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.

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.

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.

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.

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.

Various devices and monitoring systems have been developed and deployed in order to monitor the power grid. Indeed, several real-world cyberattacks on power grid systems have been publicly reported. For the transmission and distribution, Phasor Measurement Units (PMUs) constitute the main sensing equipment of the overall wide area monitoring and situational awareness systems by collecting high-resolution data and sending them to Phasor Data Concentrators (PDCs). In this paper, we consider data spoofing attacks against PMU networks. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. We consider one potential attack, where an adversary may simply keep injecting a repeated measurement through a compromised PMU to disrupt the monitoring system. This attack is referred to as a Repeated Last Value (RLV) attack. We develop and evaluate countermeasures against RLV attacks using a 2D Convolutional Neural Network (CNN)-based approach, which operates in frames for each second mimicking images, in order to avoid the computational overhead of the classical sample-based classification algorithms, such as SVM. Further, we take this frame-based approach and use it with Support Vector Machine (SVM) for performance evaluation. Our preliminary results show that frame-based CNN as well as SVM provide promising results for RLV attacks while the efficacy of CNN over SVM frame becomes more pronounced as the attack intensity increases.

Social media is an important source of real-world data for sentiment analysis. Hate speech detection models can be trained on data from Twitter and then utilized for content filtering and removal of posts which contain hate speech. This work proposes a new algorithm for calculating user hate speech index based on user post history. Three available datasets were merged for the purpose of acquiring Twitter posts which contained hate speech. Text preprocessing and tokenization was performed, as well as outlier removal and class balancing. The proposed algorithm was used for determining hate speech index of users who posted tweets from the dataset. The preprocessed dataset was used for training and testing multiple machine learning models: k-means clustering without and with principal component analysis, naïve Bayes, decision tree and random forest. Four different feature subsets of the dataset were used for model training and testing. Anomaly detection, data transformation and parameter tuning were used in an attempt to improve classification accuracy. The highest F1 measure was achieved by training the model using a combination of user hate speech index and other user features. The results show that the usage of user hate speech index, with or without other user features, improves the accuracy of hate speech detection.

This paper considers calculation methods for the electric field intensity and magnetic flux density in the vicinity of the overhead transmission lines, as well as the calculation of alternating current (AC) corona onset electric field intensity. Calculations within this paper are made using the 2D algorithms of Charge Simulation Method (CSM) and Biot-Savart (BS) law based method. In order to obtain more accurate results, calculations are made by representing each overhead transmission line conductor with a large number of electric and magnetic field sources. By applying this approach, bundle conductors can be represented in a more realistic way and also singularity problems can be avoided when calculating electric field intensity. The presented methods are applied to a real overhead transmission line configuration, and obtained results are compared with field measurement results over the lateral profile. For considered overhead transmission line, AC corona onset electric field intensity is calculated and compared with calculated electric field intensity on the conductor’s surface. A comparison of calculated and measured results shows that considered calculation methods give satisfactory results.

In the fields of computer vision and digital image processing, image segmentation denotes a process whereby an image is segmented into multiple regions. Image segmentation based on multilevel thresholding has received significant attention in recent literature. In this paper, a multilevel thresholding approach based on three different Rao algorithms and Kapur’s entropy is investigated. The performance of the considered thresholding methods is evaluated on a dataset of 10 standard benchmark images using the mean of objective function values, the standard deviation of objective function values, and the best objective function value obtained over a fixed number of independent runs. The experimental results demonstrate the effectiveness of the multilevel thresholding approach based on Rao algorithms and Kapur’s entropy.

The broader use of devices powered by rechargeable batteries, especially constrained embedded devices, makes the efficient Battery Management System (BMS) increasingly more important. The estimation accuracy of the amount of remaining charge in the battery is critical as it affects the device’s operation and reliability. For that reason, the estimation of state-of-charge (SoC) is considered one of the main functionalities of a BMS. However, SoC estimation remains a complex task that depends on a range of internal and external factors. Most traditional SoC estimation methods are either computationally complex, require special laboratory equipment or additional configuration efforts. In addition, most methods require continuous measurement of battery parameters, which, in turn, renders these methods not applicable to the class of constrained embedded devices. This paper aims to extend the Coulomb counting method to the class of duty-cycled energy-constrained devices by designing an algorithm that combines voltage-based evaluation and pre-recorded task power profiles to estimate the SoC. In addition, a setup for identifying the battery parameters and algorithm validation setup were also developed and described in the paper.

Ambient conditions, especially temperature and humidity, have a huge impact on the performance of an air quality sensor. In this paper, four correction models were built to compensate the impact of ambient conditions. Linear regression and machine learning algorithms were used for building the models. Correction models were trained by using three types of measurement data. Raw measurement data was used in the first case. Secondly, measurement data was corrected and a significant improvement was shown. Lastly, measurements of various ambient conditions were used as well. Using corrected and extended measurement data brought a great improvement in accuracy of the models. A neural network correction model proved to be the most efficient in all cases. Compensating the impact of ambient conditions on the performance of an air quality sensor by using correction models was efficient and this method could be used in the air quality monitoring applications. This is of particular importance for usage of low-cost sensors in the air quality monitoring.

The scientific discipline of Computer Vision (CV) is a fast developing branch of Machine Learning (ML). It addresses various tasks important for robotics, medicine, autonomous driving, surveillance, security or scene understanding. The development of sensor technologies enabled wide usage of 3D sensors, and therefore, it increased the interest of the CV research community in creating methods for 3D sensor data. This paper outlines seven CV tasks with 3D point cloud data, state-of-the-art techniques, and datasets. Additionally, we identify key challenges.