https://www.mdu.se/

Study design: Triangulated mixed-methods validation study.

Objectives: To validate the Danish version of the Spinal Cord Lesion-related Coping Strategies Questionnaire (SCL-CSQ).

Setting: Community in Denmark.

Methods: Participants were invited via a patient organization and its specialized hospital. Eligibility criteria were having a spinal cord injury (SCI), being 18 years or older, and able to understand and respond in Danish. Quantitative data were collected to determine internal consistency and criterion validity of the three subscales of SCL-CSQ, i.e., acceptance, fighting spirit, and social reliance. The Three-Step Test-Interview approach was employed to determine whether items measured what they were intended to measure (i.e., construct validity based on response processes).

Results: The quantitative sample consisted of 107 participants, and the interview sample comprised 11 participants. The acceptance and fighting spirit subscales showed adequate internal consistency (Cronbach's alpha of 0.72 and 0.76 respectively) and satisfactory criterion validity (expected correlations with quality of life and depression). The social reliance subscale showed inadequate internal consistency (Cronbach's alpha of 0.58) and criterion validity. All fighting spirit items and all but one acceptance items were interpreted congruently by most participants. Conversely, two social reliance items were only interpreted congruently by 9 and 27%.

Conclusion: The acceptance and fighting spirit subscales of the Danish version of the SCL-CSQ showed good psychometric properties, while the social reliance subscale showed serious issues and should be revised. Researchers and clinicians are urged to reflect on these findings when revising the SCL-CSQ or adapting it to other languages, cultural contexts, and rehabilitation settings.

Bakgrund: Tidigare forskning visar att trycksår är smärtsamt och orsakar ett stort lidande för patienterna. Trycksår ger en ökad risk för infektioner och förlängd vårdtid vilket även leder till ökade kostnader för samhället. Trycksår som drabbar patienter inom vården räknas som en vårdskada och en av sjuksköterskans arbetsuppgifter är att förhindra vårdskador. Syfte: Var att beskriva sjuksköterskors erfarenheter av att vårda trycksårsförebyggande.Metod: En allmän litteraturöversikt enligt Friberg med totalt tio artiklar fördelat på fyra kvantitativa artiklar och sex kvalitativa artiklar. Samtliga artiklar analyserades utifrån likheter och skillnader i syfte, metod och resultat. Resultat: Det framträdde tre olika teman “Betydelsen av kunskap och kompetens”, ”Betydelsen av delaktighet och kommunikation”och “Betydelsen av arbetsmiljön och resursbrist”. Sjuksköterskorna hade även erfarenheter av att trycksårförebyggande åtgärder var prioriterat men att det var svårt att genomföra på grund av en hög arbetsbelastning och brist på vårdpersonalresurser. Sjuksköterskorna erfor även att brister i kommunikationen kunde leda till en ökad risk för trycksår. När sjuksköterskorna involverade patient och anhöriga i omvårdnaden ökades förståelsen för sjuksköterskans åtgärder och samarbetet underlättades. Slutsats: Sjuksköterskorna behöver mer kunskap och kompetens för att bli mer effektiva i det trycksårförebyggande arbetet. Tid-och resursbrist gjorde att sjuksköterskorna inte kunde prioritera trycksårsförebyggande åtgärder. Risken för trycksår minskade när sjuksköterskorna involverade patienten och anhöriga i omvårdnaden.

The purpose of this study is to gain an understanding of social workers intervention programs and how they approach perpetrators of intimate partner violence. The study is based on a qualitative methodology using semi-structured interviews to answer the studies aim. The data gathered through interviews were thematically analysed where the following themes emerged; perpetrators prerequisites for change, intervention programs development areas, mapping the treatment, networking and the perpetrators risk factors. The results were analyzed by scientific articles and theoretical frameworks: power, room for maneuver, mentalization and stigma. Our findings from the data indicate that perpetrators have a lack of insight and emotional management to violence where the social workers approach becomes essential for perpetrators progress. The interventions programs foundation is based on the perpetrators individual problem areas where the social worker adapts the treatment utilizing their own knowledge and expertise. Intervention treatment places a large emphasis on partner contact to motivate and support them during the perpetrators treatment. There is a cooperation with other authorities that needs further development to enable an increased exchange of knowledge and extended reach of perpetrators. The results also show that previous exposure in childhood is a predominant pattern to perpetrators alongside other problems.

Assessing the effectiveness of inflation targeting in promoting economic growth is crucial for policymakers in determining appropriate monetary policy measures. Given the lack of consensus regarding the impact of inflation targeting on economic growth, it prompts the question of whether inflation targeting indeed exerts a significant effect on economic growth.  This bachelor's thesis investigates the impact of inflation targeting on economic growth in developed countries over a 29-year period from 1991 to 2019. Using a methodology that accounts for both static and dynamic economic growth relationships, we analyze data from 45 developed countries, some employing inflation targeting and others not. Our findings suggest a positive effect of inflation targeting on economic growth in the static model, but autocorrelation issues necessitate the use of a dynamic model. The utilization of two distinct types of dummy variables yielded disparate outcomes compared to those obtained from the static model. Results from dynamic modeling indicate a slight negative effect of inflation targeting on economic growth. Together these results point to an inconclusive effect of inflation targeting on economic growth.

 Date:                                      2023-05-30  Level:                                     Bachelor/Master thesis in Business Administration, 15 cr   Institution:                            School of Business, Society and Engineering, Mälardalen                                                 University   Authors:                                Daniela Abada                Naemi Estifanos                                                                           (99/12/11)                                       (01/06/12)  Title:                                  The influence of sales commission on the motivation of                        telemarketers  Supervisor:                       Johan Grinbergs  Keywords:                         3 keywords  Research questions          1.How do reward systems affect employee motivation? 2.What are the consequences of this impact?  Purpose:                            The purpose of this research is to investigate the effect of sales commission as a reward and how it works as a motivational factor for employees   Method:                             Qualitative research method, face to face interviews. Thematic analysis and coding of data which is then analyzed in the analysis.  Conclusion:                       Sales commissions have an effect on employee motivation, possibly not in the long term as the majority of salespeople want to change workplaces due to the side effects that are the result of sales commissions such as stress, pressure and mental illness. Development opportunities are also motivating for salespeople, but not as extensive as the commission. The topic has divided thoughts between salespeople and the management team.

With the increase in food consumption, new ideas, and technologies began to be developed. Inaddition, the developments generated by Industry 4.0 technologies have started to be applied tothe entire manufacturing sector and the indoor farming industry, which is currently trending.Many studies and articles have been prepared on this subject, and the main goal of each study isto produce quality products and to ensure continuity in production to cover the nonendingincrease in demand.

This paper discusses how simulation technology, which is one of the industry 4.0 technologies,can be used in the production system development of the indoor farming industry. According tomany researchers, the biggest obstacle for the vertical farming industry is start-up cost, andsimulation technologies can be the solution for this since it allows future production systems tobe analyzed without any investment. To have a clear vision of how these technologies can beadapted in the indoor farming industry, this paper will find the answers to these questions, RQ1:How can simulation facilitate production system development and Industry 4.0 projects withinthe indoor farming industry? RQ2: What are the benefits and challenges when using simulationas a tool for production system development within the indoor farming industry? To reach thegoal of this paper, the case study method was used, and an indoor farming company was selectedto get more realistic data about the vertical farming system. BlueRedGold AB is a start-upcompany in the indoor farming industry, and it has a huge growth potential since they aim totransform its current production lines to be fully automated. Many articles and studies were usedto approach the solution of the research questions from a more technical and academic point ofview, and the analysis of these articles was carried out with the structured literature review method.

After conducting this research, answers have been obtained for the research questions. Theauthors' solution to the layout issue, one of the case company's main challenges as indicated inthis study, was developed after extensive simulation model testing. As highlighted in this paper,it has been stated by many researchers, there are several simulation approaches to follow.However, the authors have developed a simulation modeling approach to be followed in theindoor vertical farming industry to overcome the complexity of these systems as well as thesimulation program complexity. In addition, several challenges and benefits have beenhighlighted in this paper such as the lack of ready models of the equipment used in indoorfarming which requires a knowledge of a programing language to overcome. Finally, despitechallenges, simulation technology can provide an applicable solution for production systemproblems of vertical farming companies/organizations to obtain continuous improvementphilosophy which is the main principle of Lean thinking. The generated simulation model in thisthesis project was successfully implemented, demonstrating how this technology might be aneffective solution for complex production systems as in the indoor farming sector. 

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm’s efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

Denna studie undersöker hur lärare beskriver att de arbetar för att främja läsförståelseförmågan hos elever som talar svenska som andraspråk i årskurs f-3. För att undersöka detta genomförs en tematisk analys av sex semistrukturerade intervjuer med lärare från olika kommuner som arbetar i skolor med många andraspråkselever. 

Resultatet pekar på att lärarna arbetar strukturerat med att undervisa elever om lässtrategier som kan användas för att förstå en text enligt undervisningsmodellen Reciprocal teaching. Den vanligaste strategin som används enligt lärarna är att reda ut oklarheter, då andraspråkselever ofta har ett bristande ordförråd. Strategierna summera, förutspå och ställa frågor används också men i mindre utsträckning. 

Resultatet visar dessutom att lärarna är positiva till att använda elevernas modersmål i undervisningen för att främja deras läsförståelse. Deras resonemang pekar på att de använder en del av de strategier som föreslås i ramverket pedagogiskt transspråkande, dock ofta på ett spontant och oplanerat sätt.

Slutsatsen blir således att lärare behöver mer kunskap och utbildning om hur elevernas modersmål kan användas som resurser. Detta för att underlätta för dem att utforma mer planerade, medvetna och varierade arbetssätt och komma bort från den spontana tillämpningen av pedagogiskt transspråkande som för tillfället ofta sker. Studien understryker att strategier för att främja andraspråkselevers läsförståelse i svenska inte är något som enbart SVA-lärare behöver förhålla sig till, utan en viktig fråga för alla lärare i skolan.

Syftet med studien är att undersöka hur lärare resonerar didaktiskt kring SO-undervisning i årskurs f-3 i demokratifrågor och vilka möjligheter och utmaningar som kan finnas i den undervisningen.

För att kunna besvara detta syfte har semistrukturerade intervjuer genomförts med fyra informanter med varierande yrkeserfarenhet och aktuella undervisningsårskurser.

Resultat för studien visar att informanterna har bra koll på varför det är viktigt att undervisa om demokrati, hur undervisningen ska genomföras och vad den ska innehålla. För att kunna uppnå skolans demokratiuppdrag behöver demokratiundervisningen genomsyra hela skolans verksamhet, utgå ifrån elevernas individuella förutsättningar, behov, intressen och erfarenheter, göra eleverna delaktiga i utformandet av sin utbildning och få de att inse att deras röst är betydelsefull. Kommunikation är a och o i demokratiundervisningen och ska ske både med och mellan elever, kollegor och vårdnadshavare.

Studiens syfte var att ta reda på hur förskollärare arbetar med nationella minoriteter och ta reda på vilka påverkansfaktorer som inverkar på undervisningen om de nationella minoriteterna. Studien är en flermetods-studie kring ett arbetsmaterial som skapats för förskolans arbete med minoriteter (Sametinget,2018), förskollärare har genomfört semistrukturerade enkäter och semistrukturerade intervjuer och frågorna har berört förskollärares resonemang kring arbetet med nationella minoriteter utifrån arbetsmaterialet. I analysen och diskussionen framgår påverkansfaktorer och förskollärares tolkningar och planerade undervisningar blir centrala. Vår forskning har närmat sig svaret på vad som är påverkansfaktorer för förskollärares undervisning om nationella minoriteter. Studien bidrar till forskningen kring förskolepedagogik, med fokus på förskollärares behov i sitt arbete med barn om och med nationella minoriteter. Det erbjuder insikter om hur man bäst kan stödja dessa barns utveckling och främjar en mer inkluderande utbildning. 

Sinusitis, a common disease of the maxillary sinus, is initially managed with saline solution and medication, resulting in the resolution of symptoms within a few days in most cases. However, Functional Endoscopic Sinus Surgeries are recommended if pharmacological treatments prove ineffective. This research aims to investigate the effects of maxillary sinus surgery on the airflow field, pressure distribution within the nasal cavity, and overall ventilation. This study utilized a three-dimensional realistic nasal cavity model constructed from CT images of a healthy adult. Virtual surgery including uncinectomy with Middle Meatal Antrostomy, two standard procedures performed during such surgeries, was performed on the model under the supervision of a clinical specialist. Two replicas representing pre- and post-operative cases were created using 3D printing for experimental purposes. Various breathing rates ranging from 3.8 to 42.6 L/min were examined through experimental and numerical simulations. To ensure the accuracy of the numerical simulations, the results were compared to measured pressure data, showing a reasonable agreement between the two. The findings demonstrate that uncinectomy and Middle Meatal Antrostomy significantly enhance the ventilation of the maxillary sinuses. Furthermore, increasing inspiratory rates leads to further improvements in ventilation. The static pressure distribution within the maxillary sinuses remains relatively uniform, except in regions close to the sinus ostium, even after surgical intervention.

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.

Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose. This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.

This article presents a runtime verification tool for embedded software executing under the open source real-time operating system FreeRTOS. The tool detects and diagnoses concurrency bugs such as deadlock, starvation, and suspension based-locking. The tool finds concurrency bugs at runtime without debugging and tracing the source code. The tool uses the Tracealyzer tool for logging relevant events. Analysing the logs, our tool can detect the concurrency bugs by applying algorithms for diagnosing each concurrency bug type individually. In this paper, we present the implementation of the tool, as well as its functional architecture, together with illustration of its use. The tool can be used during program testing to gain interesting information about embedded software executions. We present initial results of running the tool on some classical bug examples running on an AVR 32-bit board SAM4S. 

We study Hilbert functions, Lefschetz properties, and Jordan type of Artinian Gorenstein algebras associated to Perazzo hypersurfaces in projective space. The main focus lies on Perazzo threefolds, for which we prove that the Hilbert functions are always unimodal. Further we prove that the Hilbert function determines whether the algebra is weak Lefschetz, and we characterize those Hilbert functions for which the weak Lefschetz property holds. By example, we verify that the Hilbert functions of Perazzo fourfolds are not always unimodal. In the particular case of Perazzo threefolds with the smallest possible Hilbert function, we give a description of the possible Jordan types for multiplication by any linear form.

Purpose: This essay aims to examine the organizational and technical fire protection for the new emergency hospital in Västerås and analyze what measures are needed to ensure a safe hospital. By involving the staff and ensuring that they have the necessary knowledge and participation, one can ensure a safe care environment for the patients and create the conditions for dealing with potential dangers in an effective manner. Method: To achieve the objectives of the study the method involved carefully selected interviews with various professional roles possessing expertise in fire risks. Through these interviews, the aim was to attain a more comprehensive depiction of the results. In addition to the interviews, a versatile approach was adopted, encompassing surveys, site visits, literature reviews, and meticulous calculations of airlocks. Furthermore, data collection was augmented by utilizing documents from the Västmanland Region. Result: The outcome of the study showed a lack of knowledge, experience and ability to act when it comes to evacuation situations. The building was designed with fire safety in mind. Despite this, the opportunity to implement measures to further improve safety was identified. With regard to the building's fire protection, the possibility of implementing airlocks was identified to supplement and strengthen fire protection in departments where there were fewer fire cells. Conclusion: l the implementation of airlocks is important to improve the evacuation situation and safety. Regular evacuation drills should be prioritized to increase staff preparation and confidence. Measures such as clear fire separations, reliable fire alarm systems, automatic extinguishing systems and well-designed emergency escape routes need to be implemented to increase robustness and improve fire protection. This requires cooperation with fire safety experts. Airlocks can streamline the evacuation process for bedridden patients and staff, which is crucial for quick and safe evacuation in emergency situations. 

Non-invasive monitoring of cardiovascular health through photoplethysmography (PPG) waveforms has emerged as a crucial area of research. The Windkessel 4-Element (WK4) model is a mathematical approach used to estimate key physiological parameters related to cardiovascular health, including arterial compliance, peripheral resistance, inertance, and total arterial resistance. This study aimed to evaluate key physiological parameters associated with cardiovascular health using the WK4 model, leveraging real-life PPG waveform data obtained from volunteers across three distinct age groups. To achieve this, an algorithm was developed to automatically determine optimal parameter values for each volunteer. The results revealed a mean correlation coefficient of 0.96 between the automatically generated waveforms by the algorithm and the actual real-life PPG waveforms, indicating robust agreement. Notably, only the total arterial resistance parameter exhibited significant differences among the age groups, suggesting that the algorithm holds promise for detecting agerelated changes in cardiovascular health. These findings emphasize the potential for the development of a non-invasive tool to assess cardiovascular health status and enhance healthcare outcomes. Furthermore, they underscore the capability of the developed algorithm as a non-invasive means to evaluate various aspects of cardiovascular physiology. Additionally, the versatility of this algorithm opens doors for its application in educational settings, promoting knowledge advancement, empowering research endeavors, and facilitating advancements in the field.

Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

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.

Development in the field of smart wearable products for monitoring daily life health status is beginning to spread in society. Textile electronic methods are improving and facilitating the manufacturing of sensorized garments. This paper evaluates a newly developed t-shirt incorporating electronic sensing and interconnecting elements integrated into the T-shirt with textile-friendly techniques sensorized with a Movesense device for monitoring ECG and HR and activity. The measurement results obtained from the t-shirt are entirely in agreement with the measurements obtained with other textile garments and encourage us for a near future where wearable sensors are just textile garments sensorized seamlessly without suboptimal textile-electronic integrated elements.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

This paper presents a grid-connected double storage system (DSS) consisting of pumped-storage hydropower (PSH) and battery. The system is supplied by photovoltaics and wind turbines. In the proposed hybrid system, batteries absorb excess renewable energy that cannot be stored in PSH and they cover loads that cannot be supplied from the water turbine. To improve the system performance, a novel energy management strategy for the DSS is proposed. The strategy is based on an optimized factor that governs the charging process of the DSS. The problem of the optimal system design is solved by a non-dominated sorting genetic algorithm (NSGA-II). The multi-objective function considers simultaneously the minimal investment cost and minimal CO2 emissions. A comparative study of photovoltaic/wind/pumped-storage hydropower and photovoltaic/wind/double storage system is performed to show the effectiveness of the proposed strategy in terms of system economic and environmental performance. The considered location of the PSH station is on Attaqa Mountain at Suez (Egypt). The results indicate the effectiveness of the proposed energy management strategy for the storage system from economic and environmental perspectives. Coupling the battery with the PSH reduces the electricity cost by 22.2% and results in minimal energy exchange with the national grid (5% of the annual demand). A sensitivity analysis shows the largest variation of the electricity cost with changing the capital cost of the solar and wind generators. Also, it is observed that when the load increases, the optimal size of the system components increases, but it isn't proportional with the demand increase as could be expected. (C) 2019 Elsevier Ltd. All rights reserved.

Most construction companies want to deliver the best possible results for their customers while spending the least possible resources. Nevertheless, the construction industry has traditionally been full of unnecessary inefficiency regarding workflows. One of the most successful methods to prevent inefficiency workflow is lean construction. Lean construction consists of various methods that aim to streamline workflow as optimally as possible. Purpose: The purpose of the degree project is to use specific lean methods and tools to identify different waste and be able to use them to reduce downtime of a studied construction project. Method: A case study has been carried out where the project Heby - care homes has been studied. To gather more information a survey study has been done. To gather even further information about the project an interview was conducted with a person who has a leading role. Results: The result shows a analysis of the survey and the interviewe answers. It also includs working methods, transparency and waste minimization. Furthermore it includs solutions like identify problems, create order and structure, standardize and work on improvements. Conclusions: The conclusion that can be drawn is that standardizations in the form of checklists and guides need to be in each project. More openness and to review your chosen forms of agreement. To have a more dynamic working group that strives to achieve common goals and continuous improvements. These are the most important aspects of making a project effective.

The aim of the study is to assist the management of Ica Skrapan Västerås with promotional suggestions of innovation and creativity, as well as to analyze and address the current lack of engagement and motivation of the employees working in the grocery store. A qualitative and semi- structured research method has been conducted to collect data for the study. Using the data collected, the study has been able to analyze the current lack of engagement and motivation to see if innovative and creative suggestions can improve employee’s existence at work. The results have shown that both the management has a willingness to implement their employees ‘views, and the employees have a willingness to be involved in more creative and innovative proposals within the organization.

In today's modern work environment, the demand for innovation and creativity is crucial for the survival and success of organizations. The role of leadership is central in creating an environment that promotes creativity and idea generation among employees. This study aims to investigate the role of transformational leadership in creating an environment that promotes creativity and idea generation among employees in technology companies. The method section carefully describes the research methodology and the selection of respondents, as well as the conduct of the interviews and the J&D test. In the study, three different respondent groups were interviewed: two employees, two managers and an informant, in order to answer the research question. The results of these interviews provided insights into how transformative leadership promotes creativity and idea generation in the workplace. Furthermore, issues related to research ethics and how these have been handled and taken into account during the research process are also discussed. In the theory section, the concept of leadership is defined and three different leadership styles are discussed: transformative, transactional and servant leadership. Furthermore, the concept of creativity is explored, including individual, group and organizational creativity, as well as innovation and idea generation. In the empirical section, the results of the interviews and the J&D test are presented. The interviews have been analyzed using the thematic analysis method and organized under different themes, each of which is linked to a specific overarching theme. The analysis from the interviews shows that transformative leaders create an environment that inspires and engages employees, while challenging traditional thought patterns and encouraging divergent thinking. The results of the J&D test provide further insight into the creative abilities of the participants and show that these technology companies with a transformative leadership have a favorable work environment for individual creativity and idea generation. The conclusion is that transformative leadership is crucial for fostering creativity and idea generation within technology companies, and such a leadership style is a key factor for successful innovation processes.

Elektrifiering i transportsektorn och Rysslands anfallskrig mot Ukraina är de främsta bidragande orsak till de nuvarande höga energipriserna. Ryssland använder energi som ett politiskt vapen genom att minska gasflöde vilket har resulterat i höga gaspriser i Europa. Dessutom går elektrifieringen av transportsektorn i hög varv i många europiska länder vilket också bidrar till att energikrisen på den europeiska energimarknaden höjs ännu mer. 

Merparten av den energi som används idag kommer från fossila bränslen som bidrar till koldioxidutsläpp som påverkar miljön negativt. Något måste förändras för att minska utsläppen av växthusgaser och beroendet av fossila energikällor. En hållbar och miljövänlig lösning är att investera i solenergi som producerar förnybar energikälla. Med rätt teknik kan man enkelt installera villor och bostäder med solceller och solfångare och för att kunna producera el och värme. Syftet med detta examensarbete är att undersöka om ett solcellssystem i en villa kan tillgodose bostadens energibehov under olika årstider, och även att ta redo på om investeringen för solceller är lönsam. 

Arbetsmetoden bygger på beräkningar, simulering och litteraturstudier för att nå ett tillförlitligt resultat. Resultatet redovisar villans totala elförbrukning i 2023 samt solcellernas energiproduktion. Genom att analysera resultatet visade det sig att solcellsanläggnings elproduktion täcker 53 % av hushållens totala elförbrukning. Detta betyder att huset köper el för sex av årets 12 månader enligt tabell 10. Resultaten redovisar även beräkningen av återbetalningstiden för olika fall av solceller, solceller utan batteri och solcell med batteri. 

Av resultatet dras slutsatsen att solcellanläggningen är en bra investering 2024. Förutom att anläggningen producerar förnybara energikällor så betalar den sig även efter cirka 8-15år och ger också en besparing på cirka 649 654,75 kronor under en livslängd på 25år. Slutligen om elpriset stiger och priset på solceller fortsätter att sjunka kan återbetalningstiden bli ännu kortare. 

In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints. 

Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.

This degree project studies implementation and comparison of different AI models to predict (1) solid biofuel elements including carbon, hydrogen, nitrogen, and oxygen as well as moisture content, ash content, and higher heating value (HHV) of the fuel and (2) flue gas compositions such as concentration of carbon dioxide, carbon monoxide, nitrogen, nitrogen oxides, and water content using near-infrared spectroscopy and chemometric approaches. The study executes these predictions by simulating the operation of a combined heat and power plant (CHP) that is equipped with carbon capture and storage (CCS). The focus of this study is to investigate the possibility of using Near-Infrared spectroscopy (NIRs) technology to predict the emissions from a CHP plant, which can further improve the performance of the CCS system by providing the necessary fuel data in real time. The acquired NIR data is used to develop the Artificial Intelligence (AI) models using several regression algorithms including Linear regression, Gaussian process, Support Vector Machine, Artificial Neural Network, Ensemble Trees, and Tree regression. NIR data has been pre-processed using Savitzky-Golay (SG) and Multi scatter correction (MSC) techniques. Highest accuracy has been achieved for moisture content of the fuel using Exponential Gaussian Process, where an RMSE of 2.5687 and an R2-value of 0.9 has been obtained. Indeed, only a handful of regression algorithms have shown reasonable accuracy when predicting the fuel elements, where the HHV of the fuel has been predicted poorly as none of the algorithms have been able to execute the prediction successfully which leads to negative values of R2. Prediction of flue gas composition has been done resulting in reasonable accuracies for CO2 fraction with values of 0.1051 and 0.6 for RMSE and R2 respectively. Furthermore, the computational time of the algorithms has been investigated, which indicates that some of the pre-processing techniques could improve the computational time for a certain regression model, but not for all of them. It is conclusively possible to predict fuel elements and flue gas compositions based on data acquired from NIR spectroscopy. However, great effort must be put into model development including data treatment and AI model calibration to achieve desirable precision and reliable results.

In today's ever-automating world, businesses are increasingly focused on optimizing network operations, including configuration, management, and troubleshooting. This study scrutinizes the comparison between manual and automated methodologies within the framework of the cloud-based Cisco Meraki platform. Our objective is to establish decisive criteria for identifying the most advantageous circumstances for automation, particularly in network configuration. To achieve this, we constructed a network consisting of three Meraki devices. Automation of network configuration was facilitated through the development of Python scripts leveraging the Meraki API library in conjunction with configuration files. The configuration files were constructed in various file formats–Excel, CSV, JSON, and YAML– in order to assess their impact on automation effectiveness. Moreover, manual configuration tasks were undertaken by three network technicians with varying levels of Meraki proficiency. A comprehensive series of tests, encompassing both automated and manual methods, was subsequently conducted, focusing on metrics such as execution time and keystroke count. Through meticulous analysis, we provided valuable insights into the effectiveness of automation in network configuration processes. Results demonstrated that the use of JSON files in the automation process yielded the shortest completion time and required the fewest keystrokes compared to other automation methods. Moreover, manual configuration via the Meraki dashboard GUI exhibited comparable efficiency to automation methods in networks with a small number of devices. Nonetheless, owing to the capacity to reuse configuration files with minor adjustments for similar setups, the true advantage of automation over manual configuration becomes apparent as the device count increases.

The purpose of the thesis is to investigate what the social services work process looks like when a child is in danger or is suspected to be in danger. The study consists of three questions and in order to provide answers to the purpose, a qualitative research method is used with the help of semi-structured interviews. The result is based on five social secretaries from five different municipalities in Central Sweden. When it comes to the social secretary's investigation process, the results of the study show that all county to witch the interviews belongs have the same goal but are based on different working methods. Some common challenges for the social secretaries are a heavy workload, low resources and the loss of the clients trust in social services. An additional result shows that the study demonstrates the secretary's description of the investigation process in relation to the theoretical starting point. The system theory is essential for the social worker to gather important information from the individuals and organisations in the child’s local environment. While the attachment theory shows the child’s attachment relationship to the caregivers. Empowerment is used in the investigation process by motivating clients to regain power over their situation. 

Date :  4th June 2023

Level : Masters thesis in industrial engineering and management, advanced level, 30 ECTS

Institution : School of Innovation, Design and Engineering, Mälardalens University  

Authors :

  Afnan Abdul Amir

9 Maj 1998Onel Nabeel Polus

24 November 1998

Title: How to identify Adaptability within SMEs: A case Study

Supervisor: Mikael Johnsson

Keywords: Change Management, Adaptability, SMEs

Research Question : How can consult agencies identify the adaptability within SMEs?.

Purpose: The purpose of this study was to develop a process for the identification of  SMEs adaptability.

Method: This study adopted a qualitative research methodology using a abductive approach. This method facilitated an iterative exchange between the theoretical framework and empirical observations. The theoretical framework was based on scientific articles and the study was conducted as a case study to help the company identify the adaptability within its clients. The empirical data was collected through structured and semi-structured interviews divided between the different clients. The empirical data was structured on 7 interviews with project managers, team leaders and CEOs.

Conclusion: The conclusion that can be drawn from this study is that the adaptability to various market changes can be identified when a company analyzes the following three key factors: Leadership, Communication, and Knowledge.

In order to meet future electricity demand, Sweden needs to increase its electricity production, particularly through renewable energy sources. By the end of 2022, solar power production reached 2 TWh, an increase of 75 percent over 2021. Over the past few years, solar power has grown significantly, and it is predicted to continue to grow in the future. Furthermore, by 2045, the Swedish government aims to provide 100% of its energy needs from renewable sources and reduce greenhouse gas emissions to zero.

This study investigates whether agrivoltaic is feasible in Sweden, where solar power is coupled with agriculture. There is great potential in this technology, even though it is new. By testing different parameters, such as height and row distance, this study has also examined different agrivoltaic system designs and various geographic locations to estimate their true potential in optimizing electricity production. Using PVSyst simulations, we can then investigate how to optimize electricity and evaluate the economic feasibility based on levelized cost of energy (LCOE) and payback period of the best system. For a better understanding of this study, an extensive literature review was conducted. Although the modeling of bifacial modules is still relatively new research topic.

This study simulated three different cases using PVsyst software. Case A with a row distance of 5 meters and a system capacity of 222 kWp; case B with a row distance of 10 meters and a system capacity of 111 kWh; and case C with a row distance of 15 meters and a system capacity of 77,7 kWp. In addition, heights ranging from 0,5 to 1,5 meters were investigated, using bifacial modules. Irradiation data from PVsyst shows that the yearly global horizontal irradiance GHI in Västerås is 1004,6 [kWh/m^2] while the GHI in Trelleborg is 1023,4 [kWh/m^2]. According to the results, the most efficient system is the one-axis tracker, which produces approximately 25% more electricity than unlimited vertical sheds. It was also found that the maximum electricity was produced at 45° azimuth. The result also found a five percent increase in electricity production in Västerås over Trelleborg. Furthermore, the optimal row distance and optimal height were found to be 10 [m] and 0,5 [m]. Lastly, the most effective system was one-axis tracker system in Västerås with LCOE of 0,09 [Euro/kWh] and payback periods of 10,6 [Years].

Keywords: [Agrivoltaic, optimal system, row distance, levelized cost of energy, payback period]

This study aims to explain how many organizations in various fields are affected by the global pandemic Covid-19, which led to many organizational changes in the public sector, so those new strategies were implemented to counter these changes. Also, this research sheds light on the role of the leaders in crisis management through the sensemaking model that helps build a new story and provides logical explanations. It also seeks to study whether these new strategies are effective and how they affect the employee's performance.

The data collection for this paper uses qualitative and quantitative research through an inductive approach to answering the research questions by collecting primary and secondary data. Through qualitative research, six interviews were conducted with employees and leaders at KTH University. And the quantitative data were collected from a questionnaire sent to leaders at KTH University. 

The results show how the KTH University has faced several multiple organizational changes during the pandemic and how the leaders had a vital role in managing the crisis.

Some limitations were encountered, such as the response limitations of KTH leaders, so the researchers could not collect more than 6 interviews, which led to limiting the broad research and obtaining the required results.

Background: Compassion energy can contribute to nurses' conditions to provide patientswith meaningful care based on a compassionate approach. Nurses' empathy towards patients can mean a risk of compassion fatigue in nurses, which can impair patient safety. Nurses'responsibility over their own health is of great importance to minimize nurses' intention to leave work and to protect patients from care injury. Aim: The aim of this study was to describe nurses’ experiences of finding motivation in caring. Method: Systematic literature study according to Evans with a basis of ten different qualitative health science articles. Result: The result consisted of two themes, experiences of meaningfulness and experience sof a safe working environment. Experiences of meaningfulness yielded two subthemes,finding joy and being able to help. Experiences of a safe working environment released two subthemes, receiving emotional support from colleagues and having the opportunity for development. Conclusion: Nurses were motivated in caring by finding joy when acommunity was formed in the workplace and that joy facilitated difficult situations in caring. Being able to give help to another person in need of help was considered the basis for caring.Receiving emotional support from colleagues contributed to a sense of security in caregivingand having the opportunity for development gave the nurses a deeper understanding and facilitated different situation in care.

Kärlek är en omfattande del i människans liv, hur vi uttrycker kärlek varierar och enligt Chapman (1992) finns det fem kärleksspråk som människan använder: bekräftande ord, tjänster, fysisk beröring, kvalitetstid samt gåvor. Syftet med denna studie var att undersöka hur män och kvinnor visar kärlek i en romantisk relation samt om det finns ett dominant kärleksspråk och om det skiljer sig mellan män och kvinnor.  Tio semistrukturerade intervjuer genomfördes varav två var parintervjuer, totalt deltog sju kvinnor och fem män. Intervjuerna tematiserades med hjälp av de fem kärleksspråken och sex teman identifierades: respekt, tillit och frihet, tjänster för att förenkla vardagen, kvalitetstid genom att ha roligt ihop, kommunikation och bekräftande ord, fysisk beröring, gåvor. Resultatet visade att deltagarna uppvisade en blandning av de fem kärleksspråken men även att de uttrycker kärlek på andra sätt som visade sig vara viktiga i relationen. Resultatet visade dessutom att inget dominant kärleksspråk kunde identifieras. 

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.