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Based on social information processing theory, this research examines whether and how an employee’s proactive personality influences intrinsic and extrinsic career growth. It also examines the mediating effects of two types of proactive behaviors (voice behavior and taking charge) and the moderating effect of a leader’s proactive personality. A sample of 307 employee-leader dyads participated in this survey. Structural equation modeling was used to test the hypotheses, and the bootstrap procedure was used to test the indirect effects. Results show that an employee’s proactive personality has significant positive effects on both intrinsic and extrinsic career growth. The mediating effect of taking charge was confirmed, while the mediating effect of voice behavior was not. Leader proactive personality weakens the relationship between employee proactive personality and the two types of proactive behaviors. Employee proactive personality is more positively related to intrinsic and extrinsic career growth via proactive behaviors when a leader’s proactive personality is low. This study extends the literature on proactive personality, proactive behavior, and career development by examining the underlying determination, mediation, and moderation mechanisms.
In this paper, the essential sponsorship basics are presented and the communication instrument of sports sponsorship is illustrated. Building on this, both the perspectives of sponsors and sponsees are examined in detail. In addition, the special features of sports event sponsorships are highlighted. Finally, current developments in sports sponsorship in the context of the FIFA Soccer World Cup 2022 in Qatar and the UEFA European Soccer Championship 2024 in Germany are compared and discussed.
Wave-like differential equations occur in many engineering applications. Here the engineering setup is embedded into the framework of functional analysis of modern mathematical physics. After an overview, the –Hilbert space approach to free Euler–Bernoulli bending vibrations of a beam in one spatial dimension is investigated. We analyze in detail the corresponding positive, selfadjoint differential operators of 4-th order associated to the boundary conditions in statics. A comparison with free string wave swinging is outlined.
Automatic content creation system for augmented reality maintenance applications for legacy machines
(2024)
Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.
Tech hubs (THs) and cognate structures are nowadays ubiquitous in the innovation ecosystem of Sub-Saharan African (SSA) countries. However, the concept of THs is fuzzy due to the lack of a clear and universally accepted definition. This ambiguity is further compounded by the diverse range of organizations that self-identify as hubs, or are categorized as such by others. As a result, research on THs in SSA remained limited. Against the backdrop of established research on the interconnectedness of technology, innovation and entrepreneurship in different organizational forms, this paper is meant to provide fresh insights into the study of THs in SSA. To advance future research, first, it reveals what is special about THs in SSA and how they are related to existing concepts. I particularly argue that they contour a fourth-wave model of incubation. Second, four main categories are unfolded to delineate THs in SSA which is the cornerstone for future research.
Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
Plasmonics and nanophotonics both deal with the interaction of light with structures of typically sub-wavelength size in one of more dimensions. Over the past decade or two, interest in these topics has grown significantly. This includes basic research towards detailed understanding of light-matter interaction and the manipulation of light on the nanometer scale as well as the search for applications ranging from quantum information processing, data storage, solar cells, spectroscopy and microscopy to (bio-)sensors and biomedical devices. Key enablers for this development are advanced materials and the variety of techniques to structure them with nanometer precision on the one hand, and progress in the theoretical description and numerical implementations, on the other. Besides the traditional metals Au, Ag, Al, and Cu also compounds such as refractory metal nitrides with much higher durability as well as semiconductors, dielectrics and hybrid structures have become of interest. Structuring techniques are not only aiming at the fabrication of individual elements with highest precision for detailed interaction analysis, but also at methods for large scale, low-cost nanofabrication mostly for sensor applications. In the former case, mostly electron beam lithography and focused ion beam milling are employed, while for high throughput various forms of nanoimprint and self-assembly based techniques are favored. Thin film deposition and pattern transfer techniques are mostly derived from those developed for nano-electronics, however more recently methods such as electroless plating, atomic layer deposition or etching and 3-D additive techniques are appearing. Thus, highly specialized expertise has been acquired in the different disciplines, and successful research and technology transfer will draw from this pool of knowledge.
Digitalization and enterprise architecture management: a perspective on benefits and challenges
(2023)
Many companies digitally transform their business models, processes, and services. They have also been using Enterprise Architecture Management approaches for a long time to synchronize corporate strategy and information technology. Such digitalization projects bring different challenges for Enterprise Architecture Management. Without understanding and addressing them, Enterprise Architecture Management projects will fail or not deliver the expected value. Since existing research has not yet addressed these challenges, they were investigated based on a qualitative expert study with leading industry experts from Europe. Furthermore, potential benefits of digitalization projects for Enterprise Architecture Management were researched. Our results provide a theoretical framework consisting of five identified challenges, triggers and a number of benefits. Furthermore, we discuss in what ways digitalization and EAM is a promising topic for future research.
Do Chinese subordinates trust their German supervisors? A model of inter-cultural trust development
(2023)
In this qualitative study based on 95 interviews with Chinese subordinates and their German supervisors, we inductively develop a model which advances theoretical understanding by showing how inter-cultural trust development in hierarchical relationships is the result of six distinct elements: the subordinate trustor’s cultural profile (cosmopolitans, hybrids, culturally bounds), the psychological mechanisms operating within the trustor (role expectations and cultural accommodation), and contextual moderators (e.g., country context, time spent in foreign culture, and third-party influencers), which together influence the trust forms (e.g., presumptive trust, relational trust) and trust dynamics (e.g., trust breakdown and repair) within relationship phases over time (initial contact, trust continuation, trust disillusionment, separation, and acculturation). Our findings challenge the assumption that cultural differences result in low levels of initial trust and highlight the strong role the subordinate’s cultural profile can have on the dynamics and trajectory of trust in hierarchical relationships. Our model highlights that inter-cultural trust development operates as a variform universal, following the combined universalistic-particularistic paradigm in cross-cultural management, with both culturally generalizable etic dynamics, as well as culturally specific etic manifestations.
In the context of Industry 4.0, intralogistics faces an increasingly complex and dynamic environment driven by a high level of product customisation and complex manufacturing processes. One approach to deal with these changing conditions is the decentralised and intelligent connectivity of intralogistics systems. However, wireless connectivity presents a major challenge in the industry due to strict requirements such as safety and real-time data transmission. In this context, the fifth generation of mobile communications (5G) is a promising technology to meet the requirements of safety-critical applications. Particularly, since 5G offers the possibility of establishing private 5G networks, also referred to as standalone non-public networks. Through their isolation from public networks, private 5G networks provide exclusive coverage for private organisations offering them high intrinsic network control and data security. However, 5G is still under development and is being gradually introduced in a continuous release process. This process lacks transparency regarding the performance of 5G in individual releases, complicating the successful adoption of 5G as an industrial communication. Additionally, the evaluation of 5G against the specified target performance is insufficient due to the impact of the environment and external interfering factors on 5G in the industrial environment. Therefore, this paper aims to develop a technical decision-support framework that takes a holistic approach to evaluate the practicality of 5G for intralogistics use cases by considering two fundamental stages. The first of these analyses technical parameters and characteristics of the use case to evaluate the theoretical feasibility of 5G. The second stage investigates the application's environment, which substantially impacts the practicality of 5G, for instance, the influence of surrounding materials. Finally, a case study validates the proposed framework by means of an autonomous mobile robot. As a result, the validation proves the proposed framework's applicability and shows the practicality of the autonomous mobile robot, when integrating it into a private 5G network testbed.
Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system.
The COVID-19 pandemic necessitated significant changes in foreign language education, forcing teachers to reconstruct their identities and redefine their roles as language educators. To better understand these adaptations and perspectives, it is crucial to study how the pandemic has influenced teaching practices. This mixed-methods study focused on the less-explored aspects of foreign language teaching during the pandemic, specifically examining how language teachers adapted and perceived their practices, including rapport building and learner autonomy, during emergency remote teaching (ERT) in higher education institutions. It also explored teachers’ intentions for their teaching in the post-pandemic era. An online survey was conducted, involving 118 language educators primarily from Germany, with a smaller representation from New Zealand, the United States, and the United Kingdom. The analysis of participants’ responses revealed issues and opportunities regarding lesson formats, tool usage, rapport, and learner autonomy. Our findings offer insights into the desired changes participants envisioned for the post-pandemic era. The results highlight the opportunities ERT had created in terms of teacher development, and we offer suggestions to enhance professional development programmes based on these findings.
The present study investigated the possibilities and limitations of using a low-cost NIR spectrometer for the verification of the presence of the declared active pharmaceutical ingredients (APIs) in tablet formulations, especially for medicine screening studies in low-resource settings. Spectra from 950 to 1650 nm were recorded for 170 pharmaceutical products representing 41 different APIs, API combinations or placebos. Most of the products, including 20 falsified medicines, had been collected in medicine quality studies in African countries. After exploratory principal component analysis, models were built using data-driven soft independent modelling of class analogy (DD-SIMCA), a one-class classifier algorithm, for tablet products of penicillin V, sulfamethoxazole/trimethoprim, ciprofloxacin, furosemide, metronidazole, metformin, hydrochlorothiazide, and doxycycline. Spectra of amoxicillin and amoxicillin/clavulanic acid tablets were combined into a single model. Models were tested using Procrustes cross-validation and by projection of spectra of tablets containing the same or different APIs. Tablets containing no or different APIs could be identified with 100 % specificity in all models. A separation of the spectra of amoxicillin and amoxicillin/clavulanic acid tablets was achieved by partial least squares discriminant analysis. 15 out of 19 external validation products (79 %) representing different brands of the same APIs were correctly identified as members of the target class; three of the four rejected samples showed an API mass percentage of the total tablet weight that was out of the range covered in the respective calibration set. Therefore, in future investigations larger and more representative spectral libraries are required for model building. Falsified medicines containing no API, incorrect APIs, or grossly incorrect amounts of the declared APIs could be readily identified. Variation between different NIR-S-G1 spectroscopic devices led to a loss of accuracy if spectra recorded with different devices were pooled. Therefore, piecewise direct standardization was applied for calibration transfer. The investigated method is a promising tool for medicine screening studies in low-resource settings.
Im E-Sport-Ökosystem gibt es eine Vielzahl an Akteuren, die auf verschiedene Art und Weisen vom Erfolg des elektronischen Sports profitieren: Sponsoren präsentieren sich einer attraktiven Zielgruppe, Publisher fördern eine intensivere Nutzung ihres Videospiels und Übertragungsplattformen erhalten professionell organisierte Inhalte zum Vertrieb an eine wachsende Zuschauerschaft. Lediglich die Team-Organisationen und Veranstalter konzentrierten sich bisher allein auf die Erzielung von Erträgen durch die Ausübung oder Organisation von E-Sport-Wettkämpfen. Getrieben von Profitabilitätsschwächen versuchen sie jedoch, ihre Abhängigkeit von Sponsoring zu reduzieren und neue Ertragsquellen zu erschließen. Insbesondere Team-Organisationen sehen die Möglichkeiten hierfür eher außerhalb des originären E-Sport-Geschäfts und entwickeln sich dadurch immer mehr zu Marketing- und Unterhaltungsunternehmen.
The Circular Economy aims to reintroduce the value of products back into the economic cycle at the same value chain level. While the activities of the Circular Economy are already well-defined, there exists a gap in how returned products are treated by the industry. This study aims to examine how a process should be designed to handle returned products in the context of the Circular Economy. To achieve this, a machine learning-based algorithm is used to classify data and extract relevant information throughout the product life cycle. The focus of this research is limited to land transportation systems within the Sharing Economy sector.
Why are organizations and markets slow to transform toward sustainability despite the abundant well-recognized opportunities it provides? An important subset of the phenomena this question addresses involves decision-makers recognizing the existence of opportunities but failing to undertake ambitious, effective, sufficient, or timely action. Building on existing research on capability traps, market formation, and managing sustainability, we focus on the forces con-straining organizations from developing the capabilities and market infrastructures required for sustainability transformations. We characterize types of sustainability initiatives and, using causal loop diagramming, visualize structures that enable and constrain how organizations can navigate individually and collectively worse-before-better dynamics resulting from uncertain,nonlinear, and delayed returns. Being under day-to-day pressures and deeply intertwined within their environment, organizational actors find it difficult to recognize, undertake, maintain, and coordinate necessary efforts internally and externally. We discuss research implications and directions for future research on avoiding these traps and accelerating sustainability transformations.
Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system.
The Industry 4.0 paradigm requires concepts for integrating intelligent/ smart IoT Solutions into manufacturing. Such intelligent solutions are envisioned to increase flexibility and adaptability in smart factories. Especially autonomous cobots capable of adapting to changing conditions are a key enabler for changeable factory concepts. However, identifying the requirements and solution scenarios incorporating intelligent products challenges the manufacturing industry, especially in the SME sector. In pick and place scenarios, changing coordinate systems of workpiece carriers cause placing process errors. Using the IPIDS framework, this paper describes the development of a tool-center-point positioning method to improve the process stability of a collaborative robot in a changeable assembly workstation. Applying the framework identifies the requirement for an intelligent workpiece carrier as a part of the solution. Implementing and evaluating the solution within a changeable factory validates the IPIDS framework.
Due to constantly changing conditions, demand, and technologies, companies increasingly seek flexibility. Productivity results from automation, improved working conditions and the focus of people in production in interaction with machines. Unfortunately, the human factor is often not considered to increase flexibility and productivity with new concepts. This work aims to develop a hybrid assistance system that allows a dynamic configuration of cyber-physical production systems considering the current order situation and available resources utilizing simulation. The system also considers human factors in addition to economic factors, which contributes to the extended economic appraisal.
The hard template method for the preparation of monodisperse mesoporous silica microspheres (MPSMs) has been established in recent years. In this process, in situ-generated silica nanoparticles (SNPs) enter the porous organic template and control the size and pore parameters of the final MPSMs. Here, the sizes of the deposited SNPs are determined by the hydrolysis and condensation rates of different alkoxysilanes in a base catalyzed sol–gel process. Thus, tetramethyl orthosilicate (TMOS), tetraethyl orthosilicate (TEOS), tetrapropyl orthosilicate (TPOS) and tetrabutyl orthosilicate (TBOS) were sol–gel processed in the presence of amino-functionalized poly (glycidyl methacrylate-co-ethylene glycol dimethacrylate) (p(GMA-co-EDMA)) templates. The size of the final MPSMs covers a broad range of 0.5–7.3 µm and a median pore size distribution from 4.0 to 24.9 nm. Moreover, the specific surface area can be adjusted between 271 and 637 m2 g−1. Also, the properties and morphology of the MPSMs differ according to the SNPs. Furthermore, the combination of different alkoxysilanes allows the individual design of the morphology and pore parameters of the silica particles. Selected MPSMs were packed into columns and successfully applied as stationary phases in high-performance liquid chromatography (HPLC) in the separation of various water-soluble vitamins.
The chemical recycling of used motor oil via catalytic cracking to convert it into secondary diesel-like fuels is a sustainable and technically attractive solution for managing environmental concerns associated with traditional disposal. In this context, this study was conducted to screen basic and acidic-aluminum silicate catalysts doped with different metals, including Mg, Zn, Cu, and Ni. The catalysts were thoroughly characterized using various techniques such as N2 adsorption–desorption isotherms, FT-IR spectroscopy, and TG analysis. The liquid and gaseous products were identified using GC, and their characteristics were compared with acceptable ranges from ASTM characterization methods for diesel fuel. The results showed that metal doping improved the performance of the catalysts, resulting in higher conversion rates of up to 65%, compared to thermal (15%) and aluminum silicates (≈20%). Among all catalysts, basic aluminum silicates doped with Ni showed the best catalytic performance, with conversions and yields three times higher than aluminum silicate catalysts. These findings significantly contribute to developing efficient and eco-friendly processes for the chemical recycling of used motor oil. This study highlights the potential of basic aluminum silicates doped with Ni as a promising catalyst for catalytic cracking and encourages further research in this area.
Determination of the gel point of formaldehyde-based wood adhesives by using a multiwave technique
(2023)
Determining the instant of gelation of formaldehyde-based wood adhesives as an assessment parameter for their curing rate is important for optimizing the curing behavior. Due to the stoichiometrically imbalanced networks of formaldehyde-based adhesives, the crossover point of storage G′ and loss modulus G″ cannot unconditionally be assumed as the gel point in oscillatory time sweeps as the material response is frequency-dependent. This study aims to determine the gel point of selected adhesives by the isothermal multiwave oscillatory shear test. A thorough comparison between the gel and the crossover point of G′ and G″ is performed. Rheokinetic analysis showed no significant difference between the activation energies calculated at the gel point determined by a multiwave test and the crossover point obtained by the time sweep test. Hence, for resins with similar curing reactions, a reliable determination of gel point by applying a multiwave test is needed for a comparison of their reactivity.
Sol-gel-controlled size and morphology of mesoporous silica microspheres using hard templates
(2023)
Mesoporous silica microspheres (MPSMs) represent a promising material as a stationary phase for HPLC separations. The use of hard templates provides a preparation strategy for producing such monodisperse silica microspheres. Here, 15 MPSMs were systematically synthesized by varying the sol–gel reaction parameters of water-to-precursor ratio and ammonia concentration in the presence of a porous p(GMA-co-EDMA) polymeric hard template. Changing the sol–gel process factors resulted in a wide range of MPSMs with varying particle sizes from smaller than one to several micrometers. The application of response surface methodology allowed to derive quantitative predictive models based on the process factor effects on particle size, pore size, pore volume, and specific surface area of the MPSMs. A narrow size distribution of the silica particles was maintained over the entire experimental space. Two larger-scale batches of MPSMs were prepared, and the particles were functionalized with trimethoxy(octadecyl) silane for the application as stationary phase in reversed-phases liquid chromatography. The separation of proteins and amino acids was successfully accomplished, and the effect of the pore properties of the silica particles on separation was demonstrated.
Mesoporous silica microspheres (MPSMs) find broad application as separation materials in high liquid chromatography (HPLC). A promising preparation strategy uses p(GMA-co-EDMA) as hard templates to control the pore properties and a narrow size distribution of the MPMs. Here six hard templates were prepared which differ in their porosity and surface functionalization. This was achieved by altering the ratio of GMA to EDMA and by adjusting the proportion of monomer and porogen in the polymerization process. The various amounts of GMA incorporated into the polymer network of P1-6 lead to different numbers of tetraethylene pentamine in the p(GMA-co-EDMA) template. This was established by a partial least squares regression (PLS-R) model, based on FTIR spectra of the templates. Deposition of silica nanoparticles (SNP) into the template under Stoeber conditions and subsequent removal of the polymer by calcination result in MPSM1-6. The size of the SNPs and their incorporation depends on the pore parameters of the template and degree of TEPA functionalization. Moreover, the incorporated SNPs construct the silica network and control the pore parameters of the MPSMs. Functionalization of the MPSMs with trimethoxy (octadecyl) silane allows their use as a stationary phase for the separation of biomolecules. The pore characteristics and the functionalization of the template determine the pore structure of the silica particles and, consequently, their separation properties.
The benefits of urban data cannot be realized without a political and strategic view of data use. A core concept within this view is data governance, which aligns strategy in data-relevant structures and entities with data processes, actors, architectures, and overall data management. Data governance is not a new concept and has long been addressed by scientists and practitioners from an enterprise perspective. In the urban context, however, data governance has only recently attracted increased attention, despite the unprecedented relevance of data in the advent of smart cities. Urban data governance can create semantic compatibility between heterogeneous technologies and data silos and connect stakeholders by standardizing data models, processes, and policies. This research provides a foundation for developing a reference model for urban data governance, identifies challenges in dealing with data in cities, and defines factors for the successful implementation of urban data governance. To obtain the best possible insights, the study carries out qualitative research following the design science research paradigm, conducting semi-structured expert interviews with 27 municipalities from Austria, Germany, Denmark, Finland, Sweden, and the Netherlands. The subsequent data analysis based on cognitive maps provides valuable insights into urban data governance. The interview transcripts were transferred and synthesized into comprehensive urban data governance maps to analyze entities and complex relationships with respect to the current state, challenges, and success factors of urban data governance. The findings show that each municipal department defines data governance separately, with no uniform approach. Given cultural factors, siloed data architectures have emerged in cities, leading to interoperability and integrability issues. A city-wide data governance entity in a cross-cutting function can be instrumental in breaking down silos in cities and creating a unified view of the city’s data landscape. The further identified concepts and their mutual interaction offer a powerful tool for developing a reference model for urban data governance and for the strategic orientation of cities on their way to data-driven organizations.
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively.
The fierce market competition environment makes employees feel insecure at work. While it is difficult for enterprises to provide employees with a sense of security, they have to rely on employees’ innovative behavior to seek competitive advantage. Therefore, this study focuses on how employees engage in innovative behavior when they face job insecurity.MethodsUsing a variable-centered approach, this study aims to examine the mediating effects of intrinsic and impression management motivation in the relationship between quantitative and qualitative job insecurity and innovative behavior, including proactive and reactive innovative behavior. In addition, a person-centered approach is used to investigate whether it is possible to distinguish different combinations of quantitative and qualitative job insecurity, and examine the effect of these job insecurity profiles on motivation and innovative behavior. We used 503 data sets collected via the Credamo platform in China into the data analysis.ResultsThe study found that quantitative job insecurity affects proactive and reactive innovative behavior through impression management motivation and that qualitative job insecurity affects proactive and reactive innovative behavior through intrinsic and impression management motivation. In addition, three job insecurity profiles were identified: balanced high job insecurity, balanced low job insecurity, and a profile dominated by high quantitative job insecurity, all of which have significantly different effects on motivation and innovative behavior.DiscussionThis study contributes to provide new insights into the relationship between job insecurity and innovative behavior and compensate for the limitation of the traditional variable-centered approach that cannot capture heterogeneity within the workforce.
Mobile assistance systems (MAS) promise to overcome personnel shortages in operating theatres worldwide. A literature review inspired by the PRISMA 2020 method determines the state of the art of MAS, and identifies a lack of application areas for MAS in the operating theatre. Interviews with subject-matter experts aim to investigate application areas for MAS. The results show that most operational tasks refer to material management and patient management. MAS, with their potential to reduce the time needed for material and patient management, and the physical and mental strain of patient management, have great potential in the operating theatre.
In the past, plant layouts were regarded as highly static structures. With increasing internal and external factors causing turbulence in operations, it has become more necessary for companies to adapt to new conditions in order to maintain optimal performance. One possible way for such an adaptation is the adjustment of the plant layout by rearranging the individual facilities within the plant. Since the information about the plant layout is considered as master data and changes have a considerable impact on interconnected processes in production, it is essential that this data remains accurate and up-to-date. This paper presents a novel approach to create a digital shadow of the plant layout, which allows the actual state of the physical layout to be continuously represented in virtual space. To capture the spatial positions and orientations of the individual facilities, a pan-tilt-zoom camera in combination with fiducial markers is used. With the help of a prototypically implemented system, the real plant layout was captured and converted into different data formats for further use in exemplary external software systems. This enabled the automatic updating of the plant layout for simulation, analysis and routing tasks in a case study and showed the benefits of using the proposed system for layout capturing in terms of accuracy and effort reduction.
Hybride Arbeitsmodelle gelten als Zukunft der Arbeit. Demnach beschäftigt sich die vorliegende Forschungsarbeit mit der Untersuchung hybrider Arbeitsmodelle im Hinblick auf deutsche kleine und mittlere Unternehmen (KMU) im Vergleich zu Großbetrieben. Mithilfe einer multi-methodischen Studie, bestehend aus einer Umfrage und qualitativen Experteninterviews, wird evaluiert, in welchem Maß hybride Arbeitsmodelle in KMU bereits etabliert sind und welche Herausforderungen sie dabei bewältigen müssen. Zusätzlich wird betrachtet, ob soziodemografische Faktoren wie Alter, Geschlecht oder Rolle im Unternehmen einen Einfluss auf hybrides Arbeiten haben.Die Ergebnisse zeigen, dass die Etablierung von hybriden Arbeitsmodellen in KMU im Gegensatz zu Großbetrieben weniger vorangeschritten ist. KMUs stehen vor vielfältigen Herausforderungen, die beispielsweise auf unzureichende Digitalisierung oder traditionellere Strukturen zurückzuführen sind. Insbesondere die Unternehmenskultur sowie die Rolle im Unternehmen und der Einfluss der Führungskraft spielen eine wichtige Rolle.Praktische Relevanz: Der Großteil vorliegender Literatur zum Thema New Work und Hybride Arbeit legt den Fokus auf die Gesamtbetrachtung aller Unternehmensgrößen oder auf Großbetriebe. Aufgrund der spezifischen Merkmale, wie beispielsweise eingeschränkter Ressourcenzugang, können Ergebnisse von Großbetrieben kaum auf KMU übertragen werden. Demnach gibt diese Arbeit eine Orientierung, wie hybride Arbeitsmodelle in KMU sinnvoll und gewinnbringend umgesetzt werden und welche Herausforderungen auftreten.
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
Determinants of customer recovery in retail banking - lessons from a German banking case study
(2023)
Due to the increased willingness of retail banking customers to switch and churn their banking relationships, a question arises: Is it possible to win back lost customers, and if so, is such a possibility even desirable after all economic factors have been considered? To answer these questions, this paper examines selected determinants for the recovery of terminated customer–bank relationships from the perspective of former customers. This study therefore evaluates for the first time, empirically and systematically with reference to a German Sparkasse as a case-study setting, whether lost customers have a sufficient general willingness to return (GWR) a retail banking relationship. From our results, a correlation is shown between the GWR a banking relationship and some specific determinants: seeking variety, attractiveness of alternatives and customer satisfaction with the former business relationship. In addition, we show that a customer’s GWR varies depending on the reason for churn and is surprisingly greater when the customer defected for reasons that lie within the scope of the customer himself. Despite the case-study character, however, our results provide relevant insights for other banks and, in particular, this applies to countries with a comparable banking system.
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
The Covid-19 virus has triggered a worldwide pandemic and therefore many employees were required to work from home which caused numerous challenges. With the Covid-19 pandemic now in its third year, there are already several studies available on the subject of home offices. To investigate the impact of remote work on employee satisfaction and trust, this quantitative study aims to review existing results and formulate hypotheses based on a conceptual model created through a qualitative study and extensive literature review. The research question is as follows: Does home office during Covid-19 affect employee satisfaction and trust? To test the hypotheses, a structural equation model was constructed and analyzed. The culture of trust and flexibility are identified as the biggest influencing factors in this study.
Sleep is an essential part of human existence, as we are in this state for approximately a third of our lives. Sleep disorders are common conditions that can affect many aspects of life. Sleep disorders are diagnosed in special laboratories with a polysomnography system, a costly procedure requiring much effort for the patient. Several systems have been proposed to address this situation, including performing the examination and analysis at the patient's home, using sensors to detect physiological signals automatically analysed by algorithms. This work aims to evaluate the use of a contactless respiratory recording system based on an accelerometer sensor in sleep apnea detection. For this purpose, an installation mounted under the bed mattress records the oscillations caused by the chest movements during the breathing process. The presented processing algorithm performs filtering of the obtained signals and determines the apnea events presence. The performance of the developed system and algorithm of apnea event detection (average values of accuracy, specificity and sensitivity are 94.6%, 95.3%, and 93.7% respectively) confirms the suitability of the proposed method and system for further ambulatory and in-home use.
Healthy sleep is one of the prerequisites for a good human body and brain condition, including general well-being. Unfortunately, there are several sleep disorders that can negatively affect this. One of the most common is sleep apnoea, in which breathing is impaired. Studies have shown that this disorder often remains undiagnosed. To avoid this, developing a system that can be widely used in a home environment to detect apnoea and monitor the changes once therapy has been initiated is essential. The conceptualisation of such a system is the main aim of this research. After a thorough analysis of the available literature and state of the art in this area of knowledge, a concept of the system was created, which includes the following main components: data acquisition (including two parts), storage of the data, apnoea detection algorithm, user and device management, data visualisation. The modules are interchangeable, and interfaces have been defined for data transfer, most of which operate using the MQTT protocol. System diagrams and detailed component descriptions, including signal requirements and visualisation mockups, have also been developed. The system's design includes the necessary concepts for the implementation and can be realised in a prototype in the next phase.
The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.
Development of an expert system to overpass citizens technological barriers on smart home and living
(2023)
Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.
Managerial accountants spend a large part of their working time on more operational activities in cost accounting, reporting, and operational planning and budgeting. In all these areas, there has been increasing discussion in recent years, both in theory and practice, about using more digital technologies. For reporting, this means not only an intensified discussion of technologies such as RPA and AI but also more intensive changes to existing reporting systems. In particular, management information systems (MIS), which are maintained by managerial accountants and used by managers for corporate management, should be mentioned here. Based on an empirical survey in a large German company, this article discusses the requirements and assessments of users when switching from a regular MIS to a cloud-based system.
Introduction: Telemedicine reduces greenhouse gas emissions (CO2eq); however, results of studies vary extremely in dependence of the setting. This is the first study to focus on effects of telemedicine on CO2 imprint of primary care.
Methods: We conducted a comprehensive retrospective study to analyze total CO2eq emissions of kilometers (km) saved by telemedical consultations. We categorized prevented and provoked patient journeys, including pharmacy visits. We calculated CO2eq emission savings through primary care telemedical consultations in comparison to those that would have occurred without telemedicine. We used the comprehensive footprint approach, including all telemedical cases and the CO2eq emissions by the telemedicine center infrastructure. In order to determine the net ratio of CO2eq emissions avoided by the telemedical center, we calculated the emissions associated with the provision of telemedical consultations (including also the total consumption of physicians’ workstations) and subtracted them from the total of avoided CO2eq emissions. Furthermore, we also considered patient cases in our calculation that needed to have an in-person visit after the telemedical consultation. We calculated the savings taking into account the source of the consumed energy (renewable or not).
Results: 433 890 telemedical consultations overall helped save 1 800 391 km in travel. On average, 1 telemedical consultation saved 4.15 km of individual transport and consumed 0.15 kWh. We detected savings in almost every cluster of patients. After subtracting the CO2eq emissions caused by the telemedical center, the data reveal savings of 247.1 net tons of CO2eq emissions in total and of 0.57 kg CO2eq per telemedical consultation. The comprehensive footprint approach thus indicated a reduced footprint due to telemedicine in primary care.
Discussion: Integrating a telemedical center into the health care system reduces the CO2 footprint of primary care medicine; this is true even in a densely populated country with little use of cars like Switzerland. The insight of this study complements previous studies that focused on narrower aspects of telemedical consultations.
The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.
AbstractThrough their procyclical behavior, loan loss provisions have been determined as one of the factors that contribute to financial instability during a crisis. IFRS 9 was introduced in 2018 with an expected credit loss model replacing the incurred loss model of IAS 39 to mitigate the effect in the future. Our study aims to analyze loan loss provisions of major banks in the Eurozone to determine for the first time if the implementation of IFRS 9, as intended by regulators, has a dampening effect on procyclicality, especially during the stressed situation under COVID‐19. We analyze 51 banks from 12 countries of the European Monetary Union using 2856 firm‐year observations. While no robust evidence of less procyclicality can be found after the implementation of IFRS 9 until the pandemic, we find evidence that loan loss provisions moved countercyclical during 2020, indicating an alleviating effect at the beginning of the exogenous shock.
During the first years of the last decade, Egypt used to face recurrent electricity cut-offs in summer. In the past few years, the electricity tariff dramatically increased. Radiative cooling to the clear night sky is a renewable energy source that represents a relative solution. The dry desert climate promotes nocturnal radiative cooling applications. This study investigates the potential of nocturnal radiative cooling systems (RCSs) to reduce the energy consumption of the residential building sector in Egypt. The system technology proposed in this work is based on uncovered solar thermal collectors integrated into the building hydronic system. By implementing different control strategies, the same system could be used for both cooling and heating applications. The goal of this paper is to analyze the performance of RCSs in residential buildings in Egypt. The dynamic simulation program TRNSYS was used to simulate the thermal behavior of the system. The relevant issues of Egypt as a case-study are firstly overviewed. Then the paper introduces the work done to develop a building model that represents a typical residential apartment in Egypt. Typical occupancy profiles were developed to define the internal thermal gains. The adopted control strategy to optimize the system operation is presented as well. To fully understand and hence evaluate the operation of the proposed RCS, four simulation cases were considered: 1. a reference case (fully passive), 2. the stand-alone operation of the RCS, 3. ideal heating & cooling operation (fully-active), and 4. the hybrid-operation (when the active cooling system is supported by the proposed RCS). The analysis considered the main three distinct climates in Egypt, represented by the cities of Alexandria, Cairo and Asyut. The hotter and drier weather conditions resulted in a higher cooling potential and larger temperature differences. The simulated cooling power in Asyut was 28.4 W/m² for a 70 m² absorber field. For a smaller field area of 10 m², the cooling power reached 109 W/m² but with humble temperature differences. To meet the rigorous thermal comfort conditions, the proposed sensible RCS cannot fully replace conventional air-conditioning units, especially in humid areas like Alexandria. When working in a hybrid system, a 10% reduction in the active cooling energy demand could be achieved in Asyut to keep the cooling set-point at 24 °C. This percentage reduction was nearly doubled when the thermal comfort set-point was increased by two degrees (26 °C). In a sensitivity analysis, external shading devices as a passive measure as well as the implementation of the Egyptian code for buildings (ECP306/1–2005) were also investigated. The analysis of this study raised other relevant aspects to discuss, e.g. system-sizing, environmental effects, limitations and recommendations.
Thin, flat textile roofing offers negligible heat insulation. In warm areas, such roofing membranes are therefore equipped with metallized surfaces to reflect solar heat radiation, thus reducing the warming inside a textile building. Heat reflection effects achieved by metallic coatings are always accompanied by shading effects as the metals are non-transparent for visible light (VIS). Transparent conductive oxides (TCOs) are transparent for VIS and are able to reflect heat radiation in the infrared. TCOs are, e.g., widely used in the display industry. To achieve the perfect coatings needed for electronic devices, these are commonly applied using costly vacuum processes at high temperatures. Vacuum processes, on account of the high costs involved and high processing temperatures, are obstructive for an application involving textiles. Accepting that heat-reflecting textile membranes demand less perfect coatings, a wet chemical approach has been followed here when producing transparent heat-reflecting coatings. Commercially available TCOs were employed as colloidal dispersions or nanopowders to prepare sol-gel-based coating systems. Such coatings were applied to textile membranes as used for architectural textiles using simple coating techniques and at moderate curing temperatures not exceeding 130 °C. The coatings achieved about 90% transmission in the VIS spectrum and reduced near-infrared transmission (at about 2.5 µm) to nearly zero while reflecting up to 25% of that radiation. Up to 35% reflection has been realized in the far infrared, and emissivity values down to ε = 0.5777 have been measured.
Cotton contamination by honeydew is considered one of the significant problems for quality in textiles as it causes stickiness during manufacturing. Therefore, millions of dollars in losses are attributed to honeydew contamination each year. This work presents the use of UV hyperspectral imaging (225–300 nm) to characterize honeydew contamination on raw cotton samples. As reference samples, cotton samples were soaked in solutions containing sugar and proteins at different concentrations to mimic honeydew. Multivariate techniques such as a principal component analysis (PCA) and partial least squares regression (PLS-R) were used to predict and classify the amount of honeydew at each pixel of a hyperspectral image of raw cotton samples. The results show that the PCA model was able to differentiate cotton samples based on their sugar concentrations. The first two principal components (PCs) explain nearly 91.0% of the total variance. A PLS-R model was built, showing a performance with a coefficient of determination for the validation (R2cv) = 0.91 and root mean square error of cross-validation (RMSECV) = 0.036 g. This PLS-R model was able to predict the honeydew content in grams on raw cotton samples for each pixel. In conclusion, UV hyperspectral imaging, in combination with multivariate data analysis, shows high potential for quality control in textiles.
UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honey- dew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the valida- tion. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.
The aim of this article is to establish a stochastic search algorithm for neural networks based on the fractional stochastic processes {𝐵𝐻𝑡,𝑡≥0} with the Hurst parameter 𝐻∈(0,1). We define and discuss the properties of fractional stochastic processes, {𝐵𝐻𝑡,𝑡≥0}, which generalize a standard Brownian motion. Fractional stochastic processes capture useful yet different properties in order to simulate real-world phenomena. This approach provides new insights to stochastic gradient descent (SGD) algorithms in machine learning. We exhibit convergence properties for fractional stochastic processes.
Distributed Ledger Technologies for the energy sector: facilitating interoperability analysis
(2023)
The use of distributed data storage and management structures, such as Distributed Ledger Technologies (DLT), in the energy sector has gained great interest in recent times. This opens up new possibilities in e.g. microgrid management, aggregation of distributed resources, peer-to- peer trading, integration of electromobility or proof-of-origin strategies. However, in order to benefit from those new possibilities, new challenges have to be overcome. This work focuses on one of these challenges, which is the need to ensure interoperability when integrating DLT-enabled devices in energy use cases. Firstly, the use of DLTs in the energy sector will be analyzed and the main use cases will be presented. Then, a classification of DLT-Energy use cases will be proposed. Secondly, the need for a common reference architecture framework to analyze those use cases with a focus on interoperability will be discussed and the current activities in research and standardization in this field will be presented. Finally, a new common reference architecture framework based on current activities in standardization will be presented.
Context
Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking.
Objective
We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this.
Method
We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a rule and one that was a violation of this rule. Participants answered comprehension questions and rated the perceived difficulty.
Results
For 11 of the 12 rules, we found that violation performed significantly worse than rule for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for violation.
Conclusions
Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.
In the last few years, business firms have substantially invested into the artificial intelligence (AI) technology. However, according to several studies, a significant percentage of AI projects fail or do not deliver business value. Due to the specific characteristics of AI projects, the existing body of knowledge about success and failure of information systems (IS) projects in general may not be transferrable to the context of AI. Therefore, the objective of our research has been to identify factors that can lead to AI project failure. Based on interviews with AI experts, this article identifies and discusses 12 factors that can lead to project failure. The factors can be further classified into five categories: unrealistic expectations, use case related issues, organizational constraints, lack of key resources, and, technological issues. This research contributes to knowledge by providing new empirical data and synthesizing the results with related findings from prior studies. Our results have important managerial implications for firms that aim to adopt AI by helping the organizations to anticipate and actively manage risks in order to increase the chances of project success.
In the course of a more intensive energy generation from regenerative sources, an increased number of energy storages is required. In addition to the widespread means of storing electric energy, storing energy thermally can contribute significantly. However, limited research exists on the behaviour of thermal energy storages (TES) in practical operation. While the physical processes are well known, it is nevertheless often not possible to adequately evaluate its performance with respect to the quality of thermal stratification inside the tank, which is crucial for the thermodynamic effectiveness of the TES. The behaviour of a TES is experimentally investigated in cyclic charging and discharging operation in interaction with a cogeneration (CHP) unit at a test rig in the lab. From the measurements the quality of thermal stratification is evaluated under varying conditions using different metrics such as normalised stratification factor, modified MIX number, exergy number and exergy efficiency, which extends the state of art for CHP applications. The results show that the positioning of the temperature sensors for turning the CHP unit on and off has a significant influence on both the effective capacity of a TES and the quality of thermal stratification inside the tank. It is also revealed that the positioning of at least one of these sensors outside the storage tank, i.e. in the return line to the CHP unit, prevents deterioration of thermal stratification, thereby enhancing thermodynamic effectiveness. Furthermore, the effects of thermal load and thermal load profile on effective capacity and thermal stratification are discussed, even though these are much smaller compared to the effect of positioning the temperature sensors.
In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m-dimensional Gaussian stochastic vector process Z(t) is employed to model the temporal-spatial correlation of wind speeds at m different sites. In general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of Z(t), which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of Z(t) can be analytically obtained. Then, Fourier transformation is implemented to establish the cross-spectral density matrix of Z(t), and an analytical approach is proposed to generate samples of wind speeds at m different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.
Recent advances in artificial intelligence have enabled promising applications in neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a novel system that utilizes AI to aid neurosurgeons in precisely identifying and localizing brain tumors. The system was trained on a dataset of brain MRI scans and utilized deep learning algorithms for segmentation and classification. Evaluation of the system on a separate set of brain MRI scans demonstrated an average Dice similarity coefficient of 0.87. The system was also evaluated through a user experience test involving the Department of Neurosurgery at the University Hospital Ulm, with results showing significant improvements in accuracy, efficiency, and reduced cognitive load and stress levels. Additionally, the system has demonstrated adaptability to various surgical scenarios and provides personalized guidance to users. These findings indicate the potential for AI to enhance the quality of neurosurgical interventions and improve patient outcomes. Future work will explore integrating this system with robotic surgical tools for minimally invasive surgeries.
Purpose
For the modeling, execution, and control of complex, non-standardized intraoperative processes, a modeling language is needed that reflects the variability of interventions. As the established Business Process Model and Notation (BPMN) reaches its limits in terms of flexibility, the Case Management Model and Notation (CMMN) was considered as it addresses weakly structured processes.
Methods
To analyze the suitability of the modeling languages, BPMN and CMMN models of a Robot-Assisted Minimally Invasive Esophagectomy and Cochlea Implantation were derived and integrated into a situation recognition workflow. Test cases were used to contrast the differences and compare the advantages and disadvantages of the models concerning modeling, execution, and control. Furthermore, the impact on transferability was investigated.
Results
Compared to BPMN, CMMN allows flexibility for modeling intraoperative processes while remaining understandable. Although more effort and process knowledge are needed for execution and control within a situation recognition system, CMMN enables better transferability of the models and therefore the system. Concluding, CMMN should be chosen as a supplement to BPMN for flexible process parts that can only be covered insufficiently by BPMN, or otherwise as a replacement for the entire process.
Conclusion
CMMN offers the flexibility for variable, weakly structured process parts, and is thus suitable for surgical interventions. A combination of both notations could allow optimal use of their advantages and support the transferability of the situation recognition system.
Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today’s society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs—Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.
Twitter and citations
(2023)
Social media, especially Twitter, plays an increasingly important role among researchers in showcasing and promoting their research. Does Twitter affect academic citations? Making use of Twitter activity about columns published on VoxEU, a renowned online platform for economists, we develop an instrumental variable strategy to show that Twitter activity about a research paper has a causal effect on the number of citations that this paper will receive. We find that the existence of at least one tweet, as opposed to none, increases citations by 16-25%. Doubling overall Twitter engagement boosts citations by up to 16%.
Resilienz gewinnt für produzierende Unternehmen immer mehr an Bedeutung. Es fehlen jedoch geeignete Maßzahlen, um ein Produktionssystem auf dessen Resilienzfähigkeit zu analysieren. Dieser Beitrag stellt Resilienzmessgrößen vor, welche es ermöglichen verschiedene Produktionssysteme zu vergleichen und zusätzlich Optimierungsmaßnahmen zu bewerten.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the gm/Id method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
Towards a sustainable future, looking beyond the system boundaries of a single manufacturing company is necessary to promote meaningful collaborations in terms of circular economy principles. In this context digital data processing technologies to connect the potential collaborators are seen as enablers to make use of proven collaborative circular business models (CCBMs). Since most of such data processing technologies rely on features to describe the entities involved, it is essential to provide guidance for identifying and selecting the relevant and most appropriate ones. Defining critical success factors (CSFs) is considered a suitable instrument to describe the decisive factors. A systematic literature review (SLR), followed by a qualitative synthesis is investigating two scientific fields of work, namely (1) the general relevant features of CCBMs and, (2) methodologies for determining CSFs. This results in the development of a conceptual framework which provides guidance for digital applications that perform further digital processing based on the relevant CSFs relating to the specific CCBM.
Machine failures’ consequences – a classification model considering ultra-efficiency criteria
(2023)
To strive for a sustainable production, maintenance has to evaluate possible machine failure consequences not just economically but also holistically. Approaches such as the ultra-efficiency factory consider energy, material, human/staff, emission, and organization as optimization dimensions. These ultra-efficiency dimensions can be considered for analyzing not only the respective machine failure but also the effects on the entire production system holistically. This paper presents an easy to use method, based on a questionnaire, for assessing the failure consequences of a machine malfunction in a production system considering the ultra-efficiency dimensions. The method was validated in a battery production.
Using predictive maintenance, more efficient processes can be implemented, leading to fewer maintenance costs and increased availability. The development of a predictive maintenance solution currently requires high efforts in time and capacity as well as often interdisciplinary cooperation. This paper presents a standardized model to describe a predictive maintenance use case. The description model is used to collect, present, and document the required information for the implementation of predictive maintenance use cases by and for different stakeholders. Based on this model, predictive maintenance solutions can be introduced more efficiently. The method is validated across departments in the automotive sector.
The increasing complexity and need for availability of automated guided vehicles (AGVs) pose challenges to companies, leading to a focus on new maintenance strategies. In this paper, a smart maintenance architecture based on a digital twin is presented to optimize the technical and economic effectiveness of AGV maintenance activities. To realize this, a literature review was conducted to identify the necessary requirements for Smart Maintenance and Digital Twins. The identified requirements were combined into modules and then integrated into an architecture. The architecture was evaluated on a real AGV on the battery as one of the critical components.
Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes.
At the beginning of 2022, Frontiers in Bioengineering and Biotechnology - Biomaterials Section has published a Research Topic on “Functional Surfaces and Biomaterials.” The aim of this Research Topic is to summarize the current state of research and development in the field of functional surfaces and biomaterials with a particular focus on biotechnological and medical applications.
The guest editorial team would like to thank all colleagues from around the world who submitted their reviews and research articles for the Research Topic. By the end of August 2022, we have successfully collected 20 articles by 138 participating authors following the peer review process. We also tried to select manuscripts from different research areas to cover the most relevant Research Topic of interest, from drug delivery systems to bone tissue engineering to biosensors and general aspects in biomedicine. By the end of December, the 20 articles had been viewed for more than 21000 times with downloads more than 4,000 times, and 11 articles have reached more than 1,000 views.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.
This study examines the underexplored areas of customer success management, focusing on the impact of leadership and companywide collaboration, and the role of customer success in overall firm performance. A qualitative research approach was utilized, which involved reviewing relevant literature and conducting an interview with the Vice President of Customer Success Management in B2B at a case company. Findings revealed that both leadership and pervasive collaboration greatly enhance the customer journey experience. Given that 75% of Annual Recurring Revenue is derived from existing customers, the substantial role of customer success in propelling business growth is affirmed. The study also demonstrated the importance of proactive customer engagement, assimilating customer feedback into products and services, and nurturing personal relationships with customers for fostering innovation. It further stressed the need for service provision and decision-making at various levels, as well as the implementation of a range of communication channels, to ensure customer success.
This study examines the phenomenon of Virtual Influencer (VI) marketing and its impact on customer purchase behavior. The aim is to understand the scope and impact of VI marketing. The study compares VI marketing to traditional Human Influencer (HI) marketing and identifies the unique benefits and challenges associated with VIs. A survey was conducted to gain insight into consumer attitudes and behaviors toward VIs. Key findings reveal varying levels of trust and acceptance of VIs among consumers. While some participants expressed openness to buying products promoted by VIs, others had reservations about their authenticity. The study also explores the potential role of VIs in the metaverse, highlighting business opportunities and challenges in this evolving digital landscape. Overall, this research sheds light on the growing influence of VIs and the need for further research in the field of marketing.
In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.
Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL).
Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM).
Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy.
Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.
Diese Studie untersucht den kurzfristigen Einfluss der Tagespflege auf die kindliche Entwicklung im Vergleich zur Betreuung in der Kita. Internationale Studien deuten darauf hin, dass der Besuch einer Tagespflege im Vergleich zur Kita eher negative Auswirkungen auf Kinder hat. Mithilfe der Neugeborenen-Kohorte des NEPS können wir evaluieren, ob dies auch im deutschen Kontext gilt. Wir nutzen zwei verschiedene methodische Ansätze, um den Effekt der Tagespflege zu schätzen. Unsere Ergebnisse zeigen, dass die Tagespflege für die Mehrzahl der untersuchten Entwicklungsindikatoren keinen statistisch signifikant schlechteren Einfluss auf die kindliche Entwicklung hat, außer im Bereich der Habituation.
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
How mechanical and physicochemical material characteristics influence adipose-derived stem cell fate
(2023)
Adipose-derived stem cells (ASCs) are a subpopulation of mesenchymal stem cells. Compared to bone marrow-derived stem cells, they can be harvested with minimal invasiveness. ASCs can be easily expanded and were shown to be able to differentiate into several clinically relevant cell types. Therefore, this cell type represents a promising component in various tissue engineering and medical approaches (e.g., cell therapy). In vivo cells are surrounded by the extracellular matrix (ECM) that provides a wide range of tissue-specific physical and chemical cues, such as stiffness, topography, and chemical composition. Cells can sense the characteristics of their ECM and respond to them in a specific cellular behavior (e.g., proliferation or differentiation). Thus, in vitro biomaterial properties represent an important tool to control ASCs behavior. In this review, we give an overview of the current research in the mechanosensing of ASCs and current studies investigating the impact of material stiffens, topography, and chemical modification on ASC behavior. Additionally, we outline the use of natural ECM as a biomaterial and its interaction with ASCs regarding cellular behavior.
Dieser Beitrag untersucht, wer in Deutschland Bildungsminister:in wird. Zur Klärung dieser Frage entwickelten wir einen Datensatz, der die biografischen Merkmale aller Bildungsminister:innen der deutschen Bundesländer zwischen 1950 und 2020 enthält. Als Beispiel für die Nutzung des Datensatzes untersuchen wir die beiden Merkmale Geschlecht und frühere Berufserfahrung und verknüpfen diese Merkmale mit Indikatoren für die Größe und Entwicklung des Bildungsbudgets und die Dauer der Amtszeit. Wir zeigen, dass zwischen 1950 und 2020 deutlich mehr Männer als Frauen zum/zur Bildungsminister:in ernannt wurden, unabhängig davon, welche Parteien die Bildungsminister:innen stellten. Außerdem verfügt die Mehrheit der Bildungsminister:innen bei Amtsantritt nicht über vorherige Berufserfahrung als Lehrer:in. Die meisten Bildungsminister:innen haben jedoch bereits politische Erfahrung, wenn sie ihr Amt antreten. Unsere Datenbank, die die erste umfassende Erhebung biografischer Merkmale von Bildungsminister:innen in den deutschen Bundesländern enthält, steht allen interessierten Forscher:innen zur Verfügung.
In diesem Beitrag wird der Einfluss von Zuschauern nach den pandemiebedingten Geisterspielen auf den Heimvorteil in der ersten Fußball-Bundesliga analysiert. Für die empirische Untersuchung werden die fünf Spielzeiten 2017/18 bis 2021/22 betrachtet. Während in der Geisterspielphase in der Saison 2019/20 der Heimvorteil vollständig verschwindet, steigt der Heimvorteil in der zweiten Geisterspielphase in der Saison 2020/21 mit der Zeit wieder auf vorpandemisches Niveau an. In der Saison 2021/22 ist nach einer kurzen Phase mit eingeschränkten Zuschauerkapazitäten ein signifikant größerer Heimvorteil als in der Zeit vor der Pandemie zu beobachten. Der überdurchschnittlich positive Effekt der Zuschauer in dieser letzten Phase verschwindet mit der Zeit trotz steigender Zuschauerzahlen. Der Einfluss von Fans auf den Heimvorteil ist insgesamt insbesondere auf psychologische Effekte auf die Spieler der Heimmannschaften zurückzuführen.
This paper presents the first part of a research-work conducted at the University of Applied Sciences (HFT- Stuttgart). The aim of the research was to investigate the potential of low-cost renewable energy systems to reduce the energy demand of the building sector in hot and dry areas. Radiative cooling to the night sky represents a low-cost renewable energy source. The dry desert climate conditions promote radiative cooling applications. The system technology adopted in this work is based on uncovered solar thermal collectors integrated into the building’s hydronic system. By implementing different control strategies, the same system could be used for cooling as well as for heating applications. This paper focuses on identifying the collector parameters which are required as the coefficients to configure such an unglazed collector for calibrating its mathematical model within the simulation environment. The parameter identification process implies testing the collector for its thermal performance. This paper attempts to provide an insight into the dynamic testing of uncovered solar thermal collectors (absorbers), taking into account their prospective operation at nighttime for radiative cooling applications. In this study, the main parameters characterizing the performance of the absorbers for radiative cooling applications are identified and obtained from standardized testing protocol. For this aim, a number of plastic solar absorbers of different designs were tested on the outdoor test-stand facility at HFT-Stuttgart for the characterization of their thermal performance. The testing process was based on the quasi-dynamic test method of the international standard for solar thermal collectors EN ISO 9806. The test database was then used within a mathematical optimization tool (GenOpt) to determine the optimal parameter settings of each absorber under testing. Those performance parameters were significant to compare the thermal performance of the tested absorbers. The coefficients (identified parameters) were used then to plot the thermal efficiency curves of all absorbers, for both the heating and cooling modes of operation. Based on the intended main scope of the system utilization (heating or cooling), the tested absorbers could be benchmarked. Hence, one of those absorbers was selected to be used in the following simulation phase as was planned in the research-project.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Due to the wide variety of benign and malignant salivary gland tumors, classification and malignant behavior determination based on histomorphological criteria can be difficult and sometimes impossible. Spectroscopical procedures can acquire molecular biological information without destroying the tissue within the measurement processes. Since several tissue preparation procedures exist, our study investigated the impact of these preparations on the chemical composition of healthy and tumorous salivary gland tissue by Fourier-transform infrared (FTIR) microspectroscopy. Sequential tissue cross-sections were prepared from native, formalin-fixed and formalin-fixed paraffin-embedded (FFPE) tissue and analyzed. The FFPE cross-sections were dewaxed and remeasured. By using principal component analysis (PCA) combined with a discriminant analysis (DA), robust models for the distinction of sample preparations were built individually for each parotid tissue type. As a result, the PCA-DA model evaluation showed a high similarity between native and formalin-fixed tissues based on their chemical composition. Thus, formalin-fixed tissues are highly representative of the native samples and facilitate a transfer from scientific laboratory analysis into the clinical routine due to their robust nature. Furthermore, the dewaxing of the cross-sections entails the loss of molecular information. Our study successfully demonstrated how FTIR microspectroscopy can be used as a powerful tool within existing clinical workflows.
Flame-retardant finishing of cotton fabrics using DOPO functionalized alkoxy- and amido alkoxysilane
(2023)
In the present study, DOPO-based alkoxysilane (DOPO-ETES) and amido alkoxysilane (DOPO-AmdPTES) were synthesized by one-step and without by-products as halogen-free flame retardants. The flame retardants were applied on cotton fabric utilizing sol–gel method and pad-dry-cure finishing process. The flame retardancy, the thermal stability and the combustion ehaviour of treated cotton were evaluated by surface and bottom edge ignition flame test (according to EN ISO 15025), thermogravimetric analysis (TGA) and micro-scale combustion calorimeter (MCC). Unlike CO/DOPO-ETES sample, cotton treated with DOPO-AmdPTES nanosols exhibits self-extinguishing ehaviour with high char residue, an improvement of the LOI value and a significant reduction of the PHRR, HRC and THR compared to pristine cotton. Cotton finished with DOPO-AmdPTES reveals a semi-durability after ten laundering cycles keeping the flame-retardant properties unchanged. According to the results obtained from TGA-FTIR, Py-GC/MS and XPS, the major activity of flame retardant occurs in the condensed phase via catalytic induced char formation as physical barrier along with the activity in the gas phase derived mainly from the dilution effect. The early degradation of CO/DOPO-AmdPTES compared to CO/DOPO-ETES, triggered by the cleavage of the weak bond between P and C=O, as the DFT study indicated, provides the beneficial effect of this flame retardant on the fire resistance of cellulose.
Supply chains have evolved into dynamic, interconnected supply networks, which increases the complexity of achieving end-to-end traceability of object flows and their experienced events. With its capability of ensuring a secure, transparent, and immutable environment without relying on a trusted third party, the emerging blockchain technology shows strong potential to enable end-to-end traceability in such complex multitiered supply networks. This paper aims to overcome the limitations of existing blockchain-based traceability architectures regarding their object-related event mapping ability, which involves mapping the creation and deletion of objects, their aggregation and disaggregation, transformation, and transaction, in one holistic architecture. Therefore, this paper proposes a novel ‘blueprint-based’ token concept, which allows clients to group tokens into different types, where tokens of the same type are non-fungible. Furthermore, blueprints can include minting conditions, which, for example, are necessary when mapping assembly processes. In addition, the token concept contains logic for reflecting all conducted object-related events in an integrated token history. Finally, for validation purposes, this article implements the architecture’s components in code and proves its applicability based on the Ethereum blockchain. As a result, the proposed blockchain-based traceability architecture covers all object-related supply chain events and proves its general-purpose end-to-end traceability capabilities of object flows.
Cytocompatibility analyses of new implant materials or biomaterials are not only prescribed by the Medical Device Regulation (MDR), as defined in the DIN ISO Norm 10993-5 and -12, but are also increasingly replacing animal testing. In this context, jellyfish collagen has already been established as an alternative to mammalian collagen in different cell culture conditions, but a lack of knowledge exists about its applicability for cytocompatibility analyses of biomaterials. Thus, the present study was conducted to compare well plates coated with collagen type 0 derived from Rhizostoma pulmo with plates coated with bovine and porcine collagen. The coated well plates were analysed in vitro for their cytocompatibility, according to EN ISO 10993-5/−12, using both L929 fibroblasts and MC3T3 pre-osteoblasts. Thereby, the coated well plates were compared, using established materials as positive controls and a cytotoxic material, RM-A, as a negative control. L929 cells exhibited a significantly higher viability (#### p < 0.0001), proliferation (## p < 0.01), and a lower cytotoxicity (## p < 0.01 and # p < 0.05)) in the Jellagen® group compared to the bovine and porcine collagen groups. MC3T3 cells showed similar viability and acceptable proliferation and cytotoxicity in all collagen groups. The results of the present study revealed that the coating of well plates with collagen Type 0 derived from R. pulmo leads to comparable results to the case of well plates coated with mammalian collagens. Therefore, it is fully suitable for the in vitro analyses of the cytocompatibility of biomaterials or medical devices.
Polyester fibers are widely employed in a multitude of sectors and applications from the technical textiles to everyday life thanks to their durability, strength, and flexibility. Despite these advantages, polyester lacks in dyeability, adhesion of coating, hydrophilicity, and it is characterized by a low wettability respect to natural fibers. On this regard, beyond the harmful hydrophobic textile finishings of polyester fabrics containing fluorine-compounds, and in order to avoid pre-treatments, such as laser irradiation to improve their surface properties, research is moving towards the development of fluorine-free and safer coatings. In this work, the (3-glycidyloxypropyl)trimethoxysilane (GPTMS) and various long alkyl-chain alkoxysilanes were employed for the fabrication in the presence of a catalyst of a water-based superhydrophobic finishing for polyester fabrics with a simple sol-gel, non-fluorinated, sustainable approach and the dip-pad-dry-cure method. The finished polyester fabrics surface properties were investigated by static and dynamic water repellency tests. Additionally, the resistance to common water-based liquids, abrasion resistance, moisture adsorption, and air permeability measurements were performed. Scanning electron microscopy was employed to examine the micro- and nano-morphology of the functionalized polyester fabrics surfaces. The obtained superhydrophobic finishings displayed high water-based stain resistance as well as good hydrophobicity after different cycles of abrasion.
This paper presents a description model for smart, connected devices used in a manufacturing context. Similar to the wide spread adoption of smart products for personal and private usage, recent developments lead to a plethora of devices offering a variety of features and capabilities. Manufacturing companies undergoing digital transformation demand guidance with respect to the systematic introduction of smart, connected devices. The introduction of smart connected devices constitutes a strategic decision cost due to the high future committed cost after introduction and maintaining a smart device fleet by a vendor. This paper aims to support the introduction efforts by classifying the devices and thus helping companies identify their specific requirements for smart, connected devices before initiating widespread procurement. By mapping the features of these devices based on various attributes, allows the clustering of smart, connected devices including a requirement list for their implementation on the shopfloor. Four individual commercially available smart connected devices were analyzed using the description model.
Parallel grippers offer multiple applications thanks to their flexibility. Their application field ranges from aerospace and automotive to medicine and communication technologies. However, the application of grippers has the problem of exhibition wear and errors during the execution of their operation. This affects the performance of the gripper. In this context, the remaining useful life (RUL) defines the remaining lifespan until failure for an asset at a particular time of operation occurs. The exact lifespan of an asset is uncertain, thus the RUL model and estimation must be derived from available sources of information. This paper presents a method for the estimation of the RUL for a two-jaw parallel gripper. After the introduction to the topic, an overview of existing literature and RUL methods are presented. Subsequently, the method for estimating the RUL of grippers is explained. Finally, the results are summarized and discussed before the outlook and further challenges are presented.
Die prä-, intra- und postoperative Entitäts- und Dignitätsbestimmung von Speicheldrüsen-tumoren (ST) allein anhand von histomorphologischen Kriterien ist häufig mit großen Unsicherheiten verbunden.
Die Spektren der Raman-Spektroskopie (RS) und der Infrarot-Spektroskopie (IS) enthalten Informationen zu der molekularen Zusammensetzung des untersuchten Gewebes. Ziel der Arbeit war die Etablierung eines Gewebe-Aufarbeitungs-Workflows und die Analyse des Einflusses der Fixierung auf die spektrale Bioinformation. Zudem wird ein Überblick über den Einsatz der RS und IS im Kopf-Hals Bereich gegeben.
Es wurden 10 mm dicke, konsekutive kryo-, formalin- und paraffinfixierte ST-Gewebeschnitte von Zystadenolymphomen (n=5) und pleomorphen Adenomen (n=4) mit der RS und IS untersucht und die Daten multivariat ausgewertet. Die Messungen erfolgten in Korrelation zur Histomorphologie über einen korrespondierenden HE-Schnitt sowohl im Tumorgewebe als auch im gesunden Speicheldrüsengewebe.
In der Mittelwertspektrenanalyse zeigte sich eine deutliche Paraffin-Signatur, Formalin-Fixierung hatte keinen wesentlichen Einfluss. Dies konnte durch die Hauptkomponentenanalyse (PCA) bestätigt werden. Eine Diskriminierung von Tumor- und Nicht-Tumorgewebe durch die PCA und gekoppelte Diskriminanzanalyse war ebenfalls mit beiden spektroskopischen Methoden mit einer hohen Sensitivität möglich.
Für eine Translation von spektralen Verfahren ist das Wissen über Einflussfaktoren auf die spektrale Bioinformation der Gewebeaufarbeitung und -fixierung unabdingbar. Die Integration spektraler Verfahren additiv in bestehende Arbeitsabläufe ist möglich. Der Einfluss der Formalinfixierung auf die spektrale Bioinformation ist gering. Die bioinformatische Analyse der umfangreichen Datensätze ist herausfordernd.
IZKF Würzburg
Das Interview geht der Frage nach: Verändern neue Geschäftsmodelle die Unternehmenssteuerung? Dazu machen die Diskussionspartner am Beispiel der Automobilbranche auf vielfältige Veränderungen und Entwicklungen aufmerksam. Zentral ist die Herausbildung neuer Geschäftsmodelle, die die Funktionsweise und den Marktauftritt der Unternehmen zeitgerecht und wirtschaftlich erfolgreich gestalten sollen. Ebenso wichtig ist die Zusammenfassung der vielfältigen Steuerungsaktivitäten in einem Steuerungsmodell und deren fortlaufende Abstimmung mit den sich aus dem Geschäftsmodell jeweils ergebenden Steuerungsanforderungen.
This paper reviews suggestions for changes to database technology coming from the work of many researchers, particularly those working with evolving big data. We discuss new approaches to remote data access and standards that better provide for durability and auditability in settings including business and scientific computing. We propose ways in which the language standards could evolve, with proof-of-concept implementations on Github.
Die Lohnlücke zwischen Frauen und Männern (der sogenannte Gender Pay Gap) wird üblicherweise in Bevölkerungsgruppen untersucht, die ihre Bildungslaufbahn bereits abgeschlossen haben. In diesem Beitrag betrachten wir eine frühere Phase der Berufstätigkeit, indem wir den Gender Pay Gap unter Studierenden, die neben ihrem Studium arbeiten, analysieren. Anhand von Daten aus fünf Kohorten einer Studierendenbefragung in Deutschland beschreiben wir den Gender Pay Gap und diskutieren mögliche Erklärungen. Die Ergebnisse zeigen, dass Studentinnen im Durchschnitt etwa 6% weniger verdienen als Studenten. Nach Berücksichtigung verschiedener entlohnungsrelevanter Faktoren verringert sich die Lücke auf 4,1%. Einer der Hauptgründe für die Differenz in der Entlohnung sind die unterschiedlichen Beschäftigungen, die männliche und weibliche Studierende ausüben.
Being exposed to compulsory religious education in school can have long-run consequences for students’ lives. At different points in time since the 1970s, German states terminated compulsory religious education in public schools and replaced it by a choice between ethics classes and religious education. This article shows that the reform not only led to reduced religiosity in students’ later life, but also eroded traditional attitudes towards gender roles and increased labor-market participation and earnings.
Gender pay gaps are commonly studied in populations with already completed educational careers. We focus on an earlier stage by investigating the gender pay gap among university students working alongside their studies. With data from five cohorts of a large-scale student survey from Germany, we use regression and wage decomposition techniques to describe gender pay gaps and potential explanations. We find that female students earn about 6% less on average than male students, which reduces to 4.1% when accounting for a rich set of explanatory variables. The largest explanatory factor is the type of jobs male and female students pursue.
With the digital transformation, companies will experience a change that focuses on shaping the organization into an agile organizational form. In today's competitive and fast-moving business environment, it is necessary to react quickly to changing market conditions. Agility represents a promising option for overcoming these challenges. The path to an agile organization represents a development process that requires consideration of countless levels of the enterprise. This paper examines the impact of digital transformation on agile working practices and the benefits that can be achieved through technology. To enable a solution for today's so-called VUCA (Volatility, Uncertainty, Complexity und Ambiguity) world, agile ways of working can be applied project management requires adaptation. In the qualitative study, expert interviews were conducted and analyzed using the grounded theory method. As a result, a model can be presented that shows the influencing factors and potentials of agile management in the context of the digital transformation of medium-sized companies.
Uncontrolled movement of instruments in laparoscopic surgery can lead to inadvertent tissue damage, particularly when the dissecting or electrosurgical instrument is located outside the field of view of the laparoscopic camera. The incidence and relevance of such events are currently unknown. The present work aims to identify and quantify potentially dangerous situations using the example of laparoscopic cholecystectomy (LC). Twenty-four final year medical students were prompted to each perform four consecutive LC attempts on a well-established box trainer in a surgical training environment following a standardized protocol in a porcine model. The following situation was defined as a critical event (CE): the dissecting instrument was inadvertently located outside the laparoscopic camera’s field of view. Simultaneous activation of the electrosurgical unit was defined as a highly critical event (hCE). Primary endpoint was the incidence of CEs. While performing 96 LCs, 2895 CEs were observed. Of these, 1059 (36.6%) were hCEs. The median number of CEs per LC was 20.5 (range: 1–125; IQR: 33) and the median number of hCEs per LC was 8.0 (range: 0–54, IQR: 10). Mean total operation time was 34.7 min (range: 15.6–62.5 min, IQR: 14.3 min). Our study demonstrates the significance of CEs as a potential risk factor for collateral damage during LC. Further studies are needed to investigate the occurrence of CE in clinical practice, not just for laparoscopic cholecystectomy but also for other procedures. Systematic training of future surgeons as well as technical solutions address this safety issue.
Bioactive cations, including calcium, copper and magnesium, have shown the potential to become the alternative to protein growth factor-based therapeutics for bone healing. Ion substitutions are less costly, more stable, and more effective at low concentrations. Although they have been shown to be effective in providing bone grafts with more biological functions, the precise control of ion release kinetics is still a challenge. Moreover, the synergistic effect of three or more metal ions on bone regeneration has rarely been studied. In this study, vaterite-calcite CaCO3 particles were loaded with copper (Cu2+) and magnesium (Mg2+). The polyelectrolyte multilayer (PEM) was deposited on CaCuMg-CO3 particles via layer-by-layer technique to further improve the stability and biocompatibility of the particles and to enable controlled release of multiple metal ions. The PEM coated microcapsules were successfully combined with collagen at the outmost layer, providing a further stimulating microenvironment for bone regeneration. The in vitro release studies showed remarkably stable release of Cu2+ in 2 months without initial burst release. Mg2+ was released in relatively low concentration in the first 7 days. Cell culture studies showed that CaCuMg-PEM-Col microcapsules stimulated cell proliferation, extracellular maturation and mineralization more effectively than blank control and other microcapsules without collagen adsorption (Ca-PEM, CaCu-PEM, CaMg-PEM, CaCuMg-PEM). In addition, the CaCuMg-PEM-Col microcapsules showed positive effects on osteogenesis and angiogenesis in gene expression studies. The results indicate that such a functional and controllable delivery system of multiple bioactive ions might be a safer, simpler and more efficient alternative of protein growth factor-based therapeutics for bone regeneration. It also provides an effective method for functionalizing bone grafts for bone tissue engineering.
Cell migration plays an essential role in wound healing and inflammatory processes inside the human body. Peripheral blood neutrophils, a type of polymorphonuclear leukocyte (PMN), are the first cells to be activated during inflammation and subsequently migrate toward an injured tissue or infection site. This response is dependent on both biochemical signaling and the extracellular environment, one aspect of which includes increased temperature in the tissues surrounding the inflammation site. In our study, we analyzed temperature-dependent neutrophil migration using differentiated HL-60 cells. The migration speed of differentiated HL-60 cells was found to correlate positively with temperature from 30 to 42 °C, with higher temperatures inducing a concomitant increase in cell detachment. The migration persistence time of differentiated HL-60 cells was higher at lower temperatures (30–33 °C), while the migration persistence length stayed constant throughout the temperature range. Coupled with the increased speed observed at high temperatures, this suggests that neutrophils are primed to migrate more effectively at the elevated temperatures characteristic of inflammation. Temperature gradients exist on both cell and tissue scales. Taking this into consideration, we also investigated the ability of differentiated HL-60 cells to sense and react to the presence of temperature gradients, a process known as thermotaxis. Using a two-dimensional temperature gradient chamber with a range of 27–43 °C, we observed a migration bias parallel to the gradient, resulting in both positive and negative thermotaxis. To better mimic the extracellular matrix (ECM) environment in vivo, a three-dimensional collagen temperature gradient chamber was constructed, allowing observation of biased neutrophil-like differentiated HL-60 migration toward the heat source.
Turning students into Industry 4.0 entrepreneurs: design and evaluation of a tailored study program
(2022)
Startups in the field of Industry 4.0 could be a huge driver of innovation for many industry sectors such as manufacturing. However, there is a lack of education programs to ensure a sufficient number of well-trained founders and thus a supply of such startups. Therefore, this study presents the design, implementation, and evaluation of a university course tailored to the characteristics of Industry 4.0 entrepreneurship. Educational design-based research was applied with a focus on content and teaching concept. The study program was first implemented in 2021 at a German university of applied sciences with 25 students, of which 22 participated in the evaluation. The evaluation of the study program was conducted with a pretest–posttest-design targeting three areas: (1) knowledge about the application domain, (2) entrepreneurial intention and (3) psychological characteristics. The entrepreneurial intention was measured based on the theory of planned behavior. For measuring psychological characteristics, personality traits associated with entrepreneurship were used. Considering the study context and the limited external validity of the study, the following can be identified in particular: The results show that a university course can improve participants' knowledge of this particular area. In addition, perceived behavioral control of starting an Industry 4.0 startup was enhanced. However, the results showed no significant effects on psychological characteristics.
Hybrid organic/inorganic nanocomposites combine the distinct properties of the organic polymer and the inorganic filler, resulting in overall improved system properties. Monodisperse porous hybrid beads consisting of tetraethylene pentamine functionalized poly(glycidyl methacrylateco-ethylene glycol dimethacrylate) particles and silica nanoparticles (SNPs) were synthesized under Stoeber sol-gel process conditions. A wide range of hybrid organic/silica nanocomposite materials with different material properties was generated. The effects of n(H2O)/n(TEOS) and c(NH3 ) on the hybrid bead properties particle size, SiO2 content, median pore size, specific surface area, pore volume and size of the SNPs were studied. Quantitative models with a high robustness and predictive power were established using a statistical and systematic approach based on response surface methodology. It was shown that the material properties depend in a complex way on the process factor settings and exhibit non-linear behaviors as well as partly synergistic interactions between the process factors. Thus, the silica content, median pore size, specific surface area, pore volume and size of the SNPs are non-linearly dependent on the water-to-precursor ratio. This is attributed to the effect of the water-to-precursor ratio on the hydrolysis and condensation rates of TEOS. A possible mechanism of SNP incorporation into the porous polymer network is discussed.
Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.
The euphoria around microservices has decreased over the years, but the trend of modernizing legacy systems to this novel architectural style is unbroken to date. A variety of approaches have been proposed in academia and industry, aiming to structure and automate the often long-lasting and cost-intensive migration journey. However, our research shows that there is still a need for more systematic guidance. While grey literature is dominant for knowledge exchange among practitioners, academia has contributed a significant body of knowledge as well, catching up on its initial neglect. A vast number of studies on the topic yielded novel techniques, often backed by industry evaluations. However, practitioners hardly leverage these resources. In this paper, we report on our efforts to design an architecture-centric methodology for migrating to microservices. As its main contribution, a framework provides guidance for architects during the three phases of a migration. We refer to methods, techniques, and approaches based on a variety of scientific studies that have not been made available in a similarly comprehensible manner before. Through an accompanying tool to be developed, architects will be in a position to systematically plan their migration, make better informed decisions, and use the most appropriate techniques and tools to transition their systems to microservices.
Glioblastoma WHO IV belongs to a group of brain tumors that are still incurable. A promising treatment approach applies photodynamic therapy (PDT) with hypericin as a photosensitizer. To generate a comprehensive understanding of the photosensitizer-tumor interactions, the first part of our study is focused on investigating the distribution and penetration behavior of hypericin in glioma cell spheroids by fluorescence microscopy. In the second part, fluorescence lifetime imaging microscopy (FLIM) was used to correlate fluorescence lifetime (FLT) changes of hypericin to environmental effects inside the spheroids. In this context, 3D tumor spheroids are an excellent model system since they consider 3D cell–cell interactions and the extracellular matrix is similar to tumors in vivo. Our analytical approach considers hypericin as probe molecule for FLIM and as photosensitizer for PDT at the same time, making it possible to directly draw conclusions of the state and location of the drug in a biological system. The knowledge of both state and location of hypericin makes a fundamental understanding of the impact of hypericin PDT in brain tumors possible. Following different incubation conditions, the hypericin distribution in peripheral and central cryosections of the spheroids were analyzed. Both fluorescence microscopy and FLIM revealed a hypericin gradient towards the spheroid core for short incubation periods or small concentrations. On the other hand, a homogeneous hypericin distribution is observed for long incubation times and high concentrations. Especially, the observed FLT change is crucial for the PDT efficiency, since the triplet yield, and hence the O2 activation, is directly proportional to the FLT. Based on the FLT increase inside spheroids, an incubation time 30 min is required to achieve most suitable conditions for an effective PDT.