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Willingness-to-pay for alternative fuel vehicle characteristics : a stated choice study for Germany
(2016)
In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).
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.
Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs.
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.
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 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 data presented in this article characterize the thermomechanical and microhardness properties of a novel melamine-formaldehyde resin (MF) intended for the use as a self-healing surface coating. The investigated MF resin is able to undergo reversible crosslinking via Diels Alder reactive groups. The microhardness data were obtained from nanoindentation measurements performed on solid resin film samples at different stages of the self-healing cycle. Thermomechanical analysis was performed under dynamic load conditions. The data provide supplemental material to the manuscript published by Urdl et al. 2020 (https://doi.org/10.1016/j.eurpolymj.2020.109601) on the self-healing performance of this resin, where a more thorough discussion on the preparation, the properties of this coating material and its application in impregnated paper-based decorative laminates can be found.
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.
Theory and practice of implementing a successful enterprise IoT strategy in the industry 4.0 era
(2021)
Since the arrival of the internet and affordable access to technologies, digital technologies have occupied a growing place in industries, propelling us towards a 4th industrial revolution: Industry 4.0. In today’s era of digital upheaval, enterprises are increasingly undergoing transformations that are leading to their digitalization. The traditional manufacturing industry is in the throes of a digital transformation that is accelerated by exponentially growing technologies (e.g., intelligent robots, Internet of Things, sensors, 3D printing). Around the world, enterprises are in a frantic race to implement solutions based on IoT to improve their productivity, innovation, and reduce costs and improve their markets on the international scene. Considering the immense transformative potential that IoTs and big data have to bring to the industrial sector, the adoption of IoT in all industrial systems is a challenge to remain competitive and thus transform the industry into a smart factory. This paper presents the description of the innovation and digitalization process, following the Industry 4.0 paradigm to implement a successful enterprise IoT strategy.
Context: Development of software intensive products and services increasingly occurs by continuously deploying product or service increments, such as new features and enhancements, to customers. Product and service developers must continuously find out what customers want by direct customer feedback and usage behaviour observation. Objective: This paper examines the preconditions for setting up an experimentation system for continuous customer experiments. It describes the RIGHT model for Continuous Experimentation (Rapid Iterative value creation Gained through High-frequency Testing), illustrating the building blocks required for such a system. Method: An initial model for continuous experimentation is analytically derived from prior work. The model is matched against empirical case study findings from two startup companies and further developed. Results: Building blocks for a continuous experimentation system and infrastructure are presented. Conclusions: A suitable experimentation system requires at least the ability to release minimum viable products or features with suitable instrumentation, design and manage experiment plans, link experiment results with a product roadmap, and manage a flexible business strategy. The main challenges are proper, rapid design of experiments, advanced instrumentation of software to collect, analyse, and store relevant data, and the integration of experiment results in both the product development cycle and the software development process.
The paper describes a new stimulus using learning factories and an academic research programme - an M.Sc. in Digital Industrial Management and Engineering (DIME) comprising a double degree - to enhance international collaboration between four partner universities. The programme will be structured in such a way as to maintain or improve the level of innovation at the learning factories of each partner. The partners agreed to use Learning Factory focus areas along with DIME learning modules to stimulate international collaboration. Furthermore, they identified several research areas within the framework of the DIME program to encourage horizontal and vertical collaboration. Vertical collaboration connects faculty expertise across the Learning Factory network to advance knowledge in one of the focus areas, while Horizontal collaboration connects knowledge and expertise across multiple focus areas. Together they offer a platform for students to develop disciplinary and cross-disciplinary applied research skills necessary for addressing the complex challenges faced by industry. Hence, the university partners have the opportunity to develop the learning factory capabilities in alignment with the smart manufacturing concept. The learning factory is thus an important pillar in this venture. While postgraduate students/researchers in the DIME program are the enablers to ensure the success of entire projects, the learning factory provides a learning environment which is entirely conducive to fostering these successful collaborations. Ultimately, the partners are focussed on utilising smart technologies in line with the digitalization of the production process.
Perivascular stromal cells, including mesenchymal stem/stromal cells (MSCs), secrete paracrine factor in response to exercise training that can facilitate improvements in muscle remodeling. This study was designed to test the capacity for muscle-resident MSCs (mMSCs) isolated from young mice to release regenerative proteins in response to mechanical strain in vitro, and subsequently determine the extent to which strain-stimulated mMSCs can enhance skeletal muscle and cognitive performance in a mouse model of uncomplicated aging. Protein arrays confirmed a robust increase in protein release at 24 h following an acute bout of mechanical strain in vitro (10%, 1 Hz, 5 h) compared to non-strain controls. Aged (24 month old), C57BL/6 mice were provided bilateral intramuscular injection of saline, non strain control mMSCs, or mMSCs subjected to a single bout of mechanical strain in vitro (4 ×104). No significant changes were observed in muscle weight, myofiber size, maximal force, or satellite cell quantity at 1 or 4 wks between groups. Peripheral perfusion was significantly increased in muscle at 4 wks post-mMSC injection (p < 0.05), yet no difference was noted between control and preconditioned mMSCs. Intramuscular injection of preconditioned mMSCs increased the number of new neurons and astrocytes in the dentate gyrus of the hippocampus compared to both control groups (p < 0.05), with a trend toward an increase in water maze performance noted (p=0.07). Results from this study demonstrate that acute injection of exogenously stimulated muscle-resident stromal cells do not robustly impact aged muscle structure and function, yet increase the survival of new neurons in the hippocampus.
Marketing channels are among the most important elements of any value chain. This is because the bulk of a nation´s manufacturing output flows through them. The intermediaries (e.g., distributors, wholesalers, retailers) constituting marketing channels perform specific distribution functions,such as transportation, storage, sales, financing, and relationship building, better than most manufacturers. Over his distinguished career, Louis P. Bucklin investigated many questions about the structuring and functioning of marketing channels using conceptual, empirical, and microeconomics model-based methodologies. Today, the academic marketing literature contains hundreds of articles that have employed these three broad classes of methodologies to investigate issues of channel intermediaries´ interorganizational relationships, for example, power-dependence, relational outcomes, conflict and negotiations, and manufacturing firms´ channel strategy, for example, channel structure, selection, coordination and control. So far, however, there has been no review of how the three different methodologies have contributed to advancing knowledge across this set of channels research domains.
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.
This paper covers test and verification of a forecast-based Monte Carlo algorithm for an optimized, demand-oriented operation of combined heat and power (CHP) units using the hardware-in-the-loop approach. For this purpose, the optimization algorithm was implemented at a test bench at Reutlingen University for controlling a CHP unit in combination with a thermal energy storage, both in real hardware. In detail, the hardware-in-the-loop tests are intended to reveal the effects of demand forecasting accuracy, the impact of thermal energy storage capacity and the influence of load profiles on demand-oriented operation of CHP units. In addition, the paper focuses on the evaluation of the content of energy in the thermal energy storage under practical conditions. It is shown that a 5-layer model allows to determine the energy stored quite accurately, which is verified by experimental results. The hardware-in-the-loop tests disclose that demand forecasting accuracies, especially electricity demand forecasting, as well as load profiles strongly impact the potential for CHP electricity utilization on-site in demand-oriented mode. Moreover, it is shown that a larger effective capacity of the thermal energy storage positively affects demand-oriented operation. In the hardware-in-the-loop tests, the fraction of electricity generated by the CHP unit utilized on-site could thus be increased by a maximum of 27% compared to heat-led operation, which is still the most common modus operandi of small-scale CHP plants. Hence, the hardware-in-the-loop tests were adequate to prove the significant impact of the proposed algorithm for optimization of demand-oriented operation of CHP units.
The use of learning factories for education in maintenance concepts is limited, despite the important role maintenance plays in the effective operation of organizational assets. A training programme in a learning factory environment is presented where a combination of gamification, classroom training and learning factory applications is used to introduce students to the concepts of maintenance plan development, asset failure characteristics and the costs associated with maintenance decision-making. The programme included a practical task to develop a maintenance plan for different advanced manufacturing machines in a learning factory setting. The programme stretched over a four-day period and demonstrated how learning factories can be effectively utilized to teach management related concepts in an interdisciplinary team context, where participants had no, or very limited, previous exposure to these concepts.
Zero or plus energy office buildings must have very high building standards and require highly efficient energy supply systems due to space limitations for renewable installations. Conventional solar cooling systems use photovoltaic electricity or thermal energy to run either a compression cooling machine or an absorption-cooling machine in order to produce cooling energy during daytime, while they use electricity from the grid for the nightly cooling energy demand. With a hybrid photovoltaic-thermal collector, electricity as well as thermal energy can be produced at the same time. These collectors can produce also cooling energy at nighttime by longwave radiation exchange with the night sky and convection losses to the ambient air. Such a renewable trigeneration system offers new fields of applications. However, the technical, ecological and economical aspects of such systems are still largely unexplored.
In this work, the potential of a PVT system to heat and cool office buildings in three different climate zones is investigated. In the investigated system, PVT collectors act as a heat source and heat sink for a reversible heat pump. Due to the reduced electricity consumption (from the grid) for heat rejection, the overall efficiency and economics improve compared to a conventional solar cooling system using a reversible air-to-water heat pump as heat and cold source.
A parametric simulation study was carried out to evaluate the system design with different PVT surface areas and storage tank volumes to optimize the system for three different climate zones and for two different building standards. It is shown such systems are technically feasible today. With a maximum utilization of PV electricity for heating, ventilation, air conditioning and other electricity demand such as lighting and plug loads, high solar fractions and primary energy savings can be achieved.
Annual costs for such a system are comparable to conventional solar thermal and solar electrical cooling systems. Nevertheless, the economic feasibility strongly depends on country specific energy prices and energy policy. However, even in countries without compensation schemes for energy produced by renewables, this system can still be economically viable today. It could be shown, that a specific system dimensioning can be found at each of the investigated locations worldwide for a valuable economic and ecological operation of an office building with PVT technologies in different system designs.
The technologies of digital transformation, such as the Internet-of-Things (IoT), artificial intelligence or predictive maintenance enable significant efficiency gains in industry and are becoming increasingly important as a competitive factor. However, their successful implementation and creative, future application requires the broad acceptance and knowledge of non-IT-related groups, such as production management students, engineers or skilled workers, which is still lacking today. This paper presents a low-threshold training concept bringing IoT-technologies and applications into manufacturing related higher education and employee training. The concept addresses the relevant topics starting from IoT-basics to predictive maintenance using mobile low-cost hardware and infrastructure.
Artificial Intelligence-based Assistants AIAs are spreading quickly both in homes and offices. They already have left their original habitats of "intelligent speakers" providing easy access to music collections. The initiated a multitude of new devices and are already populating devices such as TV sets. Characteristic for the intelligent digital assistants is the formation of platforms around their core functionality. Thus, AIS capabilities of the assistants are used to offer new services and create new interfaces for business processes. There are positive network effects between the assistants and the services as well as within the services. Therefore, many companies see the need to get involved in the field of digital assistants but lack a framework to align their initiatives with their corporate strategies. In order to lay the foundation for a comprehensive method, we are therefore investigating intelligent digital assistants. Based on this analysis, we are developing a framework of strategic opportunities and challenges.
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.
Globalisation, shorter product life cycles, and increasing product varieties have led to complex supply chains. At the same time, there is a growing interest of customers and governments in having a greater transparency of brands, manufacturers, and producers throughout the supply chain. Due to the complex structure of collaborative manufacturing networks, the increase of supply chain transparency is a challenge for manufacturing companies. The blockchain technology offers an innovative solution to increase the transparency, security, authenticity, and auditability of products. However, there are still uncertainties when applying the blockchain technology to manufacturing scenarios and thus enable all stakeholders to trace back each component of an assembled product. This paper proposes a framework design to increase the transparency and auditability of products in collaborative manufacturing networks by adopting the blockchain technology. In this context, each component of a product is marked with a unique identification number generated by blockchain-based smart contracts. In this way, a transparent auditability of assembled products and their components can be achieved for all stakeholders, including the custome.
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 persistent development towards decreasing batch sizes due to an ongoing product individualization, as well as increasingly dynamic market and competitive conditions lead to new changeability requirements in production environments. Since each of the individualized products mgith require different base materials or components and manufacturing resources, the paths of the products giong through the factory as well as the required internal transport and material supply processes are going to differ for every product. Conventional planning and control systems, which rely on predifined processes and central decision-making, are not capable to deal with the arising system's complexity along the dimensions of changing goods, layouts and throughput requirements. The concepts of "self-organization" in combination with "autonomous ocntrol" provide promising solutions to solve these new requirements by using among other things the potential of autonomous, decentralized and target-optimized logistical objects (e.g. smart products, bins and conveyor systems) wich are able to communicate and interact with each other as well as with human wokers. To investigate the potential of automation and human-robot collaboration for intralogistics, a research project for the development of a collaborative tugger train has been started at the ESB Logistics Learning Factory in lin with various student projects in neighboring research areas. This collaboraive tugger train system in combination with other manual (e.g. handcarts) and (semi-) automated conveyoer systems (e.g. automated guided forklift) will be integrated into a dynamic, self-organized scenario with varying production batch sizes to develop a method for target-oriented sefl-organization and autonomous control of intralogistics systems. For a structured investigation of self-organized scenarios a generic intralogistics model as well as a criteria cataloghe has been developed. The ESB Logistics Learning will serve as a practice-oriented research, validation and demonstration environment for these purposes.
Structural and functional thermosetting composite materials are exposed to different kinds of stress which can damage the polymer matrix, thus impairing the intended properties. Therefore, self-healing materials have attracted the attention of many research groups over the last decades in order to provide satisfactory material properties and outstanding product durability. The present article provides a critical overview of promising self-healing strategies for crosslinked thermoset polymers. It is organized in two parts: an overview about the different approaches to self-healing is given in the first part, whereas the second part focuses on the specific chemistries of the main strategies to achieve self-healing through crosslinking. It is attempted to provide a comprehensive discussion of different approaches which are described in the scientific literature. By comparison of the advantages and disadvantages, the authors wish to provide helpful insights on the assessment of the potential to transfer the extensive present knowledge about self-healing materials and methods to surface varnishing thermoset coatings.
Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.
Supply chains have become increasingly complex, making it difficult to ensure transparency throughout the whole supply chain. In this context, first approaches came up, adopting the immutable, decentralised, and secure characteristics of the blockchain technology to increase the transparency, security, authenticity, and auditability of assets in supply chains. This paper investigates recent publications combining the blockchain technology and supply chain management and classifies them regarding the complexity to be mapped on the blockchain. As a result, the increase of supply chain transparency is identified as the main objective of recent blockchain projects in supply chain management. Thereby, most of the recent publications deal with simple supply chains and products. The few approaches dealing with complex parts only map sub-areas of supply chains. Currently no example exists which has the aim of increasing the transparency of complex manufacturing supply chains, and which enables the mapping of complex assembly processes, an efficient auditability of all assets, and an implementation of dynamic adjustments.
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.
Reflectometry is known since long as an interferometric method which can be used to characterize surfaces and thin films regarding their structure and,to a certain degree,composition as well.Properties like layer structures,layer thickness,density,and interface roughness can be determined by fitting the obtained reflectivity data with an appropriate model using a recursive fitting routine. However,one major drawback of the reflectometric method is its restriction to planar surfaces.In this article we demonstrate an approach to apply X-ray and neutron reflectometry to curved surfaces by means of the example of bent bare and coated glass slides.We prove the possibility to observe all features like Fresnel decay,Kiessig fringes,Bragg peaks and off-specular scattering and are able to interpret the data using common fitting software and to derive quantitative results about roughness,layer thickness and internal structure. The proposed method has become practical due to the availability of high quality 2D-detectors. It opens up the option to explore many kinds and shapes of samples,which,due to their geometry,have not been in the focus of reflectometry techniques until now.
Context: An experiment-driven approach to software product and service development is gaining increasing attention as a way to channel limited resources to the efficient creation of customer value. In this approach, software capabilities are developed incrementally and validated in continuous experiments with stakeholders such as customers and users. The experiments provide factual feedback for guiding subsequent development.
Objective: This paper explores the state of the practice of experimentation in the software industry. It also identifies the key challenges and success factors that practitioners associate with the approach.
Method: A qualitative survey based on semi-structured interviews and thematic coding analysis was conducted. Ten Finnish software development companies, represented by thirteen interviewees, participated in the study.
Results: The study found that although the principles of continuous experimentation resonated with industry practitioners, the state of the practice is not yet mature. In particular, experimentation is rarely systematic and continuous. Key challenges relate to changing the organizational culture, accelerating the development cycle speed, and finding the right measures for customer value and product success. Success factors include a supportive organizational culture, deep customer and domain knowledge, and the availability of the relevant skills and tools to conduct experiments.
Conclusions: It is concluded that the major issues in moving towards continuous experimentation are on an organizational level; most significant technical challenges have been solved. An evolutionary approach is proposed as a way to transition towards experiment-driven development.
Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999–2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are "economies of scale" (size) in pharmaceutical R&D.
Properties data of phenolic resins synthetized for the impregnation of saturating Kraft paper
(2018)
The quality of decorative laminates boards depends on the impregnation process of Kraft papers with a phenolic resin,which constitute the raw materials for the manufacture of the cores of such boards.In the laminates industries,the properties of resins are adapted via their syntheses,usually by mixing phenol and formaldehyde in a batch,where additives,temperature and stirring parameters can be controlled. Therefore, many possibilities of preparation and phenolic resins exist, that leads to different combinations of physico chemical properties. In this article, the properties data of eight phenolic resins synthetized with different parameters of pH and reaction times at 60 °C and 90 °C are presented: the losses of pH after synthesis and the dynamic viscosities measured after synthesis and one the solid content is adjusted to 45%w/w in methanol. Data aquired by Differential Scanning Calorimetry (DSC) of the resins and Inverse Gas Chromatography (IGC) of cured solids are given as well.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
Clay minerals play an increasingly important role as functional fillers and reinforcing materials for clay polymer nanocomposites (CPN) in advanced applications. Among the prerequisites necessary for polymer improvement by clay minerals are homogeneous and stable Distribution of the clay mineral throughout the CPN, good compatibility of the reinforcement with the Matrix component and suitable processability. Typically, clay minerals are surface-modified with organic interface active compounds like detergents or silanes to obtain favorable properties as filler. They are incorporated into the polymer matrix using manufacturing Equipment like extruders, batch reactors or other mixing machines. In order for the surface modification to survive the stresses and strains during incorporation, the modified clay minerals must display sufficient thermal and mechanical stability to retain the compatibilizing effect. In the present study, thermogravimetry was used in combination with isoconversional kinetic analysis to determine the thermal stability of a silane-modified clay mineral based on bentonite. These findings were compared with the stability of the same clay mineral that was only surfactant-modified. It was found that silane modification leads to significantly improved thermal stability, which depends strongly on the type of silane employed.
The powder coating of veneered particle boards by the sequence electrostatic powder application -powder curing via hot pressing is studied in order to create high gloss surfaces. To obtain an appealingaspect, veneer Sheets were glued by heat and pressure on top of particle boards and the resulting surfaceswere used as carrier substrates for powder coat finishing. Prior to the powder coating, the veneeredparticle board surfaces were pre-treated by sanding to obtain good uniformity and the boards werestored in a climate chamber at controlled temperature and humidity conditions to adjust an appropriate electrical surface resistance. Characterization of surface texture was done by 3D microscopy. The surfaceelectrical resistance was measured for the six veneers before and after their application on the particleboard surface. A transparent powder top-coat was applied electrostatically onto the veneered particleboard surface. Curing of the powder was done using a heated press at 130◦C for 8 min and a smooth, glossy coating was obtained on the veneered surfaces. By applying different amounts of powder thecoating thickness could be varied and the optimum amount of powder was determined for each veneer type.
The wet chemical deposition of solution processed transparent conducting oxides (TCO) provides an alternative low cost and economical deposition technique to realize large-areas of conducting films. Since the price for the most common TCO Indium Tin Oxide rises enormously, Aluminum Zinc Oxide (AZO) as alternative TCO reaches more and more interest. The optoelectronical properties of nanoparticle coatings strongly depend beneath the porosity of the coating on the shape and size of the used particles. By using bigger or rod-shaped particles it is possible to minimize the amount of grain boundaries resulting in an improvement of the electrical properties, whereas particles bigger than 100 nm should not be used if highly transparent coatings are necessary as these big particles scatter the visible light and lower the transmittance of the coatings. In this work we present a simple method to synthesize AZO particles with different shape and size, but comparable electronical properties. We use a simple, well reproducible polyol method for synthesis and influence the shape and size of the particles by adding different amounts of water to the precursor solution. We can show that the addition of aluminum as dopant strongly hinders the crystal growth but the addition of water counteracts this, so that both, spherical and rod-shaped particles can be obtained.
Due to the complexity of assembly processes, a high ratio of tasks is still performed by human workers. Short-cyclically changing work contents due to smaller lot sizes, especially the varied series assesmbly, increases both the need for information support as well as the risk of rising physical and psychological stress. The use of technical and digital assistance systems can counter these challenges. Through the integration of information and communication technology as well as collaborative assembly technologies, hybrid cyber-physical assembly systems will emerge. Widely established assembly planning approaches for digital and technical support systems in cyber physical assembly systems will be outlined and discussed with regard to synergies and delimitations of planning perspectives.
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.
Context: Companies increasingly strive to adapt to market and ecosystem changes in real time. Gauging and understanding team performance in such changing environments present a major challenge.
Objective: This paper aims to understand how software developers experience the continuous adaptation of performance in a modern, highly volatile environment using Lean and Agile software development methodology. This understanding can be used as a basis for guiding formation and maintenance of high-performing teams, to inform performance improvement initiatives, and to improve working conditions for software developers.
Method: A qualitative multiple-case study using thematic interviews was conducted with 16 experienced practitioners in five organisations.
Results: We generated a grounded theory, Performance Alignment Work, showing how software developers experience performance. We found 33 major categories of performance factors and relationships between the factors. A cross-case comparison revealed similarities and differences between different kinds and different sizes of organisations.
Conclusions: Based on our study, software teams are engaged in a constant cycle of interpreting their own performance and negotiating its alignment with other stakeholders. While differences across organisational sizes exist, a common set of performance experiences is present despite differences in context variables. Enhancing performance experiences requires integration of soft factors, such as communication, team spirit, team identity, and values, into the overall development process. Our findings suggest a view of software development and software team performance that centres around behavioural and social sciences.
The use of additive manufacturing technologies for industrial production is constantly growing. This technology differs from the known production proecdures. The areas for scheduling, detailed and sequence planning are particularly important for additive production due to the long print times and flexible use of the production area. Therefore, production-relevant variables are considered and used for the production planning and control (PPC) of additive manufacturing machines. For this purpose, an optimization model is presented which shows a time-oriented build space utilization. In the implementation, a nesting algorithm is used to check the combinability of different models for each individual print job.
Conventional production systems are evolving through cyber-physical systems and application-oriented approaches of AI, more and more into "smart" production systems, which are characterized among other things by a high level of communication and integration of the individual components. The exchange of information between the systems is usually only oriented towards the data content, where semantics is usually only implicitly considered. The adaptability required by external and internal influences requires the integration of new or the redesign of existing components. Through an open application-oriented ontology the information and communication exchange are extended by explicit semantic information. This enables a better integration of new and an easier reconfiguration of existing components. The developed ontology, the derived application and use of the semantic information will be evaluated by means of a practical use case.
The fiber deformations of once-dried, bleached and never-dried unbleached kraft pulps were studied with respect to their behavior in high- and low-consistency refining. The pulps were stained with congo red to experimentally highlight areas where the arrangement of the fibrils was altered by refining such as dislocated zones or slip planes. The stained fibers were analyzed with conventional Metso Fiberlab but also with a novel prototype measurement device utilizing a color imaging setup. The local intensity of the stain in the fiber was expressed as degree of overall damage (Overall fiber damage index, OFDI). The rewetted zero span tensile index (RWZSTI) was used to verify the OFDI with respect to the pulp strength. High consistency refining resulted in a clear increase in the number of kinks which negatively influenced the pulp strength. The OFDI which was used to detect the intensity of local fiber defects also responded accordingly. A higher OFDI resulted in a lower pulp strength. Low consistency refining removed a significant amount of kinks and resulted in an increase in fiber swelling. A slight increase in fibrillation and a significant increase in flake-like fines were also observed. The OFDI, however, was not reduced in low consistency refining as it would be expected by the removal of less severe dislocations. One reason proposed here is that low consistency refining created new fiber pores that allowed the dye to penetrate into the fiber wall similarly as it does in the zones of the dislocations.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
This paper is concerned with the study, optimization and control of the moisture sorption kinetics of agricultural products at temperatures typically found in processing and storage. A nonlinear autoregressive with exogenous inputs (NARX) neural network was developed to predict moisture sorption kinetics and consequently equilibrium moisture contents of shiitake mushrooms (Lentinula edodes (Berk.) Pegler) over a wide range of relative humidity and different temperatures. Sorption kinetic data of mushroom caps was separately generated using a continuous, gravimetric dynamic vapour sorption analyser at emperatures of 25-40 °C over a stepwise variation of relative humidity ranging from 0 to 85%. The predictive power of the neural network was based on physical data, namely relative humidity and temperature. The model was fed with a total of 4500 data points by dividing them into three subsets, namely, 70% of the data was used for training, 15% of the data for testing and 15% of the data for validation, randomly selected from the whole dataset. The NARX neural network was capable of precisely simulating equilibrium moisture contents of mushrooms derived from the dynamic vapour sorption kinetic data throughout the entire range of relative humidity.
Thin radio-frequency magnetron sputter deposited nano-hydroxyapatite (HA) films were prepared on the surface of a Fe-tricalcium phosphate (Fe-TCP) bioceramic composite, which was obtained using a conventional powder injection moulding technique. The obtained nano-hydroxyapatite coated Fe-TCP biocomposites (nano HA-Fe-TCP) were studied with respect to their chemical and phase composition, surface morphology, water contact angle, surface free energy and hysteresis. The deposition process resulted in a homogeneous, single-phase HA coating. The ability of the surface to support adhesion and the proliferation of human mesenchymal stem cells (hMSCs) was studied using biological short-term tests in vitro. The surface of the uncoated Fe-TCP bioceramic composite showed an initial cell attachment after 24 h of seeding, but adhesion, proliferation and growth did not persist during 14 days of culture.However, the HA-Fe-TCP surfaces allowed cell adhesion, and proliferation during 14 days. The deposition of the nano-HA films on the Fe-TCP surface resulted in higher surface energy, improved hydrophilicity and biocompatibility compared with the surface of the uncoated Fe-TCP. Furthermore, it is suggested that an increase in the polar component of the surface energy was responsible for the enhanced cell adhesion and proliferation in the case of the nano-HA Fe-TCP biocomposites.
The isothermal curing of melamine resin is investigated by in-line infrared spectroscopy at different temperatures. The infrared spectra are decomposed into time courses of characteristic spectral patterns using Multivariate Curve Resolution (MCR). It was found that depending on the applied curing temperature, melamine films with different spectral fingerprints and correspondingly different chemical network structures are formed. The network structures of fully cured resin films are specific for the applied curing temperatures used and cannot simply be compensated by changes in the curing time. For industrial curing processes, this means that cure temperature is the main system determining factor at constant M:F ratio. However, different MF resin networks can be specifically obtained from one and the same melamine resin by suitable selection of the curing time and temperatures profiles to design resin functionality. The spectral fingerprints after short curing time as well as after long curing time reflect the fundamental differences in the thermoset networks that can be obtained with industrial short-cycle and multi-daylight presses.
Model-based hearing diagnosis based on wideband tympanometry measurements utilizing fuzzy arithmetic
(2019)
Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves that represent the statistical range of normal hearing responses. Because of large inter-individual variances in the middle ear, especially in wideband tympanometry (WBT), specificity and quantitative evaluation are greatly restricted. A new model-based approach could transform today's predominantly qualitative hearing diganostics into a quantitative and tailored, patient-specific diagnosis, by evaluating WBT measurements with the aid of a middle-ear model. For this particular investigation, a finite element model of a human ear was used. It consisted of an acoustic ear canal and a tympanic cavity model, a middle-ear with detailed nonlinear models of the tympanic membrane and annular ligament, and a simplified inner-ear model. This model has made it possible to identify pathologies from measurements, by analyzing the parameters through senstivity studies and parameter clustering. Uncertainties due to the lack of knowledge, subjectivity in numerical implementation and model simplification are taken into account by the application of fuzzy arithmetic. The most confident parameter set can be determined by applying an inverse fuzzy method on the measurement data. The principle and the benefits of this model-based approach are illustrated by the example of a two-mass oscillator, and also by the simulation of the energy absorbance of an ear with malleus fixation, where the parameter changes that are introduced can be determined quantitatively through the system identification.
Human bestrophin-1 (hBest1) is a transmembrane Ca2+- dependent anion channel, associated with the transport of Cl−, HCO3- ions, γ-aminobutiric acid (GABA), glutamate (Glu), and regulation of retinal homeostasis. Its mutant forms cause retinal degenerative diseases, defined as Bestrophinopathies. Using both physicochemical - surface pressure/mean molecular area (π/A) isotherms, hysteresis, compressibility moduli of hBest1/sphingomyelin (SM) monolayers, Brewster angle microscopy (BAM) studies, and biological approaches - detergent membrane fractionation, Laurdan (6-dodecanoyl-N,N-dimethyl-2-naphthylamine) and immunofluorescence staining of stably transfected MDCK-hBest1 and MDCK II cells, we report:
1) Ca2+, Glu and GABA interact with binary hBest1/SM monolayers at 35 °C, resulting in changes in hBest1 surface conformation, structure, self-organization and surface dynamics. The process of mixing in hBest1/SM monolayers is spontaneous and the effect of protein on binary films was defined as “fluidizing”, hindering the phase-transition of monolayer from liquid-expanded to intermediate (LE-M) state;
2) in stably transfected MDCK-hBest1 cells, bestrophin-1 was distributed between detergent resistant (DRM) and detergent-soluble membranes (DSM) - up to 30 % and 70 %, respectively; in alive cells, hBest1 was visualized in both liquid-ordered (Lo) and liquid-disordered (Ld) fractions, quantifying protein association up to 35 % and 65 % with Lo and Ld. Our results indicate that the spontaneous miscibility of hBest1 and SM is a prerequisite to diverse protein interactions with membrane domains, different structural conformations and biological functions.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
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.