https://www.mdu.se/

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.

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. 

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.

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.

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.

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.

Detta examensarbete är på grundnivå som har genomförts under en period av 20 veckor undervåren 2023 och motsvarar 15 högskolepoäng. Projektet har haft som syfte att utveckla ett konceptför en kontaktor och tillämpa additiv tillverkningsteknik som en tillverkningsmetod för konceptet.Uppdragsgivaren för projektet har varit ABB Control Products i Västerås och de har identifieratbehovet av en lämplig lösning för limproblemet i släckpaketet för kontaktorer av storlek nio. Dennuvarande användningen av lim i kontaktorerna ansågs vara ohälsosam och uppfyllde intemiljökraven.

Inom ramen för projektet genomfördes en konceptutveckling för att lösa det identifieradeproblemet. Dessutom undersöktes möjligheten att tillämpa additiv tillverkningsteknik ochgenomföra en förenklad kostnadsbedömning för att visa skillnaden i kostnad mellan den nuvarandetillverkningsmetoden och additiv tillverkningsteknik. Produktutvecklingsmetoder användes för attgenomföra projektet, vilket resulterade i ett fungerande teoretiskt koncept. För att sålla ut idéeroch koncept användes en prioriteringsmatris och ett poängsystem. Det resulterande konceptetrepresenteras av CAD-modeller som består av metallplåtar och två delar av det utveckladekonceptet. Konceptet möjliggör en ny design utan användning av lim vid montering. Det har ocksåvisat sig att det är möjligt att ändra tillverkningsmetoden genom att använda additivatillverkningstekniker. Även om prototyperna ännu inte har testats, anses de teoretiska lösningarnavara fungerande.

Tests are important artifacts in the software development process. Testing activities such as test automation, test maintenance, and test suite optimization mainly rely on an in-depth understanding of test specifications. The manual process of writing testspecifications in natural language can create many different quality issues such as ambiguous, incomplete, redundant, or inconsistent test cases. Nowadays, the concept of test smells is proposed by several researchers to be used as indicators of low-qualityattributes in test specifications. Quality assurance processes for test specifications often rely on manual reviews to detect these smells. The manual process of detecting these smells is considered time consuming and costly. However, there is currently no work that implements a comprehensive quality model that classifies and identifies these smells by using a systematic strategy. As a result, there is a need for machine-supported analytical measures that decrease the time and effort needed to detect these smells manually, especially when it comes to reviewing and validating large test specifications.This study aims to investigate which natural language smell metrics implemented in NALABS can be found in test specifications and to measure the sufficiency of those smellmetrics. It also aims to extend these smell metrics by exploring, proposing, or combining with new bad smell metrics to cover more aspects of natural language test quality. The results of the study show that the smell metrics exists in real-world test specifications and can uncover many potential quality issues by assisting test designers in identifying certain types of quality issues pertaining to for example the understandability and complexity of test specifications. Moreover, the results show thatthe list of smell metrics implemented in NALABS is incomplete and can be extended to cover more aspects of test quality.

This thesis first and foremost investigates how entrepreneurial teachers today work with teachingcreativity to young entrepreneurs, at upper secondary level in Sweden. To then come toconclusions on how to improve and developed teaching of creativity in young entrepreneurs. Forthat purpose, this thesis, has the following research questions.Research question 1; How UF (entrepreneurial teachers) use creative tools today?Research question 2; How the learning of creative tools can develop in the future?The method of investigation used in this thesis is a “semi-structured interview” where sixdifferent respondents where interview two at time, separately, the process took a month to becompleted. These six respondents are teachers working in three different high schools in theMälar region of Sweden. The duration of each interview was between 20-30 minutes, theseinterviews were then analyzed, transcribed, and categorized.The answers were then analyzed by the authors and categorized with connection to the theoriesthat have formed the basis of this work. The empirical evidence could then be fed into a tablethat showed the similarities and differences in the working methods between the variousinterviewed respondents. This is to make it easier for the reader.The results of this work show that UF teachers today use creativity as a tool during the businessidea phase of UF entrepreneurship. After this phase, entrepreneurial teachers no longer see anypurpose in teaching creativity to entrepreneurial students. The authors of this thesis concludedthat creativity does not lose its value or utility after the idea generation phase ofentrepreneurship. The current way of teaching creativity creates an environment where creativityis "strangled" and there is no free space left for innovative thinking. The authors associate thislack of innovation to a lack of communication between the UF teachers and therefore the authorscall on the UF teachers to collaborate 3 times per business year.The examiner for this work is Bengt Köping Olsson, researcher in creativity, innovation anddesign at Mälardalen University. 

In order to stimulate innovation and sustainable development in a business environment that is always changing, it is important to support successful collaboration between academia, industry, and society. This project is focused on creating and implementing MDU's Creative Lab, an innovation lab based on the Quintuple Helix Model, in order to promote collaboration between students, academic institutions, and business partners and to meet the needs of all parties. The study was conducted utilizing a methodology that included a review of the literature, interviews, and data analysis. The primary issues that need to be resolved have been highlighted through the interviews as the absence of support systems, the requirement to create clear objectives at the outset of the project, and the significance of ensuring projects and authors maintain continuity.The results of the research have shown that better communication between parties, a clear connection between projects and courses, and opportunities for students to obtain knowledge and experience in the real world are all necessary. In addition, major motivators such as the desire for a social impact, interdisciplinary teamwork, and satisfaction in one's job have been highlighted. As a result, a business model has been developed that takes into account the information gleaned from the presentations and aims to facilitate collaboration and communication between academic institutions, students, and business partners, guarantee the quality of collaborations, offer mentoring and guidance to students, and plan workshops and events that encourage creativity, innovation, and entrepreneurship.This project provides a thorough analysis of how to create and implement a student-driven innovation lab that adheres to the Quintuple Helix Model and is geared toward sustainable growth in the direction of Industry 5.0. By encouraging student cooperation and contentment, it fosters an environment that fosters the development of creative ideas and the development of a team-oriented workforce that will progress society and business.

Today, well-established hazard analysis techniques are available and widely used to identify hazards for single systems in various industries. However, hazard analysis techniques for a System of Systems (SoS) are not properly investigated. SoS is a complex system where multiple systems work together to achieve a common goal. However, the interaction between systems may lead to unforeseen interactions and interdependencies between systems. This increases the difficulty of identifying and assessing system failures and potential safety hazards. In this paper, we explore whether Hazard Ontology (HO) can be applied to an SoS and whether it can identify emergent hazards, their causes, sources, and consequences. To conduct our exploration, we apply the HO to a quarry automation site (an SoS) from the construction equipment domain. The results indicate that the HO is a promising technique that facilitates the identification of emergent hazards and their components. 

While ontology comparison and alignment have been extensively researched in the last decade, there are still some challenges to these disciplines, such as incomplete ontologies, those that cover only a portion of a domain, and differences in domain modeling due to varying viewpoints. Although the literature has compared ontological concepts from the same domain, comparisons of concepts from different domains (e.g., security and safety) remain unexplored. To compare the concepts of security and safety domains, a security ontology must first be created to bridge the gap between these domains. Therefore, this paper presents a Combined Security Ontology (CSO) based on the Unified Foundational Ontology (UFO) that could be compared to or aligned with other ontologies. This CSO includes the core ontological concepts and their respective relationships that had been extracted through a previous systematic literature review. The CSO concepts and their relationships were mapped to the UFO to get a common terminology that facilitates to bridge the gap between the security and safety domains. Since the proposed CSO is based on the UFO, it could be compared to or aligned with other ontologies from different domains.

Safety and security ontologies quickly become essential support for integrating heterogeneous knowledge from various sources. Today, there is little standardization of ontologies and almost no discussion of how to compare concepts and their relationships, establish a general approach to create relationships or model them in general. However, concepts with similar names are not semantically similar or compatible in some cases. In this case, the problem of correspondence arises among the concepts and relationships found in the ontologies. To solve this problem, a comparison between the Hazard Ontology (HO) and the Combined Security Ontology (CSO) is proposed, in which the value of equivalence between their concepts and their relationships was extracted and analyzed. Although the HO covers the concepts related to the safety domain and the CSO includes securityrelated concepts, both are based on the Unified Foundational Ontology (UFO). For this study, HO and CSO were compared, and the results were summarized in the form of comparison tables. Our main contribution involves the comparisons among the concepts in HO and CSO to identify equivalences and differences between the two. Due to the increasing number of ontologies, their mapping, merging, and alignment are primary challenges in bridging the gaps that exist between the safety and security domains. 

Security ontologies have been developed to facilitate the organization and management of security knowledge. A comparison and evaluation of how these ontologies relate to one another is challenging due to their structure, size, complexity, and level of expressiveness. Differences between ontologies can be found on both the ontological and linguistic levels, resulting in errors and inconsistencies (i.e., different concept hierarchies, types of concepts, definitions) when comparing and aligning them. Moreover, many concepts related to security ontologies have not been thoroughly explored and do not fully meet security standards. By using standards, we can ensure that concepts and definitions are unified and coherent. In this study, we address these deficiencies by reviewing existing security ontologies to identify core concepts and relationships. The primary objective of the systematic literature review is to identify core concepts and relationships that are used to describe security issues. We further analyse and map these core concepts and relationships to five security standards (i.e., NIST SP 800-160, NIST SP 800-30 rev.1, NIST SP 800-27 rev.A, ISO/IEC 27001 and NISTIR 8053). As a contribution, this paper provides a set of core concepts and relationships that comply with the standards mentioned above and allow for a new security ontology to be developed.

In enterprise-scale companies, where processing complex test results is often a time-consuming task for testers and developers, the need for an Application Programming Interface (API) testing visualization tool is vital. Furthermore, as various stakeholders require insights into the API testing results, a clear dashboard can facilitate effective communication among them. 

This thesis aims to fulfill the need for a generic API testing visualization tool within the mobile network industry through the implementation of TestUp, a user-friendly solution. TestUp simplifies the analysis of complex API test results for various stakeholders, including testers, developers, managers, and product owners. To accomplish the objectives of this thesis, an industrial case study was conducted at Nokia, beginning with a pre-study phase to explore industry needs and challenges. TestUp was designed based on the Agile methodology, evolving through feedback from industry and academia. Additionally, a post-tool questionnaire was employed to gather data on the tool’s effectiveness and limitations. Subsequently, the data analysis was conducted using the thematic analysis approach. 

The thesis demonstrates that TestUp provides valuable benefits to stakeholders by effectively addressing critical issues found in existing tools. TestUp is compatible with a range of test automation frameworks and serves as a user-friendly tool for visualizing test coverage. These facts fulfill the company's requirements. All in all, this thesis improves the analysing of API testing results within the mobile network management sector. Additionally, future work can involve enhancing the tool by adding extra features, such as export functionality and the ability to indicate timeout requests. Also, integrating TestUp with the continuous integration pipelines is a potential area for future work.

With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system.

Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved. 

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.

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

Utvecklingsmetoder är olika arbetsprocesser som företag kan använda för att förbättra och effektivisera sitt arbete. Idag är de agila utvecklingsmetoderna Scrum och Kanban två av de mer använda metoderna inom företag som utvecklar mjukvara. I mitt arbete undersöks det hur nära små utvecklingsteam inom mindre företag förhåller sig till Scrum eller Kanban i verkligheten. Undersökningen görs för att se om det finns förbättringar som mindre företag med små utvecklingsteam kan ta till sig från litteratur för att efterlikna hur teorin är beskriven, samt kunna diskutera kring delar relevans som inte efterlevs.

Syftet med detta arbete är att identifiera hur nära agila utvecklingsmetoder som Scrum eller Kanban efterlevs i praktiken, hos mindre företag med små utvecklingsteam, enligt hur metoderna är teoretiskt beskrivna. Målet i detta arbete är se om det finns förbättringar som små utvecklingsteam kan ta till sig från litteratur om utvecklingsmetoderna, Scrum eller Kanban, för att effektivisera sin arbetsprocess genom att efterleva mer enligt hur dessa utvecklingsmetoder beskrivs i litteraturen.

För att uppnå arbetets syfte och mål undersökas tre frågeställningar. Ena frågeställningen är om hur litteratur beskriver metoderna för agil mjukvaruutveckling för små utvecklingsteam i mindre företag, vilket besvarades genom en litteraturstudie. Genom intervjuer besvarades frågeställningen om hur utvecklare i små utvecklingsteam inom mindre företag beskriver sin utvecklingsmetod. Tillsammans med intervjuerna och en observationsstudie besvarades den sista frågeställningen angående hur små utvecklingsteam i mindre företag arbetar med utveckling av mjukvara i praktiken.

Resultatet visade att utvecklarna från det medverkande företaget i praktiken endast följde vissa delar från Scrum men inte alla centrala delar, trots att Scrum enligt litteratur är anpassad till att användas inom små utvecklingsteam. Genom att inte följa Scrum centrala delar går det inte att säga att de använder Scrum, och därmed riskerar att inte leva upp till alla de fördelar Scrum kan bidra med.

Purpose: This research will study how hyperspectral sensoring can assess the moisture content of lettuce by monitoring its growth in indoor vertical farming.

Research questions:

“What accuracy can be achieved when using hyperspectral sensoring for assessing the moisture content of lettuce leaves grown in vertical farming?”

“How can vertical farming contribute to sustainability in conjunction with integration of NIR spectroscopy?”

Methodology: This study is an experimental study with a deductive approach in which experiments have been performed using the hyperspectral technologies singlespot sensor and the hyperspectral camera Specim FX17 to collect spectral data. To analyze the data from the experiments two regression models were used and trained to make it possible to predict future moisture content values in lettuce. In order to get a better understanding and analyze the results from the experiments, a literature review was also conducted on how hyperspectral imaging has been applied to assess the quality of food products.

Conclusion: The achieved accuracies were 58.24 % and 65.54 % for the PLS regression model and the Neural Network model respectively. Employing hyperspectral sensoring as a non-destructive technique to assess the quality of food products grown and harvested in vertical farming systems, contributes to sustainability from several aspects such as reducing food waste, minimizing costs and detecting different quality attributes that affect the food products.

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

In the literature there is no consensus regarding the meaning of the term digital transformation. Therefore, the purpose of this paper is to explore the differing views of the meaning of digital transformation. A case study has been conducted in collaboration with four Swedish manufacturing companies. The results shows that digital transformation can have different meaning within a company and the main challenge when performing digital transformation is knowledge. This study is the first investigation in a research project focusing on coordination of digital transformation. Therefore, is the underlaying goal to identify how the participating companies in the research project describe digital transformation in comparison with the literature. The research intention is not to define digital transformation rather to explore differing views of digital transformation and highlighting similarities and difference in comparison with the literature reviewed. The findings are practically relevant for manufacturing companies by highlighting differing views of digital transformation and in the creation of a common language within a company. 

The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs) have emerged to tackle the complexity of DNN accelerators, however, they are more prone to reliability issues.In this paper, a recent analytical resilience assessment method is adapted for QNNs to identify critical neurons based on a Neuron Vulnerability Factor (NVF). Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.The method is validated by experiments on different QNNs and datasets. The results demonstrate that the proposed method for correcting the faults has a twice smaller overhead than a selective Triple Modular Redundancy (TMR) while achieving a similar level of fault resiliency. 

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks.

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

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off does not hold. This paper discusses a regression problem context to predict flight take-off delays where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the XGBoost feature importance into global weights in the CBR model, the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model becomes a benchmark of accuracy and interpretability for this problem context, and hence it is used to evaluate the two additive feature attribute methods SHAP and LIME to explain the XGBoost regression model. The results with respect to local accuracy and feature attribution lead to potentially valuable future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

I dagens digitala samhälle är företag beroende av internet för att leverera tjänster till sinakunder. Det medför att de riskerar att bli utsatta för cyberattacker som kan hota derasverksamhet. Hotdetektering med logganalys innebär insamling och analys av loggdata för attidentifiera anomalier som indikerar potentiella hot. I detta examensarbete jämförs två av demest populära logganalysverktygen för att avgöra vilket av dem som är lämpligast förhotdetektering. Verktygen Graylog och ManageEngine Eventlog Analyzer jämförs utifrånvilka hot de kan detektera, hur komplexa de är, hur mycket belastning de utgör påvärdenheten och vilka kostnader verktygen har. Syftet med detta examensarbete är attunderlätta valet av logganalysverktyg som kan användas i hotdetekteringssyfte. Arbetet bestårav en litteraturstudie och en laboration. För att utvärdera verktygen har det i en litteraturstudiesamlats in information om vilka hot verktygen kan detektera, vilka kostnader verktygen haroch vilka resurskrav verktygen har. I en laboration har verktygens komplexitet utvärderats. Ilaborationen utvärderades även verktygens belastning på värdenhetens CPU och RAM-minne.Resultatet visar att ManageEngine Eventlog Analyzer är det lämpligaste logganalysverktygetför hotdetektering därför att den detekterar fler hot i jämförelse med Graylog, samtidigt somdet är mindre komplext att använda på grund av den tillgängliga tekniska supporten.

This thesis aims to investigate and enhance the accessibility of a healthcare application, with a specific focus on the design of menus and functions. The work encompasses a comprehensive literature review, needs analysis, market analysis, and user tests. Additionally, theories and prior research on accessibility and cognitive load are discussed, and how these aspects can be integrated into the design process to create a more user-friendly and inclusive healthcare application.

The study involves a detailed analysis of the design process, from sketches and wireframes to the final Hi-Fidelity prototype. The results from this process, along with insights from the needs analysis, are used to improve the design and usability of the application. Improvements include clearer menus and functions, as well as the introduction of a "readability" feature to make the content more accessible for older users.

Future research should continue to explore how digital healthcare applications can be designed to reduce cognitive load, especially considering the expected significant increase in the global population over the age of 60 by 2050. With the insights from this work, we can create more effective and inclusive digital healthcare solutions.

This study is a bachelor thesis in the field of Information design with focus on accessibility.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

The following thesis report is the result of a degree project at the basic C-level in the innovation and product design engineering program at Mälardalen University for Eskilstuna kommun. The degree project is about a product development process to develop a gutter well cover solution intended to improve the method for lifting gutter well covers.

The reader of this report will learn how scientific product development methods were used to develop a final gutter well cover tool prototype and what moments the processes included. The product development tools that were used to enable a final prototype were contributed by the SolidWorks software to construct CAD models that formed the basis for various test constructions and the final prototype.

The result of the thesis report consists of a newly developed gutter well cover opening tool designed to effectively open spring-loaded based gutter well covers.

Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.

This bachelor thesis aims to find a more sustainable and cost-effective solution for a rotation system used for turning walls. The work was carried out by the client Husmuttern AB, which requires a rotation system for rotating wall fixtures in the company's future construction projects in Scandinavia, as well as in Poland and Ukraine.

The current fixture is powered by an electric motor, which the company considers to be a costly and less sustainable solution. Therefore, this work will present a more cost-effective and sustainable solution and address the development process for the proposed design.

The research question for this thesis is: How can an existing electrically driven rotation system be replaced with a manually operated mechanical rotation system while ensuring that the design meets the internal requirements of Husmuttern AB and the external regulations from the Swedish Work Environment Authority regarding load ergonomics?

The work has been done with consideration for cost-effectiveness, ergonomics, and safety. Relevant regulations from the Swedish Work Environment Authority have been reviewed in the course of this work. The work has also focused on developing a concrete proposal where the use of standard components has been prioritized to save resources and result in a cost-effective solution based on available material dimensions from different suppliers.

Computer Aided Design (CAD) has been used to draw and assemble components that the rotation system's design consists in a virtual environment. CAD has been used with the help of Solidworks software to give a closer picture of what the construction might look like, as well as how the components sit and their position and direction.

The work has been completed within the specified time limit and delivers what was specified in the limitations. The result is a new rotation system - a manually operated mechanical rotation system that enables ergonomic and safe work tasks. The report describes the work process, calculations, drawing documents, and a user description of the rotation system's work tasks, thus constituting the delivery of the work.

Small-and medium-sized enterprises (SMEs) have inherent characteristics, which require specific solutions for improving the sustainability performance of their operations. The purpose of this paper is to increase the knowledge on barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs and to provide insights into what enablers can be used to overcome existing barriers. Taking, as a starting point, a systematic literature review, this paper presents a categorization of barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs. In total, seven categories for classifying the barriers and enablers for the adoption of sustainable manufacturing within SMEs were identified: organizational, managerial and attitudinal; informational; governmental; financial; training and skills development; market and business context; and technological. Additionally, this study elaborates on what barriers could be mitigated through the enablers. This study found specific enablers with the potential to mitigate a significantly higher number of barriers and referred to them as ‘critical enablers’. SMEs aiming to adopt sustainable manufacturing practices or improve their sustainability performance are encouraged to focus on the enablers in these categories. This paper synthesizes and facilitates interpretation of the existing body of evidence on barriers and enablers for adopting sustainable manufacturing in SMEs. 

Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions. 

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.