Search

The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.

Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices. The current TinyML research seems to focus on supporting the deployment of deep learning models on microprocessors, while the models themselves are trained on high performance computers or clouds. However, in the resource/time constrained IoT contexts, it is more desirable to perform data analytics and learning tasks directly on edge devices for crucial benefits such as increased energy efficiency, reduced latency as well as lower communication cost.To address the above challenge, this paper proposes a tiny federated learning algorithm for enabling learning of Bayesian classifiers based on distributed tiny data storage, referred to as TFL-BC. In TFL-BC, Bayesian learning is executed in parallel across multiple edge devices using local (tiny) training data and subsequently the learning results from local devices are aggregated via a central node to obtain the final classification model. The results of experiments conducted on a set of benchmark datasets demonstrate that our algorithm can produce final aggregated models that outperform single tiny Bayesian classifiers and that the result of tiny federated learning (of Bayesian classifier) is independent of the number of data partitions used for generating the distributed local training data.

This paper investigates manufacturing companies’ current and planned usage of collaborative robots along with possible reasons for the observed slow growth in implementing Collaborative Robot Applications (CRAs) in the industry. The paper also discusses whether similarities can be seen in the Gartner Hype Cycle for technology adoption. Findings from an industrial survey suggest increasingly positive attitudes towards using CRAs in manufacturing and final assembly operations as tools and support mechanisms aiding human operators. Better methodologies and best practices are urgently needed for successful CRA implementation and efficient manufacturing human-robot collaboration design., © 2023, IFIP International Federation for Information Processing.

Contemporary theories of deterrence place a strong emphasis on coherency between model and theory. Schelling’s contention of irrational threats for successful deterrence abandons the rationality assumption to explain how a player can deter, thereby departing from the standard game theoretic solution concepts. It is a misfit model in relation to a deterrence theory and, therefore, excluded. The article defends and remodels Schelling’s intuition by employing the level-k model. It is shown that an unsophisticated player that randomizes over its strategies brings about an advantageous outcome. The model also shows that the belief that a player randomizes has the same deterrent effect, as an actual stochastic choice, like Schelling suggested. While this means Schelling’s idea can be saved, it is still problematic how we should view contributions of bounded rationality in relation to current deterrence theory. The article suggests that separating the purpose of a model in conjunction with allowing other scientific ideals than model-theory coherence permits a broader and philosophically sounder approach., Not duplicate with DiVA 1617777QC 20230602

Amidst the current period of urgent and costly climate abatement policies being implemented, company cars as a fringe benefit receive surprisingly little attention from policy and research, despite evidence showing that they not only result in substantial welfare losses but also in increased car ownership and use. Therefore, this paper adds to the evidence on how company cars increase car use. We estimate how the possession of a company car impacts the households’ probability of possessing at least one car and the total car possession (the sum of privately owned and company cars). We use register micro-panel data, covering all households in Sweden, allowing us to study the effect of company cars in the full population while accounting for household-specific time-invariant unobserved preferences. It also allows us to study asymmetric effects of gaining versus losing a company car. We regress temporal changes in car possession on temporal changes in company car possession, applying a fixed effect (FE) estimator for single and couple households separately. A company car increases the probability of having at least one car in single and couple households by 38% and 14%, respectively. For couple households, we find a small asymmetric effect, such that the impact of the company car on car possession is slightly larger when the car is received than when it is lost. For single households the effect is symmetric. Moreover, a company car increases car possession by on average 0.26 cars for couple households possessing at least one car. Since roughly 80% of the mileage of these cars is attributed to private purposes in Sweden, these results indicate that the current company car taxation also increases car use.

Trigger-Action Platforms (TAPs) empower applications (apps) for connecting otherwise unconnected devices and services. The current TAPs like IFTTT require trigger services to push excessive amounts of sensitive data to the TAP regardless of whether the data will be used in the app, at odds with the principle of data minimization. Furthermore, the rich features of modern TAPs, including IFTTT queries to support multiple trigger services and nondeterminism of apps, have been out of the reach of previous data minimization approaches like minTAP. This paper proposes LazyTAP, a new paradigm for fine-grained on-demand data minimization. LazyTAP breaks away from the traditional push-all approach of coarse-grained data over-approximation. Instead, LazyTAP pulls input data on-demand, once it is accessed by the app execution. Thanks to the fine granularity, LazyTAP enables tight minimization that naturally generalizes to support multiple trigger services via queries and is robust with respect to nondeterministic behavior of the apps. We achieve seamlessness for third-party app developers by leveraging laziness to defer computation and proxy objects to load necessary remote data behind the scenes as it becomes needed. We formally establish the correctness of LazyTAP and its minimization properties with respect to both IFTTT and minTAP. We implement and evaluate LazyTAP on app benchmarks showing that on average LazyTAP improves minimization by 95% over IFTTT and by 38% over minTAP, while incurring a tolerable performance overhead.

Understanding the needs and constraints of systems in general and a system-of-systems in particular can be challenging, yet crucial. Relying only on upfront activities will not be sufficient. Important information can be gathered around the performance and behavior of the system as well as stakeholder needs in operation. A digital twin is a way to model and understand the operation of a system. To understand the challenges and enablers related to digital twins in a system-of-systems context, we performed a literature study. In total, only 10 papers were identified that explicitly address this topic, all from the last five years, indicating that this is an active field of research. The papers revealed that definitions and terminology are unclear and that similar challenges as for systems-of-systems also exist for systems-of-digital twins. The complexity and dynamic nature of systems-of-systems motivate further study of digital twins to understand needs and constraints. However, key challenges such as concepts and principles of digital twins for systems-of-systems, cost and benefits, and evolution needs to be better understood.

Additive Manufacturing (AM) has traditionally been used for prototyping of products, however, in the last few decades, it has seen a rising growth in the manufacture of final products. The addition of AM as a manufacturing method in the portfolio of a company's production capabilities increases the complexity of decision-making. This is because the decisions are often not based on the same criteria and constraints, as related to conventional manufacturing processes. In this paper, we investigate this challenge by studying how AM affects the current workflow and the associated information flow for a design-make process in a Swedish manufacturer before and after the integration of AM. In this paper, it is argued that apart from an understanding of how to design for AM, it is equally important to consider how introducing AM alters the existing information flow and how to benefit from information available in various design-make process steps to facilitate decision making process. The result clarifies that the current process relies largely on tacit and experiences-based knowledge, whereas to take advantage of AM, more precision is required to capture and process the available information.

Well-trained software development personnel, in the art and science of software testing, will effectively and efficiently develop quality software products with potentially fewer, less-critical defects. Thus software testing education is considered to be an important part of curricula for a university degree in Computer Science or Information Systems. The objective of this paper is to determine how much dedicated knowledge in the field of software testing is taught within Swedish universities. To achieve this objective, a systematic search of syllabi for software testing-related courses was done. From 25 Swedish universities offering Computer Science (or related) degrees, 14 currently offer dedicated courses in software testing. Some findings include: 32% of the individual courses were offered at the undergraduate level; 28% of the universities offer courses for specialised testing training; and, for the vast majority of the universities, dedicated software testing courses account for about 5% of the total degree credits. While some universities fare better than others, the overall state of academic software testing education in Sweden is limited but promising.

The view on safety buffers is fragmented in the current literature; some researchers argue that a safety buffer is only waste, while others see them as prerequisites to absorb variations and secure a competitive delivery capability. This study conceptualises various safety buffer types in terms of materials, capacity and lead time to mitigate the negative effects of short-term stochastic variations in supply and demand. The identified safety buffers are categorised based on a material flow perspective as inbound, process and outbound buffers. In total, seven safety buffer sub-types are identified and investigated in terms of their utilization in four manufacturing companies. The experiences from eleven respondents highlight the utilization purposes in their selection of safety buffers. The empirical investigation also indicates several concerns through four propositions that highlight the significance of decision support, providing a more holistic perspective on different types and sub-types of safety buffers and their application in practice. Finally, a conceptual framework is proposed to facilitate the selection of safety buffers in practice.

Amidst the current period of urgent and costly climate abatement policies being implemented, company cars as a fringe benefit receive surprisingly little attention from policy and research, despite evidence showing that they not only result in substantial welfare losses but also in increased car ownership and use. Therefore, this paper adds to the evidence on how company cars increase car use. We estimate how the possession of a company car impacts the households’ probability of possessing at least one car and the total car possession (the sum of privately owned and company cars). We use register micro-panel data, covering all households in Sweden, allowing us to study the effect of company cars in the full population while accounting for household-specific time-invariant unobserved preferences. It also allows us to study asymmetric effects of gaining versus losing a company car. We regress temporal changes in car possession on temporal changes in company car possession, applying a fixed effect (FE) estimator for single and couple households separately. A company car increases the probability of having at least one car in single and couple households by 38% and 14%, respectively. For couple households, we find a small asymmetric effect, such that the impact of the company car on car possession is slightly larger when the car is received than when it is lost. For single households the effect is symmetric. Moreover, a company car increases car possession by on average 0.26 cars for couple households possessing at least one car. Since roughly 80% of the mileage of these cars is attributed to private purposes in Sweden, these results indicate that the current company car taxation also increases car use.

Teaching and exploring the ethical issues brought about by digitalization is an important challenge in current higher education programs. Experiential learning through games is becoming increasingly relevant as games exert an enormous influence on the imaginarium of newer generations. This paper details how a class of international graduate students engaged in a year-long exploration of ethics, gender, and sustainability issues by playing, remixing, and designing games using an original Design Games Framework. Using a qualitative approach based on participatory observations that followed the student's entire game-making process and a series of final semi-structured interviews, the paper illustrates how game-making can enable higher education students to better understand the complex interplay of ethical issues and digitalization processes, as well as confirming that the Design Games Framework is a valid instrument for the exploration of ethics through the design of tabletop games in a higher education setting.

Current society heavily relies on software and software systems. Due to its increasing complexity, the design and operation of software systems are becoming challenging. In the last decades, a great deal of effort has been put into addressing software systems design, development, and maintenance challenges. Empirical evidence shows that one of the most critical success factors when developing software systems is their Software Architecture (SA). A SA describes software systems in terms of software components, their interactions, and critical quality attributes. Among other benefits, SAs improve the overall communication among different stakeholders, are the carriers of significant design decisions, promote the use of different abstraction levels, and allow for the early assessment of the software under development.

Wearable bioelectronics and therapeutics are a rapidly evolving area of research, with researchers exploring new materials that offer greater flexibility and sophistication. Conductive hydrogels have emerged as a promising material due to their tunable electrical properties, flexible mechanical properties, high elasticity, stretchability, excellent biocompatibility, and responsiveness to stimuli. This review presents an overview of recent breakthroughs in conductive hydrogels, including their materials, classification, and applications. By providing a comprehensive review of current research, this paper aims to equip researchers with a deeper understanding of conductive hydrogels and inspire new design approaches for various healthcare applications.

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.

The current work focuses on predicting the buckling and free vibration frequencies (fn) of cenosphere reinforced epoxy based syntactic foam beam under varying loads. Critical buckling loads (Ncr) and fn are predicted using the differential quadrature method (DQM). Ncr and fn have been calculated for beams of varying cenosphere volume fractions subjected to axial load under clamped-clamped (CC), clamped-simply (CS), simply-simply (SS), and clamped-free (CF) boundary conditions (BC′s). Upon increasing the cenosphere volume fraction, Ncr and fn of syntactic foam composites increases. These numerical outcomes are compared with the theoretical values evaluated through the Euler-Bernoulli hypothesis and further compared with experimental outcomes. Results are observed to be in precise agreement. The results of the DQM numerical analysis are given out for the different BC′s, aspect ratios, cenosphere volume fractions, and varying loads. It is perceived that depending on the BC′s, the type of axial varying loads and aspect ratios has a substantial effect on the Ncr and fn behaviour of the syntactic foam beams. A comparative study of the obtained results showed that the beam subjected to parabolic load under CC boundary conditions exhibited a higher buckling load. © 2023 The Authors

Bike-sharing could play an important role in any future circular economy since sharing solutions have the potential to challenge the current automobility regime. This paper examines the potential contributions of two Swedish bike-sharing initiatives: grassroots-initiated bike kitchens and a commercial bike-sharing service system. We explore and analyze the visions of these initiatives and how they can be nurtured in order to support a circular future. The results indicate that the incumbent regime challenges cycling initiatives through commercialization and professionalization pressures. The paper notes that the studied initiatives can contribute to a future circular economy, but in different ways. While bike kitchens support the social aspects of circularity, commercial bike-sharing is more likely to support ecological aspects. Both kinds of initiatives are crucial and rely on the support of public actors with a strong sustainability agenda.

The current work focuses on predicting the buckling and free vibration frequencies (fn) of cenosphere reinforced epoxy based syntactic foam beam under varying loads. Critical buckling loads (Ncr) and fn are predicted using the differential quadrature method (DQM). Ncr and fn have been calculated for beams of varying cenosphere volume fractions subjected to axial load under clamped-clamped (CC), clamped-simply (CS), simply-simply (SS), and clamped-free (CF) boundary conditions (BC′s). Upon increasing the cenosphere volume fraction, Ncr and fn of syntactic foam composites increases. These numerical outcomes are compared with the theoretical values evaluated through the Euler-Bernoulli hypothesis and further compared with experimental outcomes. Results are observed to be in precise agreement. The results of the DQM numerical analysis are given out for the different BC′s, aspect ratios, cenosphere volume fractions, and varying loads. It is perceived that depending on the BC′s, the type of axial varying loads and aspect ratios has a substantial effect on the Ncr and fn behaviour of the syntactic foam beams. A comparative study of the obtained results showed that the beam subjected to parabolic load under CC boundary conditions exhibited a higher buckling load. © 2023 The Authors

Bike-sharing could play an important role in any future circular economy since sharing solutions have the potential to challenge the current automobility regime. This paper examines the potential contributions of two Swedish bike-sharing initiatives: grassroots-initiated bike kitchens and a commercial bike-sharing service system. We explore and analyze the visions of these initiatives and how they can be nurtured in order to support a circular future. The results indicate that the incumbent regime challenges cycling initiatives through commercialization and professionalization pressures. The paper notes that the studied initiatives can contribute to a future circular economy, but in different ways. While bike kitchens support the social aspects of circularity, commercial bike-sharing is more likely to support ecological aspects. Both kinds of initiatives are crucial and rely on the support of public actors with a strong sustainability agenda.

The current work focuses on predicting the buckling and free vibration frequencies (fn) of cenosphere reinforced epoxy based syntactic foam beam under varying loads. Critical buckling loads (Ncr) and fn are predicted using the differential quadrature method (DQM). Ncr and fn have been calculated for beams of varying cenosphere volume fractions subjected to axial load under clamped-clamped (CC), clamped-simply (CS), simply-simply (SS), and clamped-free (CF) boundary conditions (BC′s). Upon increasing the cenosphere volume fraction, Ncr and fn of syntactic foam composites increases. These numerical outcomes are compared with the theoretical values evaluated through the Euler-Bernoulli hypothesis and further compared with experimental outcomes. Results are observed to be in precise agreement. The results of the DQM numerical analysis are given out for the different BC′s, aspect ratios, cenosphere volume fractions, and varying loads. It is perceived that depending on the BC′s, the type of axial varying loads and aspect ratios has a substantial effect on the Ncr and fn behaviour of the syntactic foam beams. A comparative study of the obtained results showed that the beam subjected to parabolic load under CC boundary conditions exhibited a higher buckling load. © 2023 The Authors

Although solar energy is a clean and abundant resource, it has an unstable nature. It is demonstrated that latent thermal energy storage (LTES) systems have been an excellent way to utilize solar energy fully and widely. However, LTES has the problem of insufficient thermal conductivity. For this reason, it is inevitable to consider effective methods to intensify the thermal conductivity of LTES system. In the current study, experiment and numerical simulation are used to study the influence of non-uniform metal foams on heat transfer during phase transition. In this study, a horizontal shell-and-tube LTES test system is established. Moreover, the phase change melting rate of radially filled metal foams with different porosity gradients is compared. According to the numerical simulation results of phase interface, velocity field and temperature field, natural convection can accelerate the melting of PCM. However, there is no distinct effect on the solidification process. When the equivalent porosity is 0.94, the optimal combination (melting process is 0.84-0.92-0.99 and solidification process is 0.87-0.94-0.97), compared with the uniform structure, can shorten the total consumption time by 9.7% and 6.2%, respectively.

Head injuries are among the most common injuries in motorcycle accidents, where the helmet is the main protection. Until recently, the test standards have only evaluated protection against linear impacts. Evaluating protection against rotational impacts has been recently introduced. The objective of this study was to evaluate how current motorcycle helmets perform in ECE R22.06 rotational impact tests. The rotational impact tests were performed on three helmet models and the linear impact tests were performed on one helmet model. All the helmets passed the rotational impact tests. The maximum value for the experimental tests was 4.5 krad/s2 for PRA and 0.48 for BrIC compared to the threshold values of 10.4 krad/s2 and 0.78. In the linear impact tests five out of twenty-two impact tests failed the threshold for peak linear acceleration or head injury criterion. The results from this study suggest that motorcycle helmets will be more optimised towards reducing linear-induced injuries and not rotational-induced injuries in the newly introduced test standard ECE R22.06. This is not responding to the protection requirements when evaluating the accident statistics, which shows that rotational-induced injuries are as common or even more common than linear-induced injuries in helmeted motorcycle accidents., QC 20230706

Sustainability is not only understood as a manner to safeguard the environment, but also to fight against injustices and inequalities that exist on social and economic level. One of the biggest challenges that exists in social sustainability is to achieve gender equality, as defended by the Sustainable Development Goal (SDG) 5 of the 2030 Agenda. But this is a complex challenge and must be addressed from different spheres and fields of knowledge. Artificial Intelligence (AI) has proven to be an essential asset in the development of new and innovative technologies. Its development, adoption, and constant use by a growing part of the world’s population demonstrates the social impact it entails and the importance of also becoming an asset for social sustainability and, in this case especially, for gender equality. That is why this study aims to collect the current knowledge about the fields of AI and gender equality, through the development of a Systematic Mapping Study (SMS) that identifies the most significant advances in this regard, as well as the main gaps that must be covered. The results and findings obtained in this work show the novelty of joint analysis of both areas, as well as increasing attention they have received in recent years. Likewise, they also demonstrate the need to address specific and urgent issues within gender equality, both in the field of AI and caused by its development. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022., QC 20230614

Biodegradable polymers complement recyclable materials in battling plastic waste because some products are difficult to recycle and some will end up in the environment either because of their application or due to wear of the products. Natural biopolymers, such as cellulose, are inherently biodegradable, but chemical modification typically required for the obtainment of thermoplastic properties, solubility, or other desired material properties can hinder or even prevent the biodegradation process. This Review summarizes current knowledge on the degradation of common cellulose derivatives in different laboratory, natural, and man-made environments. Depending on the environment, the degradation can be solely biodegradation or a combination of several processes, such as chemical and enzymatic hydrolysis, photodegradation, and oxidation. It is clear that the type of modification and especially the degree of substitution are important factors controlling the degradation process of cellulose derivatives in combination with the degradation environment. The big variation of conditions in different environments is also briefly considered as well as the importance of the proper testing environment, characterization of the degradation process, and confirmation of biodegradability. To ensure full sustainability of the new cellulose derivatives under development, the expected end-of-life scenario, whether material recycling or "biological"recycling, should be included as an important design parameter., QC 20230612

In search of high energy density cathode materials, the eldfellite mineral-type NaVIII(SO4)2 compound has been theoretically predicted to be a promising cathode insertion host for sodium-ion batteries. Synergizing computational and experimental investigations, the current work introduces NaVIII(SO4)2 as a novel versatile cathode for Li-ion and Na-ion batteries. Prepared by a low temperature sol-gel synthesis route, the eldfellite NaV(SO4)2 cathode exhibited an initial capacity approaching ∼79% (vs. Li+/Li) and ∼69% (vs. Na+/Na) of the theoretical capacity (1e− ≅ 101 mA h g−1) involving the V3+/V2+ redox potential centered at 2.57 V and 2.28 V, respectively. The bond valence site energy (BVSE) approach and DFT-based calculations were used to gain mechanistic insight into alkali ion migration and probe the redox center during (de)insertion of Li+/Na+ ions. Post-mortem and electrochemical titration tools revealed the occurrence of a single-phase (solid-solution) redox mechanism during reversible Li+/Na+ (de)insertion into NaVIII(SO4)2. With the multivalent vanadium redox center, eldfellite NaVIII(SO4)2 forms a new cathode insertion host for Li/Na-ion batteries with potential two-electron uptake., QC 20230613

Measurements of any property of a microscopic system are bound to show significant deviations from the average, due to thermal fluctuations. For time-integrated currents such as heat, work, or entropy production in a steady state, it is in fact known that there will be long stretches of fluctuations both above as well as below the average, occurring equally likely at large times. In this paper we demonstrate that for any finite-time measurement in a nonequilibrium steady state - rather counterintuitively - fluctuations below the average are more probable. This discrepancy is found to be higher when the system is further away from equilibrium. For overdamped diffusive processes, there is even an optimal time when time-integrated current fluctuations mostly lie below the average. We demonstrate that these effects are consistent with a nonmonotonic skewness of current fluctuations and provide evidence that they are easily observable in experiments. We also discuss their extensions to discrete space Markov jump processes and implications to biological and synthetic microscopic engines., QC 20230613

One way to reduce carbon dioxide emissions from the current heavy-duty vehicles fleet is to replace fossil fuel with renewable fuel. This can be done by blending so-called drop-in fuels into the standard diesel fuel. However, problems such as insoluble impurities may arise when the fuels are mixed. These precipitates, known as soft particles, can cause deposits in the fuel system, e.g., injectors and fuel filters, reducing the enginés performance. The most used drop-in fuel today is biodiesel which, is blended with different concentrations. To better understand how soft particles are formed in the vehiclés fuel system, the degradation of biodiesel blends in the engine has been investigated. This study explores biodiesel blendś degradation process by comparing the incoming fuel with the return fuel from a modern diesel engine to investigate how the fuel is affected by this process. The engine was run using different blends of biodiesel fuel. To investigate the degradation of the biodiesel, engine tests at low, medium, and high torque at two engine speeds was performed. Fuel samples were collected before and after the engine for comparison. The tested fuels were examined with different analytical techniques. Rancimat, ion chromatography, inductively coupled plasma atomic emission spectroscopy and total acid number. A filtration test method was developed to collect the soft particles from the tested fuels. The results showed that fuel properties from the fuel return in biodiesel blends with high biodiesel content were more affected compared to lower biodiesel blends. For the lower biodiesel blends both the oxidation stability (Rancimat) and the filterability improved after passing the fuel system in the engine. While for the high biodiesel content, Rancimat and filterability were reduced. In biodiesels blends lower than 10%v/v, the change in oxidation stability was positive and around 30h and for B100 the change in oxidation stability was negative around 5 to 10 h. The, QC 20230502

Optimization of lanthanide-doped luminescent nanoparticles for use in nanomedicine has encountered some difficulties due to the specific properties of water as a solvent. In this review, the current challenges for the adaptation of lanthanide-doped luminescent nanoparticles to aqueous environments, and promising strategies to optimize their colloidal dispersibility and stability in water and physiological buffers, are summarized. Moreover, the possible luminescence de-excitation paths caused by water molecule vibrations and how they can be prevented under different measurement conditions are discussed. This review also deals with the latest developments in lanthanide-doped luminescent nanoparticle design for nanomedicine, to increase the depth at which they can be monitored, which is mainly limited by the absorption bands of water. Furthermore, the anomalous temperature dependence of water and the different effects it has on lanthanide-doped luminescent nanoparticles in the physiological temperature range are commented on. Finally, a critical opinion on the possible next steps in this field is provided., QC 20230403

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

Hand rehabilitation has been widely studied since it affects the life quality and independence of those affected. Hand impairment can be caused by several conditions, among them strokes and other cerebrovascular accidents, affecting the capabilities of those who survive them in performing the activities of daily living (ADL). Rehabilitation seeks to restore the ability of a person to perform these crucial ADL. There is a current trend in using robotic rehabilitation and other industry 4.0 tools since it can provide a safe, intensive, and task-oriented at a relatively low cost, which can be combined with other technologies such as virtual and augmented reality, BCI, haptics, and others. Moreover, it can provide accessibility in the face of current panoramas such as COVID-19. Hand exoskeleton robots are one of the most extended robotic devices for rehabilitation. However, a design adapted to the patient's needs is necessary to achieve their capability fully and succeed in rehabilitation. One of the main challenges is that several considerations and parameters affect these devices' design and the broad approaches that can be followed. This brief review aims to understand and empathize as a source of inspiration during the design process of hand exoskeleton robots for rehabilitation.

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad's efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed., CC BY 4.0Creative Commons Attribution 4.0 International License (CC BY)© 2022 The Author(s)MJR was supported by the Australian Research Council Future Fellowship (FT180100447). The authors would like to thank Dr. Patrick Nalepka for his helpful comments and suggestions throughout this work.

Intelligent personal assistants (IPAs) using speech interfaces have historically been limited to monolingual use in pre-selected languages. Although recent developments in some IPAs have allowed for increased multilingual flexibility, the plurilingual competence-ability to utilise more than one language in the frame of a single interaction-of state-of-The-Art IPAs still falls short. This is demonstrated in a pilot study, where two widely used IPAs are shown to consistently fail in plurilingual interactions across 3 core tasks. This lack of plurilingual competence makes certain IPA functions virtually unusable in various contexts for users who are not native speakers of the official language(s) where they are located, and also speaks to wider problems in the treatment of multilingual use of IPAs by developers. Addressing these issues will not only make IPAs with speech interfaces considerably more functional for a large demographic of current and potential IPA users, but also enable new applications for IPAs in contexts such as self-regulated language learning.

To stay competitive, manufacturing companies are developing towards Smart Production which requires the use of digital technologies. However, there is a lack of guidance supporting manufacturing companies in selecting and integrating a combination of suitable digital technologies, which is required for Smart Production. To address this gap, the purpose of this paper is twofold: (i) to identify the main challenges of selecting and integrating digital technologies for Smart Production, and (ii) to propose a holistic concept to support manufacturing companies in mitigating identified challenges in order to select and integrate a combination of digital technologies for Smart Production. This is accomplished by using a qualitative-based multiple case study design. This paper identifies current challenges related to selection and integration of digital technologies. To overcome these challenges and achieve Smart production, the concept of data value chain was proposed, i.e., a holistic approach to systematically map and improve data flows within the production system. © 2022, IFIP International Federation for Information Processing.

In the contention of the current industrial landscape, an increasing number of manufacturing firms are experimenting with the transition from product-centric offerings to service-based value concepts and product-service bundles as high-value integrated customer solutions to increase their revenues and build sustainable competitive advantages; a phenomenon known as the “servitization” of manufacturing. Nowadays, consistently with the Industry 4.0 paradigm, these companies have therefore started a process of integrating their traditional value offerings with digital services. This recent strategy is known as “Digital Servitization” and consists of developing new services and/or improving existing ones through digital technologies. However, this transformation is challenging, and companies often struggle to achieve their expectations. Thus, this study aims to shed light on the current state of Digital Servitization strategies in the manufacturing sector based on a survey addressed to the top and middle management. The results obtained by the analysis of the data collected from the survey show an increasing trend towards the adoption of digital technologies for enabling innovation and differentiation in service delivery processes. © 2022, IFIP International Federation for Information Processing.

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad's efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed., CC BY 4.0Creative Commons Attribution 4.0 International License (CC BY)© 2022 The Author(s)MJR was supported by the Australian Research Council Future Fellowship (FT180100447). The authors would like to thank Dr. Patrick Nalepka for his helpful comments and suggestions throughout this work.

The software startup research area has grown rapidly in the recent years. It is widely known that building software startups are challenging endeavors, and the failure rate is high. However, the fascinating phenomenon keeps getting interest from academics to address those challenges, due to the potential of software startups as an effective way for disruptive innovation. The aim of this study is to provide an update on the evolution of the software startup research area through a systematic mapping study. Our contributions are two-fold. First, we provide a mapping of current research in software startups in terms of contributing disciplines and research methods and theories used. The second contribution is the identification of two new and emerging research streams termed Software Startup Education and Ethics in Software Startups. Furthermore, the findings allow us to update the research agenda and provide new examples of research questions to advance the software startup research area. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Software engineers are sedentary and need technological help for a more healthy life. Current software engineering tasks are mostly confined to the standard sedentary desktop user interface. We believe that software engineering should be restructured so that it offers a non-sedentary alternative. In this paper, we describe a new research approach, called Post-sedentary Software Engineering. Our ambition with this approach is to provide an alternative, healthier work context without decreasing productivity. We take a spatial approach to post-sedentary tool design, starting from the assumption an interactive 3D environment with appropriate metaphors is necessary for full body movement. We discuss available technologies for achieving this goal and outline four studies that incorporate the software engineering phases of code comprehension, code creation and debugging in a non-sedentary context., QC 20230307

This review paper provides a recent overview of current international research that is being conducted into the functional properties of cellulose as a nanomaterial. A particular emphasis is placed on fundamental and applied research that is being undertaken to generate applications, which are now becoming a real prospect given the developments in the field over the last 20 years. A short introduction covers the context of the work, and definitions of the different forms of cellulose nanomaterials (CNMs) that are most widely studied. We also address the terminology used for CNMs, suggesting a standard way to classify these materials. The reviews are separated out into theme areas, namely healthcare, water purification, biocomposites, and energy. Each section contains a short review of the field within the theme and summarizes recent work being undertaken by the groups represented. Topics that are covered include cellulose nanocrystals for directed growth of tissues, bacterial cellulose in healthcare, nanocellulose for drug delivery, nanocellulose for water purification, nanocellulose for thermoplastic composites, nanocellulose for structurally colored materials, transparent wood biocomposites, supercapacitors and batteries., QC 20221123

In this paper, a novel distributed control strategy addressing a (feasible) psycho-social-physical welfare problem in islanded Direct Current (DC) smart grids is proposed. Firstly, we formulate a (convex) optimization problem that allows prosumers to share current with each other, taking into account the technical and physical aspects and constraints of the grid (e.g., stability, safety), as well as psycho-social factors (i.e., prosumers' personal values). Secondly, we design a controller whose (unforced) dynamics represent the continuous time primal-dual dynamics of the considered optimization problem. Thirdly, a passive interconnection between the physical grid and the controller is presented. Global asymptotic convergence of the closed-loop system to the desired steady-state is proved and simulations based on collected data on psycho-social aspects illustrate and confirm the theoretical results., QC 20220930

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad's efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed., CC BY 4.0Creative Commons Attribution 4.0 International License (CC BY)© 2022 The Author(s)MJR was supported by the Australian Research Council Future Fellowship (FT180100447). The authors would like to thank Dr. Patrick Nalepka for his helpful comments and suggestions throughout this work.

In this paper, we present the current version of The Swedish Simplification Toolkit. The toolkit includes computational and empirical tools that have been developed along the years to explore a still neglected area of NLP, namely the simplification of “standard” texts to meet the needs of target audiences. Target audiences, such as people affected by dyslexia, aphasia, autism, but also children and second language learners, require different types of text simplification and adaptation. For example, while individuals with aphasia have difficulties in reading compounds (such as arbetsmarknadsdepartement, eng. ministry of employment), second language learners struggle with cultural-specific vocabulary (e.g. konflikträdd, eng. afraid of conflicts). The toolkit allows user to selectively select the types of simplification that meet the specific needs of the target audience they belong to. The Swedish Simplification Toolkit is one of the first attempts to overcome the one-fits-all approach that is still dominant in Automatic Text Simplification, and proposes a set of computational methods that, used individually or in combination, may help individuals reduce reading (and writing) difficulties., Funding details: VINNOVA; Funding details: Vetenskapsrådet, VR; Funding text 1: This work has been funded by The Swedish Research Council (VR) and Sweden’s innovation agency (VI-NOVA).

Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone., QC 20220607

Measuring ontology quality using metrics is far from a trivial task – one has to pick the right metrics for the right task and then interpret these values in a meaningful way. Without help, these interpretations are often highly subjective, even for trained knowledge engineers. Quality frameworks can assist and objectify the evaluation. One of the more prominent frameworks in ontology evaluation is OQuaRE, which builds upon the SQuaRE standard for software evaluation. Not only provides it tangible metrics for assessing an ontology, but it also suggests an interpretation for these values in the form of a quality rating and links these metrics to a broader quality framework. However, during an implementation effort, the authors identified some drawbacks. In the last years, various metrics have been proposed that sometimes seem to conflict with each other or are inconclusive in their descriptions. The resources on the quality framework are distributed over web pages and papers. The following paper aims first to present the drawbacks the framework currently has. At the next step, we resolve the current heterogeneities and collect the information of the various sources. We aim to provide a one-stop information resource on OQuaRE to enable our further research and applications efforts.