This paper aims to investigate the impact of water use efficiency on income inequality in European countries, exploring how financial development moderates this relationship and controlling for economic, institutional and infrastructural control variables. This study uses Method of Moments Quantile Regression on a panel data set including 32 European countries from 2000 to 2021. The analysis uses the GINI coefficient as a measure of income inequality and assesses its relationship with water use efficiency, financial development and control variables. Water use efficiency has a statistically significant negative impact on income inequality across all quantiles, suggesting that more efficient water use contributes to reducing income disparities. This effect is stronger in countries with already lower levels of inequality. Financial development unexpectedly shows a positive relationship with income inequality, potentially indicating a misallocation of financial resources across income level categories. Government efficiency and inland waterways infrastructure have inequality-reducing effects, while the rule of law augments income inequality in the European Union. This study is limited by the lack of comparable research to benchmark findings against and by potential country-specific institutional, cultural and political factors not fully captured in the model. Future research could explore the role of water efficiency technologies, the impact of climate change on water accessibility and sustainable manufacturing practices in relation to income inequality. This research addresses a significant gap in the literature by examining the impact of water use efficiency on income inequality, presenting novel insights into how water resource management affects socioeconomic disparities in European countries.

Highlights What are the main findings? Visual mental imagery engages ventral and dorsal stream systems in a content- and stage-dependent manner, with evidence for stream interaction that varies across paradigms and populations. Structural and clinico-radiological evidence is broadly consistent with disconnection frameworks, suggesting that disruption of long-range pathways (e.g., inferior longitudinal fasciculus (ILF)/inferior fronto-occipital fasciculus (IFOF)/ superior longitudinal fasciculus (SLF)) may contribute to imagery deficits beyond focal cortical damage. What are the implications of the main findings? Stream-sensitive phenotyping (object vs. spatial imagery) and stage-aware paradigms are essential to develop interpretable neuroradiological biomarkers. Multimodal protocols combining structural MRI, diffusion imaging/tractography, functional connectivity, and lesion mapping can improve clinical interpretation of imagery complaints. Abstract Visual mental imagery, the ability to generate and manipulate internal visual experiences without direct sensory input, links perception with memory, planning, and higher cognition. In this targeted narrative review, we synthesize neuroimaging and lesion evidence on the brain basis of visual imagery, with a focus on neuroradiological correlates of the ventral and dorsal visual pathways. Unlike prior cognitive neuroscience reviews that primarily emphasize functional mechanisms, this review is neuroradiology-oriented and integrates lesion patterns and white-matter disconnection to support clinico-radiological interpretation of imagery complaints. Using a dual-stream framework, we contrast ventral occipito-temporal systems that preferentially support object imagery (appearance-based features such as form, faces/objects, and color, with texture remaining under-studied) with dorsal occipito-parietal systems that preferentially support spatial imagery (relations, transformations, and navigation). Across studies, imagery recruitment is strongly task- and stage-dependent: ventral regions are most often engaged during object-focused imagery, whereas parietal regions are prominent during spatial transformation tasks, with evidence for interaction between pathways when demands require both content and spatial operations. Structural and clinico-radiological findings indicate that imagery impairment can arise from focal posterior lesions and posterior neurodegenerative syndromes but also from network disruption affecting long-range connections that support top-down access to posterior representations. Finally, emerging work on aphantasia and hyperphantasia supports a network-level view in which imagery vividness relates to how effectively higher-order systems engage visual representations. We conclude that standardized, stream-sensitive tasks and multimodal approaches combining functional and structural imaging with lesion-based evidence are key to discovering clinically actionable biomarkers of imagery dysfunction.

BACKGROUND AND OBJECTIVES Contrast-associated acute kidney injury (CA-AKI) is a potentially preventable complication after exposure to iodinated contrast media. In patients undergoing endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), the incidence and clinical impact are poorly characterized, and no validated prediction tool is currently available. The aim of this study was to assess the incidence and prognostic significance of CA-AKI in EVT-treated patients with AIS and to develop and validate a predictive score. METHODS A retrospective, multicenter cohort study was conducted involving EVT-treated patients across 73 centers in 16 countries (January-December 2023). Inclusion criteria were age ≥18 years, absence of dialysis, availability of preprocedural and 48-hour postprocedural creatinine levels, and available 90-day follow-up (modified Rankin Scale [mRS] score). The primary outcome was CA-AKI, defined by KDIGO (Kidney Disease: Improving Global Outcomes criteria;creatinine increase ≥0.3 mg/dL or ≥1.5 times baseline, within 48 hours). Secondary outcomes were (1) in-hospital mortality, (2) 90-day mRS score, and (3) 90-day severe disability or death (mRS score >3). Logistic models assessing associations with outcomes accounted for within-center clustering by applying robust standard errors. CA-AKI prediction models were developed across imputed data sets using univariable selection (p < 0.20), backward elimination (p < 0.05), and coefficient-based scoring after categorization of continuous predictors, with internal validation by bootstrap to obtain optimism-adjusted estimates. RESULTS Among 6,638 patients (median age 74 years; 48.7% male), CA-AKI occurred in 326 (4.9%) and was independently associated with in-hospital mortality (adjusted odds ratio [aOR] 2.269; 95% CI 1.615-3.190), higher 90-day mRS scores (adjusted common odds ratio 1.584; 95% CI 1.110-2.258), and 90-day severe disability or death (aOR 1.530; 95% CI 1.057-2.216). A preprocedural risk model including 12 routine clinical variables-sex, ethnicity, arterial hypertension, dyslipidemia, chronic kidney disease, antiplatelet therapy, NIH Stroke Scale score at admission, serum glucose, estimated glomerular filtration rate, hemoglobin, mean arterial pressure, and IV thrombolysis-demonstrated acceptable discrimination (area under the receiver operating characteristic curve 0.710 [95% CI 0.682-0.738]; precision-recall area under the curve 0.13 [95% CI 0.10-0.16]), good calibration (slope 0.870 [95% CI 0.759-0.928]), good overall performance (Brier score 0.045 [95% CI 0.042-0.049]). A second model that included EVT-related variables (e.g., contrast volume) showed similar performances. DISCUSSION In this large, international cohort, CA-AKI occurred in approximately 1 in 20 EVT-treated patients with AIS and was independently associated with poor outcomes. A simple preprocedural risk score enables early identification of high-risk individuals and may support preventive strategies.

Abstract Sustainable development demands research into safe, renewable energy sources. Wood briquettes offer numerous advantages, but they can contain heavy metal(oid)s, posing environmental challenges, particularly in the ash produced during combustion. This study examines the concentrations of heavy metal(oid)s (Cd, Cr, Cu, Fe, Mn, Ni, Pb, Co, Zn, and As) in wood briquettes and their residual ash. Samples were prepared via wet digestion using 65% nitric acid (HNO3) in polytetrafluoroethylene vessels, followed by analysis using flame and graphite furnace atomic absorption spectrometry. The results showed that arsenic (As) had the lowest concentration in wood briquettes, while iron (Fe) was the highest. In the ash, chromium (Cr) was detected at the lowest concentration (0.80 mg/kg), while iron (Fe) reached 5830 mg/kg. Heavy metal concentrations in wood briquettes often exceeded permissible limits, and the concentrations in ash were significantly higher, making some ash samples unsuitable for agricultural use. The ash content ranged from 0.70% to 2.34%. This study provides valuable quantitative data on heavy metal(oid)s before and after combustion, highlighting their potential environmental impact and emphasizing the need for careful management of wood briquette ash.

Psychological interventions represent a core component of contemporary interdisciplinary chronic pain treatment, yet treatment initiation following referral to pain psychology services remains consistently low. Empirical studies across behavioral health and pain medicine demonstrate that referral alone is insufficient to ensure patient engagement with psychological care. This gap between referral and treatment initiation represents a major implementation barrier limiting the impact of evidence-based psychological pain interventions. The present article synthesizes contemporary literature on behavioral health treatment initiation and chronic pain psychology to propose a structured engagement framework designed to improve initiation rates following referral. Using a targeted narrative review methodology, empirical literature published between 2021 and 2025 was examined to identify key determinants of treatment initiation across pain medicine and integrated behavioral health settings. Findings indicate that treatment initiation is best conceptualized as a multistep process involving referral communication, structural and attitudinal barriers, patient readiness, psychoeducation, and system-level facilitation. Evidence from collaborative care models suggests that active engagement strategies embedded within medical workflows can substantially improve treatment initiation rates compared with passive referral approaches. The proposed Active Engagement Model of Pain Psychology Referral integrates individual-level and system-level interventions designed to address common barriers to treatment initiation. Improving initiation requires a shift from passive referral models toward proactive engagement strategies embedded within interdisciplinary pain care. Implementing structured engagement approaches may substantially improve access to evidence-based psychological interventions for chronic pain.

The growing global demand for effective and safe therapeutics has accelerated advances in biomaterials for drug delivery applications. Biomaterials, including polymers, metals, ceramics, and composites, play a central role in modern medical devices and therapeutic systems by enabling controlled interactions with biological environments. Initially defined as inert materials interfacing with biological systems, biomaterials are now rationally engineered to treat, replace, or evaluate tissue and organ functions. Recent progress in regenerative medicine, nanotechnology, and precision healthcare has expanded their use in drug delivery, where tunable physicochemical properties—such as degradation kinetics, surface chemistry, and mechanical stability—allow controlled release, protection of labile therapeutics, and enhanced accumulation at target sites. Polymer-based biomaterials enable sustained drug release through diffusion-controlled, degradation-mediated, or stimulus-responsive mechanisms, thereby extending therapeutic exposure and reducing systemic dosing frequency compared with conventional formulations. Nanostructured carriers, including liposomes, micelles, and dendrimers, further enhance drug delivery by improving solubility, cellular uptake, and site-specific targeting via size control, surface functionalization, and ligand-mediated interactions. Despite these advances, clinical translation remains limited by challenges related to immune–biomaterial interactions, batch-to-batch variability, long-term biodegradation behavior, and the scalability of manufacturing under regulatory constraints. Future biomaterial development must therefore emphasize precision fabrication, good manufacturing practice–compatible production, and biologically informed design strategies that account for patient-specific variability. This review provides a focused overview of biomaterial-based drug delivery systems, summarizes recent technological advances, and critically discusses mechanistic and translational challenges, including immune compatibility, degradation control, and regulatory compliance, with particular emphasis on their implications for personalized drug delivery.

Malware detection using deep learning faces challenges in model selection for practical deployment. We systematically compare five transfer learning architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0) on the MaleBin RGB malware dataset ($\text{1 2, 0 0 0 +}$ images through March 2025). Experiments on NVIDIA A100 GPU evaluated accuracy, efficiency, and deployment suitability. DenseNet121 achieved highest accuracy ($91.20 \%, 8 \mathrm{M}$ parameters), MobileNetV2 provided optimal edge deployment (90.39 %, 3.5 M parameters), while ResNet50 and EfficientNetB0 unexpectedly underperformed $(77.34 \%, 71.16 \%)$. Directions for practitioners are to deploy DenseNet121 for cloud environments, prioritizing accuracy, and MobileNetV2 for resource-constrained edge devices.

Orthogonal signal generators are crucial for synchronization in single-phase systems, where accurate estimation of phase, frequency and amplitude is the focal point. Conventional generators are sensitive to a DC-offset in the input signal, which can degrade performance. This paper presents a modified Kalman-based generator with an additional feedback loop for DC elimination. A state-space model of proposed generator is developed, and parameters are calculated using a continuous Kalman estimator. The performance is validated in MATLAB/Simulink environment under several tests to determine performance of the presented orthogonal signal generator. Simulation results show that the generator is accurately tracking the input signal while generating its quadrature components demonstrating robust performance suitable for synchronization loop applications.

Chainsaw felling and processing work is conducted in various natural conditions and requires significant physical effort from the workers, movement in severe weather and environmental conditions, and has a high risk of injury. The aim of this study was to determine the physiological workload of chainsaw operators through continuous heart rate measurement during the entire working day. The research was carried out during the summer of 2024, encompassing different parts of the Federation of Bosnia and Herzegovina. Heart rate was measured using a Polar H10 Heart Rate Monitor Chest Strap with continuous data logging and storage of heart rate readings. A time study was performed based on recordings conducted simultaneously with the recording of heart rate, with the aim of determining the duration of individual work operations and identifying the work operation with the highest negative impact on the worker. The average working heart rate during productive work time for subject 1 was 104 bpm, 83 bpm for subject 2, 109 bpm for subject 3, 94 bpm for subject 4 and 129 bpm for subject 5. The results of the Kruskal-Wallis test showed a statistically significant difference in average heart rate in relation to the time study element. The heart rate reserve (%HRR) for the whole study time was estimated at 41.05 % for subject 1; 22.69% for subject 2; 44.50% for subject 3; 24.04% for subject 4, and 45.78% for subject 5. The results of the study showed that the %HRR of chainsaw operators during felling and processing exceeded the value of 40% for 3 out of 5 subjects, which corresponds to hard work and may have negative consequences for operators´ health.

With the growth of data volume and increased query complexity, the need for the application of various optimisation techniques that enable faster execution and more efficient use of resources is increasingly becoming evident. Research shows that indexing, query execution optimisation, and the use of caching significantly reduce processing time and increase system responsiveness. Given that databases are constantly growing in size due to the need to store and analyse data, efficient database architecture and organisation are imperative to the business environment. This paper deals with the topic of analysing databases with large data sets and how to retrieve them most efficiently, using web applications, which are today the most common UI for databases.

Deep learning has become increasingly significant in clinical medicine, including breast cancer detection, offering significant potential to improve patient outcomes. However, recurrent architectures like LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) remain underutilized for breast cancer prediction using structured tabular data, primarily due to the absence of explicit temporal dependencies, which are unsuitable for sequence-based modeling. This work presents a novel approach that redefines how LSTM architecture can be applied to the publicly available non-sequential Wisconsin Diagnostic Breast Cancer (WDBC), consisting of 569 samples and 30 features. The flat tabular input is reshaped into a fixed-length 3D tensor using a sliding window approach to adapt the data for sequence modeling. This transformation enables the model to leverage LSTM's sequential processing capabilities in a fundamentally new way, capturing implicit feature interactions across structured attributes without temporal context. Furthermore, Bayesian hyperparameter optimization techniques are applied to enhance the model's performance. The proposed model is evaluated against standard LSTM and state-of-the-art tabular Transformer architectures (FT-Transformer and SAINT). Results show that BiLSTM achieves the best overall performance (AUC 0.9985, accuracy 0.9824, RMSE 0.0964), while the LSTM baseline also surpasses both Transformerbased tabular models (AUC 0.9958, accuracy 0.9719). Performance gains are consistent across seven evaluation metrics, with statistical significance confirmed via paired t-tests $({p}<0.05)$. These findings demonstrate that, when appropriately adapted, recurrent architectures can outperform even advanced self-attention models in structured clinical prediction tasks.

The development of reliable Decision Support Systems (DSS) for talent identification requires a rigorous analytical framework capable of processing high-dimensional educational data. This paper presents the mathematical formulation of the machine learning pipeline utilized for classifying student potential, focusing on the algebraic structure of data representation and the optimization of predictive algorithms. We formally define the mapping of unstructured textual attributes into sparse vector spaces using One-Hot Encoding and analyze the dimensionality reduction effects. The study details the training dynamics of classification models, specifically examining the cost function minimization in Decision Trees via the Gini Impurity index and the stochastic aggregation mechanisms within Random Forest ensembles. Furthermore, to address the challenge of class imbalance, we provide a formal definition of performance metrics, including the harmonic mean of precision and recall and the arithmetic mean of indicator functions for Global Top-K Accuracy. By establishing these mathematical foundations, the paper demonstrates how formal optimization directly correlates with the discriminative power and stability of AI-driven educational assessments.

Online Analytical Processing (OLAP) technology facilitates efficient multidimensional data analysis, providing users with valuable insights for decision-making processes. Previous studies have explored the implementation of OLAP technology across various domains; however, a limited number of investigations have compared the Multidimensional Analysis Project and Pentaho on the same database or within a single study. This research contributes to existing literature by evaluating the performance and flexibility of these two tools using Microsoft SQL Server as a benchmark dataset, which represents the database of a specific blog application or an application based on user interactions with diverse posts. The manuscript details the modeling and implementation processes for OLAP cubes in both systems, emphasizing fundamental aspects of OLAP technology, key functionalities, performance metrics, and adaptability characteristics. Furthermore, a comparative analysis between Microsoft's solution and Pentaho was conducted, highlighting their respective advantages and limitations within the context of data analytics.

In the context of business systems, efficient data analysis through various Online Analytical Processing (OLAP) models represents a key challenge for performance optimization and timely decision-making. This study examines tabular and multidimensional OLAP models within the SQL Server (MSSQL) and Visual Studio environments to determine which model facilitates more effective data processing in a specific business context. An experimental analysis was conducted using the Stats dataset, where the same business question was addressed through both models, comparing their response times and query execution efficiency. Particular attention was paid to execution speed, data processing methodologies, and resource optimization strategies. Results indicated that the tabular model, which relies on in-memory technology and the Data Analysis Expressions (DAX) language, reduces data processing time by approximately 38 % and offers simpler modeling capabilities, making it suitable for analyses where rapid result retrieval is critical. In contrast, the multidimensional model, utilizing Multidimensional Expressions (MDX), provides advanced analytical features and greater scalability, rendering it more appropriate for complex analyses involving large datasets and predefined aggregations. Based on this comparison, it was concluded that the choice between tabular and multidimensional OLAP models depends on specific analytical requirements. If speed and flexibility are prioritized, the tabular model enables faster execution, whereas the multidimensional model offers enhanced control over analytical processes.