Search Results (10,399)

Background/Objectives: Poincaré plot analysis represents a nonlinear approach to heart rate variability (HRV) assessment, but the physiological meaning of several derived parameters remains unclear. This study aimed to evaluate associations between selected Poincaré plot-derived parameters, conventional HRV indices, and cardiovascular autonomic reflex tests in a real-world clinical population. Methods: This observational study included 269 adult patients referred for evaluation of suspected autonomic dysfunction. All participants underwent short-term resting ECG, cardiovascular autonomic reflex testing, and 24 h Holter ECG monitoring. Poincaré plot-derived parameters were analyzed in relation to short- and long-term HRV measures using the Spearman correlation with false discovery rate correction, and group comparisons were performed based on reflex test results. Results: Several Poincaré plot-derived parameters showed strong correlations with long-term HRV indices. VLI and LA were primarily associated with global and long-term autonomic variability, whereas VAI and SA were more closely related to parasympathetic modulation. Associations with short-term HRV were generally weak. Lower values of selected parameters were observed in patients with abnormal parasympathetic reflex tests, while no significant differences were found in relation to orthostatic hypotension. Conclusions: Poincaré plot-derived parameters capture complementary aspects of autonomic regulation beyond conventional HRV indices and may enhance autonomic phenotyping in clinical settings. Full article

Background/Objectives: Population aging has been accompanied by increased institutionalization of older adults and a high prevalence of vitamin D deficiency in this group. Although the literature suggests a possible relationship between vitamin D and cognition, findings remain inconsistent, particularly in institutional settings. This cross-sectional study aimed to investigate factors associated with vitamin D deficiency in institutionalized older adults, emphasizing the role of vitamin D supplementation and length of institutionalization, as well as to evaluate the association between serum vitamin D levels, cognitive decline, and dementia. Methods: A total of 104 older adults living in different long-term care institutions (LTCFs) in the city of Pelotas, RS, Brazil, were evaluated. Sociodemographic, clinical, and nutritional data were collected via interviews and medical record review. Serum 25-hydroxyvitamin D levels were categorized according to the Institute of Medicine cutoffs (<20 ng/mL and ≥20 ng/mL). Cognitive decline was assessed using the Mini-Mental State Examination, and dementia was evaluated with the Clinical Dementia Rating scale. Analyses included bivariate tests and binary logistic regression. Results: A high prevalence of vitamin D deficiency (52.9%), cognitive decline (83.6%), and questionable or mild dementia (79.4%) was observed. In multivariate analysis, vitamin D supplementation remained independently associated with vitamin D deficiency, whereas no significant association was observed between vitamin D levels and cognitive decline or dementia. Conclusions: Vitamin D deficiency in institutionalized older adults is predominantly associated with contextual and care-related factors rather than cognitive impairment, highlighting the importance of systematic nutritional monitoring and vitamin D supplementation strategies in institutional settings. Full article

The study presents a comprehensive spatio-temporal assessment of meteorological drought in the Upper Vistula basin, a region located in southern Poland. The analysis was based on monthly precipitation data from 30 meteorological stations covering the period 1961–2022. These data were used to calculate the Standardized Precipitation Index (SPI) for accumulation periods of 3, 6, 9, 12, 24, and 48 months. Drought events were identified using run theory, adopting a threshold of SPI < −1 for all accumulation periods. On this basis, drought characteristics were determined, including the number of identified drought episodes (N), average drought duration (ADD), average drought severity (ADS), and average drought intensity (ADI). The multi-scale analysis revealed a clear dependence of drought characteristics on the time scale. Short-term droughts (SPI-3 and SPI-6) occurred frequently and were characterized by high monthly intensity but short duration. In contrast, long-term droughts (SPI-24 and SPI-48) occurred less frequently, but were marked by much longer duration and greater cumulative severity, despite lower average intensity. Spatial analyses showed substantial heterogeneity of drought characteristics within the Upper Vistula basin. The western and south-western parts of the region were particularly exposed to frequent short-term droughts, whereas long-term droughts were less frequent, but more regional in nature and resulted from accumulated, multi-year precipitation deficits affecting groundwater resources and catchment retention. The presented findings provide valuable information for improving drought monitoring systems and adaptation strategies in the Upper Vistula basin and in other climatically diverse regions of Central Europe. Full article

Artificial Intelligence (AI)-based crack detection in buildings uses computer vision and deep learning to automatically identify structural cracks from inspection images. In recent years, many studies have explored this topic, but the overall development of the field, its methodological practices, and the remaining challenges are still not fully clear. Unlike most previous reviews that focus mainly on technical methods, this study combines a large-scale scientometric mapping of the research field with a focused technical analysis of recent AI-based crack detection methods specifically applied to building structures. This study therefore provides a dual-layer review covering research published between 2015 and 2025. A total of 146 Scopus-indexed publications were analysed using Visualization of Similarities viewer (VOSviewer) to examine publication growth, thematic evolution, collaboration patterns, and citation structures. In addition, a focused technical review of 36 highly relevant studies was carried out to analyse task formulations, model families, datasets, evaluation protocols, and methodological practices. The results show a rapid increase in research activity after 2020, largely driven by advances in deep-learning and Unmanned Aerial Vehicle (UAV)-based inspections. At the same time, collaboration networks remain uneven, and citation influence is concentrated in a limited number of research communities. The technical review further shows that most studies focus on detection-level tasks, particularly You Only Look Once (YOLO)-based models, while predictive diagnostics, automated inspection reporting, and decision-oriented Structural Health Monitoring (SHM) are still rarely addressed. Current datasets and evaluation protocols also remain mostly perception-oriented, which makes it difficult to assess robustness, generalisability and long-term predictive capability. Full article

Monitoring forest density is essential for understanding ecosystem health, wildfire risk, and post-disturbance recovery. This study proposes a robust methodology to extract forest density classes exclusively using Sentinel-2 multispectral imagery combined with vegetation indices (VIs), textural parameters, and spatial clustering metrics. The approach was applied to the northern part of Euboea Island, Greece, as a pilot area severely affected by a wildfire in August 2021. Four cloud-free Sentinel-2 images (2017–2024) were selected to capture pre- and post-fire conditions. A set of nine VIs—representing vegetation vigor, chlorophyll content, soil exposure, and canopy moisture—were calculated and statistically assessed for independence. To enhance classification accuracy, texture measures (homogeneity, correlation, and entropy) and spatial autocorrelation metrics (Moran’s I, Getis-Ord Gi) were derived for selected VIs. Supervised classification was performed using the Maximum Likelihood algorithm, yielding overall accuracies up to 89.4% and kappa coefficients above 0.85 when combining VIs with texture and spatial metrics. Results revealed a dramatic 49.3% reduction in forest cover immediately after the wildfire, with partial recovery (to 77.9% of pre-fire levels) three years later, mainly as a low-density forest. Approximately 12.1% of forest cover failed to regenerate, indicating potential long-term ecosystem degradation. The proposed approach provides a computationally efficient, high-accuracy alternative to data-fusion methods involving (Light Detection and Ranging) LiDAR or (Synthetic Aperture Radar) SAR datasets, making it suitable for operational forest monitoring and fire-risk management. Full article

Epiphytic lichens are highly sensitive components of forest ecosystems, increasingly threatened by habitat disturbance and climate change. While habitat protection remains central to lichen conservation, translocation has emerged as a promising tool to address population decline, although its global effectiveness remains poorly evaluated. This scoping review, conducted under PRISMA-ScR guidelines, analyzes 30 taxa across 12 countries to evaluate current methodologies and outcomes. The reviewed literature is largely characterized by small-scale, method-oriented interventions, with a strong predominance of thallus fragment translocation over diaspore-based approaches. Success is most often evaluated through short-term survival and persistence of transplanted material, whereas indicators of long-term population self-maintenance and reproductive viability are rarely considered. Major limitations emerge from technical constraints, including early sample loss due to inadequate fixation, as well as from mismatches between donor requirements and recipient-site microhabitat conditions. Although high initial survival is frequently reported, evidence for long-term population stability, secondary colonization, and genetic resilience remains scarce. Overall, translocation may support short-term establishment under favorable environmental conditions, mainly at local scales, but its reliability as a long-term conservation strategy requires further validation. This review identifies a critical gap in long-term monitoring and highlights the need for research priorities that enhance the effectiveness, conceptual clarity, and technical precision of future translocation efforts to ensure the persistence of epiphytic lichen populations within changing forest landscapes. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean $-75.51$ dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), $4.98\%$ (humidity), and a stable pressure offset of $-9.58$ hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications. Full article

This study presents a comprehensive investigation of OPE residues, distribution patterns, and dietary exposure risks in crustaceans from southeast China. OPEs were detected in over 90% of samples, with mean total concentrations (ΣOPEs) of 5.80 μg/kg wet weight (ww) in freshwater shrimp, 6.52 μg/kg ww in marine prawn, and 1.25 μg/kg ww in marine crab. Tributyl phosphate (TiBP), triethyl phosphate (TEP), and tris(2-chloroethyl) phosphate (TCEP) emerged as the dominant congeners, accounting for 68.1% of ΣOPEs, which indicates inputs from industrial emissions, plastic waste leaching, and aquaculture equipment. Spatial analysis revealed striking regional differences: coastal industrial cities (Zhoushan, Taizhou) exhibited ΣOPE levels up to 12-fold higher than inland mountainous areas (Quzhou, Lishui), while no significant temporal variations were observed. Human health risk evaluation, based on estimated daily intake (EDI) and target hazard quotient (THQ), demonstrated negligible non-carcinogenic risks for the general population (HI < 1), though children and frequent seafood consumers have slightly elevated exposure. These findings indicate the value of crustaceans as bioindicators for OPE contamination and require long-term monitoring of emerging OPEs and their synergistic effects with co-occurring pollutants. Full article

Background: Our study retrospectively investigated the therapeutic effects of SGLT2 inhibitors on multiple outcomes in patients with Type 2 Diabetes, capitalizing on the agent’s proven benefits in glycemic, cardiovascular, and renal systems. Methods: This retrospective cohort study investigated a total of 200 patients with T2DM, 100 SGLT2I-treated and 100 treated without SGLT2Is. Clinical data were retrieved from the electronic health record system of the hospital. Patients were followed for more than 6 months to assess the effects of SGLT2Is on metabolic, biochemical, and renal parameters. Results: In the SGLT2I-treated cohort, a higher prevalence of males, non-geriatrics, and comorbidities such as HF and ASCVD was observed with greater use of concomitant medications (beta-blockers, antithrombotics, antilipidemics). SGLT2I treatments show a greater reduction in FBG (control: −6.3 mg/dL vs. treatment: −24.2 mg/dL; p ≤ 0.05), HbA1c (control: −0.093% vs. treatment: −0.76%; p ≤ 0.001), weight (control: −0.6 kg vs. treatment: −3.6 kg; p ≤ 0.001), SBP (control: 5.8 mmHg vs. treatment: −9.2 mmHg; p ≤ 0.001), and DBP (control: 2.2 mmHg vs. treatment: −4.7 mmHg; p ≤ 0.05) compared to the control group. The analysis of the mean change in eGFR showed no statistically significant difference in both groups. The SGLT2I’s safety profile was favorable, with no difference in adverse events and no cases of euglycemic ketoacidosis or Fournier’s gangrene. Conclusions: In this study, SGLT2Is demonstrated strong clinical efficacy in improving multiple cardiometabolic parameters without compromising patient safety in short-term follow-up. Large-scale and long-term real-world studies are needed to monitor the long-term safety profile, characterize the incidence of rare adverse events in general clinical practice, and validate results from this study. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

Gangue backfilling has become an important technique for promoting environmentally friendly and low-carbon coal mining. The long-term creep behavior of cemented backfill plays a critical role in maintaining stope stability and controlling surface subsidence during long-term service. Although considerable research has been conducted on cemented tailings backfill, systematic investigations on the triaxial creep evolution, long-term strength characteristics, confining pressure effects, and the applicability of the classical Burgers model for gangue–gypsum cemented backfill under engineering-relevant confining pressures remain limited. In this study, the experimental scheme was designed based on field monitoring data from practical backfill mining operations, which indicate that the in situ backfill generally remains stable without significant deformation or instability under normal working conditions. Multi-stage loading triaxial creep tests were conducted on gangue–gypsum cemented backfill under confining pressures of 1, 2, 3, and 4 MPa. The creep deformation characteristics were analyzed using Chen’s superposition method, while the long-term strength was computed via inflection point method of isochronous stress–strain curves. The parameters of the Burgers creep model were identified using the Levenberg–Marquardt optimization algorithm, and numerical verification was performed using FLAC3D. Our findings demonstrate that the creep deformation process of the backfill consists of three typical stages: instantaneous deformation, attenuated creep, and steady-state creep, and no accelerated creep was observed within the applied stress range. The absolute creep strain surges nonlinearly with increasing stress level (S_L), whereas higher confining pressure significantly suppresses the creep response of the material. Within the investigated stress range, the backfill exhibits mainly linear viscoelastic behavior, and its critical long-term strength is not less than 0.9 times the failure deviatoric stress (qf). Although confining pressure enhances the long-term strength, the strengthening effect weakens as the confining pressure increases. Model fitting outcomes imply that Burgers model precisely describes the creep behavior of gangue–gypsum cemented backfill under all test conditions, with correlation coefficients (R²) exceeding 0.97. The identified parameters show systematic variation with S_L, reflecting stiffness degradation and viscous evolution during loading. Numerical simulation results agree well with the experimental data, providing theoretical guidance for mixture proportion optimization, long-term stability evaluation, and stope support parameter design in gangue backfill mining engineering. Full article

Background: Adolescence is a critical developmental period during which dietary habits are formed and may influence long-term health outcomes. Monitoring changes in adolescents’ eating behaviors and nutrition-related knowledge over time is important for developing effective health promotion strategies. The aim of this study was to compare adolescents’ (Lower Silesia, Poland) dietary habits and nutritional knowledge between two study periods (2011 and 2023) using comparable survey methods. Methods: A repeated cross-sectional comparison of two independent cohorts was conducted using an identical questionnaire in both study periods. The 2023 cohort included 14-year-old primary school students (n = 100; 48 girls and 52 boys), while the comparison group consisted of adolescents aged 13–15 years assessed in 2011 (n = 377; 202 girls and 175 boys). Anthropometric measurements and self-reported data on dietary habits and nutritional knowledge were analyzed using descriptive statistics and group comparison tests. Results: The findings indicate changes in selected dietary behaviors and levels of nutritional knowledge among adolescents over the studied period. A higher percentage of students in 2023 reported eating four meals per day and obtaining information about healthy eating from the Internet rather than from television. Students in 2023 were also more likely to recognize the relationship between diet and attention, identify the harmful effects of energy drinks and excessive fast-food consumption, and provide correct answers regarding proper nutrition. Nutritional knowledge improved over time, with a mean percentage of correct responses of 71.9% in 2023 compared with 63.7% in 2011. Although nutritional awareness improved in several areas, certain unhealthy eating habits remained prevalent, including irregular breakfast consumption and frequent intake of sweets. Changes in the distribution of body weight categories were also observed, with gender-specific differences between cohorts. Conclusions: The results suggest that improvements in nutritional knowledge alone may not be sufficient to ensure positive changes in dietary behavior among adolescents. Continued monitoring of adolescent nutrition and the development of comprehensive health promotion strategies addressing both knowledge and environmental influences remain necessary. Full article

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change. Full article

Effective online bond wire degradation assessment of power modules is crucial for ensuring long-term stability. However, its electrical aging indicators are often influenced by junction temperature ($T_j$), and conventional $T_j$ monitoring methods are also affected by the aging process itself, creating a contradiction. This paper proposes a thermal–electrical co-modeling method designed to reduce reliance on accurate $T_j$. A major challenge of the method is the traditional thermal network models, which rely on case temperature ($T_c$). These models are affected by thermal coupling and have a slow dynamic response, making them difficult to integrate with electrical models. To overcome this, a $T_j$ monitoring method based on in situ sensor fabrication is employed to shorten thermal conduction path and simplify thermal network. This method results in a much faster dynamic process and is unaffected by thermal coupling, as confirmed through both theoretical analysis and finite element simulation. To validate the proposed method, bond wire degradation assessment is conducted using the on-state voltage drop ($V_{ce}$). Tested in practical circuits, this design successfully enables online evaluation of bond wire degradation, which is unaffected by $T_j$. Full article