# Blekinge Institute of Technology

• Karlskrona, Blekinge, Sweden
Recent publications
Swedish municipalities are obliged to formulate housing provision policies in housing programs, as part of municipal strategic planning. This article explores how municipalities interpret this responsibility. We analyze housing provision programs by drawing from prospective responsibility and policy analysis. Our analysis shows three different prospective responsibilities in the municipality’s production of housing provision responsibility. The results show that municipalities take actions by different means, leading to ambiguities and inequalities in housing provision planning.
Non-robust (fragile) test execution is a commonly reported challenge in GUI-based test automation, despite much research and several proposed solutions. A test script needs to be resilient to (minor) changes in the tested application but, at the same time, fail when detecting potential issues that require investigation. Test script fragility is a multi-faceted problem. However, one crucial challenge is how to reliably identify and locate the correct target web elements when the website evolves between releases or otherwise fail and report an issue. This paper proposes and evaluates a novel approach called similarity-based web element localization (Similo), which leverages information from multiple web element locator parameters to identify a target element using a weighted similarity score. This experimental study compares Similo to a baseline approach for web element localization. To get an extensive empirical basis, we target 48 of the most popular websites on the Internet in our evaluation. Robustness is considered by counting the number of web elements found in a recent website version compared to how many of these existed in an older version. Results of the experiment show that Similo outperforms the baseline; it failed to locate the correct target web element in 91 out of 801 considered cases (i.e., 11%) compared to 214 failed cases (i.e., 27%) for the baseline approach. The time efficiency of Similo was also considered, where the average time to locate a web element was determined to be four milliseconds. However, since the cost of web interactions (e.g., a click) is typically on the order of hundreds of milliseconds, the additional computational demands of Similo can be considered negligible. This study presents evidence that quantifying the similarity between multiple attributes of web elements when trying to locate them, as in our proposed Similo approach, is beneficial. With acceptable efficiency, Similo gives significantly higher effectiveness (i.e., robustness) than the baseline web element localization approach.
Over the past 16 years, the concept of crowdsourcing has rapidly gained traction across many research fields. While related debates focused mainly on its importance for business, the public and non-governmental sectors, its relevance for generating scientific knowledge is increasingly emphasized. This rising interest remains in contradiction with its feeble recognition, and excessive simplifications reducing crowdsourcing in science to citizen science. Conceptual clarity and a coherent framework would help integrate the various research streams. The aim of this paper is to extend reflection on crowdsourcing in science by analyzing the characteristics of the phenomenon. We synthesize a consensual definition from the literature, and structure key characteristics into a coherent framework, useful in guiding further research. We use a systematic literature review procedure to generate a pool of 42 definitions from a comprehensive set of 62 articles spanning different literatures, including: business and economics, education, psychology, biology, and communication studies. We follow a mixed-method approach that combines bibliometric and frequency analyses with deductive coding and thematic analysis. Based on triangulated results we develop an integrative definition: crowdsourcing in science is a collaborative online process through which scientists involve a group of self-selected individuals of varying, diverse knowledge and skills, via an open call to the Internet and/or online platforms, to undertake a specified research task or set of tasks. We also provide a conceptual framework that covers four key characteristics: initiator, crowd, process, and technology.
Aim To describe the experiences of dignity encounters from the perspective of people with long-term illness and their close relatives within a primary healthcare setting. Background The importance of dignity as a concept in nursing care is well known, and in every healthcare encounter, the patient’s dignity has to be protected. Methods A purposive sample of 10 people (5 couples) participated in this qualitative descripted study. One person in each of the couples had a long-term illness. Conjoint interviews were conducted and analyzed with an inductive qualitative content analysis. Results The analysis resulted in three themes: i) Being supported by an encouraging contact ; ii) Being listen to and understood ; and iii) Being met with respect . Couples described being encountered with dignity as having accessibility to care in terms of being welcomed with their needs and receiving help. Accessibility promoted beneficial contact with healthcare personnel, who empowered the couples with guidance and support. Couples described a dignity encounter when healthcare personnel confirmed them as valuable and important persons. A dignity encounter was promoted their sense of feeling satisfied with the care they received and promoted safe care. Treated with dignity had a positive impact on the couples’ health and well-being and enhanced their sense of a good impression of the healthcare personnel within the primary health care. Conclusions Healthcare personnel must regard and consider people with long-term illnesses and their close relatives’ experiences of dignity encounters to gain an understanding that enables them to support their needs and to know that the care is directed toward them.
Fibromyalgia (FM) patients have dysfunctional endogenous pain modulation, where opioid and serotonergic signaling is implicated. The aim of this study was to investigate whether genetic variants in the genes coding for major structures in the opioid and serotonergic systems can affect pain modulation in FM patients and healthy controls (HC). Conditioned pain modulation (CPM), evaluating the effects of ischemic pain on pressure pain sensitivity, was performed in 82 FM patients and 43 HC. All subjects were genotyped for relevant functional polymorphisms in the genes coding for the μ-opioid receptor (OPRM1, rs1799971), the serotonin transporter (5-HTT, 5-HTTLPR/rs25531) and the serotonin 1a receptor (5-HT1a, rs6295). Results showed the OPRM1 G-allele was associated with decreased CPM. A significant gene-to-gene interaction was found between the OPRM1 and the 5-HT1a gene. Reduced CPM scores were seen particularly in individuals with the OPRM1 G*/5-HT1a CC genotype, indicating that the 5-HT1a CC genotype seems to have an inhibiting effect on CPM if an individual has the OPRM1 G-genotype. Thus, regardless of pain phenotype, the OPRM1 G-allele independently as well as with an interaction with the 5-HT1a gene influenced pain modulation. FM patients had lower CPM than HC but no group differences were found regarding the genetic effects on CPM, indicating that the results reflect more general mechanisms influencing pain modulatory processes rather than underlying the dysfunction of CPM in FM. In conclusion, a genetic variant known to alter the expression of, and binding to, the my-opioid receptor reduced a subject's ability to activate descending pain inhibition. Also, the results suggest a genetically inferred gene-to-gene interaction between the main opioid receptor and a serotonergic structure essential for 5-HT transmission to modulate pain inhibition. The results in this study highlight the importance of studying joint synergistic and antagonistic effects of neurotransmittor systems in regard to pain modulation.
Aim: The aim of the study was to deepen the current knowledge of how patients with chronic obstructive pulmonary disease and long-term oxygen treatment think about and expect end-of-life. Design: A qualitative design was used. Methods: A purposeful sample of 19 patients with oxygen-dependent chronic obstructive pulmonary disease was obtained from the Swedish National Registry on Respiratory Failure (Swedevox). Data was collected with semi-structured interviews and analysed using a hermeneutic approach. Results: The analysis revealed three themes: Living in the present without a future; difficulty talking about the uncertainty; and feeling anxious about leaving family behind. Participants indicated that healthcare professionals should invite them to mutual discussions as it was easier to reject an invitation if they could not talk right then, than to initiate a discussion themselves. Start of home oxygen or a deteriorating health status may be an important time to clinically address existential and end-of-life issues.
Contribution: In this study, we accumulated the knowledge and generated evidence on how and in what context CDIO framework has been used in software engineering (SE) education. The aggregated evidence will enable SE academics in making informed decisions while adopting CDIO for SE education and build upon it. Background: CDIO framework is relevant for SE as it focuses on enabling engineering graduates in conceiving, designing, implementing, and operating complex systems and products. We were not able to find any study that identifies and aggregates the evidence on the use of CDIO for SE education. Research Questions: This study attempts to answer the following research questions: 1) how CDIO has been used in SE education? and 2) what are the experiences of academics in applying the CDIO framework in SE education? Methodology: Using a mixed-method approach (systematic mapping study and interview study with experienced academics in SE), we established the state of the art and practice on the use of CDIO in SE education. Findings: Getting a commitment from the higher management, teachers, and students is a major challenge in the adoption of the CDIO initiative followed by a lack of competence, finance, and resources. Ownership, motivation, persistence, and training of teachers and students are required not only to adopt CDIO for SE but also to sustain it.
Context Code smells are patterns in source code associated with an increased defect rate and a higher maintenance effort than usual, but without a clear definition. Code smells are often detected using rules hard-coded in detection tools. Such rules are often set arbitrarily or derived from data sets tagged by reviewers without the necessary industrial know-how. Conclusions from studying such data sets may be unreliable or even harmful, since algorithms may achieve higher values of performance metrics on them than on models tagged by experts, despite not being industrially useful. Objective Our goal is to investigate the performance of various machine learning algorithms for automated code smell detection trained on code smell data set(MLCQ) derived from actively developed and industry-relevant projects and reviews performed by experienced software developers. Method We assign the severity of the smell to the code sample according to a consensus between the severities assigned by the reviewers, use the Matthews Correlation Coefficient (MCC) as our main performance metric to account for the entire confusion matrix, and compare the median value to account for non-normal distributions of performance. We compare 6720 models built using eight machine learning techniques. The entire process is automated and reproducible. Results Performance of compared techniques depends heavily on analyzed smell. The median value of our performance metric for the best algorithm was 0.81 for Long Method, 0.31 for Feature Envy, 0.51 for Blob, and 0.57 for Data Class. Conclusions Random Forest and Flexible Discriminant Analysis performed the best overall, but in most cases the performance difference between them and the median algorithm was no more than 10% of the latter. The performance results were stable over multiple iterations. Although the F-score omits one quadrant of the confusion matrix (and thus may differ from MCC), in code smell detection, the actual differences are minimal.
In this article, we give a complete characterization of semigroup graded rings which are graded von Neumann regular. We also demonstrate our results by applying them to several classes of examples, including matrix rings and groupoid graded rings.
In early 2020, the Covid-19 pandemic forced employees in tech companies worldwide to abruptly transition from working in offices to working from their homes. During two years of predominantly working from home, employees and managers alike formed expectations about what post-pandemic working life should look like. Many companies are experimenting with new work policies that balance employee- and manager expectations regarding where, when and how work should be done in the future. In this article, we gather experiences of the new trend of remote working based on the synthesis of 22 company-internal surveys of employee preferences for WFH, and 26 post-pandemic work policies from 17 companies and their sites, covering 12 countries in total. Our results are threefold. First, through the new work policies, all companies formally give employees more flexibility regarding working time and location. Second, there is a great variation in how much flexibility the companies are willing to yield to the employees. The paper details the different formulations that companies adopted to document the extent of permitted WFH, exceptions, relocation permits and the authorization procedures. Third, we document a change in the psychological contract between employees and managers, where the option of working from home is converted from an exclusive perk that managers could choose to give to the few, to a core privilege that all employees feel they are entitled to. Finally, there are indications that as the companies learn and solicit feedback regarding the efficiency of the chosen strategies, we will see further developments and changes in the work policies concerning how much flexibility to work whenever and from wherever they grant. Through these findings, the paper contributes to a growing literature about the new trends emerging from the pandemic in tech companies and spells out practical implications onwards.
Objectives To investigate differences in antibiotic prescription for patients with hard-to-heal ulcers assessed using a digital decision support system (DDSS) compared with those assessed without using a DDSS. A further aim was to examine predictors for antibiotic prescription. Design Register-based study. Setting In 2018–2019, healthcare staff in primary, community and specialist care in Sweden tested a DDSS that offers a mobile application for data and photograph transfer to a platform for multidisciplinary consultation and automatic transmission of data to the Registry of Ulcer Treatment (RUT). Register-based data from patients assessed and diagnosed using the DDSS combined with the RUT was compared with register-based data from patients whose assessments were merely registered in the RUT. Participants A total of 117 patients assessed using the DDSS combined with the RUT (the study group) were compared with 1784 patients whose assessments were registered in the RUT without using the DDSS (the control group). Primary and secondary outcome measures The differences in antibiotic prescription were analysed using the Pearson’s χ2 test. A logistic regression analysis was used to check for influencing factors on antibiotic prescription. Results Patients assessed using a DDSS in combination with the RUT had significantly lower antibiotic prescription than patients entered in the RUT without using the DDSS (8% vs 26%) (p=0.002) (only healed ulcers included). Predictors for antibiotic prescription were diabetes; long healing time; having an arterial, neuropathic or malignant ulcer. Conclusions A DDSS with data and photograph transfer that enables multidisciplinary communication appears to be a suitable tool to reduce antibiotic prescription for patients with hard-to-heal ulcers.
Let N and H be groups, and let G be an extension of H by N. In this article, we describe the structure of the complex group ring of G in terms of data associated with N and H. In particular, we present conditions on the building blocks N and H guaranteeing that G satisfies the zero-divisor and idempotent conjectures. Moreover, for central extensions involving amenable groups we present conditions on the building blocks guaranteeing that the Kadison–Kaplansky conjecture holds for the group C∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^*$$\end{document}-algebra of G.
Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions.
Alumni engagement plays a crucial role in driving innovation in university-based entrepreneurship ecosystems. We employ an inductive, informant-centric research design to explore the pro- cessual dynamics surrounding the early alumni engagement of entrepreneurship graduates and how these translate into enter- prising behaviors that foster technology transfer and knowl- edge-intensive entrepreneurship. Our inductive analysis advances the theoretical understanding of the beginning phases of the alumni engagement process among entrepre- neurship graduates, the key drivers that make them gravitate toward different forms of alumni engagement, and the role and impact of their engagement in the surrounding ecosystem.
There is broad consensus among policymakers about the urgency of developing healthy, inclusive, and socially sustainable cities. In the Swedish context, social services are considered to have knowledge that needs to be integrated into the broader urban development processes in order to accomplish such ends. This article aims to better understand the ways in which social service officials collaborate in urban development processes for developing the social dimensions of healthy cities. We draw from neo-institutional theories, which set out actors (e.g., social service officials) as acting according to a logic of appropriateness , which means that actors do what they see as appropriate for themselves in a specific type of situation. Based on semi-structured interviews with social services officials in 10 Swedish municipalities on their experiences of collaboration in the development of housing and living environments for people with psychiatric disabilities, we identified that they act based on (a) a pragmatic rule of conduct through the role of the problem solver, (b) a bureaucratic rule of conduct through the role of the knowledge provider, and (c) activist rule of conduct through the role of the advocator. In these roles, they have little authority in the development processes, and are unable to set the agenda for the social dimensions of healthy cities but act as the moral consciousness by looking out for everyone’s right to equal living conditions in urban development.
Millimeter-wave (mm-wave) frequency modulated continuous wave (FMCW) radars are increasingly being deployed for scenario perception in various applications. It is expected that the mutual interference between such radars will soon become a significant problem. Therefore, to maintain the reliability of the radar measurements, there must be procedures in place to mitigate this interference. This paper proposes a novel interference mitigation technique that utilizes the pulse compression principle for interference compression and mitigation. The interference in the received time-domain signal is compressed using an estimated matched filter. Afterwards, the compressed interference is discarded, and the signal is repaired in the pulse-compressed domain using an autoregressive (AR) model. Since the interference spans fewer samples after compression, the signal can be restored more accurately in the compressed domain. Real outdoor measurements show that the interference is effectively suppressed down to the noise floor using the proposed scheme. A signal to interference and noise ratio (SINR) gain of approximately 14 dB was achieved in the experimental data supporting this study. Moreover, the results indicate that this method is also applicable to situations where multiple interference sources are present.
Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.
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