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.

 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. 

Problem: The goal of a software product line is to aid quick and quality delivery of software products, sharing common features. Effectively achieving the above-mentioned goals requires reuse analysis of the product line features. Existing requirements reuse analysis approaches are not focused on recommending product line features, that can be reused to realize new customer requirements. Hypothesis: Given that the customer requirements are linked to product line features' description satisfying them: then the customer requirements can be clustered based on patterns and similarities, preserving the historic reuse information. New customer requirements can be evaluated against existing customer requirements and reuse of product line features can be recommended. Contributions: We treated the problem of feature reuse analysis as a text classification problem at the requirements-level. We use Natural Language Processing and clustering to recommend reuse of features based on similarities and historic reuse information. The recommendations can be used to realize new customer requirements.

[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. 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 categorization.

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.

The software system controlling a train is typically deployed on various hardware architectures and is required to process various signals across those deployments. Increases of such customization scenarios, as well as the needed adherence of the software to various safety standards in different application domains, has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected by means of a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss research opportunities, focusing in particular on the areas of product line evolution and test automation.

Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards coverage-based test case selection based on extra-functional properties. We report a preliminary evaluation of industrial data for test categorization for safety aspects. Results show that keyword-based approaches can be used to categorize tests for extra-functional properties and can be improved by considering contextual information of keywords.

This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.

Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.

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.

The connection between objects and information exchange has been possible in recent years, with the advent of the Internet of Things (IoT) in different industries. We can meet different requirements in each industry utilizing this feature. Intelligent transportation uses the Internet of Vehicles (IoV) as a solution for communication among vehicles. It improves traffic management applications and services to guarantee safety on roads. We categorize services, applications, and architectures and propose a taxonomy for IoV. Then, we study open issues and challenges for future works. We highlighted applications and services due to drivers' requirements and nonfunctional requirements, considering the qualitative characteristic. This paper summarizes the current state of the IoV in architectures, services, and applications. It can be a start view to provide the solutions for challenges in traffic management in cities. The present study is beneficial for smart city developments and management. According to this paper's result, the services and applications evaluate performance with 34% frequency, safety and data accuracy, and security with a 13% frequency in selected papers. These measurements are essential due to the IoV characteristics such as real-time operation, accident avoidance in applications, and complicated user data management. 

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. 

Concurrent programming puts demands on software debugging and testing, as concurrent software may exhibit problems not present in sequential software, e.g., deadlocks and race conditions. In aiming to increase efficiency and effectiveness of debugging and bug-fixing for concurrent software, a deep understanding of concurrency bugs, their frequency and fixing-times would be helpful. Similarly, to design effective tools and techniques for testing and debugging concurrent software, understanding the differences between non-concurrency and concurrency bugs in real-word software would be useful. This paper presents an empirical study focusing on understanding the differences and similarities between concurrency bugs and other bugs, as well as the differences among various concurrency bug types in terms of their severity and their fixing time, and reproducibility. Our basis is a comprehensive analysis of bug reports covering several generations of five open source software projects. The analysis involves a total of 11860 bug reports from the last decade, including 351 reports related to concurrency bugs. We found that concurrency bugs are different from other bugs in terms of their fixing time and severity while they are similar in terms of reproducibility. Our findings shed light on concurrency bugs and could thereby influence future design and development of concurrent software, their debugging and testing, as well as related tools.

Multicore hardware development increases the popularity of parallel and multicore software, while testing and debugging the software become more difficult, frustrating and costly. Among all types of software bugs, concurrency bugs are both important and troublesome. This type of bugs is increasingly becoming an issue, particularly due to the growing prevalence of multicore hardware. Suspension-based-locking bug is one type of concurrency bugs. This position paper proposes a model based on runtime verification and reflection technique in the context of multicore and parallel software to monitor and detect suspension-based-locking bugs. The model is not only able to detect faults, but also diagnose and even repair them. The model is composed of four layers: Logging, Monitoring, Suspension Bug Diagnosis and Mitigation. The logging layer will observe the events and save them into a file system. The monitoring layer will detect the presents of bugs in the software. The suspension bug diagnosis will identify Suspension bugs by comparing the captured data with the suspension bug properties. Finally, the mitigation layer will reconfigure the software to mitigate the suspension bugs. A functional architecture of a runtime verification tool is also proposed in this paper. This architecture is based on the proposed model and is comprised of different modules. 

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

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.

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.

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.

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.

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.

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.

Administering the medications and fluids intravenously is a frequent practice in modern medical procedures, which plays a vital role in the treatment of certain acute conditions which require immediate action by drugs or fluids. This paper covers the design of a low-cost, wireless drip monitoring system for use in the hospital environment. The device is equipped with the Bluetooth low energy based battery-operated microcontroller, an infrared based drops counting system and a digital servo motor to control the drip flow rate, and it is attached to an existing intravenous stand. A LabView graphical user interface has also been developed to provide sets of input to the system to calculate the desired drip rate and the amount of pressure that digital servo motor must apply to achieve it. The system shows an average accuracy of 96% when compared with the measured and calculated values. This allows accurate computation of the level of the drip.

This qualitative vignette study aims to examine how social workers within social services assess honor-related violence directed towards women, and whether the ethnic background of the victim affects the assessment. Additionally, the study aims to investigate the interventions offered by social services for victims living in an honor context and whether they differ from interventions for intimate partner violence. Data collection was conducted through six semi-structured interviews with social workers from different municipalities, based on a vignette. The results are analyzed using previous research and theoretical frameworks such as intersectionality and systems theory. The study's findings indicate that all participants identified honor in the case description, regardless of whether the woman had a Swedish-sounding name or a Middle Eastern-sounding name. The participants argue that ethnicity should not be decisive in the assessment, but it needs to be taken into account to evaluate the severity of honor practices. The interventions offered for victims in an honor context include sheltered accommodation and counseling support. It is also evident that the interventions for honor-related violence and oppression do not differ from interventions for intimate partner violence. Finally, all participants believe that more research and specialized interventions are needed for honor-related violence and oppression.

The purpose of this thesis is to be able to contribute a small insight into how first-generationSomali women experience higher education and what they experience as opportunities andobstacles. The data has been collected through a semi-structured qualitative telephoneinterview. The result is based on five interviews with first-generation Somali femalestudents who have lived in Sweden for less than ten years and who are currently pursuingbachelors or master's level of education. The results showed that higher education plays animportant role in the lives of all the students, it also shows that the students socio-culturalupbringing is the driving force behind their pursuing of higher education. Education is alsoseen as crucial for gaining social cultural and economic capital. The results also shows thatthe female students face challenges such as prejudices, socio-cultural barriers and evendifficulties in finding work after their education. 

This paper aims at an accurate and efficient computation of effective quantities, e.g., the homogenized coefficients for approximating the solutions to partial differential equations with oscillatory coefficients. Typical multiscale methods are based on a micro-macro coupling, where the macromodel describes the coarse scale behaviour, and the micro model is solved only locally to upscale the effective quantities, which are missing in the macro model. The fact that the micro problems are solved over small domains within the entire macroscopic domain, implies imposing artificial boundary conditions on the boundary of the microscopic domains. A naive treatment of these artificial boundary conditions leads to a first order error in ε/δ, where ε < δ represents the characteristic length ofthe small scale oscillations and δ^d is the size of micro domain. This error dominates all other errors originating from the discretization of the macro and the micro problems, and its reduction is a main issue in today’s engineering multiscale computations. The objective of the present work is to analyse a parabolic approach, first announced in [A. Abdulle,D. Arjmand, E. Paganoni, C. R. Acad. Sci. Paris, Ser. I, 2019], for computing the homogenized coefficients with arbitrarily high convergence rates in ε/δ. The analysis covers the setting of periodic microstructure,and numerical simulations are provided to verify the theoretical findings for more general settings, e.g. non-periodic micro structures.

Background: Person-centered caregiving aims to reorganize care with a different focus on the patient's experiences in purpose to give the patient more influence in their caretaking. The method has been shown to increase understanding between the caregiver and the patient. There are three core components of person-centred care; partnership, the patient's story and documentation. Purpose: The aim of this thesis was to describe nurses' experiences of working person-centred in hospital care. Method: A general literature review is conducted for this thesis where 10 scientific articles were chosen to be analyzed, of which seven are qualitative and three are quantitative. Results: The following themes was shown in the results of this thesis: the importance of participation, the importance of communication, the importance of communication in the care team, negative work environment and nurses' satisfaction. The teamwork and ability for dialogue between the professional groups also proved to be important for the development of person-centred caregiving as a method. Conclusion: Person-centred care was proved to be desirable according to the nurses' experiences. Existing legislation, regulations and the patients themselves requested the method. Although the nurses felt that the way of working was worth striving for, it was difficult to achieve and reliant on good working conditions. The nurses felt that they were dependent on both the organization and the patients to together create the best conditions for the method.

Residential segregation is a significant problem in most cities around Sweden. Resulting in political, social, and economic exclusion for citizens that live in residential segregated areas. Therefore, the purpose of this bachelor thesis is to create an increased understanding of what residential segregation means and highlight the consequences, and what it entails for citizens in ethnic and socioeconomic segregated areas. An additional purpose of this study is to examine how citizens in residential segregated neighborhoods experience residential segregation, and how inhabitants experience their citizenship in the context to residential segregation. We have also chosen to study how Västerås and Örebro municipalities designed policies contribute to preventing residential segregation. The chosen methodology for this study is qualitative research interviews. We have chosen to do ten semi-structured interviews, five from each neighborhood. This study has been narrowed down to only research citizens who live in Bäckby and Vivalla and are over the age of eighteen. 

The material collected will be analyzed and examined based on the municipal policies and the chosen theories. The chosen theories are citizenship and urban citizenship from Marshall and Bottommore (1992), Ruth Lister (2007), Patricia Burke Wood (2017)  and Gilbert & Phillips  (2003), and residential segregation from the view of Iris Young (1999) and (2002) and Irene Molina (1997). In conclusion, the participants feel that their life opportunities are affected by living in an ethnic-and socioeconomic segregated area, they also express that the opportunities they receive are not equal to people from areas with good conditions. The participants express that their citizenship differs in practice from in theory. An additional conclusion is that the policies were found to be deficient and the municipalities were incapable of not only implementing but also pursuing a strategy, for these reasons, we can not determine if the objectives of the policies were achieved. 

A work can be a risk or a protection factor and the demands it puts on the employee can have different consequences. Work environment includes both physical and psychosocial work environment, the physical part is about noise, ventilation and light. The psychosocial work environment is about social support, influence and demands. Work environment is an important pillar in public health because people spend a lot of time in their work. The purpose of this study is to describe the prison guards experience their work environment. The Demand-Control-Support-model have been used in this study as theoretical perspective.

To get the purpose answered, a qualitative method was done with a goal-directed selection where five prison guards were interviewed individually. All the interviews were semi-structured and recorded then transcribed, and afterwards analysed. The interview person who was interviewed are working in the same prison and have worked there for different amount of time.

The results showed that the interview persons felt that the management put a high pressure even if they are short of staff and nothing has changed even if the staff remarks it. The psychosocial work environment for the interview persons was that the work is psychologically stressful because of the high demands from the management and even the daily threat from the inmates. But the interview persons felt that collaboration among the colleagues is so good that the job satisfaction increase.

Keywords: Demand-Control-Support model, Prison guard, Qualitative method, Work environment.

Background: Stress can have both positive and negative effects on the human body. Prolonged stress without recovery can lead to public health issues. The use of social media is increasing among young people. Even lot of use of social media can lead to serious consequences. The stress-vulnerability model has been applied to this study as a theoretical framework.

Aim: The aim of the study was to study if there is a relationship between use of social media and stress among high school students. 

Method: A quantitative study with a cross-sectional design was conducted among high school students. Convenience selection was used, with a response rate of 100 precent among 105 respondents. Spearman's rho correlation was used to analyze the relationship. Three research questions were formulated to answer the purpose of the study.

Result: The majority of respondents use social media for at least three hours per day. Three out of four respondents experience daily stress. The study showed a weak positive correlation between social media use and stress.

Conclusion: The study showed that almost all respondents use social media. The majority spent more than three hours per day on social media. The study also revealed a weak positive significant associations between social media and stress.