This study is about increasing engagement in understanding complex energy related information. The purpose of the study is to provide insights on how to create a  concept for increasing engagement in understanding complex energy related information, primarily for young adults, and to provide concept how storytelling  and other techniques be used to engage users in complex energy related information and how design principles be applied with the aim of conveying complex energy related information  in an understandable way. The goal is to increase the understanding on how to engage young adults and to enable a design concept with the collaboration partner Energy Evolution Center. The methods used in the thesis are AEIOU method, user workshop, questionnaire, user observation, storyboard, prototyping, focus groups, cognitive walkthrough and design principles. The design process builds upon a human centered design approach. The design concept was tested, iterated and produced with close collaboration with the target audience and experts within the field. The results show signs that the target group's engagement could be increased through interactive activities that are similar to games and contain experiments, if placed within an educational context and be able to participate during school hours. Thus, the target group raised the issue of engaging voluntarily in activities with subjects in which they have no existing interest in. However, the target group expressed a greater commitment by participating in the activities that the design concept suggested, and to creating engagement to topics such as energy by participating in educational contexts. The conclusion is that the design concept allows room for failure, playfulness, exploration and teamwork, that is conceptualized through interaction of multiple senses. This study would also benefit from larger research and create space for more research especially in the societal aspects of inclusion, such as gender for example. 

This study is about inclusion in digital environments with a focus on videogames. The purpose of the study is to provide insight into how to increase inclusion in the digital environment, primarily for women, and provide examples of how to apply good information design to digital spaces to contribute to increased inclusion in videogames. The aim is also to increase the understanding of how spatial design affects inclusion for users and which parts of the spatial aspect are important for the environment to be perceived as inclusive or not. The goal is to try to raise awareness about inclusion in the gaming environment and to get people to question the design. The methods used are questionnaire, interview workshop, site analysis and traditional masculine and feminine scale, as well as human centered design as a method and process in the work. Of these methods, prototypes were made and used to produce different notions of what is masculine or feminine, as well as what the users in the study perceived as more inclusive designs. The results show signs that the spatial environment gives signals to be able to behave in a certain way that can be linked to hypermasculinity. The users in the study perceived the most popular games to be in the more masculine direction with Counterstrike as the most masculine direction. The attributes of the game's environment and spatial elements were perceived by the users in the study as violence and terror, which may in this way send signals to the users that it is okay to behave in a more hypermasculine way as one of the attributes of hypermasculinity is violence. The conclusion is that a larger study would need to be carried out on the interaction between the environment and its signals to the users to see if it has a greater impact on the inclusion of the users, as there are signs that the spatial design also contributes to a hypermasculine environment in the digital space. 

PID controllers are still popular in a wide range of engineering practices due to their simplicity and robustness. Traditional design of a PID controller needs manual setting of its parameters in advance. This paper proposes a new method for online tuning of PID controllers based on hybridized neural membrane computing. A neural network is employed to adaptively determine the proper values of the PID parameters in terms of evolving situations/stages in the control process. Further the learning of the neural network is performed based on a membrane algorithm, which is used to locate the weights of the network to optimize the control performance. The effectiveness of the proposed method has been demonstrated by the preliminary results from simulation tests.

In this thesis a system that evaluates projection systems is designed and tested. The type of projection systems of concern feature display surfaces that are curved in two axes. The system uses a calibration image featuring evenly spaced circles. This image is projected onto the display surface. Photos of the projection, captured using a camera placed in the design eye position of the projection, are stitched into an equirectangular projection of the display surface. The resulting system identifies both absolute and relative pixel position errors of the projection by comparing the location of the projected circles with their intended location. The developed system can detect faults in systems projecting on curved surfaces. There are several sources of error in the resulting system. The panoramic stitching and is a minor source of error. The largest source of error is the Hough transform used to detect and locate circles in the projection. This transform is not very robust. In pixel perfect images the system returns perfect measurements, however, if images are captured by a camera, the Hough transform lacks accuracy. The largest measured measurement error for the absolute pixel position error is approximately 10.6 arcminutes. For the relative pixel position error the largest measured measurement error is approximately 0.85 arcminutes per degree. The developed system can be used to acceptance test projection systems as well as indicate needs for maintenance of them.

Purpose: The use of quality management (QM) to achieve the United Nations Sustainable Development Goals (UNSDGs) is a topic of growing interest in academia and industry. The IAQ (International Academy for Quality) established Quality Sustainability Award in 2020, a testament to this growing interest. This study aims to investigate how QM philosophies, methodologies and tools can be used to achieve sustainable development in organizations. Design/methodology/approach: Five large manufacturing organizations – three from India and two from China – who reported their achievements about using QM in achieving Sustainable Development Goals (SDGs) were studied using multiple sources of data collection. A detailed within-case and cross-case analysis were conducted to unearth this linkage's practical and theoretical aspects. Findings: The study finds that QM methodologies effectively met the five organizations' UNSDGs. These organizations successfully used OPEX (Operational Excellence) methodologies such as Lean, Kaizen and Six Sigma to meet UNSDGs 7, 11, 12 and 13. Moreover, UNSG 12 (Responsible Consumption and Production) is the most targeted goal across the case studies. A cross-case analysis revealed that the most frequently used quality tools were Design of Experiments (DoE), Measurement Systems Analysis (MSA), C&E analysis and Inferential statistics, among other essential tools. Research limitations/implications: The study's sample size was limited to large-scale manufacturing organizations in the two most populous countries in the world. This may limit the study's generalizability to other countries, continents, or micro-, small- and medium-sized enterprises (SMEs). Additionally, the study's conclusions would be strengthened if tested as hypotheses in a follow-up survey. Practical implications: This practical paper provides case studies on how to use QM to impact SDGs. It offers both descriptive and prescriptive solutions for practitioners. The study highlights the importance of using essential QM tools in a structured and systematic manner, with effective teams, to meet the SDGs of organizations. Social implications: The study shows how QM can be used to impact UNSDGs, and this is very important because the UNSDGs are a set of global objectives that aim to address a wide range of social and environmental issues. This study could motivate organizations to achieve the UNSDGs using essential QM tools and make the world a better place for the present and future generations. Originality/value: This case study is the first to investigate at a micro-level how QM can impact UNSDGs using live examples. It uses data from the IAQ to demonstrate how QM can be integrated into UNSDGs to ensure sustainable manufacturing.

PurposeIn a highly competitive and globalised era, agile organisations proactively steer towards sustainability. This situation persuaded the organisations to align Quality Management (QM) initiatives to achieve sustainable outcomes. This study aims to explore quality-sustainability linkage, explicitly focusing on attaining the prestigious IAQ Quality Sustainability Award. Further it investigates, the impact of QM as a strategy for promoting sustainability to meet sustainable development goals (SDGs).Design/methodology/approachDue to the lack of substantial literature connecting QM to sustainability, the current research adopted an explanatory multiple-case study. Six cases were purposively chosen for the study. Three cases of those who have achieved the prestigious IAQ Quality Sustainability Award and remaining have been selected that have fallen short of receiving the award. A detailed within-case and cross-case examinations involving six cases that reported their QM achievements aligned with SDGs.FindingsThe findings demonstrate the significant role of QM adoption in achieving positive results from the perspective of SDGs, such as reduced environmental impacts, improved operational efficiency and enhanced quality of life. Effective stakeholder collaboration, proficiency in analytical tools and strategic alignment with SDGs emerged as critical success factors. Conversely, weak linkage with sustainability and unclear approaches were crucial challenges in attaining the IAQ Quality Sustainability Award.Research limitations/implicationsThis paper outlines essential commandments for organisations actively seeking to promote sustainability. It offers valuable insights for decision-makers, facilitating a profound understanding of the challenges and opportunities in pursuing sustainable performance.Originality/valueThe distinctive nature of this study lies in its dedicated exploration of the intricate relationship between QM deployment and its true impact on the achievement of the SDGs.

Product line architectures play a vital role in the automotive industry in supporting cross-product development involving several hardware and software variation points for different vehicle variants. The effective resolution of multiple variation points for the generation of valid variants is complex, especially when dealing with both software and hardware components implying timing constraints, simultaneously. EAST-ADL is a well-known domain-specific modelling language supporting cross-product development using different levels of abstraction. Furthermore, it offers timing extensions to perform system and component-level timing verification. In this article, we propose an EAST-ADL-compliant and timing-Aware variability resolution approach to generate valid product variants effectively. The method relies on existing EAST-ADL product line architecture for system modelling, where several variation points at different levels of abstraction are identified. We propose a variability resolution algorithm where several configuration decisions, starting from the topmost vehicle level down to the design level, are incor-porated for seamless variability resolution. Furthermore, timing decisions based on analysis and design prototypes are provided to generate variant-specific timing constraints. The approach is validated on the car wiper use case provided by our industrial partner, Volvo, an international original equipment manufacturer in the automotive domain. In the use case, three product variants comprising a full system model with associated timing constraints are generated successfully. The results show the feasibility of the proposed approach and indicate its effectiveness in managing timing-Aware product variants.

Design and development of domain-specific modeling languages are crucial activities in model-driven engineering. At the core of these languages we find metamodels, i.e. descriptions of concepts and rules to combine those concepts in order to build valid models. Both in research and practice, metamodels are created and updated more or less frequently to meet certain business requirements. Although there exist several workbenches for metamodeling, some textual (e.g., JetBrains MPS) and some graphical (e.g., Eclipse Modeling Framework - EMF), it still remains a sensitive and complex task, where several stakeholders with different skill-sets need to be able to properly exchange ideas and reach agreements.To maximize the throughput of metamodeling activities, in this paper we propose a Blended Metamodeling Framework (BMF) that enables the development of metamodels through both graphical and textual (natural language) notations interchangeably, by utilizing the concepts of Natural Language Processing and model-driven engineering. The feasibility of the framework is demonstrated via the Portable test and Stimulus Standard (PSS) use case, where a DSML is developed by seamlessly blending the use of textual (natural language) and graphical (EMF) notations. Moreover, for demonstration purposes we also generate a domain-specific language structure reflecting the metamodel in JetBrains MPS.

EAST-ADL is a domain-specific modelling language for the design and analysis of vehicular embedded systems. Seamless modelling through multiple concrete syntaxes for the same language, known as blended modelling, offers enhanced modelling flexibility to boost collaboration, lower modelling time, and maximise the productivity of multiple diverse stakeholders involved in the development of complex systems, such as those in the automotive domain. Together with our industrial partner, which is one of the leading contributors to the definition of EAST-ADL and one of its main end-users, we provided prototypical blended modelling features for EAST-ADL. In this article, we report on our language engineering work towards the provision of blended modelling for EAST-ADL to support seamless graphical and textual notations. Notably, for selected portions of the EAST-ADL language (i.e., timing and variability packages), we introduce ad-hoc textual concrete syntaxes to represent the language's abstract syntax in alternative textual notations, preserving the language's semantics. Furthermore, we propose a full-fledged runtime synchronisation mechanism, based on the standard EAXML schema format, to achieve seamless change propagation across the two notations. As EAXML serves as a central synchronisation point, the proposed blended modelling approach is workable with most existing EAST-ADL tools. The feasibility of the proposed approach is demonstrated through a car wiper use case from our industrial partner - Volvo. Results indicate that the proposed blended modelling approach is effective and can be applied to other EAST-ADL packages and supporting tools.

Blended modeling is an emerging trend in Model-Driven Engineering for complex systems. It enables the modeling of diverse system-related aspects through multiple editing notations seamlessly, interchangeably, and collaboratively. Blended modeling is expected to significantly improve productivity and user-experience for multiple stakeholders. Case-specific solutions providing blended modeling, to a certain extent, for domain specific languages have been provided in the last few years. Nevertheless, a generic and language-agnostic full-fledged blended modeling framework has not been proposed yet.

In this paper, we propose a comprehensive and generic blended modeling framework prototype that provides automated mechanism to generate graphical and textual notations from a given domain-specific modeling language. Moreover, it offers a flexible editor to get expert’s feedback on the mapping between graphical and textual notations. The proposed prototype is validated through a proof-of-concept on the Portable test and Stimulus Standard use-case. Our initial results indicate that the proposed framework is capable of being applied in different application scenarios and dealing with multiple domain-specific modeling standards.

The design and implementation of vehicle suspension systems is complex and time-consuming process that usually leads to production delays. Although different Model Driven Engineering (MDE) technologies like EAST-ADL/AUTOSAR are frequently applied to expedite vehicle development process, a framework particularly dealing with design and analysis of vehicle suspension is hard to find in literature. This rises the need of a framework that not only supports the analysis of suspension system at higher abstraction level but also complements the existing standards like EAST-ADL. In this article, a Model driven framework for Vehicle Suspension System (MVSS) is proposed. Particularly, a meta-model containing major vehicle suspension aspects is introduced. Subsequently, a modeling editor is developed using Eclipse Sirius platform. This allows the modeling of both simple as well as complex vehicle suspension systems with simplicity. Moreover, Object Constraint Language (OCL) is utilized to perform early system analysis in modeling phase. Furthermore, the target MATLAB-Simulink models are generated from source models, using model-to-text transformations, to perform advanced system analysis. The application of proposed framework is demonstrated through real life Audi A6L Hydraulic active suspension use case. The initial results indicate that proposed framework is highly effective for the design and analysis of vehicle suspension systems. In addition to this, the analysis results could be propagated to EAST-ADL toolchains to support full vehicle development workflow. 

Futures Literacy is the capability to imagine and understand potential futures to prepare ourselves to act and innovate in the present. This pilot study aims to understand how artistic methodologies and speculative design can support the collaborative exploration of futures in the context of work and contribute to developing peoples' capability of futures literacy. Our premise is that technologies such as Artificial Intelligence and the Internet of things can augment people and support their needs at work. To illustrate this process, we have presented a collaborative method that integrates an artistic intervention with speculative design activities. We tested the method in a full-day workshop with seventeen (17) participants from a Swedish academy responsible for enabling learning and competence development at work in the healthcare sector. The results indicate that the artistic intervention, combined with the speculative design activities, can challenge current participants' perspectives and offer them new ways of seeing futures with technologies. These new ways of seeing reveal underlying premises crucial in developing the capability of futures literacy. 

The availability of new research for IoT support and the human-centric perspective of industry 4.0 opens a gap to support operators in unleashing their creativity so they can provide improvements opportunities with IoT technology. This paper presents a case-study carried out in four Swedish manufacturing companies, where four different workshops were facilitated to support operators in the conceptualization of manufacturing improvements with IoT technologies. The empirical material gathered during these workshops has been analyzed in five different reflective sessions and discussed in light of previous research from industry 4.0, operators, and IoT support. Results indicate that operators can collaboratively create conceptual IoT solutions and that expressiveness in communicating their ideas and needs using IoT technology is more relevant than technical aspects and details of their proposed IoT solutions. This technological expressiveness is identified as a necessary skill to be cultivated on the shop floor and can potentially contribute to making a more effective and socially sustainable industrial landscape in the future.

Artificial Intelligence offers a wide variety of capabilities that can potentially address people's needs and desires in their specific contexts. This pilot study presents a collaborative method using a deck of AI cards tested with 58 production, AI, and information science students, and experts from an accessible media agency. The results suggest that, with the support of the method and AI cards, participants can ideate and reach conceptual AI solutions. Such conceptualisations can contribute to a more inclusive integration of AI solutions in society.

This paper presents a collaborative solution developed to enable people without prior Internet of Things (IoT) knowledge to ideate, conceptualise, role-play and prototype potential improvements to their work processes and environments. The solution, called the Karakuri IoT toolkit and method, was tested in two workshops with eight production leaders at a Swedish manufacturing company. Outcomes were analysed from the perspectives of materials interaction and instruments of inquiry. Results indicate the solution can help people conceive and prototype improvement ideas at early design stages.

The rapid development and integration of IoT, AI, and digital twin technologies into work environments create new demands and challenges for organisations, including the need to upskill and prepare their workforces for new technological applications and capabilities. The collaborative design tradition draws upon participatory notions of joint inquiry to help people in the ideation and conception of alternative futures; however, given the novelty and the rapidity of the technological transformations, there is an opportunity to engage people unfamiliar with technology and design in the ideation and conceptualisation of work-oriented improvements using these technologies.

This dissertation investigates what dimensions are necessary to support participatory processes for identifying and creating work-oriented improvements with technology and how design practitioners can apply these dimensions to stage these joint inquiry situations. To achieve these aims, explorations of and reflections on design are guided by a research-through-design approach that builds on three collaborative design cases that address real-world situations in a variety of contexts and participants: the joint inquiry processes of factory workers in the ideation, conceptualisation and prototyping of IoT work-oriented improvements; the joint inquiry process of ideation and conceptualisation of a digital twin in a manufacturing environment; and the joint inquiry processes of workers (predominantly from the healthcare sector) in ideating, conceptualising, and prototyping roles, skills, and products relying on IoT and AI technologies for their work futures. 

The research and design practice is guided by Deweyan pragmatism, underscoring the role and nature of materials (design methods, tools, and practices) in participatory design processes. Drawing from the three collaborative design cases and these theoretical notions, this thesis addresses two research questions: “What dimensions are needed to support participants in creating work-oriented improvements using technology?” and “How can these dimensions inform designers in staging joint inquiry situations of work-oriented improvements using technology?” The research methods consist of audio-recorded interviews, field notes, and collective reflective sessions to analyse the empirical material and video recordings. 

The main contributions are the identification of dimensions that underscore technology and work-oriented themes in joint inquiry and the framework of “thinking with and through technology”, which integrates these dimensions into a guided reflective and analytical design process. These contributions can help design and innovation practitioners and researchers prepare and stage materials, methods, and perspectives of joint inquiry situations concerning technology. The framework presents a “thinking with” perspective that underscores the material properties of technology and what the technology can offer to participants, and a “thinking through” perspective to contest the role of technology in organisations and open the design space to consider more sustainable and responsible futures. These results contribute to the collaborative design domain by developing knowledge and new nuances when staging joint inquiry situations of work-oriented improvements with technology. New understandings of these dimensions can contribute to an organisational landscape where workers can exercise their creativity, upskill their capacities, and voice their ideas and concerns concerning the technologies being integrated into their work environments.

Over the past three decades, automation in software development has gone mainstream. Software development teams strive to automate as much of the software development activities as possible, spanning requirements specification, system modeling, code generation, testing, deployment, verification, as well as release phases, project status reporting and system maintenance. Automation helps to reduce development time and cost, as well as to concentrate knowledge by bringing quality into every step of the development process. The Workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) provided a forum to share and discuss innovative contributions to research and practice related to novel software engineering approaches to automated and verifiable development of software systems. The 5th edition took place on September 11th, 2023, in Kirchberg, Luxembourg. Notably, this marked the inaugural co-location of ASYDE with the IEEE/ACM International Conference on Automated Software Engineering (ASE).

Radio links in wireless body area networks (WBANs) are highly subject to short and long-term attenuation due to the unstable network topology and frequent body blockage. This instability makes it challenging to achieve reliable and energy-efficient communication, but on the other hand, provides a great potential for the sending nodes to dynamically schedule the transmissions at the time with the best expected channel quality. Motivated by this, we propose improved Gilbert-Elliott Markov chain model (IGE), a memory-efficient Markov chain model to monitor channel fluctuations and provide a long-term channel prediction. We then design adaptive transmission power selection (ATPS), a deadline-constrained channel scheduling scheme that enables a sending node to buffer the packets when the channel is bad and schedule them to be transmitted when the channel is expected to be good within a deadline. ATPS can self-learn the pattern of channel changes without imposing a significant computation or memory overhead on the sending node. We evaluate the performance of ATPS through experiments using TelosB motes under different scenarios with different body postures and packet rates. We further compare ATPS with several state-of-the-art schemes, including the optimal scheduling policy, in which the optimal transmission time for each packet is calculated based on the collected received signal strength indicator (RSSI) samples in an off-line manner. The experimental results reveal that ATPS performs almost as efficiently as the optimal scheme in high-date-rate scenarios and has a similar trend on power level usage.

The traditional slow-start mechanism in TCP can result in slow ramping-up of the data delivery rate, inefficient bandwidth utilization, and prolonged completion time for small-size flows, especially in networks with a large bandwidth-delay product (BDP). Existing solutions either only work in specific situations, or require network assistance, making them challenging (if even possible) to deploy. This paper presents SUSS (Speeding Up Slow Start): a lightweight, sender-side add-on to the traditional slow-start mechanism, that aims to safely expedite the growth of the congestion window when a flow is significantly below its optimal fair share of the available bandwidth. SUSS achieves this by accelerating the growth in cwnd when exponential growth is predicted to continue in the next round. SUSS employs a novel combination of ACK clocking and packet pacing to effectively mitigate traffic burstiness caused by accelerated increases in cwnd. We have implemented SUSS in the Linux kernel, integrated into the CUBIC congestion control algorithm. Our real-world experiments span many device types and Internet locations, demonstrating that SUSS consistently outperforms traditional slow-start with no measured negative impacts. SUSS achieves over 20% improvement in flow completion time in all experiments with flow sizes less than 5MB and RTT larger than 50 ms.

Today, the construction sector accounts for one-fifth of Sweden's climate impact. The Swedish Parliament has decided that Sweden should have net zero greenhouse gas emissions by 2045. This is happening at a time when Sweden is constantly growing. In 2025, we will have 11 million inhabitants. That means hundreds of thousands of new homes, more infrastructure and more public facilities. It is therefore critical that the building and construction sector collectively adjust its approach to climate neutrality. To achieve this vision, it is essential that construction companies drive the work throughout their value chain with a holistic focus across all environmental areas, from design and production to demolition.

If Sweden is to achieve net zero greenhouse gas emissions by 2045, public procurement must take the lead and become fossil-free or climate-neutral within a few years Construction and real estate contracts, together with civil engineering and infrastructure, account for a significant proportion of public purchases in Sweden, and what is built today will affect nature, the environment and people for a long time to come. One way for Sweden to achieve these goals is to set more and higher requirements in public procurement. 

Public clients have a major influence through the choices they make in their investments and construction contracts to achieve the vision of climate neutrality. When a public works procurement is carried out, the contractor usually has limited possibilities to make decisions that have a significant impact on the CO2e emissions of the project. This means that the contractor is critically dependent on the climate requirements set by the clients in order for the company itself to achieve its climate targets. The overall aim of this study was thus to contribute to a better understanding of how public clients currently reason - and what can be done to bring about a positive shift - in order for public clients to set higher climate requirements in construction contracts and in the production of dwellings. 

This scientific study is conducted as a qualitative case study at Skanska Sweden, where an abductive research approach has been used for data collection. The study used a quantitative method in the form of a questionnaire survey where 16 municipal housing and real estate companies from the western geography of Sweden participated as respondents in the form of their respective Chief Executive Officer. 

The results of the study highlighted the opportunities that the Paris Agreement and other climate policy measures have brought to actors in the Swedish building and construction sector. Furthermore, the results show that each actor in the construction sector needs to work strategically to set their own climate targets and implement them throughout their operations, with production methods that enable the use of materials with low or net zero climate impact and increase their reuse in order to succeed in achieving Sweden's goal of achieving climate neutrality by 2045. The municipal housing and property companies that participated as respondents have shown a willingness to act sustainably, this is considered to be very helpful for the development of sustainable public procurement in Sweden, these are also consistent with what previous research has found. There is a need for collaboration between municipal housing and property companies and the construction sector where, by identifying the specific challenges faced by municipalities, it is recommended that they set higher climate and competence requirements in public procurement and allow these to permeate the entire value value chain with systematic monitoring for a successful transition.

Denna studie är ett examensarbete inom Informationsdesign inriktning Rumslig Gestaltning. Syftet med denna studie är att undersöka cyklisters navigationsupplevelse i blandtrafik och utveckla ett gestaltningsförslag för ett wayshowingsystem som förbättrar platsens visuella läsbarhet genom grafisk form. Studien fokuserar på att observera och inkludera förstagångsanvändare i designprocessen för att identifiera hur den visuella och rumsliga informationen kan kommuniceras effektivt till cyklister. Genom en kombination av metodresultat från användarna och teorier om kognition, wayshowing, mentala kartor samt visuell och rumslig kommunikation har ett förslag utvecklats som syftar till att informera och guida cyklister. Målet var att skapa en läsbar och informativ miljö som underlättar för cyklister men som även kom att informera chaufförer i linjetrafik om deras delade väg och väjningsplikt mot cyklister. En viktig upptäckt är att det finns lite tidigare forskning om cyklisters uppfattning av wayshowing i urbana miljöer, vilket visade sig vara en utmaning. Den användarinvolverade metoden, som inkluderade observationer och dokumentation av känslor, visade sig vara mycket värdefull för att styra arbetets framfart och säkerställa att designen möter användarnas behov.

The purpose of this literature study is to explore the most important ergonomic factors that play a role in shaping the work environment of assembly line systems. Since the subject of ergonomics in production systems has continuously gained great interest in the past years, it was decided to conduct research around this topic. The literature study type was chosen to answer the research questions. This was done because even though various articles discuss the mentioned areas, they usually still have a specific focus. For example, human errors, automation, simulation, virtual reality, etc. Therefore, it was decided to carry on with a more comprehensive review that takes into account the most important ergonomic factors in general and how they influence assembly workstations both positively and negatively. Thus, two research questions were explored:

Research question:  What are the most important ergonomic factors that influence an assembly line system’s work environment?

Sub-research question: What are the positive and negative effects and what causes them?

In order to proceed with the study, a systematic literature review and thematic analysis were conducted through the use of secondary data only. This was done by searching for different articles through two academic databases; ScienceDirect and Scopus. Lastly, the words that were used to search for articles were highly relevant in terms of the research questions.

In regards to the analysis and conclusion, different factors were found including automation and cobots, job rotation, the implementation of human factors, and repetitive manual tasks. The results showed that all these factors can affect an assembly line system’s work environment to a great extent, both positively and negatively. First of all, cobots contribute by helping human operators with difficult tasks, yet, the collaboration of humans and robots is viewed as risky to some extent. Furthermore, the level of the implementation of ergonomics at workplaces is crucial to provide a healthy work environment. Ultimately, repetitive tasks can have a great impact on workers, and thereby the whole work environment becomes affected. Therefore, convenient training sessions are highly important to ensure safety in such cases.

In this paper, we apply model-based testing (MBT) to automate the creation of hardware-in-loop (HIL) test cases. In order to select MBT tools, different tools' properties were compared to each other through a literature study, with the result of selecting GraphWalker and MoMuT tools to be used in an industrial case study. The results show that the generated test cases perform similarly to their manual counterparts regarding how the test cases achieved full requirements coverage. When comparing the effort needed for applying the methods, a comparable effort is required for creating the first iteration, while with every subsequent update, MBT will require less effort compared to the manual process. Both methods achieve 100% requirements coverage, and since manual tests are created and executed by humans, some requirements are favoured over others due to company demands, while MBT tests are generated randomly. In addition, a comparison between the used tools showcased the differences in the models' design and their test case generation. The comparison showed that GraphWalker has a more straightforward design method and is better suited for smaller systems, while MoMuT can handle more complex systems but has a more involved design method.

While most large web-based software systems (e.g., Amazon, Google) release a new software version every almost a minute, in the context of Cyber-Physical Systems (CPSs), this is still far. However, the software of CPSs needs to evolve while these are in operation to fix bugs, add new functionalities, carry out refactoring activities and deal with unforeseen situations that were discovered while the CPS was operating. In the last three years, the Adeptness project has been developing in a solution to help speedup the software release of CPSs that are in operation while guaranteeing their reliability. In this paper, we summarize the objectives, results and lessons learned from this H2020 project.

Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques’ evidence occurring in the period 2010–2021. This paper discusses testing data processing by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our findings show that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing challenges have been faced during the MapReduce validation phase. In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence testing) to find various functional faults through Big Data testing.

Research question:

What challenges exists in the internal supply chain of an ETO-producing company?

How can OTE, 5s Kanban, Ishikawa diagrams, and 5 Whys contribute to a more efficiently controlled internal supply chain in ETO-producing companies?

Aim: Describe how OTE, 5s, Kanban, Ishikawa diagrams, and 5 Whys can get implemented at one ETO-producing company to streamline the internal supply chain.

Method: This qualitative study had a deductive approach, and the theoretical framework developed from a literature study. The empirical material could get obtained by a case study.

Conclusion: The study demonstrated several conclusions concerning lean manufacturing and ETO-producing companies. One of the most important aspects for companies is that the flow of information must be systematic and standardised. The study has also shown that agile and lean manufacturing can work in symbiosis for ETO-producing companies. From a holistic perspective, the study has also shown that it is possible to implement five lean tools at an ETO-producing company.

This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a generative adversarial network (GAN) along with a neural architecture search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99.0% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by [Formula: see text] and [Formula: see text].

In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

MDU, in collaboration with several other universities, plans to join the World Robotic Sailing Championship (WRSC), where in certain sub-challenges some object detection is necessary. Such as for detecting objects such as boats, buoys, and possibly other items. Utilizing a camera system could significantly aid in these tasks, and in this research, an omnidirectional camera is proposed. This is a camera that offers a wide field of view of 360 degrees and could provide comprehensive information about the boat’s surroundings. However, these images use a spherical camera model, which projects the image on a sphere and, when saved to a 2D format, becomes very distorted. To be able to use state-of-the-art vision algorithms for object detection and tracking, this research proposes to project these images to other formats. As such, four systems using object detection and tracking are made that uses different image representation projected from the spherical images. One system uses spherical images and is used as a baseline, while the three remaining systems use some form of projection. The first is cubemap projection, which projects the spherical image to a cube and unfolds this image on a 2D plane. The two other image representations used perspective projections, which are when the spherical image is projected to small sub-images. The two image representations that used perspective projections had 4 and 8 perspective images. None of the systems ultimately performed very well but did have some advantages and disadvantages.

Med hjälp av den digitala tekniken i vår vardag kan vi kommunicera, betala och använda oss av sociala medier som är en stor del av samhället. Personer med finmotoriska svårigheter upplever att det finns ett fysiskt hinder att använda sig av den digitala tekniken och lever därför i digitalt utanförskap. Vi har utvecklat en prototyp till ett hjälpmedel som känner av en användares armrörelser och används för att navigera på en webbplats. Syftet med vårt arbete är att utvärdera hur personer med finmotoriska svårigheter upplever navigationen på en webbplats med hjälp av en extern enhet som känner av personens egna armrörelser. För att uppnå syftet har vi ställt oss frågeställningarna: (1) Hur kan ett tydligt och lätthanterligt användargränssnitt som använder sig av en extern smartklockas inbyggda sensorer utvecklas för att hjälpa personer med finmotoriska svårigheter att navigera på en webbplats? och (2) Hur upplever personer med finmotoriska svårigheter navigation på en webbplats när de använder en smartklocka för att navigera? Vi har skapat en prototyp och utvärderat den med personer ur målgruppen genom intervjuer och observationer. Intervjuerna och observationerna utvärderades genom tematisk analys som gav oss tre huvudteman. Den tematiska analysen har lett fram till våra slutsatser vilket är att det finns ett behov av ett externt hjälpmedel och att hjälpmedlet bör samtidigt ha möjlighet att anpassas för olika individers behov och förmågor. Vidare kan vi säga att målgruppen uppskattar att använda en extern enhet och armrörelser för att navigera på en webbplats.

Manufacturing industries have adopted globalization as a strategy to improve the reach of their products/services, bringing them closer to the customer and utilizing the cost advantage in emerging economies, better availability of resources such as raw materials, and cheaper workforce. However, with globalization came a fair share of obstacles in effectively managing the operations of a complex supply chain network spread across the globe. Failure of manufacturing firms in controlling their operations has led to a generation of wastes such as underutilization of resources, inefficient by-product disposal systems, and increased emissions from poorly managed logistics networks. Research on understanding the environmental impact of ineffectively managed supply chain networks shows that industrial operations contribute up to 20% of global carbon dioxide emissions creating a serious impact on the environment. 

Realizing these adverse effects, many countries across the globe have taken steps to regulate and monitor the operations and by-products produced by manufacturing firms. Multiple countries and local governments have come together to become environmentally more responsible through encouraging sustainability initiatives. They have laid more stringent regulations to pressure the manufacturing firms to transform into more sustainable and reduce their carbon footprint. Moreover, the manufacturing firms are also experiencing pressure from local governments and private NGOs. With increasing awareness of sustainability, customers have also slowly started transforming towards sustainable products and boycotting products from firms that do not comply with sustainability standards.

Consequently, manufacturing firms have realized the need to become more sustainable in order to adapt to the changing regulations and customer needs. To achieve the level of sustainability in their supply chain operations, manufacturing firms have adopted innovative strategies and made changes in their policies making sustainability one of their prime goals. To achieve the desired level of sustainability, firms need to transform their current ways by incorporating a sustainability perspective into their operations. However, integrating sustainability at the operations level is not as easy as said due to existing governance gaps which are a result of ineffective control and management in global supply chain networks. 

This thesis aims at identifying, understanding, and analyzing the gaps in integrating sustainability into supply chains and providing suggestions on how to fulfill these gaps. Through this research three innovative sustainability strategies which can be applied at operational, organizational, and system levels are analyzed, and conclusions are drawn on how successful implementation of these strategies would help in fulfilling the governance gaps. Further, the research also analyses the plausible effects of implementing the strategies by understanding and comparing the firm performance before and after implementation. The firm performance is measured in three categories: environmental performance, operational performance, and financial performance.

During the last three decades, automation in software development has gone mainstream. Software development teams strive to automate as much of the software development activ-ities as possible, spanning requirements specification, system modeling, code generation, testing, deployment, verification, as well as release phases, project status reporting and system maintenance. Automation helps to reduce development time and cost, as well as to concentrate know ledge by bringing quality into every step of the development process. 

The 6th edition of the workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) provided a forum to share and discuss innovative contributions to research and practice related to novel software engineering approaches to automated and verifiable development of software systems.

Outcome-Based Education (OBE) is widely recognized for its goal-oriented approach in education. The key elements of outcome-based education are learning outcomes which are measured at student, course, program, and institution levels. The evaluation of learning outcomes is usually performed manually in isolation which leads to several issues like assessment delays and impaired judgement. Furthermore, integration of OBE techniques with existing Learning Management System (LMS) becomes impracticable. To handle such issues, in this article, we propose a framework comprising Metamodel for streamlining evaluation processes of OBE. Moreover, a set of text-to-model transformations is implemented for the automatic gen-eration of high-level models from traditional documents containing information about students, courses, grades etc. Furthermore, the model-to-text transformations are implemented to generate the target model in JAVA for the assessment of learning outcomes. This facilitates OBE evaluation straightforwardly. The efficacy of the framework is validated through a case study conducted within the Department of Software Engineering at University of AJK. The results are encouraging, and OBE assessment is successfully performed for software engineering courses.

In most systems-of-systems (SoS) and ecosystems, peer-to-peer relations are insufficient to provide the desired emergent behavior, but the support of mediators is necessary. Mediators are elements that facilitate the collaboration between the constituents of the SoS or ecosystem, without having a role outside it. The topic has previously been studied in the SoS field, mainly from a software engineering perspective, and separately also in research on software, business, and innovation ecosystems. This paper presents a systematic literature review on mediators across scholarly work in SoS as well as ecosystems. It identifies mediator functionality and implementation concerns, and discusses alternative terminology. Based on findings from the literature, a suggestion for an improved conceptualization is presented, which also includes the information, processing and interactions involved in the mediation.

Design patterns are an established approach for reusing knowledge about good solutions to recurring problems. Patterns can be seen as a way of describing the best practices, and have been used in many different fields, ranging from building architecture and city planning to software development. There are also scattered results relating to patterns for systems-of-systems. The purpose of this paper is to summarize and review the literature on patterns for systems-of-systems and make a synthesis of a recommended approach for the field. Specifically, the novel contributions of the paper are to propose a consolidated structure for describing individual patterns and suggest the dimensions along which a pattern catalog can be organized. The paper also summarizes the concrete patterns suggested in the existing literature and classifies them according to the recommended structure.

The concept of emergence refers to phenomena that occur on a system level without being present at the level of elements in the system. Since a system is created to achieve certain emergent system-level behavior, while avoiding other emergent properties, a deeper understanding of emergence is crucial to further the field of systems engineering. It has also been identified as one of the key aspects of systems-of-systems. However, the concept has been the topic of much debate in philosophy, systems science, and complexity science for a long time, and there is yet no precise characterization on which there is general agreement. In this paper, a selection of the literature on emergence is reviewed to identify some key characteristics and disputes. The various philosophical points of view are analyzed from the perspective of systems engineering, to sort out what characteristics have practical implications, and which philosophical quiddities are merely of theoretical interest. The paper also relates emergence to systems engineering practices and suggests some tactics for dealing with emergence. Key results are that the inclusion of an explicit observer is essential for understanding and handling emergence, and that emergence is closely related to the amount of information required to describe the system which is also a defining characteristic of complexity.

This paper describes the Architecture Life Cycle Effect Analysis (ALCEA) method, a structured method for evaluating proposed new architectures for software-intensive systems. The method evaluates a proposed architecture by quantifying its effect on the performance of system life-cycle phases. The method is instantiated by identifying (i) the relevant life-cycle phases of the system under investigation and (ii) a set of evaluation functions that capture, in terms of basic factors, the effect of different architectural decisions on key life-cycle performance attributes, such as revenue, operating resources, and investments. The method results in a transparent cost and revenue structure, documented in a tabular form, based on quantifiable factors from the developing organization. The results of the method can be used directly as part of a business case, and their robustness can be estimated by sensitivity analysis. The ALCEA method is designed for system-level architectural analysis, covering both software and hardware aspects. In this paper, we introduce the ALCEA method and provide a detailed example of how to apply it in the evolution of embedded systems. Moreover, we share early experiences of using the method in large-scale industrial settings.

In a system-of-systems, independent constituent systems collaborate to achieve broader capabilities they cannot provide on their own. This paper investigates the nature of the constituent system capabilities beyond basic operational actions, to achieve a deeper understanding of what is required to participate in a system-of-systems. Through a case study of industrial ecosystems, the need is shown for planning how to use basic operational capabilities, for dynamic capabilities to achieve long-term evolution, and for resilience capabilities to deal with perturbations. This also affects the governance of the system. The findings are used to extend an existing conceptual model of constituent systems and to characterize collaboration in a system-of-systems that implements a value network. 

Systems-of-systems are designed to provide a capability that their constituent systems cannot achieve individually. A key property is that the constituent systems have some degree of operational and managerial independence. The concepts of capability and independence are thus central to the field of systems-of-systems. Yet the contemporary literature and standards only give vague definitions of these terms. This vagueness is a barrier to progress in the field, and this paper aims at contributing with a more detailed conceptualization. It describes a system capability as a state-transforming process that uses certain resources. Independence means that the system has a choice about when and how its capabilities should be activated. This requires that the system is an intelligent agent with a notion of utility, a perception of the world around it, and a decision-making capability. When given a mission, the system can complete that mission by activating appropriate combinations of capabilities. A system-of-systems can decompose its mission into parts that correspond to the capabilities of various constituent systems.