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.

Objectives. Our objective was to study the ecological relationship of many risk factors and personal characteristics with mean age at death (AD) after a 50-year follow-up of nearly extinct cohorts. Material and Methods. There were 16 cohorts totaling 12,763 middle-aged men enrolled in the Seven Countries Study (SCS), and 58 variables were measured, including traditional risk factors, dietary nutrition and anthropometric variables. A follow-up of 50 years allowed the use of AD as the end-point. Analysis included simple linear regression correlation and multivariate modelling using Principal Component Analysis and regression and Ridge regression. Results. Out of 58 variables, only 11 (10 nutrition-dietary items plus age) showed a significant linear correlation coefficient (R) ≥ 0.50 and a p value ≤ 0.05. Linear regression was computed by using as a predictor the dietary factor score derived from a Principal Component Analysis of the 11 significant variables, which were used as independent variables, whose coefficients were significantly related with AD, and the final R2 was 0.52. The Principal Component regression and Ridge regression documented the direct relationship of food groups of vegetable origin (including olive oil) with the AD and the inverse relationship for food groups of animal origin. Conclusions. A few variables, all related to diet and nutrition, were able to statistically explain about 50% of the different AD in 16 cohorts of men followed up with nearly until death. Other variables, including traditional cardiovascular disease risk factors, did not contribute in a significant way for this purpose.

Background/Objectives: Hidradenitis suppurativa (HS) is a chronic, immune-mediated inflammatory skin disease characterized by painful nodules, abscesses, sinus tracts, and progressive fibrosis. Vascular activation is becoming increasingly acknowledged as an important factor in HS pathogenesis; however, the effects of tumor necrosis factor alpha (TNF-α) blockade on vascular remodeling in HS remain poorly characterized. This study investigated the impact of TNF-α inhibition by adalimumab (ADA) on endothelial and fibroblast-associated markers in HS lesions. Methods: Formalin-fixed paraffin-embedded skin samples from 71 HS patients were analyzed, including treatment-naive (n = 38) and adalimumab-treated (n = 33) cases. Histopathology and immunofluorescence were performed using antibodies against CD31, von Willebrand factor (vWF), α-smooth muscle actin (αSMA), vimentin, Ki-67 (proliferation), and cleaved Caspase-3 (apoptosis). ImageJ software was used to determine the immunoexpression of selected markers and vascular density. Vascular density, assessed as vessel count per mm2, was designated as the primary endpoint. Sex-related differences were analyzed as exploratory endpoints. Results: Adalimumab-treated tissue exhibited significantly reduced vascular density (p < 0.01) compared to the treatment-naive group. Conversely, vimentin immunoexpression was significantly higher (p < 0.01) in the adalimumab-treated group. No significant differences were found in endothelial Ki-67 or cleaved Caspase-3 expression between treatment groups, indicating that the observed reduction in vascular density is not associated with direct effects on endothelial cell proliferation or apoptosis, but rather may occur indirectly through attenuation of the pro-angiogenic inflammatory milieu. Exploratory sex-stratified analysis revealed that treatment-naive males had significantly higher endothelial proliferation (Ki-67; p = 0.031) and vimentin expression (p = 0.017) compared to treatment-naive females. In the ADA-treated group, males exhibited significantly lower vascular density (p = 0.036) and higher endothelial apoptosis (p = 0.039) compared to females, whereas females showed a significant increase in vimentin expression following treatment (p = 0.008), suggesting possible sex-dependent differences in vascular remodeling. Conclusions: TNF-α blockade is associated with reduced vascular density, consistent with indirect anti-angiogenic effects, suggesting that adalimumab exerts disease-modifying effects on the microenvironment beyond inflammatory cytokine suppression. Sex-dependent differences in vascular regression underscore the importance of considering sex as a biological variable in HS pathogenesis and treatment response. These results highlight the significance of vascular interactions in HS and support adalimumab as a disease-modifying treatment. These exploratory findings require confirmation in longitudinal studies with paired biopsies.

Recognizing the problems and shortcomings of the intersection “Vraca”, as well as its importance in the economic, social and economic sense, it was undertaken a detailed operational and planning analysis in order to determine the need for capacity expansion and the selection of the most favorable solution for reconstruction, which would ensure the normal and safe traffic flow with a satisfactory level of service. The analysis was performed on the basis of data recorded in the field, traffic counting, measurement, visual inspection, as well as on the geodetic base data. In order to perform an operational intersection analysis, an analysis of its existing design and technical elements was performed in order to define the road conditions that are important for the capacity analysis. Intersection traffic conditions analysis was also carried out in terms of traffic control elements and traffic flows determined by traffic counting, as well as an analysis of intersection traffic safety. Guided by the principles of functionality, permeability, rational use of space and safety, the conceptual solution of the future intersection was proposed along with the pavement structure dimensioning and the proposal for intersection traffic management.

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.

Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography, where Multiple Instance Learning (MIL) often offers the best formulation. While recent foundation models provide strong semantic representations out of the box, effective augmentation of such representations of MIL data remains limited, as existing methods operate at the instance level and fail to capture intra-bag dependencies. In this work, we introduce SetFlow, a generative architecture that models entire MIL bags (i.e., sets) directly in the representation space. Our approach leverages the flow matching paradigm combined with a Set Transformer-inspired design, enabling it to handle permutation-invariant inputs while capturing interactions between instances within each bag. The model is conditioned on both class labels and input scale, allowing it to generate coherent and semantically consistent sets of representations. We evaluate SetFlow on a large-scale mammography benchmark using a stateof-the-art MIL-PF classification pipeline. The generated samples are shown to closely match the original data distribution and even improve downstream performance when used for augmentation. Furthermore, training on synthetic data alone shows competitive results, demonstrating the effectiveness of representation-space generative modeling for data-scarce and privacy-sensitive tasks.

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.

Context. Gamma-ray burst GRB 241030A ( z  = 1.411) exhibited a particularly bright afterglow (similar to the ‘BOAT’, GRB 221009A), detected across gamma-ray, X-ray, UV, and optical bands. The extensive, multi-wavelength observations of this remarkable event provide a valuable opportunity to advance our understanding of GRB afterglow physics. Aims. We aim to constrain the physical properties of the jet, its microphysics, and the characteristics of the circumburst environment in the context of forward-shock emission. Methods. We compiled multi-wavelength observations spanning from a minute to a week after the prompt emission, processing the data through a unified photometry pipeline. Leveraging this comprehensive dataset, we analysed the observations both analytically and using Bayesian inference with two independent models. Our models assume that the afterglow emission arises from the strong forward shock of a laterally structured jet, with possible contributions from synchrotron self-Compton (SSC) scatterings. Results. We find that our models do reproduce the afterglow observations accurately, from the X-rays to the optical, favouring a jet propagating into a constant-density interstellar medium, with a viewing angle within the jet core. However, both analyses – with and without the inclusion of SSC scatterings – require parameter values that are extreme compared to expectations from standard theory. In particular, our results imply extremely energetic jets despite regular prompt energy, leading to a very inefficient prompt emission. Furthermore, the jets are particularly inefficient at accelerating particles, with low ϵ e and ϵ B , leading to significant SSC emission. Finally, our analyses indicate that the jets have large opening angles and propagate in high-density media. Conclusions. If the afterglow is indeed powered by radiation emitted behind a strong forward shock, our results place GRB 241030A within a sub-class of GRBs characterised by extreme kinetic energies, large jet opening angles, and very low prompt emission efficiencies, below 10 −3 , with strong SSC radiation. These predictions are difficult to reconcile with typical expectations from other GRBs. We therefore suggest that the afterglow of GRB 241030A is not solely powered by forward shock emission, and we discuss other options such as a long-lasting reverse-shock contribution.

Perfluorination of the terminal methyl group in ethanol gives rise to different thermodynamics of mixing with water. To understand its origin, we probe structure, thermodynamics and collective vibration modes in aqueous solutions of ethanol (EtOH) or 2,2,2-trifluoroethanol (TFE) by using Terahertz (THz) spectroscopy and molecular dynamics simulations. The THz spectra show mainly two features: a mostly entropy-related peak (below 200 cm-1) related to weaker water-water hydrogen bonds, and an enthalpy-related large band above 200 cm-1 related to water-solute hydrogen bonds. The entropic feature is red-shifted for TFE relative to EtOH, consistent with TFE's subpopulation of weaker solvation shell water-water hydrogen bonds found in the simulations. By contrast, the thermodynamics of mixing is dominated by three effects: the higher probability of forming water-water hydrogen bonds in the solvation shell of either solute than in the bulk; the fact that TFE induces a smaller perturbation per water molecule than EtOH, despite perturbing a slightly larger number of water molecules than EtOH, and TFE's weaker solute-water hydrogen bond. The three effects determine the more negative (favourable) enthalpy of mixing and more negative (unfavourable) entropy of mixing of EtOH relative to TFE at low concentrations. The results confirm that hydrophobic solvation of perfluorinated groups is fundamentally different from that of their alkylated equivalents and have implications for the development of models to predict solubility of perfluorinated molecules.

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.

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.

Immunotherapy with immune checkpoint inhibitors (ICI) has become a transformative pillar in cancer treatment, offering significant improvements in survival and reducing treatment-related side effects compared to traditional therapies. In gynecologic cancers, ICIs have transformed the treatment of endometrial (EC) and cervical cancers, whereas they have not demonstrated clinical benefit in ovarian cancer. This review examines the current state of ICI advancements in EC. Given the unique immunological characteristics of EC, a comprehensive understanding of advancements is crucial for optimizing decision-making and patient outcomes. While ICIs have demonstrated robust and durable efficacy in dMMR/MSI-H EC, the magnitude of benefit in pMMR disease remains modest. Additionally, we examine promising future directions, including personalized immunotherapy approaches and novel combination therapies (e.g. antibody-drug conjugates, PARP inhibitors, antiangiogenic drugs).

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.