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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.
In the context of Industry 4.0, intralogistics faces an increasingly complex and dynamic environment driven by a high level of product customisation and complex manufacturing processes. One approach to deal with these changing conditions is the decentralised and intelligent connectivity of intralogistics systems. However, wireless connectivity presents a major challenge in the industry due to strict requirements such as safety and real-time data transmission. In this context, the fifth generation of mobile communications (5G) is a promising technology to meet the requirements of safety-critical applications. Particularly, since 5G offers the possibility of establishing private 5G networks, also referred to as standalone non-public networks. Through their isolation from public networks, private 5G networks provide exclusive coverage for private organisations offering them high intrinsic network control and data security. However, 5G is still under development and is being gradually introduced in a continuous release process. This process lacks transparency regarding the performance of 5G in individual releases, complicating the successful adoption of 5G as an industrial communication. Additionally, the evaluation of 5G against the specified target performance is insufficient due to the impact of the environment and external interfering factors on 5G in the industrial environment. Therefore, this paper aims to develop a technical decision-support framework that takes a holistic approach to evaluate the practicality of 5G for intralogistics use cases by considering two fundamental stages. The first of these analyses technical parameters and characteristics of the use case to evaluate the theoretical feasibility of 5G. The second stage investigates the application's environment, which substantially impacts the practicality of 5G, for instance, the influence of surrounding materials. Finally, a case study validates the proposed framework by means of an autonomous mobile robot. As a result, the validation proves the proposed framework's applicability and shows the practicality of the autonomous mobile robot, when integrating it into a private 5G network testbed.
Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system.
The 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.
The Circular Economy aims to reintroduce the value of products back into the economic cycle at the same value chain level. While the activities of the Circular Economy are already well-defined, there exists a gap in how returned products are treated by the industry. This study aims to examine how a process should be designed to handle returned products in the context of the Circular Economy. To achieve this, a machine learning-based algorithm is used to classify data and extract relevant information throughout the product life cycle. The focus of this research is limited to land transportation systems within the Sharing Economy sector.
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.
Context
In a world of high dynamics and uncertainties, it is almost impossible to have a long-term prediction of which products, services, or features will satisfy the needs of the customer. To counter this situation, the conduction of Continuous Improvement or Design Thinking for product discovery are common approaches. A major constraint in conducting product discovery activities is the high effort to discover and validate features and requirements. In addition, companies struggle to integrate product discovery activities into their agile processes and iterations.
Objective
This paper aims at suggests a supportive tool, the “Discovery Effort Worthiness (DEW) Index”, for product owners and agile teams to determine a suitable amount of effort that should be spent on Design Thinking activities. To operationalize DEW, proposals for practitioners are presented that can be used to integrate product discovery into product development and delivery.
Method
A case study was conducted for the development of the DEW index. In addition, we conducted an expert workshop to develop proposals for the integration of product discovery activities into the product development and delivery process.
Results
First, we present the "Discovery Effort Worthiness Index" in form of a formula. Second, we identified requirements that must be fulfilled for systematic integration of product discovery activities into product development and delivery. Third, we derived from the requirements proposals for the integration of product discovery activities with a company's product development and delivery.
Conclusion
The developed "Discovery Effort Worthiness Index" provides a tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. Integrating product discovery with product development and delivery should ensure that the results of product discovery are incorporated into product development. This aims to systematically analyze product risks to increase the chance of product success.
The Industry 4.0 paradigm requires concepts for integrating intelligent/ smart IoT Solutions into manufacturing. Such intelligent solutions are envisioned to increase flexibility and adaptability in smart factories. Especially autonomous cobots capable of adapting to changing conditions are a key enabler for changeable factory concepts. However, identifying the requirements and solution scenarios incorporating intelligent products challenges the manufacturing industry, especially in the SME sector. In pick and place scenarios, changing coordinate systems of workpiece carriers cause placing process errors. Using the IPIDS framework, this paper describes the development of a tool-center-point positioning method to improve the process stability of a collaborative robot in a changeable assembly workstation. Applying the framework identifies the requirement for an intelligent workpiece carrier as a part of the solution. Implementing and evaluating the solution within a changeable factory validates the IPIDS framework.
Due to constantly changing conditions, demand, and technologies, companies increasingly seek flexibility. Productivity results from automation, improved working conditions and the focus of people in production in interaction with machines. Unfortunately, the human factor is often not considered to increase flexibility and productivity with new concepts. This work aims to develop a hybrid assistance system that allows a dynamic configuration of cyber-physical production systems considering the current order situation and available resources utilizing simulation. The system also considers human factors in addition to economic factors, which contributes to the extended economic appraisal.
Film formation of self synthesized Polymer EPM–g–VTMDS (ethylene–propylene rubber, EPM, grafted with vinyltetramethyldisiloxane, VTMDS) was studied regarding bonding to adhesion promoter vinyltrimethoxysilane (VTMS) on oxidized 18/10 chromium/nickel–steel (V2A) stainless steel surfaces. Polymer films of different mixed solutions including commercial siloxane and silicone, dimethyl, vinyl group terminated crosslinker (HANSA SFA 42100, CAS# 68083-19-2, 0.35 mmol Vinyl/g) and platinum, 1,3-diethenyl-1,1,3,3-tetramethyldisiloxane complex Karstedt's catalyst (ALPA–KAT 1, CAS# 68478-92-2) were spin coated on V2A stainless steel surfaces with adsorbed VTMS thin layers in order to analyze film formation of EPM–g–VTMDS at early stages. Surface topography and chemical bonding of the high performance polymers on different oxidized V2A surfaces were investigated with X–ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), scanning electron microscopy (SEM) and surface enhanced Raman spectroscopy (SERS). AFM and SEM as well as XPS results indicated that the formation of the polymer film proceeds via growth of polymer islands. Chemical signatures of the essential polymer contributions, linker and polymer backbones, could be identified using XPS core level peak shape analysis and also SERS. The appearance of signals which are related to Si–O–Si can be seen as a clear indication of lateral crosslinking and silica network formation in the films on the V2A surface.
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.
High-performance liquid chromatography is one of the most important analytical tools for the identification and separation of substances. The efficiency of this method is largely determined by the stationary phase of the columns. Although monodisperse mesoporous silica microspheres (MPSM) represent a commonly used material as stationary phase their tailored preparation remains challenging. Here we report on the synthesis of four MPSMs via the hard template method. Silica nanoparticles (SNPs) which form the silica network of the final MPSMs were generated in situ from tetraethyl orthosilicate (TEOS) in the presence of (3-aminopropyl) triethoxysilane (APTES) functionalized p(GMA-co-EDMA) as hard template. Methanol, ethanol, 2-propanol, and 1-butanol were applied as solvents to control the size of the SNPs in the hybrid beads (HB). After calcination, MPSMs with different sizes, morphology and pore properties were obtained and characterized by scanning electron microscopy, nitrogen adsorption and desorption measurements, thermogravimetric analysis, solid state NMR and DRIFT IR spectroscopy. Interestingly, the 29Si NMR spectra of the HBs show T and Q group species which suggests that there is no covalent linkage between the SNPs and the template. The MPSMs were functionalized with trimethoxy (octadecyl) silane and used as stationary phases in reversed-phase chromatography to separate a mixture of eleven different amino acids. The separation characteristics of the MPSMs strongly depend on their morphology and pore properties which are controlled by the solvent during the preparation of the MPSMs. Overall, the separation behavior of the best phases is comparable with those of commercially available columns. The phases even achieve faster separation of the amino acids without loss of quality.
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
The Covid-19 virus has triggered a worldwide pandemic and therefore many employees were required to work from home which caused numerous challenges. With the Covid-19 pandemic now in its third year, there are already several studies available on the subject of home offices. To investigate the impact of remote work on employee satisfaction and trust, this quantitative study aims to review existing results and formulate hypotheses based on a conceptual model created through a qualitative study and extensive literature review. The research question is as follows: Does home office during Covid-19 affect employee satisfaction and trust? To test the hypotheses, a structural equation model was constructed and analyzed. The culture of trust and flexibility are identified as the biggest influencing factors in this study.
Sleep is an essential part of human existence, as we are in this state for approximately a third of our lives. Sleep disorders are common conditions that can affect many aspects of life. Sleep disorders are diagnosed in special laboratories with a polysomnography system, a costly procedure requiring much effort for the patient. Several systems have been proposed to address this situation, including performing the examination and analysis at the patient's home, using sensors to detect physiological signals automatically analysed by algorithms. This work aims to evaluate the use of a contactless respiratory recording system based on an accelerometer sensor in sleep apnea detection. For this purpose, an installation mounted under the bed mattress records the oscillations caused by the chest movements during the breathing process. The presented processing algorithm performs filtering of the obtained signals and determines the apnea events presence. The performance of the developed system and algorithm of apnea event detection (average values of accuracy, specificity and sensitivity are 94.6%, 95.3%, and 93.7% respectively) confirms the suitability of the proposed method and system for further ambulatory and in-home use.
Healthy sleep is one of the prerequisites for a good human body and brain condition, including general well-being. Unfortunately, there are several sleep disorders that can negatively affect this. One of the most common is sleep apnoea, in which breathing is impaired. Studies have shown that this disorder often remains undiagnosed. To avoid this, developing a system that can be widely used in a home environment to detect apnoea and monitor the changes once therapy has been initiated is essential. The conceptualisation of such a system is the main aim of this research. After a thorough analysis of the available literature and state of the art in this area of knowledge, a concept of the system was created, which includes the following main components: data acquisition (including two parts), storage of the data, apnoea detection algorithm, user and device management, data visualisation. The modules are interchangeable, and interfaces have been defined for data transfer, most of which operate using the MQTT protocol. System diagrams and detailed component descriptions, including signal requirements and visualisation mockups, have also been developed. The system's design includes the necessary concepts for the implementation and can be realised in a prototype in the next phase.
The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.
Development of an expert system to overpass citizens technological barriers on smart home and living
(2023)
Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.
Managerial accountants spend a large part of their working time on more operational activities in cost accounting, reporting, and operational planning and budgeting. In all these areas, there has been increasing discussion in recent years, both in theory and practice, about using more digital technologies. For reporting, this means not only an intensified discussion of technologies such as RPA and AI but also more intensive changes to existing reporting systems. In particular, management information systems (MIS), which are maintained by managerial accountants and used by managers for corporate management, should be mentioned here. Based on an empirical survey in a large German company, this article discusses the requirements and assessments of users when switching from a regular MIS to a cloud-based system.
The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.
During the first years of the last decade, Egypt used to face recurrent electricity cut-offs in summer. In the past few years, the electricity tariff dramatically increased. Radiative cooling to the clear night sky is a renewable energy source that represents a relative solution. The dry desert climate promotes nocturnal radiative cooling applications. This study investigates the potential of nocturnal radiative cooling systems (RCSs) to reduce the energy consumption of the residential building sector in Egypt. The system technology proposed in this work is based on uncovered solar thermal collectors integrated into the building hydronic system. By implementing different control strategies, the same system could be used for both cooling and heating applications. The goal of this paper is to analyze the performance of RCSs in residential buildings in Egypt. The dynamic simulation program TRNSYS was used to simulate the thermal behavior of the system. The relevant issues of Egypt as a case-study are firstly overviewed. Then the paper introduces the work done to develop a building model that represents a typical residential apartment in Egypt. Typical occupancy profiles were developed to define the internal thermal gains. The adopted control strategy to optimize the system operation is presented as well. To fully understand and hence evaluate the operation of the proposed RCS, four simulation cases were considered: 1. a reference case (fully passive), 2. the stand-alone operation of the RCS, 3. ideal heating & cooling operation (fully-active), and 4. the hybrid-operation (when the active cooling system is supported by the proposed RCS). The analysis considered the main three distinct climates in Egypt, represented by the cities of Alexandria, Cairo and Asyut. The hotter and drier weather conditions resulted in a higher cooling potential and larger temperature differences. The simulated cooling power in Asyut was 28.4 W/m² for a 70 m² absorber field. For a smaller field area of 10 m², the cooling power reached 109 W/m² but with humble temperature differences. To meet the rigorous thermal comfort conditions, the proposed sensible RCS cannot fully replace conventional air-conditioning units, especially in humid areas like Alexandria. When working in a hybrid system, a 10% reduction in the active cooling energy demand could be achieved in Asyut to keep the cooling set-point at 24 °C. This percentage reduction was nearly doubled when the thermal comfort set-point was increased by two degrees (26 °C). In a sensitivity analysis, external shading devices as a passive measure as well as the implementation of the Egyptian code for buildings (ECP306/1–2005) were also investigated. The analysis of this study raised other relevant aspects to discuss, e.g. system-sizing, environmental effects, limitations and recommendations.
This article examines the risks and societal costs associated with flexible average inflation targeting in the United States and symmetric inflation targeting in the Eurozone. Employing an empirical approach, we analyze monthly cumulative inflation gaps over a monetary policy horizon of 36 months. By investigating the trajectories of the cumulative inflation gaps, we find a heavy tailed distribution and a 20 percent probability of over- and undershooting the inflation target. We exhibit that the offsetting mechanism introduced in the revised monetary strategies lack credibility in ensuring price stability during a period of persistent inflation. Consequently, the credibility of central banks may be compromised. The policy implications are the integration of an escape clause and prompt monetary corrections in cases where the inflation goal is not achieved. This study provides insights for policymakers and central banks, emphasizing challenges in maintaining credibility and price stability within the new monetary strategies.
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.
The properties of polyelectrolyte multilayers are ruled by the process parameters employed during self-assembly. This is the first study in which a design of experiment approach was used to validate and control the production of ultrathin polyelectrolyte multilayer coatings by identifying the ranges of critical process parameters (polyelectrolyte concentration, ionic strength and pH) within which coatings with reproducible properties (thickness, refractive index and hydrophilicity) are created. Mathematical models describing the combined impact of key process parameters on coatings properties were developed demonstrating that only ionic strength and pH affect the coatings thickness, but not polyelectrolyte concentration. While the electrolyte concentration had a linear effect, the pH contribution was described by a quadratic polynomial. A significant contribution of this study is the development of a new approach to estimate the thickness of polyelectrolyte multilayer nanofilms by quantitative rhodamine B staining, which might be useful in all cases when ellipsometry is not feasible due to the shape complexity or small size of the coated substrate. The novel approach proposed here overcomes the limitations of known methods as it offers a low spatial sampling size and the ability to analyse a wide area without restrictions on the chemical composition and shape of the substrate.
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%.
Towards a sustainable future, looking beyond the system boundaries of a single manufacturing company is necessary to promote meaningful collaborations in terms of circular economy principles. In this context digital data processing technologies to connect the potential collaborators are seen as enablers to make use of proven collaborative circular business models (CCBMs). Since most of such data processing technologies rely on features to describe the entities involved, it is essential to provide guidance for identifying and selecting the relevant and most appropriate ones. Defining critical success factors (CSFs) is considered a suitable instrument to describe the decisive factors. A systematic literature review (SLR), followed by a qualitative synthesis is investigating two scientific fields of work, namely (1) the general relevant features of CCBMs and, (2) methodologies for determining CSFs. This results in the development of a conceptual framework which provides guidance for digital applications that perform further digital processing based on the relevant CSFs relating to the specific CCBM.
Machine failures’ consequences – a classification model considering ultra-efficiency criteria
(2023)
To strive for a sustainable production, maintenance has to evaluate possible machine failure consequences not just economically but also holistically. Approaches such as the ultra-efficiency factory consider energy, material, human/staff, emission, and organization as optimization dimensions. These ultra-efficiency dimensions can be considered for analyzing not only the respective machine failure but also the effects on the entire production system holistically. This paper presents an easy to use method, based on a questionnaire, for assessing the failure consequences of a machine malfunction in a production system considering the ultra-efficiency dimensions. The method was validated in a battery production.
Using predictive maintenance, more efficient processes can be implemented, leading to fewer maintenance costs and increased availability. The development of a predictive maintenance solution currently requires high efforts in time and capacity as well as often interdisciplinary cooperation. This paper presents a standardized model to describe a predictive maintenance use case. The description model is used to collect, present, and document the required information for the implementation of predictive maintenance use cases by and for different stakeholders. Based on this model, predictive maintenance solutions can be introduced more efficiently. The method is validated across departments in the automotive sector.
The increasing complexity and need for availability of automated guided vehicles (AGVs) pose challenges to companies, leading to a focus on new maintenance strategies. In this paper, a smart maintenance architecture based on a digital twin is presented to optimize the technical and economic effectiveness of AGV maintenance activities. To realize this, a literature review was conducted to identify the necessary requirements for Smart Maintenance and Digital Twins. The identified requirements were combined into modules and then integrated into an architecture. The architecture was evaluated on a real AGV on the battery as one of the critical components.
Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes.
Herein, biochar from biomass residues is demonstrated as active materials for the catalytic cracking of waste motor oil into diesel-like fuels. Above all, alkali-treated rice husk biochar showed great activity with a 250% increase in the kinetic constant compared to the thermal cracking. It also showed better activity than synthetic materials, as previously reported. Moreover, much lower activation energy (185.77 to 293.48 kJ/mol) for the cracking process was also obtained. According to materials characterization, the catalytic activity was more related to the nature of the biochar’s surface than its specific surface area. Finally, liquid products complied with all the physical properties defined by international standards for diesel-like fuels, with the presence of hydrocarbons chains between C10 - C27 similar to the ones obtained in commercial diesel.
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.
In countries such as Germany, where municipalities have planning sovereignty, problems of urban sprawl often arise. As the dynamics of land development have not substantially subsided over the last years, the national government decided to test the instrument of ‘Tradable Planning Permits’ (TPP) in a nationwide field experiment with 87 municipalities involved. The field experiment was able to implement the key features of a TPP system in a laboratory setting with approximated real socioeconomic and planning conditions. In a TPP system allocated planning permits must be used by municipalities for developing land. The permits can be traded between local jurisdictions, so that they have flexibility in deciding how to comply with the regulation. In order to evaluate the performance of such a system, specific field data about future building areas and their impact on community budgets for the period 2014–2028 were collected. The field experiment contains several sessions with representatives of the municipalities and with students. The participants were confronted with two (municipalities) and four (students) schemes. The results show that a trading system can curb down land development in an effective and also efficient manner. However, depending on the regulatory framework, the trading schemes show different price developments and distributional effects. The unexperienced representatives of the local authorities can easily handle with the permits in the administration and in the established market. A trading scheme sets very high incentives to save open space and to direct development activities to areas within existing planning boundaries. It is therefore a promising instrument for Germany and also other regions or countries with an established land-use planning system.
We analyze economics PhDs’ collaborations in peer-reviewed journals from 1990 to 2014 and investigate such collaborations’ quality in relation to each co-author’s research quality, field and specialization. We find that a greater overlap between co-authors’ previous research fields is significantly related to a greater publication success of co-authors’ joint work and this is robust to alternative specifications. Co-authors that engage in a distant collaboration are significantly more likely to have a large research overlap, but this significance is lost when co-authors’ social networks are accounted for. High quality collaboration is more likely to emerge as a result of an interaction between specialists and generalists with overlapping fields of expertise. Regarding interactions across subfields of economics (interdisciplinarity), it is more likely conducted by co- authors who already have interdisciplinary portfolios, than by co-authors who are specialized or starred in different subfields.
COVID-19 and educational inequality: How school closures affect low- and high-achieving students
(2021)
In spring 2020, governments around the globe shut down schools to mitigate the spread of the novel coronavirus. We argue that low-achieving students may be particularly affected by the lack of educator support during school closures. We collect detailed time-use information on students before and during the school closures in a survey of 1099 parents in Germany. We find that while students on average reduced their daily learning time of 7.4 h by about half, the reduction was significantly larger for low-achievers (4.1 h) than for high-achievers (3.7 h). Low-achievers disproportionately replaced learning time with detrimental activities such as TV or computer games rather than with activities more conducive to child development. The learning gap was not compensated by parents or schools who provided less support for low-achieving students.
This paper presents a description model for smart, connected devices used in a manufacturing context. Similar to the wide spread adoption of smart products for personal and private usage, recent developments lead to a plethora of devices offering a variety of features and capabilities. Manufacturing companies undergoing digital transformation demand guidance with respect to the systematic introduction of smart, connected devices. The introduction of smart connected devices constitutes a strategic decision cost due to the high future committed cost after introduction and maintaining a smart device fleet by a vendor. This paper aims to support the introduction efforts by classifying the devices and thus helping companies identify their specific requirements for smart, connected devices before initiating widespread procurement. By mapping the features of these devices based on various attributes, allows the clustering of smart, connected devices including a requirement list for their implementation on the shopfloor. Four individual commercially available smart connected devices were analyzed using the description model.
Parallel grippers offer multiple applications thanks to their flexibility. Their application field ranges from aerospace and automotive to medicine and communication technologies. However, the application of grippers has the problem of exhibition wear and errors during the execution of their operation. This affects the performance of the gripper. In this context, the remaining useful life (RUL) defines the remaining lifespan until failure for an asset at a particular time of operation occurs. The exact lifespan of an asset is uncertain, thus the RUL model and estimation must be derived from available sources of information. This paper presents a method for the estimation of the RUL for a two-jaw parallel gripper. After the introduction to the topic, an overview of existing literature and RUL methods are presented. Subsequently, the method for estimating the RUL of grippers is explained. Finally, the results are summarized and discussed before the outlook and further challenges are presented.
The general conclusion of climate change studies is the necessity of eliminating net CO2 emissions in general and from the electric power systems in particular by 2050. The share of renewable energy is increasing worldwide, but due to the intermittent nature of wind and solar power, a lack of system flexibility is already hampering the further integration of renewable energy in some countries. In this study, we analyze if and how combinations of carbon pricing and power-to-gas (PtG) generation in the form of green power-to-hydrogen followed by methanation (which we refer to as PtG throughout) using captured CO2 emissions can provide transitions to deep decarbonization of energy systems. To this end, we focus on the economics of deep decarbonization of the European electricity system with the help of an energy system model. In different scenario analyses, we find that a CO2 price of 160 €/t (by 2050) is on its own not sufficient to decarbonize the electricity sector, but that a CO2 price path of 125 (by 2040) up to 160 €/t (by 2050), combined with PtG technologies, can lead to an economically feasible decarbonization of the European electricity system by 2050. These results are robust to higher than anticipated PtG costs.
With the digital transformation, companies will experience a change that focuses on shaping the organization into an agile organizational form. In today's competitive and fast-moving business environment, it is necessary to react quickly to changing market conditions. Agility represents a promising option for overcoming these challenges. The path to an agile organization represents a development process that requires consideration of countless levels of the enterprise. This paper examines the impact of digital transformation on agile working practices and the benefits that can be achieved through technology. To enable a solution for today's so-called VUCA (Volatility, Uncertainty, Complexity und Ambiguity) world, agile ways of working can be applied project management requires adaptation. In the qualitative study, expert interviews were conducted and analyzed using the grounded theory method. As a result, a model can be presented that shows the influencing factors and potentials of agile management in the context of the digital transformation of medium-sized companies.
The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.
Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.
Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.
Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
The citizen-centered health platform project is intended to provide a platform that can be used in EU cross-border regions, where social and economic exchange occurs across national borders. The overriding challenges are: (a) social: improving citizen-centered health and care provision; (b) technical: providing a digital platform for networking citizens, service providers, and municipal actors; (c) economic: developing long-term successful (sustainable) business models/value chains. The platform should strengthen and expand existing networks and establish new regional networks. Each network addresses particular challenges and apply them in a region-specific manner. Here, the national boundary conditions and the interregional needs play an essential role. These objectives require sufficient participation of civil society representatives. Furthermore, the platform will establish an overarching, sustainable, and knowledge-based network of health experts. The platform is to be jointly developed and implemented in the regions and follow an open-access approach. Therefore, synergies will be shared more quickly, strengthening competencies and competitiveness. In addition to practice partners, scientific and municipal institutions and SMEs are involved. The actors thus contribute to scientific performance, innovative strength, and resilience.
Modern production systems are characterized by the increasingly use of CPS and IoT networks. However, processing the available information for adaptation and reconfiguration often occurs in relatively large time cycles. It thus does not take advantage of the optimization potential available in the short term. In this paper, a concept is presented that, considering the process information of the individual heterogeneous system elements, detects optimization potentials and performs or proposes adaptation or reconfiguration. The concept is evaluated utilizing a case study in a learning factory. The resulting system thus enables better exploitation of the potentials of the CPPS.
The global demand for resources such as energy, land, or water is constantly increasing. It is therefore not sur- prising that research on the Food-Energy-Water (FEW) nexus has become a scientific as well as a general focus in recent years. A significant increase in publications since 2015 can be observed, and it can be expected that this trend will continue. A multilevel (macro, meso, and micro) perspective is essential, as the FEW nexus has cross- sectoral interdependencies. Several review studies on the FEW nexus can be found in the literature, in general, it can be concluded that the FEW nexus is a multi-disciplinary and complex topic. The studies examined identify essential fields of action for research, policy, and society. However, questions such as what are the main research fields at each level? Is it possible to divide the research into specific clusters? and do the clusters correlate with the levels, and what are the methods of modeling used in the clusters and levels? are still not fully discussed in the literature. An extensive literature review was conducted to get insight into the existing research areas. Especially in such fields as the FEW nexus, the amount of literature can get huge, and a human could get lost analyzing the literature manually. For that, we created word clouds and performed a cluster- and network-analysis to support the selection of most relevant papers for a detailed reading. In 2021, the most publications were published, with 173 publications, which corresponds to a share of 26.6 %. There has been a significant increase since 2015, and it can be expected that this trend will continue in the coming years. Most of the first authors come from the USA (25.4 %), followed by China with 22.4 %. From the word cloud and the top 20 words, which appear in the title and abstract, it can be deduced that the topic water is the most represented. However, the terms system, resource, model, study, change, development, and management also appear to be very important, which indi- cates the importance of a holistic approach to the topic. In total 9 clusters could be identified at the different levels. It can be seen that three clusters form well. For the others, a rather diffuse picture can be observed. In order to find out which topics are hidden behind the individual clusters, 6 publications from each cluster were subjected to a more detailed examination. With these steps, a number of 54 publications were identified for de- tailed consideration. The modeling approaches that are currently being applied in research can be classified into domain-specific tools (e. g. global water models, crop models or global climate models) and into more general tools to perform for example a life cycle analysis, spatial analysis using geographic information system, or system dynamics for a general understanding of the links between the domains. With the domain-specific tools, detailed research questions can be addressed to answer questions for a specific domain. However, these tools have the disadvantage that especially the links between the sectors food, energy, and water are not fully considered. Many implementations that are made today are at lowest level (micro) relate to bounded spatial areas and are derived from macro and meso level goals.
The transmembrane Ca2+ − activated Cl− channel - human bestrophin-1 (hBest1) is expressed in retinal pigment epithelium and mutations of BEST1 gene cause ocular degenerative diseases colectivelly referred to as “bestrophinopathies”. A large number of genetical, biochemical, biophysical and molecular biological studies have been performed to understand the relationship between structure and function of the hBest1 protein and its pathophysiological significance. Here, we review the current understanding of hBest1 surface organization, interactions with membrane lipids in model membranes, and its association with microdomains of cellular membranes. These highlights are significant for modulation of channel activity in cells.
The fifth mobile communications generation (5G) offers the deployment scenario of licensed 5G standalone non-public networks (NPNs). Standalone NPNs are locally restricted 5G networks based on 5G New Radio technology which are fully isolated from public networks. NPNs operate on their dedicated core network and offer organizations high data security and customizability for intrinsic network control. Especially in networked and cloud manufacturing, 5G is seen as a promising enabler for delay-sensitive applications such as autonomous mobile robots and robot motion control based on the tactile internet that requires wireless communication with deterministic traffic and strict cycling times. However, currently available industrial standalone NPNs do not meet the performance parameters defined in the 5G specification and standardization process. Current research lacks in performance measurements of download, upload, and time delays of 5G standalone-capable end-devices in NPNs with currently available software and hardware in industrial settings. Therefore, this paper presents initial measurements of the data rate and the round-trip delay in standalone NPNs with various end-devices to generate a first performance benchmark for 5G-based applications. In addition, five end-devices are compared to gain insights into the performance of currently available standalone-capable 5G chipsets. To validate the data rate, three locally hosted measurement methods, namely iPerf3, LibreSpeed and OpenSpeedTest, are used. Locally hosted Ping and LibreSpeed have been executed to validate the time delay. The 5G standalone NPN of Reutlingen University uses licensed frequencies between 3.7-3.8 GHz and serves as the testbed for this study.
Towards a model for holistic mapping of supply chains by means of tracking and tracing technologies
(2022)
The usage of tracking and tracing technologies not only enables transparency and visibility of supply chains but also offers far-reaching advantages for companies, such as ensuring product quality or reducing supplier risks. Increasing the amount of shared information supports both internal and external planning processes as well as the stability and resilience of globally operating value chains. This paper aims to differentiate and define the functionalities of tracking and tracing technologies that are frequently used interchangeably in literature. Furthermore, this paper incorporates influencing factors impacting a sequencing of the connected world in Industry4.0 supply chain networks. This includes legal influences, the embedment of supply chain-related standards, and new possibilities of emerging technologies. Finally, the results are summarized in a model for the holistic mapping of supply chains by means of tracking and tracing technologies. The resulting technological solutions that can be derived from the model enable companies to address missing elements in order to enable the holistic mapping of supply chain events as well as the transparent representation of a digital shadow throughout the entire supply chain.
The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.