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Understanding “efficiency roll-off” (i.e., the drop in emission efficiency with increasing current) is critical if efficient and bright emissive technologies are to be rationally designed. Emerging light-emitting electrochemical cells (LECs) can be cost- and energy-efficiently fabricated by ambient-air printing by virtue of the in situ formation of a p-n junction doping structure. However, this in situ doping transformation renders a meaningful efficiency analysis challenging. Herein, a method for separation and quantification of major LEC loss factors, notably the outcoupling efficiency and exciton quenching, is presented. Specifically, the position of the emissive p-n junction in common singlet-exciton emitting LECs is measured to shift markedly with increasing current, and the influence of this shift on the outcoupling efficiency is quantified. It is further verified that the LEC-characteristic high electrochemical-doping concentration renders singlet-polaron quenching (SPQ) significant already at low drive current density, but also that SPQ increases super-linearly with increasing current, because of increasing polaron density in the p-n junction region. This results in that SPQ dominates singlet-singlet quenching for relevant current densities, and significantly contributes to the efficiency roll-off. This method for deciphering the LEC efficiency roll-off can contribute to a rational realization of all-printed LEC devices that are efficient at highluminance., The authors wish to acknowledge generous financial support from the Swedish Research Council (2019‐02345 and 2021‐04778), the Swedish Energy Agency (50779‐1 and P2021‐00032), “Bertil och Britt Svenssons stiftelse för belysningsteknik,” Kempe Foundations, the Knut and Alice Wallenberg Foundation for a Proof of concept grant (KAW 2022.0381), the Wallenberg Initiative Materials Science for Sustainability (WISE) funded by the Knut and Alice Wallenberg Foundation (WISE‐AP01‐D02), and the European Research Council for an ERC Advanced Grant (project 101096650).

Current granite magma generation models essentially reduce to two groups: (1) intra-crustal melting and (2) basaltic origin. A mixed, crustal, and basaltic origin and therefore a mantle filiation has been proposed for most granite magma types. In contrast, strongly peraluminous silicic magmas such as two-mica leucogranites have been classically interpreted as products of pure crustal melting. In this paper, we re-examine this interpretation and the evidence for considering leucogranites as unique among granite types. In the first part, some key aspects of the intra-crustal melting model are reviewed. Classical assumptions are discussed, such as the use of migmatites to infer granite generation processes. Our knowledge of crustal melt production is still incomplete, and fluid-present H2O-undersaturated melting should be considered in addition to mica dehydration melting reactions. The source rock remains essential as a concept despite difficulties in the identification of source lithologies from their geochemical and mineralogical signatures. Incorporating spatial and temporal variability at the source and the possibility of external inputs (fluids, magmas) would represent useful evolutions of the model. Thermal considerations bring strong constraints on the intra-crustal melting model since the absence of mafic magmas reduces possible external heat sources for melting. In the second part, the origin of a strongly peraluminous silicic volcanic suite, the Macusani Volcanics (SE Peru), is detailed. Magmas were generated in a mid-crustal anatectic zone characterized by high temperatures and heat fluxes. Crustal metamorphic rocks (metapelites) were dominant in the source region, although Ba-, Sr- and La-rich calcic plagioclase cores and some biotite and sanidine compositions point to the involvement of a mantle component. The heat necessary for melting was supplied by mafic mainly potassic-ultrapotassic magmas which also partly mixed and hybridized with the crustal melts

The current European data strategy foresees a novel ecosystem of data sharing and data trading among public and private sector organizations in the EU member states. The focus is on enabling and fostering data sharing among the stakeholders while maintaining compliance with existing EU and national data protection legislation, such as the European General Data Protection Regulation (GDPR). However, managing data sharing in such a compliant manner requires additional metadata to be exchanged amongst the actors in this ecosystem. Therefore, this paper proposes a novel data model for managing data sharing activities. This model takes current and planned regulations (e.g., the Data Governance Act) and the resulting data ecosystem architectures (e.g. data intermediaries) into account and is applicable to different actions that are necessary for compliant data exchange, like data subject rights requests or intellectual property enforcement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

This study investigates the evolving landscape of nursing informatics by conducting a follow-up survey initiated by the International Medical Informatics Association (IMIA) Students and Emerging Professionals (SEP) Nursing Informatics (NI) group in 2015 and 2019. The participants were asked to describe what they thought should be done in their institutions and countries to advance nursing informatics in the next 5-10 years. For this paper, responses in English acquired by December 2023 were analysed using inductive content analysis. Identified needs covered a) recognition and roles, b) educational needs, c) technological needs, and d) research needs. The initial findings indicate that, despite significant progress in nursing informatics, the current needs closely mirror those identified in the 2015 survey.

There is still a lack of approaches to easily help users cope with the complex and challenging tasks related to Internet of Things (IoT) application development. The absence of standardized procedures and the complexity and heterogeneity of the IoT landscape are perceived as the main challenges. To address the complexity of IoT application development, Model-Driven Development (MDD) has emerged as an effective technique. By means of a Systematic Literature Review (SLR), we provide an overview of the current state of research in MDD for IoT applications. It shows that current approaches often neglect the role of organizational factors. This paper presents a method for MDD of IoT applications that considers both the stated challenges and organizational integration. The method includes a Domain-Specific Modeling Language (DSML) and several functionalities that assist in the modeling and development processes. This eliminates the necessity for any particular IT expertise at the application level. An industrial use case in the field of air conditioning facilities where the method has been implemented is described. Requirements for the methodological and technical support for IoT development were derived from a Small and Medium-Sized Enterprise (SME) during the development process.

Managing a system can be critical for its successful functioning. This is especially crucial for the socio-technical systems of systems (SoS) that characterize many of modern society's critical operations. However, the management of SoS becomes complex as systems are increasingly interconnected and the dependence among connected systems intensifies. This paper explores the current state of the art on SoS management and governance from a risk management perspective. Our findings show a higher focus on SoS management; however, many studies do not holistically deal with the SoS. Moreover, our findings indicate fewer studies in SoS governance. Hence there is still a research gap. The study contributes to the body of knowledge by adding insight into how risk management fits in these domains and provides direction on the possible areas of mitigating risks.

The functional suitability of digital twin systems relies on accurately capturing, modelling, and exchanging data from their corresponding assets or processes. Consequently, achieving interoperability among various components and different digital twin systems is crucial. In the current landscape, characterized by various languages supporting diverse semantic models, achieving interoperability is a complex task. Achieving interoperability might involve translating diverse models, either peer-to-peer or through a central pivotal model. In this study, we propose a model-driven engineering approach that leverages higher-order transformations in conjunction with the Asset Administration Shell, acting as the pivotal model to tackle the interoperability challenges associated with digital twin systems. Higher-order transformations are a specific type of model transformation, characterized by their input and/or output being model transformations themselves. Our hypothesis is that such transformations would eliminate the need to manually craft multiple translations toward the Asset Administration Shell. In-stead, a single higher-order transformation would automatically generate these translations. We chose the Asset Administration Shell as our pivotal model, because it is widely recognized as a foundational element for application interoperability in Industry 4.0/5.0. We illustrate our approach through a vehicle use case represented using the Systems Modeling Language version 2 and consolidating this description into an Asset Administration Shell model. Hence, we showcase the applicability and suitability of our proposed approach. To the best of our knowledge, our work represents the first effort to address the translation of Systems Modeling Language version 2 into the Asset Administration Shell.

AMD; IEEE Computer Society; IEEE Computer Society Technical Committee on Computer Architecture; IEEE Computer Society Technical Community on Microprogramming and Microarchitecture (TCuARCH); Intel; NSF, 2024 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2024 -- 5 May 2024 through 7 May 2024 - Indianapolis -- 201090, Single-Instruction Multiple-Thread (SIMT) computing has enabled a revolution in graphics, high-performance computing, and artificial intelligence. However, despite its benefits in these domains, SIMT processing has been relegated to accelerators rather than becoming a feature of general-purpose computing. Although a number of recent works have explored the potential benefits of 'G PSIMT,' current research infrastructures to study high performance general-purpose CPUs provide no support for the SIMT architecture and its programming model. This work presents our initial efforts toward developing a full-system GPSIMT research infrastructure. We first describe how we extend the QEMU emulator with SIMT features, enabling ISA pathfinding for GPSIMT hardware and providing a platform for the rapid development of system software for GPSIMT. We then present our approach to leveraging the Chip yard hardware gen-eration framework to develop a full-system GPSIMT exploration platform on an FPGA by extending the RISC- V Rocket Core. © 2024 IEEE.

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.

Digital twins have emerged as a critical technology to enable smart production. Digital twins can enhance the current production system by optimizing the current setup and facilitating decision-making based on facts rather than gut feeling. Despite the numerous benefits explored, digital twins have faced many challenges in developing and implementing production systems. Their complexity is causing a lack of digital twin implementations in the production system. This complexity can be traced back to physical and virtual entities and the digital twin development process. By conducting a case study in a global manufacturing company, this publication explores the sources of complexity when developing digital twins. The findings are organized around the digital twin development steps and their corresponding complexity. The number of different types of entities being modeled, the choice of the modeling approach, modeling low-frequency events, emergent phenomena, and the unpredictability and variability of the manufacturing process are all examples of structural and dynamic complexity that have been found to impede success in digital twin applications. This research has implications for managers who are involved in the development of digital twins in their organizations. It can help with methodological guidance when dealing with an undefined and complicated process of digital twin development.

Combining CO2 electrochemical reduction (CO2R) and biomass gasification for producing methanol (CH3OH) is a promising option to increase the carbon efficiency, reduce total production cost (TPC), and realize the utilization of byproducts of CO2R system, but its viability has not been studied. In this work, systematic techno-economic assessments for the processes that combined CO2R to produce CO/syngas/CH3OH with biomass gasification were conducted and compared to stand-alone biomass gasification and CO2R processes, to identify the benefits and analyze the commercialization potential of different pathways under current and future conditions. The results demonstrated that the process that combined biomass gasification with CO2R to CO represents a viable pathway with a competitive TPC of 0.39 €/kg-CH3OH under the current condition. For all the combined cases, electricity usage for CO2R accounts for 36–76% of total operating cost, which plays a key role for TPC. Sensitivity analysis confirmed that the process that combined biomass gasification with CO2R to CO is sensitive to the price of electricity, while both CO2R performance and prices of stack and electricity are important for the processes that combined with CO2R to syngas/CH3OH.

Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this article, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users’ engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 ∼ 70% in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research. © 2024 Copyright held by the owner/author(s).

Anger is a common emotion in everyday life, and in the context of driving, the experience of greater anger has been positively associated with aggressive behaviour, near-misses and crashes. Although the effect of anger on driver behaviour has been studied, the role of the source of anger and the perceived level of skill of the driver experiencing anger, based on self-reported behaviour, is not well understood. In this context, the aim of the current study was to investigate the relationships of trait anger and driving anger with driver behaviours in relation to driver skills. A total of 369 drivers aged 18–58 years (M = 24.53, SD = 7.65) completed a questionnaire consisting of the Trait Anger Scale (TAS), the Measure for Angry Drivers (MAD), the Driver Skills Inventory (DSI), the Driver Behaviours Questionnaire (DBQ), the Positive Driver Behaviour Scale (PDBS) and the Driving Anger Expression Inventory (DAX). The factor analyses for the Turkish adaptations of the MAD and DAX supported the original factorial structures. For less skilled drivers, experiencing more anger due to the behaviours of others was associated with more errors. For skilled drivers, higher levels of anger due to the behaviours of others were associated with more personal physical anger expression and positive behaviours. Similarly, for the same group of drivers, higher levels of anger due to traffic delays were associated with more errors. The findings revealed a complex relationship between driving-specific anger and driver behaviours based on different levels of driver skills, contrary to non-significant relations of trait anger. Anger provoked by the context/situation-based reason, as opposed to trait anger, was associated with more general driving style (i.e., errors and positive behaviours) through driver skills. For the first time in literature, the relationship between anger and driver behaviour has been addressed in this study within the context of driver skill. The results provided valua

The tremendous growth of RAM capacity - now exceeding multiple terabytes - necessitates a reevaluation of traditional memory-management methods, which were developed when resources were scarce. Current virtual-memory subsystems handle address-space regions as sets of individual 4-KiB pages with demand paging and copy-on-write, resulting in significant management overhead. Although huge pages reduce the number of managed entities, they induce internal fragmentation and have a coarse copy granularity.To address these problems, we introduce Morsels, a novel virtual-memory-management paradigm that is purely based on hardware data structures and enables the efficient sharing of virtual-memory objects between processes and devices while being well suited for non-volatile memory. Our benchmarks show that Morsels reduce the mapping time for a 6.82-GiB machine-learning model by up to 99.8 percent compared to conventional memory mapping in Linux.

In reinforcement learning (RL) an agent usually learns the specifics and rules of the environment via interaction. This limits the agent in taking the best action only from the current observation and past experience. Therefore, providing relevant external knowledge for RL agents, as well as incorporating learned knowledge in the RL process can be a critical part of agent’s successful training in real-world tasks. We propose a method, an architecture and experimental results for injecting expert knowledge in the form of RDF knowledge graphs (KGs) into the RL processes, showing how knowledge consumption increases sample efficiency. Furthermore, we investigate the scalability of our approach concerning the complexity of the underlying task showing injection of KGs is beneficial to the solution of more complex RL tasks. For experimental evaluation we used the Minigrid environment, which is a standard benchmark for RL. For this environment, we designed an ontology and generated a KG, that promotes reusability and interoperability across heterogeneous data of the environment. We show that adding knowledge to the agent’s learning process improves sample efficiency and the benefits increase with the complexity of the environment.

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.

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

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.

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

Trigger-Action Platforms (TAPs) enable applications to integrate various devices and services otherwise unconnected. Recent features of TAPs introduce additional sources of data such as queries in IFTTT. The current TAPs, like IFTTT, demand that trigger and query services transmit excessive amounts of user data to the TAP. To limit the data to what is actually necessary for the execution to comply with the principle of data minimization, input services should send no more than the necessary data. LazyTAP proposes a new paradigm of data minimization by construction in TAPs, introducing a novel perspective for data collection from input services. While the existing push-all approach of TAPs entails coarse-grained data over-approximation, LazyTAP pulls input data on-demand at the level of attributes, once accessed by the app execution. Thanks to the fine granularity provided by LazyTAP, multiple trigger and query services can be naturally minimized while the behavior of app executions is preserved. In addition, a great benefit of LazyTAP is being seamless for third-party app developers. By leveraging laziness, LazyTAP defers computation and proxies objects to load necessary remote data behind the scenes. Our evaluation study on app benchmarks shows that on average LazyTAP improves minimization by 95% over IFTTT and by 38% over minTAP, with a tolerable performance overhead. This poster goes into further details about LazyTAP and elaborates on its prototype implementation.

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.

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.

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.

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.

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.

In many industrial sectors, the current digitalization trend resulted in new products and services that exploit the potential of built-in sensors, actuators, and control systems. The business models related to these products and services usually are data-driven and integrated into digital ecosystems. Quantified products (QP) are a new product category that exploits data of individual product instances and fleets of instances. A quantified product is a product whose instances collect data about themselves that can be measured or, by design, leave traces of data. The QP design has to consider what dependencies exist between the actual product, services related to the product, and the digital ecosystem of the services. By investigating three industrial case studies, the paper contributes to a better understanding of typical features of QP and the implications of these features for the design of products and services. For this purpose, we combine the analysis of features of QP potentially affecting design with an analysis of dependencies between features. The main contributions of the work are (1) three case studies describing QP design and development, (2) a set of recurring features of QPs derived from the cases, and (3) a feature model capturing design dependencies of these features.

A readily available and sustainable biomass resource that could be used as a bi-tumen extender or modifier is the biopolymer lignin. Nevertheless, there isn’tmuch agreement regarding how well lignin-based bituminous mixtures crack. The fracture performance of half-warm mix recycled asphalt (HWMRA), made with 100%reclaimed asphalt pavement (RAP) and 5% lignin-rich industrial waste in place of bitumen mulsion, was examined in the current research in this regard. The Fenix test was carried out with this goal in mind. The results were compared to those obtained for a control mixture (HWMRA with 0% of lignin rich industrial waste). At 20oC the results were slightly better for the control mixture. But at -5oC and 5oC the results were similar for both mixtures. Therefore, it can be concluded that replacing 5% of the bitumen emulsion with lignin-rich industrial waste will not be detrimental to the cracking performance of half-warm mix recycled asphalt. © 2024 The Author(s)., Peer Reviewed, Postprint (author's final draft)

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.

Automated test generation has been proposed to allow test cases to be created with less effort. While much progress has been made, it remains a challenge to automatically generate strong as well as small test suites that are also relevant to engineers. However, how these automated test generation approaches compare to or complement manually written test cases is still an open research question. In the light of the potential benefits of automated test generation in practice, its long history, and the apparent lack of summative evidence supporting its use, the present study aims to systematically review the current body of peer-reviewed publications comparing automated test generation and manual test design performed by humans. We conducted a literature review and meta-analysis to collect data comparing manually written tests with automatically generated ones regarding test efficiency and effectiveness. The overall results of the literature review suggest that automated test generation outperforms manual testing in terms of testing time, the number of tests created and the code coverage achieved. Nevertheless, most of the studies report that manually written tests detect more faults (both injected and naturally occurring ones), are more readable, and detect more specific bugs than those created using automated test generation. Our results suggest that just a few studies report specific statistics (e.g., effect sizes) that can be used in a proper meta-analysis, and therefore, results are inconclusive when comparing automated test generation and manual testing due to the lack of sufficient statistical data and power. Nevertheless, our meta-analysis results suggest that manual and automated test generation are clearly outperforming random testing for all metrics considered., Conference code: 177266; Export Date: 8 June 2022; Conference Paper; Funding details: Horizon 2020 Framework Programme, H2020, 957212; Funding text 1: The work has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957212, by Software Center and Vinnova (XIVT and SmartDelta).

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.

On the basis of literature sources analysis, the paper provides the rationale for the necessity of considering the limited digital devices capabilities when designing closed digital control systems for the complex electrical power grids. The problem of design is decomposed into two subproblems: design of current state observation vector digital transmission systems and current controlled process state estimation; design of digital systems for optimal control vector calculation, transmission and control actions realization. The paper presents consideration of the former problem, i.e. design of current state observation vector digital transmission systems and current controlled process state estimation: the mathematical model of digital system of information transmission and state estimation considering speed and reliability of technical means of implementation is presented; the functional structure of simulation complex is developed; the paper provides the formulation of the problem of estimating the optimal amount of information about the control object state, resulting in a solution of computational experiments simulating complex. © 2021 The Author(s).

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

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.

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., QC 20230614