Mälardalen University

Energy transition has brought widespread attentions to the concept of coupled utilization of the geothermal and solar energy. This paper provides an integrated assessment on developing a nanofluid geothermal-photovoltaic hybrid system that addresses the multi-objective optimization and multi-criteria evaluation difficulties. The coupling system design and dispatch are optimized by considering the multiple objectives from the microscopic particles to the system. The life cycle cost, levelized cost of energy, levelized cost of heat, and the irreversibility are introduced in the optimization stage. The optimization parameters include the pipe arrangement, type of nanoparticles, and the concentration of the nanoparticles in nanofluids. A combined analysis including the energy, exergy, economy, and the environment is proposed to evaluate the various objectives and cases. The results show that the combination of 2% Al 2 O 3 nanofluid and spiral pipe has the optimum performance. The monocrystalline solar panels with the nanofluids-aided heat pump create the least CO 2 emissions (550 kg/year), the least LCOE (198.18 $), and the highest exergy efficiency. However, the LCOH (211.78 $/MWh) is still much high. Only when the electricity cost is higher than 0.11$/kWh, the proposed coupling system would show competitiveness. In summary, these results effectively prove the robustness and superiority of the hybrid system.

Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.

Studies have found that bilinguals respond differently to personality measures in their two languages, indicating that bilinguals change their personality as they switch between their two languages and/or cultures. Across two experiments, we attempted to investigate the effect of language and culture separately on how bilingual speakers rate themselves on the personality dimensions on the Big-5 Personality Inventory. Swedish speakers were asked to imagine applying for a job either at a Swedish (home country/own culture) or an American (foreign country/culture) company, and they responded to the Big-5 questions in either their first language Swedish, or their second language English, in a 2 by 2 design. Overall, differences on several of the personality dimensions were found, mainly affected by the language factor, generally replicating previous research. These results suggest that separate processes may be driving previously found differences on personality measures in bilinguals’ two languages to some extent, and that these processes affect the personality dimensions (as measured by the Big-5 personality inventory) differently, even if the language variable seems to be the stronger indicator.

Research Findings: Few studies address the contextual conditions for preschool staff in supporting children in preschool, especially in classrooms with high proportions of early second language learners (L2-learners). The aim of the study was to describe preschool staff’s support of children’s engagement in units with high proportions of L2-learners. In an exploratory mixed-methods approach, eleven interviews (11 units and 27 staff) were conducted, analyzed through content analysis. For the observations, 121 preschool staff (31 units) were added, and 594 children were observed (42 classrooms), analyzed through T-tests to investigate staff’s contextual differences in units with high proportions (>80%) of L2-learners (L2-groups) and child groups with high proportions (>80%) of L1-learners. Content analysis revealed a main theme of a commitment to establish relationships with the children while managing cultural values and norms. In interacting with children individually, the staff wanted to be close to the children and acknowledging them. In these aspects, no differences were found between staff between groups. Differences occurred in interaction with children in groups. Staff in L2-groups struggled with routines, mediating values and encouraging children learning from each other. Policy/practice: Staff in L2-groups need further encouragement from the organization to manage the needs of children in L2-groups without straining their commitments.

Robotic animals are increasingly discussed as a solution to challenges connected to the aging population and limited resources in care. While previous research focuses on the robots' effect on the patients' well-being, there is a general lack of knowledge regarding the hands-on experience of caregivers' use of robots. Therefore, the aim of the study is to explore the competences that caregivers draw upon when facilitating interaction between residents and robots. The study was conducted through ethnographic observations and interviews with caregivers at dementia care homes in Sweden. The notion of 'competence' is understood as knowledge about the ways of working and social norms that are valued within a community of practice, which members develop through engagement in the community. The findings show that caregivers' use of robotic animals as caregiving tools rests on embodied, social, and ethical competences. KEYWORDS Caregiver / dementia care / embodied competence / ethical competence / social competence / robotic animal / working life

Background: The inflammatory responses are central components of diseases associated with particulate matter (PM) exposure, including systemic diseases such as cardiovascular diseases (CVDs). The aim of this study was to determine if exposure to PM, including respirable dust or quartz in the iron foundry environment mediates systemic inflammatory responses, focusing on the NLRP3 inflammasome and novel or established inflammatory markers of CVDs. Methods: The exposure to PM, including respirable dust, metals and quartz were determined in 40 foundry workers at two separate occasions per worker. In addition, blood samples were collected both pre-shift and post-shift and quantified for inflammatory markers. The respirable dust and quartz exposures were correlated to levels of inflammatory markers in blood using Pearson, Kendall τ and mixed model statistics. Analyzed inflammatory markers included: 1) general markers of inflammation, including interleukins, chemokines, acute phase proteins, and white blood cell counts, 2) novel or established inflammatory markers of CVD, such as growth/differentiation factor-15 (GDF-15), CD40 ligand, soluble suppressor of tumorigenesis 2 (sST2), intercellular/vascular adhesion molecule-1 (ICAM-1, VCAM-1), and myeloperoxidase (MPO), and 3) NLRP3 inflammasome-related markers, including interleukin (IL)-1β, IL-18, IL-1 receptor antagonist (IL-1Ra), and caspase-1 activity. Results: The average respirator adjusted exposure level to respirable dust and quartz for the 40 foundry workers included in the study was 0.65 and 0.020 mg/m3, respectively. Respirable quartz exposure correlated with several NLRP3 inflammasome-related markers, including plasma levels of IL-1β and IL-18, and several caspase-1 activity measures in monocytes, demonstrating a reverse relationship. Respirable dust exposure mainly correlated with non-inflammasome related markers like CXCL8 and sST2. Conclusions: The finding that NLRP3 inflammasome-related markers correlated with PM and quartz exposure suggest that this potent inflammatory cellular mechanism indeed is affected even at current exposure levels in Swedish iron foundries. The results highlight concerns regarding the safety of current exposure limits to respirable dust and quartz, and encourage continuous efforts to reduce exposure in dust and quartz exposed industries.

Among 15-year-olds, boys tend to report higher life satisfaction than girls. Recent research has shown that this gender gap tends to be larger in more gender-egalitarian countries. We shed light on this apparent paradox by examining the mediating role of two psychological dispositions: competitiveness and fear of failure. Using data from the 2018 PISA study, we analyze the life satisfaction, competitiveness, and fear of failure of more than 400,000 15-year-old boys and girls in 63 countries with known levels of gender equality. We find that competitiveness and fear of failure together mediate more than 40 percent of the effects on life satisfaction of gender and its interaction with gender equality. Thus, interventions targeting competitiveness and fear of failure could potentially have an impact on the gender gap in life satisfaction among adolescents in gender equal countries.

Abstract: Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results

Modeling is an essential and challenging activity in any engineering environment. It implies some hard-to-train skills such as abstraction and communication. Teachers, project leaders, and tool vendors have a hard time teaching or training their students, co-workers, or users. Gamification refers to the exploitation of gaming mechanisms for serious purposes, like promoting behavioral changes, soliciting participation and engagement in activities, etc. We investigate the introduction of gaming mechanisms in modeling tasks with the primary goal of supporting learning/training. The result has been the realization of a gamified modeling environment named PapyGame. In this article, we present the approach adopted for PapyGame implementation, the details on the gamification elements involved, and the derived conceptual architecture required for applying gamification in any modeling environment. Moreover, to demonstrate the benefits of using PapyGame for learning/training modeling, a set of user experience evaluations have been conducted. Correspondingly, we report the obtained results together with a set of future challenges we consider as critical to make gamified modeling a more effective education/training approach.

Objective A behavioral medicine approach, incorporating a biopsychosocial view and behavior change techniques, is recommended in physical therapy for the management of musculoskeletal pain. However, little is known about physical therapists’ actual practice behavior regarding the behavioral medicine approach. The aim of this study was to examine how physical therapists in primary health care judge their own practice behavior of a behavioral medicine approach in the assessment and treatment of patients with persistent musculoskeletal pain versus how they practice a behavioral medicine approach as observed by independent experts in video recordings of patient consultations. Methods A prospective cohort study was conducted. Video recordings of 23 physical therapists’ clinical behavior in 139 patient consultations were observed by independent experts and compared to the physical therapists’ self-reported practice behavior, using a protocol including 24 clinical behaviours. The difference between observed and self-reported clinical behaviors was analyzed with a chi-square test and a Fisher exact test. Results The behavioral medicine approach was, in general, practiced to a small extent and half of the self-reported clinical behaviors were over-estimated compared to the observed behaviors. According to the observations, the physical perspective dominated in assessment and treatment, the functional behavioral analysis was never performed, and the mean number of behavior change techniques used was 0.7. Conclusion There was a discrepancy between how physical therapists perceived the extent to which they practiced a behavioral medicine approach in their clinical behavior compared with what the independent researchers observed in the video recordings. Impact This study demonstrates the importance of using observations instead of using self-reports when evaluating professionals’ clinical behavior. The results also suggest that—to ensure that physical therapy integrates the biopsychosocial model of health—physical therapists need to increase their focus on psychosocial factors in clinical practice.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Counterfeit drugs have long been a major concern. In search of a solution, this study conducted a systematic literature review. Using an automated content analysis of pharmaceutical blockchains, this study identifies the (1) unique characteristics of smart contracts on blockchain platforms in the pharmaceutical supply chain, (2) role of smart contracts to combat counterfeit drugs, and (3) avenues for future studies. Theoretical and managerial implications are discussed at the end of this paper.

Many manufacturing companies are adopting servitization as a competitive business strategy to offer product-service combinations. The ongoing shift to digitalization and Industry 4.0 provides novel opportunities and benefits to industrial firms in this regard, and researchers termed the adoption of digital technologies to servitization as “digital servitization”. In order to successfully transition towards digital servitization, fundamental reconfiguration of resources, organizational structures, work practices, infrastructure, culture, etc. are required. Hence, this paper performs a systematic literature review on prior studies covering dynamic capabilities for servitization and digital servitization. The purpose is to identify and compare the dynamic capabilities needed to facilitate a transition from “traditional” servitization to digital servitization. In doing so, this paper presents an integrated framework of dynamic capabilities to enable digital servitization, providing 22 micro-foundations for servitization and digital servitization, as well as the key challenges and enablers related to the transition from one to the other.

The increasing worldwide population is leading to a continuous increase in energy and food demand. These increasing demands have led to fierce land-use conflicts as we need agricultural land for food production while striving towards renewable energy systems such as large-scale solar photovoltaic (PV) systems, which also require in most of the cases agricultural flat land for implementation. It is therefore essential to identify the interrelationships between the food, and energy sectors and develop sustainable solutions to achieve global goals such as food and energy security. A technology that has shown promising potential in supporting food and energy security, as well as supporting water security, is agrivoltaic (AV) systems. This technology combines conventional farm activities with PV systems on the same land. Understanding the microclimatic conditions in an AV system is essential for an accurate assessment of crop yield potential as well as for the energy performance of the PV systems. Nevertheless, the complex mechanisms governing the microclimatic conditions under agrivoltaic systems represent an underdeveloped research area. In this study, a computational fluid dynamics (CFD) model for a vertical AV system is developed and validated. The CFD model showed PV module temperature estimation errors in the order of 0–2 °C and ground temperature errors in the order of 0–1 °C. The shading caused by the vertical PV system resulted in a reduction of solar irradiance by 38%. CFD modelling can be seen as a robust approach to analysing microclimatic parameters and assessing AV system performance.

Background: Patient participation is considered to promote well-being and is, therefore, central in care contexts. Care-dependent older persons living at home constitute a vulnerable population with increased ambulance care needs. Care transfers risk challenging participation in care, a challenge that can be accentuated in situations involving acute illness. Aim: To illuminate meanings of older persons' participation in ambulance care in the presence of municipal care personnel from the perspective of ambulance personnel. Method: A phenomenological hermeneutical method was used to analyse transcripts of narrative interviews with 11 ambulance personnel. Results: The ambulance personnel's lived experience of older persons' participation includes passive and active dimensions and involves a balancing act between an exercise of power that impedes participation and equalisation of power that empowers participation. The main theme 'Balancing dignity in relation to manipulating the body' included the themes Providing a safe haven and Complying with bodily expressions, which means shouldering responsibility for existential well-being and being guided by reactions. The main theme 'Balancing influence in relation to perceived health risks' included the themes Agreeing on a common perspective, Directing decision-making mandate, and Sharing responsibility for well-being, which means shouldering responsibility for health focusing on risks. Influence is conditional and includes performance requirements for both the older person and municipal care personnel. Conclusion: Care-dependent older persons' participation in care from the perspective of ambulance personnel means recognising passive and active dimensions involving human dignity, the ability to influence care, and optimising care efforts through collaboration. This study provides a deepened understanding of the balancing of power involved in ambulance care determining participation, where power is equalised or exercised depending on personal engagement, health risks, and available care options. The knowledge provided holds the potential to improve ambulance care to benefit older persons in critical life situations.

Complete hom-Lie superalgebras are considered and some equivalent conditions for a hom-Lie superalgebra to be a complete hom-Lie superalgebra are established. In particular, the relation between decomposition and completeness for a hom-Lie superalgebra is described. Moreover, some conditions that the linear space of αs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha ^{s}$$\end{document}-derivations of a hom-Lie superalgebra to be complete and simply complete are obtained.

Motivation & challenge: Computer Science suffers from a lack of diversity that gets perpetuated by the most dominant and visible role models. The community is doing itself a disservice by upholding techno-solutionism, short-term efficiency, and busyness as central values. Those models are created and consolidated over time through social and cultural interactions that increase the perpetration of gender stereotypes. Exposing people to diverse types of role models and stories can contribute to making them more aware of the complexity of reality and inspire them taking better informed decisions-making on their career paths. Likewise, showing different role models to stakeholders in society and industry can contribute to increase the workforce diversity in the profession of computing as well as to make a shift towards the consolidation of different role models. This, in turn, may contribute to strengthen resilience and adequacy for solving issues related to diversity, equality and inclusion in Computer Science and more importantly allowing women take the ownership of their career path. Goal: To encourage the dissemination, sharing and creation of stories that show diverse career pathways to address gender stereotypes created by dominant stories in Computer Science. We tackle this issue by developing a framework for storytelling around female scientists and professionals to show a diversity of possibilities for women in pursuing an academic career based on the ownership of their pathways. Method: We apply a qualitative approach to analyse stories collected using the auto-ethnography and use thematic analysis to unpack the components of what in these stories contribute to building the academic path in the field of Computer Science. Authors used their own professional histories and experiences as input. They highlighted the central values of their research visions and approaches to life and emphasised how they have helped to take decisions that shaped their professional paths. Results: We present a framework made of the nine macro-themes emerging from the autoethnography analysis and two dimensions that we pick from the literature (interactions and practices). The framework aims to be a reflecting storytelling tool that could support women in Computer Sciences to create their own paths. Specifically, the framework addresses issues related to communication, dissemination to the public, community engagement, education, and outreach to increase the diversity within Computer Science, AI and STEM in general. Impact: The framework can help building narratives to showcase the variety of values supported by Computer Science. These stories have the power of showing the diversity of people as well as highlighting the uniqueness of their research visions in contributing to transformation of our global society into a supportive, inclusive and equitable community. Our work aims to support practitioners who design outreach activities for increasing diversity and inclusion, and will help other stakeholders to reflect on their own reality, values and priorities. Additionally, the outcomes are useful for those who are working in improving the gender gap in Computer Science in academia and industry. Finally, they are meant for women who are willing to proceed into an academic career in this area by offering a spur for reflection and concrete actions that could support them in their path from PhD to professorship.

Background In this study, the focus is on how to support the competence development needed for nursing assistants in home care. Home care services for older persons can be challenging concerning the nature of the interpersonal interaction and communication needed to care for and respond to the diverse needs of older people who seek to live well in our communities. This implies a need to offer more person-centred care (PCC) to older persons. However, there is a lack of knowledge on how to develop such competence. We, therefore, developed A Person-centred CommunicaTION (ACTION) programme, which is a web-based educational intervention aimed at supporting competence development for nursing assistants. The research objective is to evaluate the ACTION programme with respect to participants’ responses to and the effect of the intervention. Methods A multicentre case–control study with pre- and post-assessments was designed. The ACTION programme will be implemented at home care units, in two different geographic areas in Sweden. A total of 300 nursing assistants will be recruited: 150 for the intervention group and 150 for the control group. We will evaluate the impact measures and the process. Pre- and post-assessments will be performed with data collected via a) audio recordings of communication, b) a questionnaire on self-efficacy communication skills, PCC, empathy and job satisfaction, c) user data, evaluation forms, field notes and observations, and d) interviews. The data will be analysed with descriptive and analytic statistics and/or qualitative methods for meanings. Discussion This study has the potential to contribute to the evidence supporting competence development required to offer person-centred and quality home care to older persons and to meet upcoming needs for flexible and easily accessible competence development. Trial registration ISRCTN64890826. Registered 10 January 2022, https://www.isrctn.com/ISRCTN64890826

Understanding individual car drivers' behavioural variations and heterogeneity is a significant aspect of developing car simulator technologies, which are widely used in transport safety. This also characterizes the heterogeneity in drivers' behaviour in terms of risk and hurry, using both real-time on-track and in-simulator driving performance features. Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain drivers' behaviour while being classified and the explanations for them are evaluated. However, the high predictive power of ML algorithms ignore the characteristics of non-stationary domain relationships in spatiotemporal data (e.g., dependence, heterogeneity), which can lead to incorrect interpretations and poor management decisions. This study addresses this critical issue of 'interpretability' in ML-based modelling of structural relationships between the events and corresponding features of the car drivers' behavioural variations. In this work, an exploratory experiment is described that contains simulator and real driving concurrently with a goal to enhance the simulator technologies. Here, initially, with heterogeneous data, several analytic techniques for simulator bias in drivers' behaviour have been explored. Afterwards, five different ML classifier models were developed to classify risk and hurry in drivers' behaviour in real and simulator driving. Furthermore, two different feature attribution-based explanation models were developed to explain the decision from the classifiers. According to the results and observation, among the classifiers, Gradient Boosted Decision Trees performed best with a classification accuracy of 98.62%. After quantitative evaluation, among the feature attribution methods, the explanation from Shapley Additive Explanations (SHAP) was found to be more accurate. The use of different metrics for evaluating explanation methods and their outcome lay the path toward further research in enhancing the feature attribution methods.

Good performance of the Machine Learning (ML) model is an important requirement associated with ML-integrated manufacturing. An increase in performance improvement methods such as hyperparameter tuning, data size increment, feature extraction, and architecture change leads to random attempts while improving performance. This can result in unnecessary consumption of time and performance improvement solely depending on luck. In the proposed study, a quantitative performance analysis on the case study of chip detection is performed from six perspectives: hyperparameter change, feature extraction method, data size increment, and concatenated Artificial Neural Network (ANN) architecture. The focus of the analysis is to create a consolidated knowledge of factors affecting ML model performance in turning process quality prediction. Metal peels such as chips are designed at the time of metal cutting (turning process) and the shape of these chips indicates the quality of the turning process. The result of the proposed study shows that following a fixed recipe does not always improve performance. In the case of performance improvement, data quality plays the main role. Additionally, the choice of an ML algorithm and hyperparameter tuning plays an essential role in performance.