Search Results (2,166)

This study addresses the management and optimization of decentralized bioresource energy generation under conditions of political instability, using Ukraine as a representative case. The research aims to enhance energy security and operational resilience where centralized energy infrastructure is vulnerable to disruption. A high-efficiency technology for decentralized heat generation is proposed, based on the direct combustion of non-standard agricultural biomass with a one-year renewal cycle. The methodology combines experimental and statistical analysis of biomass feeding processes with advanced three-dimensional modeling of mixture formation and combustion, as well as the development of an artificial intelligence-driven automated control system. The system enables the use of sunflower, rapeseed, wheat, corn, and other agricultural residues with variable particle size and moisture content of up to 40%, without the need for pre-drying or pelletization. An original jet–vortex bioheat generator and optimized dosing systems were designed to ensure continuous and stable combustion. An operational algorithm allowing stable performance within 25–100% of nominal capacity was formulated based on statistical evaluation of screw feeder behavior and optimization of adjustable electric drive parameters, ensuring thermal carrier temperature stability within ±1–2 °C. The main novelty lies in the integrated optimization framework combining unconventional biomass utilization, adaptive electric drive control, and AI-based automation to achieve high energy efficiency and environmental performance. The results indicate that such decentralized systems can substantially strengthen national energy security and support sustainable energy supply in unstable political environments. Full article

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and emphysema is present in the majority of affected patients and can be identified on computed tomography (CT). This study investigated whether radiomic features derived from automatically and adaptively segmented low-attenuation lung regions can capture distinct imaging characteristics of COPD beyond conventional emphysema measures. Radiomic features were extracted from 6078 chest CT scans of 2243 participants from the COPDGene cohort. Emphysematous regions were segmented using the MimSeg method based on Gaussian mixture modelling with patient-adjusted thresholding, and radiomic features were computed for individual lesion clusters and aggregated per patient using summary statistics, yielding 780 features per subject. Uniform Manifold Approximation and Projection (UMAP) was used to generate a low-dimensional embedding, and feature contributions were evaluated using SHAP analysis and statistical testing. The resulting embedding demonstrated structured patterns broadly aligned with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages, with greater overlap among GOLD 0–2 and more consolidated groupings for GOLD 3 and 4, reflecting differences in disease severity. The most influential features were predominantly derived from Grey Level Run Length Matrix measures, capturing textural heterogeneity and spatial organisation of emphysematous changes that are not directly described by standard density-based metrics. These findings suggest that radiomic analysis of adaptively segmented CT data may provide complementary and structurally distinct information relative to conventional emphysema measures, supporting a more nuanced characterisation of emphysema patterns in COPD. Full article

Telegram, along with WhatsApp and Signal, has become very popular due to its hybrid capabilities, including both instant private and public messaging, making it an effective tool for quickly broadcasting content to a wide audience. This article presents TGEconomicDataset, a new dataset containing more than 2.9 million messages from the most popular Russian-language Telegram channels in the field of economics, as well as synthetically generated labeled mixtures of these channels. These mixtures are specifically designed to model authorship change scenarios for testing various methods for solving the problem of continuous authentication, which is of particular interest due to the need for organizations and companies to rely on data posted on social media. The presented dataset is enriched with quotes of important financial instruments such as gold futures, the USD/RUB currency pair, BRENT oil, the dollar index (DXY), and bitcoin (BTC), synchronized with the message timestamps. A detailed joint analysis of the collected data is provided. In addition to the presented dataset, we publish the scripts used to collect the data, integrate the financial indicators, and generate the synthetic mixtures for the continuous authentication task, ensuring full reproducibility of the research. Full article

Detection and simultaneous monitoring of multiple trace gases is vital in scientific and industrial processes. Here, we use coherent control in quartz-enhanced photoacoustic spectroscopy (COCO-QEPAS) with an in situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at very low concentrations, without prior knowledge of gas composition. We validate this on various mixtures, including CH₄/C₂H₂/C₂H₄/C₂H₆/NO₂/NH₃. To this end, we demonstrate real-time analysis of mixtures containing up to four trace gases at ppm-level, monitoring changes in seconds using linear regression. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand fingerprinting to 10 ppm with a detection limit of 180 ppb CH₄, and apply empirical mode decomposition as an adaptive, data-driven filtering method to recover characteristic spectral features at the noise floor. For quantitative analysis in the ppb regime, we employ principal component regression as a calibration model that exploits correlations across the full spectrum. Consequently, our method offers significant potential for sensing applications where speed, accuracy, and simplicity are critical. Full article

Recognizing emotions accurately in virtual reality (VR) enables adaptive and personalized experiences across gaming, therapy, and other domains. However, most existing facial emotion recognition models rely on acted expressions collected under controlled settings, which differ substantially from the spontaneous and subtle emotions that arise during real VR experiences. To address this challenge, the objective of this study is to develop and evaluate generalizable emotion recognition models that jointly learn from both acted and natural facial expressions in virtual reality. We integrate two complementary datasets collected using the Meta Quest Pro headset, one capturing natural emotional reactions and another containing acted expressions. We evaluate multiple model architectures, including convolutional and domain-adversarial networks, and a mixture-of-experts model that separates natural and acted expressions. Our experiments show that models trained jointly on acted and natural data achieve stronger cross-domain generalization. In particular, the domain-adversarial and mixture-of-experts configurations yield the highest accuracy on natural and mixed-emotion evaluations. Analysis of facial action units (AUs) reveals that natural and acted emotions rely on partially distinct AU patterns, while generalizable models learn a shared representation that integrates salient AUs from both domains. These findings demonstrate that bridging acted and natural expression domains can enable more accurate and robust VR emotion recognition systems. Full article

Introduction: Operative time is commonly used as a surrogate marker for operative difficulty in liver resection, but the contribution of other intraoperative factors is less understood. This study aimed to develop an objective, composite score to assess operative difficulty and evaluate its association with postoperative and oncological outcomes in liver surgery. Methods: A retrospective cohort study was conducted on patients who underwent liver resection for malignant disease between 1999 and 2023 at an Australian tertiary hospital, using a prospectively maintained database. Principal component analysis (PCA) was applied to operative time, estimated blood loss, total time of hepatic inflow occlusion and the number of packed red bloods transfused intraoperatively to derive a composite operative difficulty score. Patients were then stratified into low, moderate and high difficult groups using Gaussian mixture models (GMM). Comparison of textbook oncological outcomes (TOO) achievement and futile resection rates were assessed using Chi-squared analysis. Kaplan-Meier analysis was used to assess recurrence-free and overall survival in subgroup analysis. Results: Of 729 patients, 699 met the inclusion criteria. GMM identified three distinct operative difficulty groups: low (n = 540), moderate (n = 143), and high (n = 16). TOO and non-futile resection rates declined with increasing difficulty: 77% and 58% (low), 47% and 52% (moderate), and 6% and 19% (high), respectively (p < 0.001, p = 0.004 respectively). Among patients with cholangiocarcinoma, median overall survival was inversely correlated with operative difficulty (40 months low, 16 months moderate, 7 months high, p = 0.004). In patients with colorectal liver metastases, there was a trend towards worse overall survival and disease-free survival with increasing operative difficulty, however, this did not reach statistical significance. Conclusions: An objective intraoperative difficulty score was developed and demonstrated a significant inverse association with both quality and oncological outcomes. While external validation is required, these findings support the potential of operative difficulty assessment to enhance perioperative decision-making, inform patient counselling, and optimise postoperative care planning. Full article

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

Owing to their high strength characteristics, chemical stability, and piezoelectric activity, vinylidene fluoride (VDF) copolymers have become promising materials for creating implants to replace bone tissue defects. However, a significant drawback of these materials is the biological inertness of their surface, which leads to unsatisfactory integration with the patient’s bone tissue. In this study, we propose a single-step approach for immobilizing hydroxyapatite (HAp) on the surface of porous implants made of vinylidene fluoride and tetrafluoroethylene copolymer (P(VDF-TeFE)). This method consists of treating the surface of the product with a mixture of solvents while simultaneously capturing HAp microparticles. Using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), it was shown that the proposed method preserves the morphology of model implants (pore diameter and printed line thickness) and allows HAp to cover up to 63 ± 14% of their surface, reaching concentrations of calcium and phosphorus up to 6.0 ± 1.3 and 3.6 ± 0.7 at. %, respectively, imparting superhydrophilic properties to them. Optical profilometry revealed that the surface roughness of samples increased by more than seven times as a result of HAp immobilization. X-ray diffraction analysis (XRD) confirmed that the piezoelectric phase of P(VDF-TeFE) is preserved after treatment, as are the compressive strength characteristics of the samples. Hydroxyapatite immobilization significantly improved the adhesion and osteogenic differentiation of multipotent stem cells cultured with P(VDF-TeFE)-based samples. Thus, the proposed method can significantly enhance the biological activity of implants based on the piezoelectric VDF copolymer. Full article

Polyurethane (PU) mixtures exhibit superior mechanical performance compared to traditional asphalt mixtures, owing to the excellent engineering properties of the PU binder. This study investigates the dynamic rheological properties of an open-graded polyurethane mixture (PUM−OGFC) in comparison with a dense-graded polyurethane mixture (PUM−AC). The time−temperature superposition principle and three rheological models (Standard Logistic Sigmoid (SLS), Generalized Logistic Sigmoid (GLS), and Havriliak−Negami (HN)) were employed to construct and analyze master curves. The results show that while PUM−AC possesses a higher dynamic modulus, PUM−OGFC exhibits a lower phase angle, indicating a more elastic response. Critically, PUM−OGFC demonstrated superior rutting resistance, as evidenced by its higher rutting parameter (|E*|/sin δ). Aggregate gradation significantly influenced all rheological properties. The master curve analysis further revealed that PUM−OGFC exhibits greater temperature sensitivity than PUM−AC. The SLS and GLS models provided excellent fits for both dynamic modulus and phase angle data, whereas the HN model was suitable only for dynamic modulus. In summary, the open-graded structure, when combined with a PU binder, creates a high-performance composite with an exceptional balance of elasticity and rutting resistance, showcasing its potential for demanding pavement applications. Full article

High-dimensional regression with multivariate responses poses significant challenges when data are collected across multiple platforms, each with potentially correlated outcomes. In this paper, we introduce a multi-platform multivariate high-dimensional linear regression (MM-HLR) model for simultaneously modeling within-platform correlation and cross-platform information fusion. Our approach incorporates a mixture of Lasso and group Lasso penalties to promote both individual predictor sparsity and cross-platform group sparsity, thereby enhancing interpretability and estimation stability. We develop an efficient computational algorithm based on iteratively reweighted least squares and block coordinate descent to solve the resulting regularized optimization problem. We establish theoretical guarantees for our estimator, including oracle bounds on prediction error, estimation accuracy, and support recovery under mild conditions. Our simulation studies confirm the method’s strong empirical performance, demonstrating low bias, small variance, and robustness across various dimensions. The analysis of real financial data further validates the performance gains achieved by incorporating multivariate responses and integrating data across multiple platforms. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Background/Objectives: Family caregivers of individuals with dementia frequently experience substantial psychological distress, yet their emotional responses are heterogeneous. Depression, anxiety and psychological well-being may co-occur in distinct patterns, and socio-economic resources such as education and income are often hypothesized to buffer caregiver distress. This study aimed to identify latent psychological profiles among dementia caregivers and to examine whether education and income moderate the association between affective symptoms and well-being. Methods: A cross-sectional study was conducted with 73 family caregivers of dementia patients attending the Neurology–Psychiatry Department of C.F.2 Clinical Hospital, Bucharest (November 2023–April 2024). Participants completed the PHQ-9 (depression), the COVI Anxiety Scale and Ryff’s Psychological Well-Being Scales. Care recipients’ cognitive status was extracted from medical records using the MMSE. Gaussian Mixture Modeling was used for latent profile analysis (LPA). Between-profile differences were examined using one-way ANOVAs and Tukey post-hoc tests and Pearson correlations were used to assess associations between affective symptoms and psychological well-being, and examined whether education and income were associated with profile membership and psychological well-being. Results: LPA supported a three-profile solution: (1) lower depressive symptoms with moderate anxiety (33%), (2) severe combined depression and anxiety (18%) and (3) moderately severe depression with severe anxiety (49%). Profiles differed significantly in depressive symptom severity, whereas anxiety severity did not differ significantly across profiles. Caregivers in Profile 3 (moderately severe depression–severe anxiety) reported significantly higher overall psychological well-being than those in Profile 1 (moderate depression–moderate anxiety). In contrast, caregivers in Profile 2 (severe depression–severe anxiety), who exhibited the highest affective symptom burden, showed intermediate levels of overall well-being, with comparatively lower scores on specific dimensions such as purpose in life. Depressive symptoms were weakly but significantly associated with autonomy and self-acceptance, whereas anxiety symptoms showed no significant associations with psychological well-being. Education level and household income were not significantly associated with profile membership or psychological well-being. Conclusions: Family caregivers of individuals with dementia can be meaningfully described as forming three exploratory psychological profiles characterized by different configurations of depressive and anxiety symptoms. These findings indicate that caregiver distress does not follow a simple severity gradient and that psychological well-being is not solely determined by symptom burden. Socio-economic characteristics did not account for differences in caregiver adjustment, underscoring the importance of individualized psychological assessment and tailored interventions to support caregiver mental health. Full article

The structural origin of the germanate anomaly in glasses, which involves complex Ge–O coordination environments, is frequently studied using crystalline analogs. This study aims to provide reliable spectroscopic fingerprints by performing a detailed structural and thermal analysis of crystalline A₄Ge₉O₂₀ model systems with A = Li, Na, K. The compounds were synthesized via melt crystallization and characterized using powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and Raman spectroscopy techniques. The results demonstrate clear cation-dependent crystallization pathways. The Li-containing system predominantly forms Li₂Ge₇O₁₅ in mixture with Li₄Ge₉O₂₀, indicating a preference for thermodynamically stable phases. The Na-system successfully yields the target Na₄Ge₉O₂₀ compound. In contrast, the K-system primarily produces the likely metastable K₂Ge₄O₉ phase with a significant amorphous fraction, highlighting the role of kinetic limitations. This comparative study demonstrates that the size of the alkali cation is a critical factor for controlling phase formation under identical stoichiometric and thermal conditions. Full article

Folate receptor alpha is expressed at low levels in normal tissues, but is elevated in aggressive breast cancer types and can be utilized for targeted nanoparticle delivery. Hence, we prepared a hybrid nanocarrier based on in-house synthesized mesoporous silica nanoparticles (MSNs) which were further lipid-coated and reinforced with folic acid (FA). Thorough physicochemical evaluation was performed including dynamic light scattering (DLS), powder x-ray diffraction (PXRD), thermogravimetric analysis (TGA), and nitrogen physisorption. In vitro dissolution of the model drug doxorubicin was carried out in release media with pH 7.4 and pH 5.5. The cytotoxic potential and cellular uptake were investigated in MCF-7 breast cancer cells via the MTT assay, doxorubicin fluorescence measurement, and microscopy. The potential amelioration of doxorubicin’s cardiotoxicity was evaluated in vitro on the H9c2 cell line. The results showed MSNs with significant pore volume (1.38 cm³/g) and relatively small sizes (98.05 ± 1.34 nm). The lipid coat and FA attachment improved the physicochemical stability and sustained release pattern over 24 h. MSNs were non-toxic, while when doxorubicin-loaded, they caused moderate cytotoxicity. The highest cytotoxic activity was observed with folate-functionalized, doxorubicin-loaded nanoparticles (NPs). Even though non-loaded folate-functionalized NPs exhibited significant cytotoxicity, their physical mixture with doxorubicin was inferior in MCF-7 cytotoxicity as opposed to the corresponding loaded nanocarrier. Fluorescence-based quantification showed a higher intracellular accumulation of doxorubicin when delivered via NPs. These results demonstrate the potential to use folate-functionalized NPs as carriers for doxorubicin delivery in breast cancer cells. Its cardiotoxicity was significantly reduced in the case of loading onto the folic acid-functionalized lipid-coated MSNs. All these findings provide a promising proof-of-concept, although further experimental validation, particularly regarding targeting selectivity and safety, is required. Full article