Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.

The continued development of robots has enabled their wider usage in human surroundings. Robots are more trusted to make increasingly important decisions with potentially critical outcomes. Therefore, it is essential to consider the ethical principles under which robots operate. In this paper we examine how contrastive and non-contrastive explanations can be used in understanding the ethics of robot action plans. We build upon an existing ethical framework to allow users to make suggestions about plans and receive automatically generated contrastive explanations. Results of a user study indicate that the generated explanations help humans to understand the ethical principles that underlie a robot’s plan.

Energy transition from predominantly fossil fuel driven to renewables driven system is one of the key problems human civilization is facing. Solutions for this problem come in many forms and shapes, but most of them are often one sided and limited in perspective for such a complex problem. Modelling is of great importance and provides an opportunity to better understand the problem, still it also often provides just a view of some aspects of the issue. This literature review attempts to highlight these points and provide some perspective conclusions for future effort while focusing its scope on the case of Bosnia and Herzegovina and Western Balkans, where such lack of efforts is particularly pronounced.

We aimed to assess the association of diabetes mellitus (DM) and admission hyperglycaemia (AH), respectively, and outcome in patients with acute ischaemic stroke with large vessel occlusion in the anterior circulation treated with endovascular therapy (EVT) in daily clinical practice.

Introduction Resource-oriented interventions can be a low-cost option to improve care for patients with severe mental illnesses in low-resource settings. From 2018 to 2021 we conducted three randomized controlled trials testing resource-oriented interventions in Bosnia and Herzegovina (B&H), i.e. befriending through volunteers, multi-family groups, and improving patient-clinician meetings using the DIALOG+ intervention. All interventions were applied over 6 months and showed significant benefits for patients’ quality of life, social functioning, and symptom levels. In this study, we explore whether patient experiences point to common processes in these interventions. Methods In-depth semi-structured interviews were conducted with 15 patients from each intervention, resulting in a total sample of 45 patients. Patients were purposively selected at the end of the interventions including patients with different levels of engagement and different outcomes. Interviews explored the experiences of patients and were audio-recorded, transcribed, and analysed using the thematic analysis framework proposed by Braun and Clark. Results Three broad themes captured the overall experiences of patients receiving resource-oriented interventions: An increased confidence and agency in the treatment process; A new and unexpected experience in treatment; Concerns about the sustainability of the interventions. Conclusions The findings suggest that the three interventions – although focusing on different relationships of the patients – lead to similar beneficial experiences. In addition to being novel in the context of the mental health care system in B&H, they empower patients to take a more active and confident role in treatment. Whilst strengthening patients’ agency in their treatment may be seen as a value in itself, it may also help to achieve significantly improved treatment outcomes. This shows promise for the implementation of these interventions in other low-resource countries with similar settings.

The ability to robustly quantify the potential for strontium precipitation and scaling in both natural surface waters and water infrastructure systems is limited. In some regions, both surface and ground water supplies contain significant concentrations of naturally occurring radionuclides, such as strontium, that can accumulate in water, soils and sediments, media, and living tissues. Methods for quantifying and predicting the potential for these occurrences are not readily available nor have they been tested and calibrated to cold region aquatic environments. Through extensive literature review, it was determined that a modified calcium carbonate precipitation potential (CCPP) model offered a scientifically credible approach to filling that knowledge gap in both the science and engineering of strontium fate and transport in water. The results from previous field and laboratory experiments were compiled to not only elucidate the fate and transport of strontium in water systems, but also to calculate the logarithmic distribution coefficient, λ, for strontium under co-precipitation conditions. Lambda (λ) is both time- and water-quality sensitive and must be measured as water mixes from source to receiving environment to determine continuous loss of Sr from the water phase. The data were collected to develop the strontium precipitation potential model that can be used in surface water quality assessment. The tool was then applied to pre-existing, publicly available, and extensive datasets for several rivers in Saskatchewan, Canada, to validate the model and produce estimates for strontium precipitation potential in those rivers.

ABSTRACT A working memory (WM) deficit is a reliable observation in people experiencing anxiety. Whether the level of anxiety is related to the severity of WM difficulties is still an open question. In the present experiment, we investigated this aspect by testing the WM performance of people with different levels of anxiety symptoms. Participants were grouped according to self-report anxiety into a control group with low anxiety scores and an experimental group with clinically relevant anxiety. The experimental group was then divided into a high anxiety group and a severe anxiety group. Participants performed a battery of WM tasks tagging different WM processes. The results showed that, compared to participants with low anxiety, participants with clinically relevant anxiety scores had reduced accuracy in all the WM tasks. Interestingly, participants with high and severe anxiety did not present any significant difference. Anxious participants showed difficulties also in cognitive domains other than WM. Hence, these results supply reliable evidence that people with clinically relevant anxiety scores present WM difficulties, irrespective of symptoms severity. The observation that anxiety compromises performance also in cognitive domains other than WM suggests that the deficit might affect fluid cognition.

ABSTRACT In this study, 3D-printed connectors to replace the typical L-shaped joints in the construction of a chair were developed, tested and numerically analysed. Different connectors were designed and manufactured with a fused deposition modelling (FDM) 3D printer using acrylonitrile butadiene styrene (ABS) with the aim to find a simple shaped connector which could be used to build chairs and withstand standard chair loading requirements. The strength and stiffness of the joints were tested and compared with traditional beech mortise-and-tenon joints. Numerical stress and strain analyses were performed with the finite element method for an orthotropic linear-elastic model. The experimental results showed that joints with 3D-printed connectors achieved lower strength than the traditional wooden mortise-and-tenon joints with similar dimensions. The results indicate that the effect of reinforcement of the connector were not recognised due to the small thickness and inadequate geometric position and arrangement of the reinforcement ABS material. The chair assembled with 3D-printed connectors could withstand the loads for seating, but failed the backrest test according to standard EN 1728:2002. The connectors need to be optimised and reinforced to withstand standard loads.

Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS.

The application of spectral analysis methods to the heart rate (HR) signal is challenging due to the nature of the signal itself, which is non-uniform. Methods for non-uniform signals can be applied directly, whilst the methods designed for uniform signals can be used after the signal is adequately preprocessed beforehand. Preprocessing consists of interpolation and resampling. In this paper, we have implemented a tool for explorative evaluation of various spectral analysis methods applied to HR signal. The tool is based on heat maps used for visualization of frequency metrics for the ECG signals selected from the MIT-BIH Arrhythmia Database. Evaluated methods are the Lomb-Scargle method for nonuniform signal analysis and Welch's method which is applied in conjunction with different interpolation approaches. A set of frequency-domain metrics are evaluated with the proposed tool for exploratory analysis. The evaluation indicates that the Lomb-Scargle method produces a loss of information in certain frequency bands. Furthermore, Welch method better demonstrates the difference in spectral power metrics for frequency bands of interest, irrespective of the type of interpolation used.

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus. Diagnosis of the disease itself is difficult because the incubation period is long, often the disease is initially without some characteristic symptoms, but also due to a lack of laboratory methods. Artificial intelligence is increasingly being used nowadays to make it easier and faster to assess the illness. As hepatitis C is a rising healthcare burden it is of utmost importance to construct effective and reliable screening methods. As AI has already proven useful for diagnosis of a variety of conditions based on clinical parameters, this study focuses on the application of artificial neural network (ANN) for hepatitis C diagnosis. In this study, a database of 1000 respondents divided into two groups was used to develop the ANN: healthy (n = 200) and sick (n = 800). Monitoring parameters were: albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, acetylcholinesterase and anti-HCV antibodies. The overall accuracy of the developed ANN was 97,78%, which indicates that the potential of artificial intelligence in diagnosing hepatitis C is enormous, and in the future, attention should be paid to the development of new systems with as much data as possible.

Background: Rising expenditure for new cancer medicines is accelerating concerns that their costs will become unsustainable for universal healthcare access. Moreover, early market access of new oncology medicines lacking appropriate clinical evaluation generates uncertainty over their cost-effectiveness and increases expenditure for unknown health gain. Patient-level data can complement clinical trials and generate better evidence on the effectiveness, safety and outcomes of these new medicines in routine care. This can support policy decisions including funding. Consequently, there is a need for improving datasets for establishing real-world outcomes of newly launched oncology medicines. Aim: To outline the types of available datasets for collecting patient-level data for oncology among different European countries. Additionally, to highlight concerns regarding the use and availability of such data from a health authority perspective as well as possibilities for cross-national collaboration to improve data collection and inform decision-making. Methods: A mixed methods approach was undertaken through a cross-sectional questionnaire followed-up by a focus group discussion. Participants were selected by purposive sampling to represent stakeholders across different European countries and healthcare settings. Descriptive statistics were used to analyze quantifiable questions, whilst content analysis was employed for open-ended questions. Results: 25 respondents across 18 European countries provided their insights on the types of datasets collecting oncology data, including hospital records, cancer, prescription and medicine registers. The most available is expenditure data whilst data concerning effectiveness, safety and outcomes is less available, and there are concerns with data validity. A major constraint to data collection is the lack of comprehensive registries and limited data on effectiveness, safety and outcomes of new medicines. Data ownership limits data accessibility as well as possibilities for linkage, and data collection is time-consuming, necessitating dedicated staff and better systems to facilitate the process. Cross-national collaboration is challenging but the engagement of multiple stakeholders is a key step to reach common goals through research. Conclusion: This study acts as a starting point for future research on patient-level databases for oncology across Europe. Future recommendations will require continued engagement in research, building on current initiatives and involving multiple stakeholders to establish guidelines and commitments for transparency and data sharing.