Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.

Messaging apps, such as Telegram and WhatsApp, are routinely used to communicate, chat and make decisions. Group Recommender Systems (GRSs) have been introduced as self standing tools to support group interactions and decision-making. We present here a TelegramBot, named CHARM, that supports groups to make a decision on an arbitrary topic by leveraging GRSs techniques. CHARM helps elicit the group members’ preferences, ranks the items that the members have suggested to be considered, provides a summary of the current status of the discussion, and finally recommends a fair choice. A focus group study has revealed that the designed functionality includes features that users expect to find in a bot aimed at supporting group decision-making.

Intracerebral aneurysms (IAs) are pathological dilatations of cerebral arteries whose rupture leads to subarachnoid hemorrhage, a significant cause of disability and death. Inflammation is recognized as a critical contributor to the formation, growth, and rupture of IAs; however, its precise actors have not yet been fully elucidated. Here, we report CNS-associated macrophages (CAMs), also known as border-associated macrophages, as one of the key players in IA pathogenesis, acting as critical mediators of inflammatory processes related to IA ruptures. Using a new mouse model of middle cerebral artery (MCA) aneurysms we show that CAMs accumulate in the IA walls. This finding was confirmed in a human MCA aneurysm obtained after surgical clipping, together with other pathological characteristics found in the experimental model including morphological changes and inflammatory cell infiltration. In addition, in vivo longitudinal molecular MRI studies revealed vascular inflammation strongly associated with the aneurysm area, i.e., high expression of VCAM-1 and P-selectin adhesion molecules, which precedes and predicts the bleeding extent in the case of IA rupture. Specific CAM depletion by intracerebroventricular injection of clodronate liposomes prior to IA induction reduced IA formation and rupture rate. Moreover, the absence of CAMs ameliorated the outcome severity of IA ruptures resulting in smaller hemorrhages, accompanied by reduced neutrophil infiltration. Our data shed light on the unexplored role of CAMs as main actors orchestrating the progression of IAs towards a rupture-prone state. Graphical abstract

Background: Understanding the relationship between teachers’ physical activity (PA) and quality of life (QoL), which is impacted by work-related stress, could help develop guidelines for improvement. The purpose of this study was to investigate the impact of physical activity on high school teachers’ quality of life and the differences in QoL and PA between male and female teachers. Methods: The sample consisted of 499 respondents (193 men and 306 women), all working in the educational system. The International Physical Activity Questionnaire (short form) was used for PA assessment, and the WHOQoL questionnaire to measure QoL. Results: Physical health and Psychological health domains were areas where male teachers scored better (p < 0.01, both), while female teachers had higher scores in Social relationships domain (p < 0.05). Regression analysis showed that PA affects Physical health: Sig. = 0.056; Psychological health: Sig. = 0.000; Social relationships: Sig. = 0.001; Environment: Sig. = 0.021 in men, and Physical health (Sig. = 0.009) and Psychological health (Sig. = 0.039) in women. Conclusions: The findings of this study allow us to conclude that, whereas female teachers’ PA primarily impacts their physical and psychological domain, male teachers’ PA has an impact on their overall QoL.

Purpose. The purpose of this paper is to determine and analyze the determinants of the capital structure of joint-stock companies in Bosnia and Herzegovina that are listed on the Banja Luka Stock Exchange. This study will answer the question as to which factors determine the capital structure of BiH companies and whether existing financial theories of the capital structure hold true in that context. The main research objective is to estimate the effects of a firm’s level determinants on its capital structure measures in different ownership structures. Those findings will certainly advance our understanding of listed companies financing behavior. Methodology. For the research, we took into account firm-specific characteristics and divided joint-stock companies into private and state-owned. The results of the research show that the capital structure of these two groups of enterprises is differently affected by individual determinants. Findings and implications. While state-owned enterprises rely more on borrowed resources to finance both short-term and long-term assets, private enterprises even finance part of their short-term assets with their own capital. However, the most important determinant in both groups of enterprises is the share of inventories in short-term assets, which confirms that short-term liabilities, i.e. free sources of financing in the form of liabilities to suppliers are the determinant that most positively affect the indebtedness of all enterprises. Unlike previous research that observed enterprises according to their size or affiliation to individual industries, the focus of our research is enterprises of different ownership structures. The empirical statistical results provide basis for logical conclusion and appropriate policy implications. The study points to the specifics of the capital structure in private and publicly listed joint-stock companies. The stated opposite influence of certain ratios on the indebtedness of the enterprises is explained by a number of factors. Limitations. This study focuses only on the presentation of the recent indicators of capital structure of listed companies - listed on the Banja Luka Stock Exchange, which is one of its major limitations. The limitation of this search is the sample size which can be considered low. Further research may be conducted by using other capital markets to explore more information regarding the effect of the variables affecting the capital structure. In addition, further research may also be conducted by using other proxies or by adding more variables, sample size, and research period to get a better result. Originality. The study is an original research paper. It has not been published in any other peer-reviewed journal not under consideration for publication by any other journal. The paper adds to the existing literature on Bosnia and Hezegovina by giving an overview of recent developments in the flexi purity concept, pointing out the areas that require policy response.

The development of communication systems for in-telligent transportation systems (ITS) relies on their performance in high-mobility scenarios. Such scenarios introduce rapid fluctuations in wireless channel properties. As a promising solution for vehicle-to-everything (V2X) communication, the orthogonal time frequency space (OTFS) approach has emerged. Nevertheless, the performance of OTFS systems is closely tied to time- and frequency diversity of the wireless propagation channel. However, there is a lack of understanding of the stationarity of the wireless channels, especially in the millimeter wave (mmWave) frequency bands. In this paper, we address this research gap by conducting a comprehensive stationarity analysis of measured sub-6 GHz and mmWave high-speed wireless channels. We evaluate the spatial stationarity of a scenario, where the transmitter is moving at high velocity. Furthermore, we investigate the influence of the transmit antenna orientation on the channel spatial stationarity. We could show that the spatial stationarity is proportional to the wavelength.

The increase in soil salinity has a negative effect on the growth and yield of plants. Mitigating the negative effects of soil salinity is therefore a difficult task and different methods are being used to overcome the negative effects of salt stress on crop plants. One of the often-used approaches is seed priming that can increase plants’ vigor and resilience. In this paper, we tested the effects of hydropriming, proline priming, and salicylic acid priming on the mitigation of the negative effects of salt stress on two bell pepper varieties (Capsicum annuum L.): Herkules and Kurtovska kapija. Sweet bell pepper seeds were primed following desiccation to achieve the original water content, and subsequently cultivated in salt-supplemented medium. The positive effects on vigor (in the form of increased germination and seedling establishment) as well as on level of tolerance for salt stress were recorded for both cultivars. The positive effects varied between the priming treatments and pepper cultivar used. The results of germination, seedling performance, photosynthetic pigments, and osmolytes were measured for seedlings grown from unprimed and primed seeds with under 0, 25, and 50 mM of NaCl. Both cultivars demonstrated greater germination when primed with proline and salicylic acid, while the Herkules cultivar demonstrated a higher tolerance to salt when proline was used as the priming agent. Priming with salicylic acid and proline in the seed improved germination and seedling performance, which could be related to the increase in proline content in the seedlings.

The objective of this study was to assess the impact that diets supplemented with grape seed cake rich in polyphenols had on lactating goats. The study investigated the quantity and quality of goat milk, the metabolic profile of blood, and the antioxidative status. The study involved 24 French Alpine dairy goats throughout their lactation period. The goats were, on average, 5 years old (±three months) and in the fourth lactation. The experiment lasted for 58 days. The control group (CON) had a diet without grape seed cake (GSC). The experimental groups were given a diet containing 5% and 10% GSC on a dry matter basis (GSC5 and GSC10, respectively). A slightly higher milk production, as well as protein and fat milk content, were found in GSC5 and GSC10, but the differences were not significant. Goat milk in the GSC10 group exhibited significantly higher activity of superoxide dismutase and glutathione reductase, as well as decreased concentrations of GUK and SCC. The feeding treatments did not affect significant differences in hematological and biochemical indicators, except for the BHB content, which can be associated with a higher energy value of feed containing GSC. There was an observed elevation in the activity of SOD within the blood of GSC5, and GSC10 was measured as well. The determined changes justify the supplementation of GSC rich in polyphenols to goat feed, especially in the amount of 10%, as it can reduce stress caused by lactation, which is known as a very stressful production period for animals.

In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation Models (FMs) offer an alternative, demonstrating promise in zero-shot and few-shot settings. However, they can be unreliable due to limited physical and spatial understanding. We introduce ExploRLLM, a method that combines the strengths of both paradigms. In our approach, FMs improve RL convergence by generating policy code and efficient representations, while a residual RL agent compensates for the FMs' limited physical understanding. We show that Explorllm outperforms both policies derived from FMs and RL baselines in table-top manipulation tasks. Additionally, real-world experiments show that the policies exhibit promising zero-shot sim-to-real transfer. Supplementary material is available at https://explorllm.github.io.

A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a ‘guide towards substantiating freedom from infection’ that describes both all assumptions-limitations and available methods that can be applied in different settings.

Background To evaluate long-term outcomes and prognostic factors in patients with juvenile idiopathic arthritis (JIA), presenting as oligoarthritis, who received IAC as the first treatment for their disease. Methods We conducted retrospective study at the University Children’s Hospital Ljubljana, Slovenia, from January 2015 to May 2023 in children with JIA, clinically presenting as oligoarthritis receiving intra-articular corticosteroid injection (IAC) as the initial treatment. Patient and treatment data were collected, and the outcomes were categorized into three groups based on the later need for therapy: no therapy needed, only additional IAC needed and systemic therapy needed. The last group was further divided based on the requirement of bDMARD. Log-rank (Mantel-Cox) survival analyses compared different outcome groups. Results We included 109 patients with JIA, presenting as oligoarthritis (63% female), who were first treated with IAC. The mean age at IAC was 8.0 years, with a 4.3-year follow-up. Notably, 38.5% of patients did not require additional therapy post-IAC, whereas 15.5% required only additional IAC. Systemic therapy, mainly methotrexate (MTX), was necessary for 45.9% of patients, initiated in average 7.8 months post-IAC. Biologic therapy was initiated in 22% in average 2.2 years post-IAC. Number of injected joints correlated with the need for biologics. At the last follow-up, 88.9% had inactive disease. ANA positivity ( P  = 0.049, chi square 3.89) and HLA B27 antigen presence ( P  = 0.050, chi square 3.85) were associated with the need for systemic therapy. A subgroup of children older than 8 years, ANA and HLA B27 negative required significantly less systemic (25.8%) and biologic therapy (9.6%) compared to other patients ( p  = 0.050, chi square 3.77). Conclusion Almost 40% of children with oligoarticular JIA requiring IAC did not progress to chronic disease. Younger age, ANA positivity, and HLA B27 presence were predictive factors for systemic therapy, while the number of injected joints predicted the future need for biologic therapy.

The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.

Intratumoral heterogeneity arises as a result of genetically distinct subclones emerging during tumor progression. These subclones are characterized by various types of somatic genomic aberrations, with single nucleotide variants (SNVs) and copy number aberrations (CNAs) being the most prominent. While single-cell sequencing provides powerful data for studying tumor progression, most existing and newly generated sequencing datasets are obtained through conventional bulk sequencing. Most of the available methods for studying tumor progression from multi-sample bulk sequencing data are either based on the use of SNVs from genomic loci not impacted by CNAs or designed to handle a small number of SNVs via enumerating their possible copy number trees. In this paper, we introduce DETOPT, a combinatorial optimization method for accurate tumor progression tree inference that places SNVs impacted by CNAs on trees of tumor progression with minimal distortion on their variant allele frequencies observed across available samples of a tumor. We show that on simulated data DETOPT provides more accurate tree placement of SNVs impacted by CNAs than the available alternatives. When applied to a set of multi-sample bulk exome-sequenced tumor metastases from a treatment-refractory, triple-positive metastatic breast cancer, DETOPT reports biologically plausible trees of tumor progression, identifying the tree placement of copy number state gains and losses impacting SNVs, including those in clinically significant genes.

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.