This study investigates the impact of thermal modification on the water resistance and colour stability of Indigenous wood species from Bosnia and Herzegovina, including Norway spruce (Picea abies), silver fir (Abies alba), oak (Quercus sp.), and walnut (Juglans regia). Thermal modification, conducted in the presence of air, enhanced the hydrophobicity of all tested wood species by reducing their water absorption and limiting their susceptibility to moisture-driven dimensional changes. The modification process also induced notable colour changes, darkening each species in line with the degradation of lignin and other wood constituents. The degree of colour change was observed to increase with higher treatment temperatures, particularly at 210 °C, as measured by the CIE Lab colour system. These findings suggest that thermal modification provides an environmentally friendly method to improve both durability and aesthetic appeal in wood, extending the material’s application range for humid or outdoor environments.

Background and purpose Physical activity has been shown to produce numerous beneficial effects, including improved cardiovascular function, reduced body fat percentage, increased muscular strength, enhanced endurance, and greater flexibility. Accordingly, physical activity is considered a crucial factor in enhancing the quality of life in older adults. The aim of this study was to analyze literature that investigates the effects of resistance training on fitness parameters in older adults. Material and methods A systematic search was conducted using databases such as Google Scholar, PubMed, and Research Gate, focusing on studies published between 2015 and December 31, 2024. Keywords used included: elderly, physical fitness, strength training, health outcomes. The inclusion criteria comprised original scientific studies, tested at least one measure of fitness parameters and involving older adult participants and experimental interventions with resistance training. A total of 17 studies met the criteria and were analyzed in detail. Results The findings indicate that resistance training regardless of intensity level (low, moderate, or high) has a consistently positive effect on muscle strength, endurance, flexibility, cardiovascular function, body composition, balance, and overall functional mobility in older individuals. Training modalities included the use of weights, bodyweight exercises, resistance bands, and unstable surfaces. The frequency of interventions ranged from 8 weeks to 2.3 years, with training frequency mostly between two and three sessions per week. Studies combining resistance training with balance training or aerobic exercises demonstrated additional benefits. Conclusions The reviewed evidence suggests that resistance training is an effective and safe strategy for improving the physical fitness and health of older adults, potentially reducing the risk of falls and chronic diseases. It is recommended that resistance training be incorporated into regular physical activity routines, ideally alongside aerobic and flexibility exercises, to maximize overall health outcomes in the aging population.

Purpose: This research examines and contributes to the behavioural lit­erature on voluntary tax compliance. It focuses on the use and potential of machine-learning (ML) methods and models to predict individual tax morale across Europe, and it identifies the factors that influence predic­tive accuracy.Design/Methodology/Approach: Using data from the fifth wave (2017– 2020) of the European Values Survey (EVS), a data-driven, systematic approach employing six ML methods is applied to predict individual tax morale across Europe. The importance of formal, informal and socio-de­mographic factors is assessed, and the study tests whether incorporating the Corruption Perception Index (CPI) improves predictive accuracy.Findings: The results indicate that ML methods and models can enhance understanding and prediction of individual tax morale in Europe. Among the deployed models, artificial neural networks (ANNs) achieved the highest accuracy. Accuracy increased across all ML methods when the CPI was included. Attitudes towards bribery, perceptions of immigrants’ im­pact on the national welfare system, and gender emerged as significant formal, informal and socio-demographic factors.Academic contribution to the field: The study offers a novel application of data-driven ML methods to the prediction of individual tax morale. Given the scarcity of empirical ML research in the social sciences, the find­ings provide valuable insights in a European context and may serve as a basis for further global research.Practical Implications: The conclusions are particularly relevant for gov­ernments and tax administrations seeking to improve tax compliance and revenue collection. In the European context, the results confirm the virtu­ous circle linking effective government performance, high tax morale and voluntary tax compliance—insights that are crucial for decision-makers, regulators, European institutions and tax-policy makers.Originality/Value: The findings confirm that, when ML methods are ap­plied, individual tax morale can be viewed as an outcome of interactions between formal and informal institutions. They also show that predictive accuracy is higher in countries with lower corruption, as indicated by a higher CPI.    

ABSTRACT The information age has led to extensive personal data aggregation, posing various challenges for posthumous privacy and digital remains. Users are often unaware of tools for managing their digital remains, laws in the area are predominantly silent or inadequate, and the scholarship has argued for appropriate legal and technological solutions. Our study of 1766 adult UK residents’ attitudes, the first in the UK, reveals a desire for control over digital remains but low awareness and utilisation of existing tools. These phenomena are known in the literature as ‘the posthumous privacy paradox’ and ‘the inverted posthumous privacy paradox’ – our data and findings offer strong evidence for future policy and law reform. Based on our findings, complemented by earlier theoretical and doctrinal research, our recommendations include law reforms in data protection, recognition of online tools for managing digital remains and a more comprehensive UK and EU-wide reform that encompasses several relevant areas of law.

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit research scalability and accessibility. We introduce BehaveAgent, an autonomous multimodal AI agent designed to automate behavior analysis from video input without retraining or manual intervention. Unlike conventional methods that require manual behavior annotation, video segmentation, task-specific model training, BehaveAgent leverages the reasoning capabilities of multimodal large language models (LLM) to generalize across novel behavioral domains without need for additional training. It integrates LLMs, vision-language models (VLMs), and large-scale visual grounding modules, orchestrated through a multimodal context memory and goal-directed attention mechanism, to enable robust zero-shot visual reasoning across species and experimental paradigms, including plants, insects, rodents, primates, and humans. Upon receiving a video input, BehaveAgent autonomously identifies the correct analysis strategy and performs end-to-end behavior analysis and interpretation without human supervision. Leveraging vision-language representations, it performs general-purpose tracking, pose estimation and segmentation. We demonstrate BehaveAgent’s universal applicability to autonomously (1) identify the behavioral paradigm and develop an action plan specialized for the identified paradigm, (2) identify relevant subjects and objects, (3) track those features, (4) identify behavioral sequences with explicit reasoning, (5) generate and execute code for targeted analysis and (6) generate comprehensive research reports that integrate behavioral findings with relevant scientific literature. Through interpretable agentic reasoning, BehaveAgent makes its internal decision-making process transparent, clarifying why particular features are tracked or behaviors inferred. By reducing the time and expertise required for behavior analysis, BehaveAgent introduces a scalable, generalizable, and explainable paradigm for advancing biological and behavioral research.

COVID-19, caused by the SARS-CoV-2, poses significant global health challenges. A key player in its pathogenesis is the nucleocapsid protein (NP), which is crucial for viral replication and assembly. While NPs from other coronaviruses, such as SARS-CoV and MERS-CoV, are known to increase inflammation and cause acute lung injury, the specific effects of the SARS-CoV-2 NP on host cells remain largely unexplored. Recent findings suggest that the NP acts as a pathogen-associated molecular pattern (PAMP) that binds to Toll-like receptor 2 (TLR2), activating NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) and MAPK (mitogen-activated protein kinase) signaling pathways. This activation is particularly pronounced in severe COVID-19 cases, leading to elevated levels of soluble ICAM-1 (intercellular adhesion molecule 1) and VCAM-1 (vascular cell adhesion molecule 1), which contribute to endothelial dysfunction and multiorgan damage. Furthermore, the NP is implicated in hyperinflammation and thrombosis—key factors in COVID-19 severity and long COVID. Its potential to bind with MASP-2 (mannan-binding lectin serine protease 2) may also be linked to persistent symptoms in long COVID patients. Understanding these mechanisms, particularly the role of the NP in thrombosis, is essential for developing targeted therapies to manage both acute and chronic effects of COVID-19 effectively. This comprehensive review aims to elucidate the multifaceted roles of the NP, highlighting its contributions to viral pathogenesis, immune evasion, and the exacerbation of thrombotic events, thereby providing insights into potential therapeutic targets for mitigating the severe and long-term impacts of COVID-19.

Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information from the sub-6 GHz band. The first method employs a convolutional neural network (CNN), while the second method utilizes a UNet architecture. We compare these proposed methods against deep-learning methods that rely solely on in-band information and with other state-of-the-art out-of-band aided methods. Simulation results show that our proposed out-of-band aided deep-learning methods outperform existing alternatives in terms of achievable spectral efficiency.

The application of additive manufacturing technologies for producing parts from polymer composite materials has gained significant attention due to the ability to create fully functional components that leverage the advantages of both polymer matrices and fiber reinforcements while maintaining the benefits of additive technology. Polymer composites are among the most advanced and widely used composite materials, offering high strength and stiffness with low mass and variable resistance to different media. This study aims to experimentally investigate the impact of selected process parameters, namely, wall thickness, raster angle, printing temperature, and build plate temperature, on the flexural properties of carbon fiber reinforced polyamide (CFrPA) fused deposition modeling (FDM) printed samples, as per ISO 178 standards. Additionally, regression and artificial neural network (ANN) models have been developed to predict these flexural properties. ANN models are developed for both normal and augmented inputs, with the architecture and hyperparameters optimized using random search technique. Response surface methodology (RSM), which is based on face centered composite design, is employed to analyze the effects of process parameters. The RSM results indicate that the raster angle and build plate temperature have the greatest impact on the flexural properties, resulting in an increase of 51% in the flexural modulus. The performance metrics of the optimized RSM and ANN models, characterized by low MSE, RMSE, MAE, and MAPE values and high R2 values, suggest that these models provide highly accurate and reliable predictions of flexural strength and modulus for the CFrPA material. The study revealed that ANN models with augmented inputs outperform both RSM models and ANN models with normal inputs in predicting these properties.

This paper introduces affordance-based explanations of robot navigational decisions. The rationale behind affordance-based explanations draws on the theory of affordances, a principle rooted in ecological psychology that describes potential actions the objects in the environment offer to the robot. We demonstrate how affordances can be incorporated into visual and textual explanations for common robot navigation and path-planning scenarios. Furthermore, we formalize and categorize the concept of affordance-based explanations and connect it to existing explanation types in robotics. We present the results of a user study that shows participants to be, on average, highly satisfied with visual-textual, i.e., multimodal, affordance-based explanations of robot navigation. Furthermore, we investigate the complexity of different types of textual affordance-based explanations. Our research contributes to the expanding domain of explainable robotics, focusing on explaining robot actions in navigation.

Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows.

This paper aims to examine the lightfastness of yellow water-based ink using a light-accelerated aging chamber. Mondy Maestro Print uncoated offset paper (120 g/m²) was used as printing substrate. The printing was carried out on an inkjet printing machine using water-based inks, and the light exposure was tested within the Solarbox climate chamber. The study was conducted through the analysis of colour differences, as well as changes in chroma and lightness, across light, mid and dark tonal patches, as well as the paper substrate. It is hypothesised that the paper substrate will influence the tonal patches through its changes, and that the colour differences will be greater with higher tonal coverage. Experimental results obtained throught spectrophotometric measurements indicate significant color differences. It was found that the paper substrate, through its alterations, influenced the yellow tonal patches. Furthermore, higher tonal coverage resulted in more pronounced colour changes. An exception was observed in the 70% tonal value (TV), which exhibited greater changes compared to the solid patch. These studies are crucial for better understanding of the application and efficiency of this type of ink in the graphic arts industry.