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This article proposes several modified quasi Z-source dc/dc boost converters. These can achieve soft-switching by using a clamp-switch network comprised of an active switch and a diode in parallel with a capacitor connected across one of the inductors of the Z-source network. In this way, ringing at the transistor switching node is mitigated, and the voltage at the turn-on of the transistor is reduced. Even a zero voltage switching (ZVS) of the main transistor is possible if the capacitor in the clamp-switch network is adequately chosen. The proposed circuit structure and operating mode are described and validated through simulations and measurements on a low-power prototype.
Introduction: Even if there is a standard procedure of CI surgery, especially in pediatric surgery surgical steps often differ individually due to anatomical variations, malformations or unforseen events. This is why every surgical report should be created individually, which takes time and relies on the correct memory of the surgeon. A standardized recording of intraoperative data and subsequent storage as well as text processing would therefore be desirable and provides the basis for subsequent data processing, e.g. in the context of research or quality assurance.
Method: In cooperation with Reutlingen University, we conducted a workflow analysis of the prototype of a semi-automatic checklist tool. Based on automatically generated checklists generated from BPMN models a prototype user interface was developed for an android tablet. Functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OP documentation could be implemented. The system was tested in a remote usability test on a petrous bone model.
Result: The user interface allows a simple intuitive handling, which can be well implemented in the intraoperative setting. Clinical data as well as surgical steps could be individually recorded and saved via DICOM. An automatic surgery report could be created and saved.
Summary: The use of a dynamic checklist tool facilitates the capture, storage and processing of surgical data. Further applications in clinical practice are pending.
Natural wood colors occur within a wide range from almost white (e.g., white poplar), various yellowish, reddish, and brownish hues to almost black (e.g., ebony). The intrinsic color of wood is basically defined by its chemical composition. However, other factors such as specific anatomical formations or physical properties further affect the optical impression. Starting with the chemical composition of wood and anatomical basics, wood color and its modifications are discussed in this chapter. The classic method of coloring or re-coloring wood-based material surfaces is the application of a coating containing appropriate dyes or pigments. Different concepts for wood coating and coloration are presented. Another method used dyes for coloration of the wood structure. As alternative techniques, physical methods, for example, drying, steaming, ammoniation, bleaching, enzyme treatment, as well as treatment with electromagnetic irradiation (e.g., UV), are explained in this chapter.
Purpose
The purpose of this paper is to explore why men do not rent luxury fashion to explain why the demand for luxury fashion rental services for men is so low and to contribute to science by collecting high-quality data for the research fields gender differences in barriers to renting fashion, barriers to participating in renting luxury fashion in general and to increase the amount of data on men consumption behavior in the field of fashion and luxury fashion research. Furthermore, this study aims not only to make a theoretical contribution, but also to provide practical implications for the luxury fashion rental industry.
Design/methodology/approach
To answer the research question, qualitative semi-structured interviews with seven men were conducted, who are interested in fashion and spend at least 10% of their monthly net income on luxury fashion per month. Through a deductively-inductively category-based qualitative content analysis of the interviews supported by the software MAXQDA, not only were the reasons found why many men refuse to rent luxury fashion, but also characteristics were discovered that make luxury fashion rental services more attractive to men, as well as two fashion segments and a product category in which men can imagine renting fashion or luxury fashion under certain circumstances.
Findings
Men reject the concept of renting primarily because of the nonexistence of ownership, which has to do with loss of emotional value, loss of functional value, fear of social rejection, and identity concerns; other reasons include lack of individualism, lack of habit and their own subjective standards. Except for two outliers, the remaining men surveyed could imagine using a luxury rental service under certain conditions. The most frequently mentioned features were omnichannel approach, transparency of the entire rental process provided by reviews and feedback about both the borrower and the lender, information about the cleaning process, and proof of authenticity. Also mentioned was the maintenance of exclusivity and the fact that rental services should be offered directly by the company. In the convenience category, the purchase option and insurance were mentioned most often. In addition, some men could imagine renting event-related clothing, very trendy and expensive luxury clothing, and luxury watches. However, none of the respondents would give up owning clothes and primarily use the LFRS.
Value/Practical Implications
So that marketers do not have to go through trial and error to figure out which of these characteristics works best for which male target group, the work developed five types that can be targeted with selected characteristics and their marketing, and thus perhaps persuaded to participate in the LFRS. The social type needs the feature of maintenance of exclusivity, the emotional type needs the purchase option and an omnichannel experience, the flexibility type needs the same day delivery and free exchange possibilities, the cost-benefit type needs analytical tools to maximize his rental income or to calculate whether it is cheaper to buy or rent this particular item for this particular period of time, the rule-governed type needs an added value in addition to renting such as a top service.
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.
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.
Product engineering and subsequent phases of product lifecycles are predominantly managed in isolation. Companies therefore do not fully exploit potentials through using data from smart factories and product usage. The novel intelligent and integrated Product Lifecycle Management (i²PLM) describes an approach that uses these data for product engineering. This paper describes the i²PLM, shows the cause-and-effect relationships in this context and presents in detail the validation of the approach. The i²PLM is applied and validated on a smart product in an industrial research environment. Here, the subsequent generation of a smart lunchbox is developed based on production and sensor data. The results of the validation give indications for further improvements of the i²PLM. This paper describes how to integrate the i²PLM into a learning factory.
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.
Applications often need to be deployed in different variants due to different customer requirements. However, since modern applications often need to be deployed using multiple deployment technologies in combination, such as Ansible and Terraform, the deployment variability must be considered in a holistic way. To tackle this, we previously developed Variability4TOSCA and the prototype OpenTOSCA Vintner, which is a TOSCA preprocessing and management layer that implements Variability4TOSCA. In this demonstration, we present a detailed case study that shows how to model a deployment using Variability4TOSCA, how to resolve the variability using Vintner, and how the result can be deployed.
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.
In modern collaborative production environments where industrial robots and humans are supposed to work hand in hand, it is mandatory to observe the robot’s workspace at all times. Such observation is even more crucial when the robot’s main position is also dynamic e.g. because the system is mounted on a movable platform. As current solutions like physically secured areas in which a robot can perform actions potentially dangerous for humans, become unfeasible in such scenarios, novel, more dynamic, and situation aware safety solutions need to be developed and deployed.
This thesis mainly contributes to the bigger picture of such a collaborative scenario by presenting a data-driven convolutional neural network-based approach to estimate the two-dimensional kinematic-chain configuration of industrial robot-arms within raw camera images. This thesis also provides the information needed to generate and organize the mandatory data basis and presents frameworks that were used to realize all involved subsystems. The robot-arm’s extracted kinematic-chain can also be used to estimate the extrinsic camera parameters relative to the robot’s three-dimensional origin. Further a tracking system, based on a two-dimensional kinematic chain descriptor is presented to allow for an accumulation of a proper movement history which enables the prediction of future target positions within the given image plane. The combination of the extracted robot’s pose with a simultaneous human pose estimation system delivers a consistent data flow that can be used in higher-level applications.
This thesis also provides a detailed evaluation of all involved subsystems and provides a broad overview of their particular performance, based on novel generated, semi automatically annotated, real datasets.
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%.
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.
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.
Blockchains have become increasingly important in recent years and have expanded their applicability to many domains beyond finance and cryptocurrencies. This adoption has particularly increased with the introduction of smart contracts, which are immutable, user-defined programs directly deployed on blockchain networks. However, many scenarios require business transactions to simultaneously access smart contracts on multiple, possibly heterogeneous blockchain networks while ensuring the atomicity and isolation of these transactions, which is not natively supported by current blockchain systems. Therefore, in this work, we introduce the Transactional Cross-Chain Smart Contract Invocation (TCCSCI) approach that supports such distributed business transactions while ensuring their global atomicity and serializability. The approach introduces the concept of Resource Manager Smart Contracts, and 2PC for Blockchains (2PC4BC), a client-driven Atomic Commit Protocol (ACP) specialized for blockchain-based distributed transactions. We validate our approach using a prototypical implementation, evaluate its introduced overhead, and prove its correctness.
The aim of this work is the development of artificial intelligence (AI) application to support the recruiting process that elevates the domain of human resource management by advancing its capabilities and effectiveness. This affects recruiting processes and includes solutions for active sourcing, i.e. active recruitment, pre-sorting, evaluating structured video interviews and discovering internal training potential. This work highlights four novel approaches to ethical machine learning. The first is precise machine learning for ethically relevant properties in image recognition, which focuses on accurately detecting and analysing these properties. The second is the detection of bias in training data, allowing for the identification and removal of distortions that could skew results. The third is minimising bias, which involves actively working to reduce bias in machine learning models. Finally, an unsupervised architecture is introduced that can learn fair results even without ground truth data. Together, these approaches represent important steps forward in creating ethical and unbiased machine learning systems.
Smart cities are considered data factories that generate an enormous amount of data from various sources. In fact data is the backbone of any smart services. Therefore, the strategic beneficial handling of this digital capital is crucial for cities. Some smart city pioneers have already written down their approach to data in the form of data strategies, but what should a city's data strategy include, and how can the goals and measures defined in the strategies be operationalized? This paper addresses these questions by looking closely at the data strategies of cities in Germany and the top three countries in the EU Digital Economy and Society Index. The in-depth analysis of 8 city data strategies has yielded 11 dimensions that cities should consider in their data strategy. These are relevance of data, principles, methods, data sharing, technology, data culture, data ethics, organizational structure, data security and privacy, collaborations, data literacy. In addition, data governance is a concept to put these 11 strategic dimensions into practice through standardization measures, training programs, and defining roles and responsibilities by developing a data catalog.
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.
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.
Titanium(IV) surface complexes bearing chelating catecholato ligands for enhanced band-gap reduction
(2023)
Protonolysis reactions between dimethylamido titanium(IV) catecholate [Ti(CAT)(NMe2)2]2 and neopentanol or tris(tert-butoxy)silanol gave catecholato-bridged dimers [(Ti(CAT)(OCH2tBu)2)(HNMe2)]2 and [Ti(CAT){OSi(OtBu)3}2(HNMe2)2]2, respectively. Analogous reactions using the dimeric dimethylamido titanium(IV) (3,6-di-tert-butyl)catecholate [Ti(CATtBu2-3,6)(NMe2)2]2 yielded the monomeric Ti(CATtBu2-3,6)(OCH2tBu)2(HNMe2)2 and Ti(CATtBu2-3,6)[OSi(OtBu)3]2(HNMe2)2. The neopentoxide complex Ti(CATtBu2-3,6)(OCH2tBu)2(HNMe2)2 engaged in further protonolysis reactions with Si–OH groups and was consequentially used for grafting onto mesoporous silica KIT-6. Upon immobilization, the surface complex [Ti(CATtBu2-3,6)(OCH2tBu)2(HNMe2)2]@[KIT-6] retained the bidentate chelating geometry of the catecholato ligand. This convergent grafting strategy was compared with a sequential and an aqueous approach, which gave either a mixture of bidentate chelating species with a bipodally anchored Ti(IV) center along with other physisorbed surface species or not clearly identifiable surface species. Extension of the convergent and aqueous approaches to anatase mesoporous titania (m-TiO2) enabled optical and electronic investigations of the corresponding surface species, revealing that the band-gap reduction is more pronounced for the bidentate chelating species (convergent approach) than for that obtained via the aqueous approach. The applied methods include X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, and solid-state UV/vis spectroscopy. The energy-level alignment for the surface species from the aqueous approach, calculated from experimental data, accounts for the well-known type II excitation mechanism, whereas the findings indicate a distinct excitation mechanism for the bidentate chelating surface species of the material [Ti(CATtBu2-3,6)(OCH2tBu)2(HNMe2)2]@[m-TiO2].
The increase in distributed energy generation, such as photovoltaic systems (PV) or combined heat and power plants (CHP), poses new challenges to almost every distribution network operator (DNO). In the low-voltage (LV) grids, where installed PV capacity approaches the magnitude of household load, reverse power flow occurs at the secondary substa-tions. High PV penetration leads to voltage rise, flicker and loading problems. These problems have been addressed by the application of various techniques amongst which is the deployment of step voltage regulators (SVR). SVR can solve the voltage problem, but do not prevent or reduce reverse power flows. Therefore, the application of SVR in low voltage grids can result in significant power losses upstream. In this paper we present part of a research project investi-gating the application of remote-controlled cable cabinets (CC) with metering units in a low-voltage network as a possible alternative for SVR. A new generation of custom-made remote-control cable cabinets has been deployed and dynamic network reconfigurations (NR) have been realized with the following objectives: (i) reduction of reverse power flow through the secondary substation to the upstream network and therefore a reduction of upstream losses, (ii) reduction of the voltage rise caused by distributed energy resources and (iii) load balancing in the low-voltage grid. Secondary objec-tives are to improve the DNO's insight into the state of the network and to provide further information on future smart grid integration.
This study introduces a straightforward approach to construct three-dimensional (3D) surface-enhanced Raman spectroscopy (SERS) substrates using chemically modified silica particles as microcarriers and by attaching metal nanoparticles (NPs) onto their surfaces. Tollens’ reagent and sputtering techniques are utilized to prepare the SERS substrates from mercapto-functionalized silica particles. Treatment with Tollens’ reagent generates a variety of silver NPs, ranging from approximately 10 to 400 nm, while sputtering with gold (Au) yields uniformly distributed NPs with an island-like morphology. Both substrates display wide plasmon resonances in the scattering spectra, making them effective for SERS in the visible spectral range, with enhancement factors (ratio of the analyte’s intensity at the hotspot compared to that on the substrate in the absence of metal nanoparticles) of up to 25. These 3D substrates have a significant advantage over traditional SERS substrates because their active surface area is not limited to a 2D surface but offers a much greater active surface due to the 3D arrangement of the NPs. This feature may enable achieving much higher SERS intensity from within streaming liquids or inside cells/tissues.
For large-scale processes as implemented in organizations that develop software in regulated domains, comprehensive software process models are implemented, e.g., for compliance requirements. Creating and evolving such processes is demanding and requires software engineers having substantial modeling skills to create consistent and certifiable processes. While teaching process engineering to students, we observed issues in providing and explaining models. In this paper, we present an exploratory study in which we aim to shed light on the challenges students face when it comes to modeling. Our findings show that students are capable of doing basic modeling tasks, yet, fail in utilizing models correctly. We conclude that the required skills, notably abstraction and solution development, are underdeveloped due to missing practice and routine. Since modeling is key to many software engineering disciplines, we advocate for intensifying modeling activities in teaching.
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.
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.
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.
Purpose
The authors study the valuation effect of corporate diversification in the initial phase of the COVID-19 pandemic in 2020 in Europe.
Design/methodology/approach
Applying a cross-sectional regression model to a sample of public companies headquartered in the European Union, the authors investigate the existence of and the change in a diversification discount between 2018 and 2020. By applying the Excess Q methodology, the authors make an industry adjustment of diversified companies to measure the value effect of corporate diversification.
Findings
The authors find an economically and statistically significant diversification discount that increases from an average Excess Q of −0.05 in 2019 to −0.10 in 2020. The diversified companies' inferior fundamental financial performance in 2020 accompanies the discount. The results deviate from those of previous research, which mostly show a decrease in the diversification discount in economic crises, and thereby, shed doubt on whether diversification provides insurance against pandemic-induced adverse value effects.
Originality/valueThe study distinguishes the role of corporate diversification during recessionary periods by establishing that the valuation effect of diversification depends on the nature of the crisis. The analysis incorporates criticism of previous studies concerning a biased methodology and uniform data source by applying the Excess Q methodology and using FactSet industry segment data.
Most Question-answering (QA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose TFCSG, an unsupervised similar question retrieval approach that leverages pre-trained language models and multi-task learning. Firstly, topic keywords in question sentences are extracted sequentially based on a latent topic-filtering algorithm to construct unsupervised training corpus data. Then, the multi-task learning method is used to build the question retrieval model. There are three tasks designed. The first is a short sentence contrastive learning task. The second is the question sentence and its corresponding topic sequence similarity judgment task. The third is using question sentences to generate their corresponding topic sequence task. The three tasks are used to train the language model in parallel. Finally, similar questions are obtained by calculating the cosine similarity between sentence vectors. The comparison experiment on public question datasets that TFCSG outperforms the comparative unsupervised baseline method. And there is no need for manual marking, which greatly saves human resources.
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.
The 17 SDGs, as agreed upon by the international community, are designed to be implemented across all levels of human activity. Alongside the level of international politics, this also includes the local levels, national politics, wider society, and the economic sphere. Many channels are called on to further implementation, including the transfer of technology to developing and emerging countries. As the patent holders, this must include the active participation of companies. While the literature examines the important role of technology transfer in North-South business-to-business (B2B) partnerships, studies on the technology transfer between European and African companies are scarce. Therefore, in this study we use original data from 26 interviews conducted with managers engaged in sales partnerships between German manufacturers and their distributors in African markets to examine the existence and forms of technology transfer. We find that training and marketing excellence are the predominant forms of technology transfer and based on that suggest a refinement of established frameworks on B2B technology transfer.
Ecuador, traditionally an agricultural based economy, has a great potential for valorizing their industrial residues. This study, presents a techno-economic analysis for applying a novel biomass oxidation method to produce formic and acetic acids from coffee husk residues in Machala, Ecuador. The analysis determined that the time of return of investment was lower than 5 years, making this project economically feasible, when producing approx. 1000 tons of formic acid per year, which is enough for supplying the Ecuadorian market. This production, would reduce imports costs and develop the chemical industry in the country.
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 targeted design of monodisperse, mesoporous silica microspheres (MPSMs) as HPLC separation phases is still a challenge. The MPSMs can be generated via a multi-step template-assisted method. However, this method and the factors affecting the individual process steps and resulting material properties are scarcely understood, and specific control of the complex multi-step process has been hardly discussed. In this work, the key synthesis steps were systematically investigated by means of statistical Design of Experiment (DoE). In particular, three steps were considered in detail: 1) the synthesis of porous poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) (p(GMA-co-EDMA)) particles, which as template particles, determine the structure for the final MPSMs. In this context, functional models were generated, which allow the control of the template properties pore volume, pore size and specific surface area. 2) In the presence of amino-functionalized template particles, the sol-gel process was carried out under Stöber process conditions. The water to tetraethyl orthosilicate (TEOS) ratio, as well as the concentration of ammonia as basic catalyst were varied according to a face-centered central composite design (FCD). The incorporation of silica nanoparticles (SNPs) into the pore network of the porous polymers was investigated by scanning electron microscopy (SEM), evaluation of the pore properties assessed by nitrogen sorption measurements and determination of the inorganic content by thermogravimetric analysis (TGA). Here, the material properties, such as the amount of attached silica, can be specifically controlled in the resulting organic/silica hybrid material (hybrid beads, HBs). Furthermore, depending on the sol-gel conditions three, potentially four, reaction regimes were identified, leading to different HBs. These range from porous polymer particles coated with a thin protective silica layer, to interpenetrating networks of polymer and silica, to potential particles consisting of a porous polymer core coated with a silica shell. Also, the effects of the use of different precursors and solvents on silica incorporation were investigated. 3) To obtain MPSMs from the HBs, the organic polymer template was removed by calcination. The effects of sol-gel process conditions on the resulting MPSMs were evaluated and relationships between process conditions and material properties were shown in predictive models. Fully porous, spherical, monodisperse silica particles with sizes ranging from 0.5 µm to 7.8 µm and pore sizes from 3.5 nm to 72.4 nm can be prepared specifically. Subsequent to organo-functionalization, prepared MPSMs were applied as reversed-phase HPLC column materials. Here, the columns were successfully applied for the separation of proteins and amino acids. The separation performance of the materials depends largely on the property profile of the MPSMs, which is predetermined during the preparation of the HBs.
Organizational agility may be an antidote against threats from volatile, uncertain, complex, or ambiguous corporate environments. While agility has been extensively examined in manufacturing enterprises, comparably less is known about agility in knowledge-intensive organizations. As results may not be transferable, there is still some confusion about how agility in knowledge-intensive organizations can be characterized, what factors facilitate its development, what its organizational effects are, and what environmental conditions favor these effects. This study closes these gaps by presenting a systematic literature review on agility in knowledge-intensive organizations. A systematic literature search led to a sample of 37 relevant papers for our review. Integrating the knowledge-based view and a dynamic capabilities perspective, we (1) present different relevant conceptualizations of organizational agility, (2) discuss relevant knowledge management-related as well as information technology-related capabilities that support the development of organizational agility, and (3) shed light on the moderating role of environmental conditions in enhancing organizational agility and its effect on organizational performance. This academic paper adds value to theory by synthesizing existing research on agility in knowledge-intensive organizations. It furthermore may serve as a map for closing research gaps by proposing an extensive agenda for future research. Our study expands existing literature reviews on agility with its specific focus on a knowledge-intensive context and its integration of the research streams of knowledge management capabilities as well as information technology capabilities. It integrates relevant organizational knowledge management practices and the use of knowledge management systems to ensure superior performance effects. Our study can serve as a base for future examinations of organizational agility by illustrating fruitful topics for further examination as well as open questions. It may also provide value to practitioners by showing what factors favor the development of agility in knowledge-intensive organizations and what organizational effects can be achieved under which conditions.
Facing ever-looming climate change, studying the drivers for individuals' Information Systems (IS) Use to reduce environmental harm gains momentum. While extant research on the antecedents of sustainable IS Use has focused on specific theories, interventions, contexts, and technologies, a holistic understanding has become increasingly elusive, with a synthesis remaining absent. We employ a systematic literature review methodology to shed light on the driving antecedents for sustainable IS Use among individual consumers. Our results build on findings of 29 empirical studies drawn from 598 articles retrieved from our premier outlets and a forward/backward search. The analysis reveals six salient complementary antecedents: Relief, Empowerment, Default, User-centricity, Salience, and Encouragement. We recommend considering these concepts when developing, deploying, promoting, or regulating digital technologies to mitigate individual consumers' emissions. Along with memorable and implementable concepts, our theoretical framework offers a novel conceptualization and four promising avenues for researchers on sustainable IS Use.
The fifth generation of mobile communication (5G) is a wireless technology developed to provide reliable, fast data transmission for industrial applications, such as autonomous mobile robots and connect cyber-physical systems using Internet of Things (IoT) sensors. In this context, private 5G networks enable the full performance of industrial applications built on dedicated 5G infrastructures. However, emerging wireless communication technologies such as 5G are a complex and challenging topic for training in learning factories, often lacking physical or visual interaction. Therefore, this paper aims to develop a real-time performance monitoring system of private 5G networks and different industrial 5G devices to visualise the performance and impact factors influencing 5G for students and future connectivity experts. Additionally, this paper presents the first long-term measurements of private 5G networks and shows the performance gap between the actual and targeted performance of private 5G networks.
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.
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.
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.
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.
Purpose
Digital transformation of organizations has major implications for required skills and competencies of the workforce, both as a prerequisite for implementation, and, as a consequence of the transformation. The purpose of this study is to analyze required skills and competencies for digital transformation using the context of robotic process automation (RPA) as an example.
Design/methodology/approach
This study is based on an explorative, thematic coding analysis of 119 job advertisements related to RPA. The data was collected from major online job platforms, qualitatively coded and subsequently analyzed quantitatively.
Findings
The research highlights the general importance of specific skills and competencies for digital transformation and shows a gap between available skills and required skills. Moreover, it is concluded that reskilling the existing workforce might be difficult. Many emerging positions can be found in the consulting sector, which raises questions about the permanent vs temporary nature of the requirements, as well as the difficulty of acquiring the required knowledge.
Originality/value
This paper contributes to knowledge by providing new empirical findings and a novel perspective to the ongoing discussion of digital skills, employment effects and reskilling demands of the existing workforce owing to recent technological developments and automation in the overall context of digital transformation.
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.
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
Governments and public institutions increasingly embrace digital opportunities to involve citizens in public issues and decision making. While public participation is generally seen as an important and promising venture, the design of the participation processes and the utilized digital infrastructure poses challenges, especially to the public sector. Instead of limiting conceptual guidance and exchange to one domain, we therefore develop a taxonomy for digital involvement projects that unites the domains of e-participation, citizen science and crowd-X. Embedded in a design science research approach, we follow an iterative design process to elaborate the key characteristics of a digital involvement project based on the participation process, its individuals and digital infrastructure. Through evaluating the artifact in a focus group with domain practitioners, we find support for the usefulness of our taxonomy and its ability to provide guidance and a basis for discussion of digital involvement projects across domains.
Since its first publication in 2015, the learning factory morphology has been frequently used to design new learning factories and to classify existing ones. The structuring supports the concretization of ideas and promotes exchange between stakeholders.
However, since the implementation of the first learning factories, the learning factory concept has constantly evolved.
Therefore, in the Working Group "Learning Factory Design" of the International Association of Learning Factories, the existing morphology has been revised and extended based on an analysis of the trends observed in the evolution of learning factory concepts. On the one hand, new design elements were complemented to the previous seven design dimensions, and on the other hand, new design dimensions were added. The revised version of the morphology thus provides even more targeted support in the design of new learning factories in the future.
The paper “focuses on the critique of economic rationality” (p. 2). The author analyses the work by Amartya Sen with a somewhat interdisciplinary approach. The author concludes that Sen has greatly shifted our paradigm of economic rationality. The nexus of ethics and economics as well as the two types of rationality (consistency versus optimization) are major contributions of Sen, according to the author. In a nutshell, Sen’s work is reconfiguring economic rationality until today.
Rapid and robust quality monitoring of the composition of meat pastes is of fundamental importance in processing meat and sausage products. Here, an in-line near-infrared spectroscopy/micro-electro-mechanical-system-(MEMS)-based approach, combined with multivariate data analysis, was used for measuring the constituents fat, protein, water, and salt in meat pastes within a typical range of meat paste recipes. The meat pastes were spectroscopically characterized in-line with a novel process analyzer prototype. By integrating salt content in the calibration set, robust predictive PLSR models of high accuracy (R2 > 0.81) were obtained that take interfering matrix effects of the minor and NIR-inactive meat paste recipe component “salt” into account as well. The nonlinear blending behavior of salt concentration on the spectral features of meat pastes is discussed based on a designed mixture experiment with four systematically varied components.
The blockchain technology represents a decentralised database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. This paper proposes a prototypical blockchain application that adopts an authority concept and a concept of smart non-fungible tokens. The application enables the mapping of complex products in dynamic supply chains that require the auditability of changeable assembling processes on the blockchain. Finally, the paper demonstrates the practical feasibility of the proposed application based on a prototypical implementation created on the Ethereum blockchain.
The increase in product variance and shorter product lifecycles result in higher production ramp-up frequencies and promote the usage of mixed-model lines. The ramp-up is considered a critical step in the product life cycle and in the automotive industry phases of the ramp-up are often executed on separated production lines (pilot lines) or factories (pilot plants) to verify processes and to qualify employees without affecting the production of other products in the mixed-model line. The required financial funds for planning and maintaining dedicated pilot lines prevent small and medium-sized enterprises (SMEs) from the application. Hence, SMEs require different tools for piloting and training during the production ramp-up. Learning islands on which employees can be trained through induced and autonomous learning propose a solution. In this work, a concept for the development and application which contains the required organization, activities, and materials is developed through expert interviews. The results of a case study application with a medium-sized automotive manufacturer show that learning islands are a viable tool for employee qualification and process verification during the ramp-up of mixed-model lines.
For optimization of production processes and product quality, often knowledge of the factors influencing the process outcome is compulsory. Thus, process analytical technology (PAT) that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality. The present study aims at characterizing a well-known industrial process, the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters (FAME) for usage as biodiesel in a continuous micro reactor set-up. To this end, a design of experiment approach is applied, where the effects of two process factors, the molar ratio and the total flow rate of the reactants, are investigated. The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield. The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression. The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis. A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination (R²) of 0.9608. Thus, we applied a PAT approach to generate further insight into this established industrial process.
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.
With the rapid development of globalization, the demand for translation between different languages is also increasing. Although pre-training has achieved excellent results in neural machine translation, the existing neural machine translation has almost no high-quality suitable for specific fields. Alignment information, so this paper proposes a pre-training neural machine translation with alignment information via optimal transport. First, this paper narrows the representation gap between different languages by using OTAP to generate domain-specific data for information alignment, and learns richer semantic information. Secondly, this paper proposes a lightweight model DR-Reformer, which uses Reformer as the backbone network, adds Dropout layers and Reduction layers, reduces model parameters without losing accuracy, and improves computational efficiency. Experiments on the Chinese and English datasets of AI Challenger 2018 and WMT-17 show that the proposed algorithm has better performance than existing algorithms.
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 performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-bound. Processing-in-Memory (PIM) designs seek to overcome these limitations by integrating memory and processing functionality on the same chip. PIM targets near- or in-memory data processing, leveraging the greater in-situ parallelism and bandwidth.
In this paper, we introduce pimDB and provide an initial comparison of processor-centric and PIM-DBMS approaches under different aspects, such as scalability and parallelism, cache-awareness, or PIM-specific compute/bandwidth tradeoffs. The evaluation is performed end-to-end on a real PIM hardware system from UPMEM.
In clothing e-commerce, the challenge of optimally recommending clothing that suits a user’s unique characteristics remains a pressing issue. Many platforms simply recommend best-selling or popular clothing, without taking into account important attributes like user’s face color, pupil color, face shape, age, etc. To solve this problem, this paper proposes a personalized clothing recommendation algorithm that incorporates the established 4-Season Color System and user-specific biological characteristics. Firstly, the attributes and colors of clothing are classified by Fnet network, that can learn disjoint label combinations and mitigate the issue of excessive labels. Secondly, on the basis of the 4-Season Color System, the user’s face color model is trained by combined MobileNetV3_DTL, which ensures the model’s generalization and improves the training speed. Thirdly, user’s face shape and age are divided into different categories by an Inception network. Finally, according to the users’ face color, age, face shape and other information, personalized clothing is recommended in a coarse-to-fine manner. Experiments on five datasets demonstrate that the algorithm proposed in this paper achieves state-of-the-art results.
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.
We present the results of an extensive characterization of the performance and stability of a third-order continuous-time delta-sigma modulator with active coefficient error compensation. Using our previously published coefficient tuning technique, process variation induced R-C time-constant (TC) errors in the forward signal path can be compensated indirectly using continuously tunable DACs in the feedback path. To validate our technique experimentally with a range of real TC variations, we designed a modulator with discretely configurable integration capacitor arrays in a 0.35-μm CMOS process. We configured the capacitors of the fabricated device for a range of total TC variations from -28.4 % to +19.3 % and measured the signal-to-noise ratio (SNR) as a function of the input amplitude before and after compensating the variations electrically using the feedback DACs. The results show that our tuning technique is capable of restoring the desired nominal modulator performance over the entire parameter variation range, including the system’s nominal maximum stable amplitude (MSA).
AI-based prediction and recommender systems are widely used in various industry sectors. However, general acceptance of AI-enabled systems is still widely uninvestigated. Therefore, firstly we conducted a survey with 559 respondents. Findings suggested that AI-enabled systems should be fair, transparent, consider personality traits and perform tasks efficiently. Secondly, we developed a system for the Facial Beauty Prediction (FBP) benchmark that automatically evaluates facial attractiveness. As our previous experiments have proven, these results are usually highly correlated with human ratings. Consequently they also reflect human bias in annotations. An upcoming challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. In this work, we introduce AntiDiscriminationNet (ADN), a superior attractiveness prediction network. We propose a new method to generate an unbiased convolutional neural network (CNN) to improve the fairn ess of machine learning in facial dataset. To train unbiased networks we generate synthetic images and weight training data for anti-discrimination assessments towards different ethnicities. Additionally, we introduce an approach with entropy penalty terms to reduce the bias of our CNN. Our research provides insights in how to train and build fair machine learning models for facial image analysis by minimising implicit biases. Our AntiDiscriminationNet finally outperforms all competitors in the FBP benchmark by achieving a Pearson correlation coefficient of PCC = 0.9601.
The proliferation of smart technologies transforms the way individual consumers perform tasks. Considerable research alludes that smart technologies are often related to domestic energy consumption. However, it remains unclear how such technologies transform tasks and thereby impact our planet. We explore the role of technological smartness in personal day-to-day tasks that help create a more sustainable future. In the absence of theory, but facing extensive changes in everyday life enabled by smart technologies, we draw on phenomenon-based theorizing (PBT) guidelines. As anchor, we refer to task endogeneity related to task-technology fit theory (TTF). As infusion, we employ theory on public goods. Our model proposes novel relations between the concepts of smart autonomy and -transparency with sustainable task outcomes, mediated by task convenience and task significance. We discuss some implications, limitations, and future research opportunities.
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.
Near-Data Processing (NDP) is a key computing paradigm for reducing the ever growing time and energy costs of data transport versus computations. With their flexibility, FPGAs are an especially suitable compute element for NDP scenarios. Even more promising is the exploitation of novel and future non-volatile memory (NVM) technologies for NDP, which aim to achieve DRAM-like latencies and throughputs, while providing large capacity non-volatile storage.
Experimentation in using FPGAs in such NVM-NDP scenarios has been hindered, though, by the fact that the NVM devices/FPGA boards are still very rare and/or expensive. It thus becomes useful to emulate the access characteristics of current and future NVMs using off-the-shelf DRAMs. If such emulation is sufficiently accurate, the resulting FPGA-based NDP computing elements can be used for actual full-stack hardware/software benchmarking, e.g., when employed to accelerate a database.
For this use, we present NVMulator, an open-source easy-to-use hardware emulation module that can be seamlessly inserted between the NDP processing elements on the FPGA and a conventional DRAM-based memory system. We demonstrate that, with suitable parametrization, the emulated NVM can come very close to the performance characteristics of actual NVM technologies, specifically Intel Optane. We achieve 0.62% and 1.7% accuracy for cache line sized accesses for read and write operations, while utilizing only 0.54% of LUT logic resources on a Xilinx/AMD AU280 UltraScale+ FPGA board. We consider both file-system as well as database access patterns, examining the operation of the RocksDB database when running on real or emulated Optane-technology memories.
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively.
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.
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.
Smart factories, driven by the integration of automation and digital technologies, have revolutionized industrial production by enhancing efficiency, productivity, and flexibility. However, the optimization and continuous improvement of these complex systems present numerous challenges, especially when real-world data collection is time-consuming, expensive, or limited. In this paper, we propose a novel method for semi-automated improvement of smart factories using synthetic data and cause-effect-relations, while incorporating the aspect of self-organization. The method leverages the power of synthetic data generation techniques to create representative datasets that mimic the behaviour of real-world manufacturing systems. These synthetic datasets serve together with the cause-and-effect relationships as a valuable resource for factory optimization, as they enable extensive experimentation and analysis without the constraints of limited or costly real-world data. Furthermore, the method embraces the concept of self organization within smart factories. By allowing the system to adapt and optimize itself based on feedback from the synthetic data, cause-effect-relationships, the factory can dynamically reconfigure and adjust its processes. To facilitate the improvement process, the method integrates the synthetic data with advanced analytics and machine learning algorithms as well as and the cause-and-effect relationships. This synergy between human expertise and technological advancements represents a compelling path towards a truly optimized smart factory of the future.
Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.
Application systems often need to be deployed in different variants if requirements that influence their implementation, hosting, and configuration differ between customers. Therefore, deployment technologies, such as Ansible or Terraform, support a certain degree of variability modeling. Besides, modern application systems typically consist of various software components deployed using multiple deployment technologies that only support their proprietary, non-interoperable variability modeling concepts. The Variable Deployment Metamodel (VDMM) manages the deployment variability across heterogeneous deployment technologies based on a single variable deployment model. However, VDMM currently only supports modeling conditional components and their relations which is sometimes too coarse-grained since it requires modeling entire components, including their implementation and deployment configuration for each different component variant. Therefore, we extend VDMM by a more fine-grained approach for managing the variability of component implementations and their deployment configurations, e.g., if a cheap version of a SaaS deployment provides only a community edition of the software and not the enterprise edition, which has additional analytical reporting functionalities built-in. We show that our extended VDMM can be used to realize variable deployments across different individual deployment technologies using a case study and our prototype OpenTOSCA Vintner.
Most digital innovations fail when they transition from the exploring to the scaling stage. We describe how freeyou, the digital innovation spinoff of a major German insurer, successfully scaled online-only car insurance, focusing particularly on how it managed the IT-related challenges. The stark differences between the stages required very different approaches to application development, IT organization and data analytics. Based on freeyou’s experience, we provide recommendations for successful transitioning from exploring to scaling.
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.
Transforming our food system is important to achieving global climate neutrality and food security. Germany has set a national target of reaching a 30% share in organic farming to support the goal. When looking at the transformation process from conventional to organic farming, it becomes apparent that measures need to be taken to reach this anticipated goal. A particular emphasis of this work is placed on finding a digital solution and process improvements to ensure longevity and efficiency. Interviews with actors along the farm-to-fork value chain were conducted to identify central barriers and drivers of organic transformation. The results of the interviews show firstly, that three subsystems need to be distinguished when talking about the farm-to-fork value chain: (1) farmers, (2) intermediaries, and (3) the canteen system. Although all three subsystems can be combined to form a coherent value chain, they rarely act and communicate beyond the boundaries of their subsystem. Secondly, we were able to allocate primary barriers and drivers to each of the subsystems, highlighting the need to include all three in the transformation process and aim for a comprehensive digital solution. This work explores the potential of a network-based platform to improve the current practice of rigid and strictly hierarchical value chains. We focus on deriving user requirements from the interviews to describe the necessary functionality of the platform to address the identified barriers and exploit existing drivers.
This research evaluates current measurement scales for ambidexterity and proposes a new approach for the measurement of this important construct. We argue that current measurement approaches may be unsuitable to capture the concept of ambidexterity. Through a systematic scale development process, we derive a measurement scale with dual items that simultaneously refer to both dimensions, exploitation and exploration, thus reflecting the true nature of ambidexterity. An extensive pre-test with 39 executives suggests that our scale is suitable for capturing ambidexterity. Our measurement model enhances conceptual clarity of ambidexterity and can serve as a base for future investigations of the concept.
The dawn of the 21st Century has witnessed a tremendous increase in trade pacts among nations, resulting in renewed hopes for sustainable enterprise development in emerging economies worldwide. Ghana and other sub-Saharan African (SSA) countries have signed onto several North-South and South-South free trade agreements with the hope of strengthening their presence in the international trade arena, and to promote economic growth in SSA. For over two decades, however, very little has changed, and many have dashed their high hopes as enterprises continue to struggle in SSA. Not even the African Continental Free Trade Agreement (AfCFTA) could renew the hopes of sceptics. Several studies opined that enterprises in SSA could improve their domestic and international competitiveness by establishing mutually beneficial partnerships with their counterparts from the Global North and South. This study delved into the issues that affect North-South and South-South business collaborations and recommends key success factors that could help promote mutually beneficial cross-border business partnerships. The research includes both literature and empirical information on the key success factors of business partnerships between African enterprises as well as between African enterprises and firms from the Global North. We approached the study qualitatively using a phenomenological research design. Research participants included important stakeholders in Africa and Europe's international trade and sustainable enterprise development ecosystem. The study identified several challenges with the current business collaborations and recommended new ways of making such partnerships more beneficial.
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.
Introduction to the special issue on self‑managing and hardware‑optimized database systems 2022
(2023)
Data management systems have evolved in terms of functionality, performance characteristics, complexity, and variety during the last 40 years. Particularly, the relational database management systems and the big data systems (e.g., Key-Value stores, Document stores, Graph stores and Graph Computation Systems, Spark, MapReduce/Hadoop, or Data Stream Processing Systems) have evolved with novel additions and extensions. However, the systems administration and tasks have become highly complex and expensive, especially given the simultaneous and rapid hardware evolution in processors, memory, storage, or networking. These developments present new open problems and challenges to data management systems as well as new opportunities.
The SMDB (International Workshop on Self-Managing Database Systems) and HardBD&Active (Joint International Workshop on Big Data Management on Emerging Hardware and Data Management on Virtualized Active Systems) workshops organized in conjunction with the IEEE ICDE (International Conference on Data Engineering) offered two distinct platforms for examining the above system-related challenges from different perspectives. The SMDB workshop looks into developing autonomic or self-* features in database and data management systems to tackle complex administrative tasks, while the HardBD&Active workshop focuses on harnessing hardware technologies to enhance efficiency and performance of data processing and management tasks. As a result of these workshops, we are delighted to present the third special issue of DAPD titled “Self-Managing and Hardware-Optimized Database Systems 2022,” which showcases the best contributions from the SMDB 2021/2022 and HardBD&Active 2021/2022 workshops.
Because of a high product and technology complexity, companies involve external partners in their research and development (R&D) processes. Interorganizational projects result, which represent temporary organizations. In these projects heterogenous organizations work closely together. Since project work is always teamwork, these projects face due to their characteristic’s major challenges on an organizational, relational, and content-related collaboration level. Thus, this paper raises the following research question: “How can a project team be supported on an organizational, relational, and content-related level in an interorganizational new product development setting?” To answer this research question, an explorative expert study was set up with two digital workshops using the interactive presentation tool Mentimeter. The results show that a cooperative innovation culture could support project teams on an organizational and relational level in the future in minimizing predominant problems. Moreover, it supports project teams for example in a functional communication. Furthermore, 18 values of a cooperative innovation culture result which are for example openness and transparency, risk and failure tolerance or respect. On a content-related level the results show that an adaptable tool which promotes creativity and collaboration method as well as content-related input support could be beneficial for problem-solving in an interorganizational new product development setting in the future. Because the tool can guide product developers through the process with suitable creativity and collaboration methods, can give content-related input and can enable interactive interchange on a table-top. Future research could mainly focus on the connection of the cooperative innovation culture and the tool since these potentially influence each other.
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.
Modern component-based architectural styles, e.g., microservices, enable developing the components independently from each other. However, this independence can result in problems when it comes to managing issues, such as bugs, as developer teams can freely choose their technology stacks, such as issue management systems (IMSs), e.g., Jira, GitHub, or Redmine. In the case of a microservice architecture, if an issue of a downstream microservice depends on an issue of an upstream microservice, this must be both identified and communicated, and the downstream service’s issues should link to its causing issue. However, agile project management today requires efficient communication, which is why more and more teams are communicating through comments in the issues themselves. Unfortunately, IMSs are not integrated with each other, thus, semantically linking these issues is not supported, and identifying such issue dependencies from different IMSs is time-consuming and requires manual searching in multiple IMS technologies. This results in many context switches and prevents developers from being focused and getting things done. Therefore, in this paper, we present a concept for seamlessly integrating different IMS technologies into each other and providing a better architectural context. The concept is based on augmenting the websites of issue management systems through a browser extension. We validate the approach with a prototypical implementation for the Chrome browser. For evaluation, we conducted expert interviews, which approved that the presented approach provides significant advantages for managing issues of agile microservice architectures.
The market for indoor positioning systems for a variety of applications has grown strongly in recent years. A wide range of systems is available, varying considerably in terms of accuracy, price and technology used. The suitability of the systems is highly dependent on the intended application. This paper presents a concept to use a single low-cost PTZ camera in combination with fiducial markers for indoor position and orientation determination. The intended use case is to capture a plant layout consisting of position, orientation and unique identity of individual facilities. Important factors to consider for the selection of a camera have been identified and the transformation of the marker pose in camera coordinates into a selectable plant coordinate system is described. The concept is illustrated by an exemplary practical implementation and its results.
Mobile monitoring of outpatients during cancer therapy becomes possible through technological advancements. This study leveraged a new remote patient monitoring app for in-between systemic therapy sessions. Patients’ evaluation showed that the handling is feasible. Clinical implementation must consider an adaptive development cycle for reliable operations.
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 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.
Impact of a large distribution network on radiation characteristics of planar spiral antenna arrays
(2023)
Designing antenna arrays with a central feed point has gained ground in the antenna technique. This approach, which is usually applied because of manufacturing costs, is difficult to achieve and leads to a large feeding network. The impact of which is numerically investigated in the present work. Upon comparing three different antennas, it is shown that the enlargement of the feed strongly affects the antenna's overall dimensions and the antenna's radiation characteristics. The antenna with the plug-in solution is not only small in size but also performs better compared to antennas with a central feed point. Considering the high effort in designing the feed network with a central point and the influence of the resulting enlarged network on the dimensions and radiation characteristics of the antenna, the cost saving in production can be put into perspective.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
The basis for developing future products in the automotive industry is finding creative and innovative solutions. Ideas can be found by means of creativity methods that support product developers throughout the creative process. Product developers are provided with a variety of different and new methods. This leads to a “method jungle” in which it is difficult for product developers to find the most suitable path. The successful use of methods in product development goes hand in hand with the acceptance and implementation of the methods. Despite the added value, only a low use is observed in the development process. The field of Creativity Support Tools also offers a wide variety of different tools that support the creativity process. Although a chasm exists between the many CSTs that are developed and what creative practitioners actually use. Therefore, previous studies iteratively developed a user-centered tool called “IDEA” that tries to provide a tool that responds to users' needs. The question arises how the developed tool IDEA performs in “real life setting” regarding its UX and usability as well as the creativity method acceptance and level of mental workload.
Different network architectures are being used to build remote laboratories. Historically, it has been difficult to integrate industrial control systems with higher level IT systems like enterprise resource planning (ERP), manufacturing execution systems (MES), and manufacturing operations management (MOM). Getting these systems to communicate with one another has proven to be relatively difficult due to the absence of shared protocols between them. The Open Platform Communications United Architecture (OPC-UA) protocol was introduced as a remedy for this issue and is gaining popularity, but what if open-source protocols that are widely used in the IT industry could be used instead? This paper presents the development of an IT-Architecture for a cyber-physical industrial control systems laboratory that enables a seamless interconnection and integration of its elements. The architecture utilises Node-Red technology. Node-RED is an open-source programming platform developed by IBM that is focused on making it simple to link physical components, APIs, and web services. This cyber-physical laboratory is for learning principles of an industrial cascaded process control factory. Finally, this text will also discuss future work relating to digital twin (DT). A coupled tank system is selected as a teaching factory to illustrate a range of fluid control application in a typical chemical process factory.
In a recently developed study programme at Reutlingen University, which focuses on practical orientations, an innovative product with solid company references is to be defined and realised by student teams. On the basis of this product, all subjects of the business engineering study programme “Sustainable Production and Business” are taught. By focusing on three main paths of future skills that have been developed by NextSkills to analyse upcoming social changes, global challenges and fields of work that are innovation-driven and agile, the new study programme aims to create responsible leaders who will shape global businesses respectfully. Thereby, different TRIZ tools help to support students in developing their own products with a focus on sustainability and pay off on the future skills enhancement. Further, students get to know TRIZ tools in an unbiased way, unburdened by too much theory, and are thus continuously supported in the progressing product development process that accompanies their studies. Hence, students perceive TRIZ on the one hand as a method to develop sustainable products and, on the other hand, to find sustainable solutions for everyday problems. The knowledge and positive experiences gained in this way should then arouse curiosity for the TRIZ class at the end of the study programme. The students can graduate with a TRIZ Level 1 certificate. Thereby, as many students as possible are introduced to the TRIZ methods, and the TRIZ tool is spread widely.
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.
Knowledge-intensive organizations primarily rely on knowledge and expertise as key strategic resources. In light of economic, social, and health-related crises in recent years, such organizations increasingly need to operate in dynamic environments. However, examinations on dynamic capabilities specifically in knowledge-intensive organizations remain scarce. This is remarkable given the role that knowledge holds as an economic resource in developed countries. To provide an explanation of how knowledge-intensive organizations can prevail among competitors under dynamic conditions, the authors integrate two literature streams in a knowledge-intensive context: the knowledge-based view and the dynamic capabilities approach. The knowledge-based view focuses on the nature of organizational knowledge as a critical resource and illustrates specific properties of knowledge in contrast to traditional means of labor such as capital. The dynamic capabilities approach on the other hand is about a firm's ability to integrate, build, and reconfigure internal and external resources and can be drawn on to explain organizational success through adaptation to dynamic contexts. In this conceptual study, the authors propose a research model linking knowledge processes to organizational performance through two different paths: (1) Operational capabilities permit organizations to make their living in the present and refer to efficiency. (2) Dynamic capabilities allow organizations to change their resource base and, therefore, enable their long-term survival in dynamic environments by focusing on effectiveness. Additionally, the authors hypothesize a moderating effect of environmental dynamics on the relationship between dynamic capabilities and performance. The study offers a comprehensive overview on the interplay between dynamic capabilities and the knowledge-based view, offering valuable insights for both researchers and practitioners in the field.
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.
The replacement of conventional material with recyclates affects product personality, particularly regarding sustainability aspects influencing consumer behaviour. A definition of personality for products made of recyclates is missing in literature. As these products require appropriate aesthetics based on material origin to communicate the advantage concerning sustainability, there is a need for research in this regard. This paper aims to develop an adequate personality of a reusable water bottle made of ocean plastic by collecting personality traits that evoke associations related to the material's origin and sustainability. We conducted two quantitative field studies. Study 1 collected associated visual perceived attributes and context-related personality traits in order to develop and visualize a preliminary design. Study 2 evaluated the design regarding associated personality traits. The overall outcome was a product personality scale consisting of 23 items plus a concrete design recommendation for a water bottle made of recycled ocean plastic. The assessment of degree of sustainability was strongly influenced by participants’ associations with personal use, familiarity with usage and the factor of stability and resilience.
Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the details of such formulations in the physical database, and this approach brings significant advantages in that the model can be enforced across a range of applications for a single database. In previous work, we have discussed the advantages for enterprise integration of typed graph data models (TGM), which can play a similar role in graphical databases, leveraging the existing support for the unified modelling language UML. Ideally, the integration of systems designed with different models, for example, graphical and relational database, should also be supported. In this work, we implement this approach, using metadata in a relational database management system (DBMS).
In recent years, 3D facial reconstructions from single images have garnered significant interest. Most of the approaches are based on 3D Morphable Model (3DMM) fitting to reconstruct the 3D face shape. Concurrently, the adoption of Generative Adversarial Networks (GAN) has been gaining momentum to improve the texture of reconstructed faces. In this paper, we propose a fundamentally different approach to reconstructing the 3D head shape from a single image by harnessing the power of GAN. Our method predicts three maps of normal vectors of the head’s frontal, left, and right poses. We are thus presenting a model-free method that does not require any prior knowledge of the object’s geometry to be reconstructed.
The key advantage of our proposed approach is the substantial improvement in reconstruction quality compared to existing methods, particularly in the case of facial regions that are self-occluded in the input image. Our method is not limited to 3d face reconstruction. It is generic and applicable to multiple kinds of 3D objects. To illustrate the versatility of our method, we demonstrate its efficacy in reconstructing the entire human body.
By delivering a model-free method capable of generating high-quality 3D reconstructions, this paper not only advances the field of 3D facial reconstruction but also provides a foundation for future research and applications spanning multiple object types. The implications of this work have the potential to extend far beyond facial reconstruction, paving the way for innovative solutions and discoveries in various domains.
Gamification has been increasingly applied to software engineering education in the past. The approaches vary from applying game elements on a conceptual phase in the course to using specific tools to engage the students more and support their learning goals. However, existing tools usually have game elements, such as quizzes or challenges, but do not provide a more computer game-like experience. Therefore, we try to raise the level of gamified learning experience to another level by proposing Gamify-IT. Gamify-IT is a Unity- and web-based game platform intended to help students learn software engineering. It follows an immersive role-play game characteristic where the students explore a world, find and solve minigames and clear dungeons with SE tasks. Lecturers can configure the worlds, e.g., to add content hints. Furthermore, they can add and configure minigames and dungeons to include exercises in a fully gamified way. Thereby, they customize their course in Gamify-IT to adapt the world very precisely to other materials such as lectures or exercises. Results of an evaluation of our initial prototype show that (i) students like to engage with the platform, (ii) students are motivated to learn when using Gamify-IT, and (iii) the minigames support students in understanding the learning objectives.
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.
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.
Framework for integrating intelligent product structures into a flexible manufacturing system
(2023)
Increasing individualisation of products with a high variety and shorter product lifecycles result in smaller lot sizes, increasing order numbers, and rising data and information processing for manufacturing companies. To cope with these trends, integrated management of the products and manufacturing information is necessary through a “product-driven” manufacturing system. Intelligent products that are integrated as an active element within the controlling and planning of the manufacturing process can represent flexibility advantages for the system. However, there are still challenges regarding system integration and evaluation of product intel-ligence structures. In light of these trends, this paper proposes a conceptual frame-work for defining, analysing, and evaluating intelligent products using the example of an assembly system. This paper begins with a classification of the existing problems in the assembly and a definition of the intelligence level. In contrast to previous approaches, the analysis of products is expanded to five dimensions. Based on this, a structured evaluation method for a use case is presented. The structure of solving the assembly problem is provided by the use case-specific ontology model. Results are presented in terms of an assignment of different application areas, linking the problem with the target intelligence class and, depending on the intelligence class of the product, suggesting requirements for implementation. The conceptual frame-work is evaluated by utilising a case study in a learning factory. Here, the model-mix assembly is controlled actively by the workpiece carrier in terms of transferring the variant-specific work instructions to the operator and the collaborative robot (cobot) at the workstations. The resulting system thus enables better exploitation of the poten-tials through less frequent errors and shorter search times. Such an implementation has demonstrated that the intelligent workpiece carrier represents an additional part for realising a cyber-physical production system (CPPS).
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.