Search Results (9,858)

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

Background: Special Forces Operators often carry out missions in conditions where the use of motor vehicles is impossible. Additional external load across areas with variable inclination may reduce walking efficiency and consequently limit the combat capability of soldiers. The aim of the study was to determine how ground inclination affects the spatiotemporal structure of gait in Special Forces Operators (SFO) with different military loads. Methods: The study included 50 operators from Polish special forces units. Measurements of walking were performed using the h/p/cosmos Gaitway 1D + 3D treadmill. Tests were conducted at four uphill inclination levels: 0%, 5%, 10%, and 15%. Each participant completed trials both without external load and with a 27 kg load (helmet, tactical vest, and backpack). Statistical analyses were performed using the Friedman test, the Durbin–Conover post hoc test, and linear mixed models (LMM) to assess interaction effects. The Robinson Symmetry Index (SI) was calculated to assess asymmetry between the dominant and non-dominant limbs. Results: Increasing inclination caused statistically significant changes in the spatiotemporal structure of gait. The greatest modifications were observed at 10–15% inclinations, particularly under the maximum load of 27 kg. A significant shortening of step length and gait cycle time was noted, while cadence showed a slight upward trend, especially at a 15% inclination with the highest load. Step width remained stable. Conclusions: Ground inclination, especially when combined with the additional mass of military equipment, significantly affects the locomotion of Special Forces Operators. The stable SI values and consistent step width indicate a high level of gait stability and effective adaptive mechanisms. However, the extent of spatiotemporal modifications observed at inclinations of 10–15% with a 27 kg load may increase the risk of overuse injuries among operators. Full article

Financial exclusion remains a critical barrier to sustainable economic development in emerging economies, particularly among microentrepreneurs who depend on informal financial credit (IFC) to sustain their livelihoods. This study aims to examine the determinants and consequences of IFC utilization and their relationship with distinct delinquency patterns among microentrepreneurs in the Peruvian Amazon. A cross-sectional survey was administered to 310 microentrepreneurs from the central market of Yurimaguas during the first quarter of 2024 using partial least squares structural equation modeling (PLS-SEM). Four determinants of IFC—motivation, lender choice, loan conditions, and financial stress—were tested alongside their influence on three delinquency types: accidental, intentional, and negligent. The results indicate that psychological motivation and social lender choice are the primary and statistically significant drivers of IFC utilization, whereas loan conditions showed no significant association. Regarding delinquency outcomes, IFC is significantly and positively associated with accidental and intentional delinquency, yet paradoxically shows a significant negative association with negligent delinquency, suggesting that trust-based social enforcement mechanisms embedded in informal lending relationships may constrain negligent default behavior. These differentiated effects underscore the dual nature of informal credit as both a livelihood-sustaining mechanism and a source of financial vulnerability. The findings contribute to the understanding of financial sustainability in excluded populations by providing empirical evidence that effective interventions must address the psychological and relational dimensions of credit behavior, rather than focusing solely on structural loan characteristics. Key limitations include the cross-sectional design, which precludes causal inference, and the geographic focus on a single market in the Peruvian Amazon, which restricts generalizability. This study offers actionable insights for policymakers and microfinance institutions seeking to design inclusive financial strategies aligned with Sustainable Development Goals 1 (No Poverty), 8 (Decent Work and Economic Growth), and 10 (Reduced Inequalities). Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Work–life balance (WLB) has become a critical component of social sustainability, yet empirical evidence remains uneven across economic sectors. While existing research predominantly focuses on service-oriented and public-sector occupations, comparatively little is known about the determinants of WLB in manufacturing, where high job demands, limited flexibility, and structural constraints on autonomy often characterize work. Addressing this gap, the present study examines how job characteristics, support mechanisms, and individual resources shape the likelihood of achieving WLB among manufacturing employees in a rapidly developing European economy. Drawing on the Job Demands–Resources (JD–R) framework, the study employs survey data from 361 manufacturing employees and estimates a series of Probit regression models. To facilitate a meaningful analysis, composite indices were constructed to capture job demands, job flexibility, organizational and social support, psychological boundaries, and overall well-being. Predicted probabilities were used to evaluate both direct effects and interaction patterns in the Probit models. The findings indicate that manageable job demands and individual resources, particularly well-being and effective self-management, are the strongest predictors of WLB. Job flexibility demonstrates a slight positive effect; however, when accounting for individual and structural factors, formal organizational and social support mechanisms do not show statistically significant direct effects. Furthermore, our analysis provides no empirical support for moderating effects, as the interaction terms between job characteristics and support variables are not statistically significant. This suggests that support mechanisms do not consistently modify the relationship between job demands, flexibility, and WLB within the analyzed sample. Overall, the findings underscore the importance of combining supportive organizational contexts with manageable work demands and individual resources to promote sustainable work–life balance in manufacturing. The study contributes sector-specific empirical evidence to sustainability research and offers practical insights for designing socially sustainable work environments in industrial settings. Full article

This work considers optimal mechanical–electrical power conversion across rigid linkages equipped with current-controlled actuators. A novel cost function derived from a generalization of the Second Law of Thermodynamics is adopted from our previous work, where cycle-averaged energies are interpreted as generalized temperatures. A cost function based on generalized entropy generation is used to formulate an optimal control problem yielding a decoupled velocity feedback controller. Suboptimal gains are found, which are independent of both the excitation characteristics and the mechanical subsystem dynamics, and yield closed-loop stability. The effectiveness and simplicity of the resulting controller is demonstrated by a Monte Carlo simulation study, where random episodes of unknown, periodic forcing are applied under the proposed controller and compared with a maximum-efficiency controller. Results show that the proposed controller offers a higher statistical expectation for the average harvested power. Full article

To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side consistency-aware aggregation, while incorporating governance as a system-level support module. Under strong non-IID conditions, the proposed soft-weight aggregation strategy mitigates update mismatch and improves convergence stability without hard filtering legitimate but distributionally shifted client contributions. Meanwhile, the risk-aware perturbation mechanism adaptively adjusts clipping and noise strength across clients to better balance privacy protection and model utility. An on-chain governance and off-chain training coordination mechanism is further introduced to support auditable and traceable collaboration without interfering with the main optimization process. Experimental results on EuroSAT_RGB with ResNet-18 show that the proposed design achieves more stable training and better overall performance than the compared baselines, especially under severe heterogeneity. These findings highlight the value of jointly considering privacy-aware perturbation and consistency-aware aggregation for improving training stability and preserving utility in geospatial federated learning under statistically heterogeneous settings. Full article

Introduction: Accurately meeting the energy requirements of patients in intensive care units (ICUs) is crucial to prevent catabolism, muscle loss, and complications. We assessed their energy needs in this study using indirect calorimetry (IC) and predictive formulas, comparing the results with delivered energy intake and evaluating agreement. Materials and Methods: A total of 38 mechanically ventilated patients in seven ICUs at Kayseri City Hospital were included; eligible patients were ≥18 years old and mechanically ventilated for at least 24 h. Disease severity and nutritional risk were evaluated using validated indices (prognostic nutritional index (PNI) and Modified Nutrition Risk in the Critically Ill (mNUTRIC)), and basal energy expenditure (BEE) was measured by IC and calculated using the Harris–Benedict (HB) and ESPEN formulas. IC measurements lasted 15 min under resting conditions in conscious patients and, according to acute phase criteria, in unconscious patients in a quiet, temperature-controlled environment. Nutrition was provided enterally or parenterally based on patient condition and disease severity. Agreement between IC and predictive formulas was assessed using Bland–Altman analysis, a statistical method that evaluates agreement between two measurement techniques. Results: Estimated energy requirements differed significantly from delivered energy intake (p < 0.001). IC-derived values were significantly lower than those estimated by the HB equation and ESPEN recommendations (p < 0.001), suggesting that predictive equations may overestimate energy requirements in this population. By contrast, delivered energy intake was lower than IC-measured values, with a mean difference of approximately 503 kcal, indicating a potential risk of underfeeding in clinical practice. Weak correlations were observed between methods (IC vs. HB: r = 0.35, p = 0.003; IC vs. ESPEN: r = −0.21, p = 0.02), indicating limited agreement between predictive equations and IC measurements, and Passing–Bablok regression analysis further supported this lack of agreement between methods. Conclusions: The energy intake delivered to patients was lower than the calculated values. Indirect calorimetry is important for accurately monitoring and determining energy requirements based on delivered energy intake, and further research in this area is needed. These findings highlight the importance of individualized monitoring of energy expenditure in critically ill patients and suggest that reliance solely on predictive equations may lead to clinically relevant discrepancies in energy delivery. Full article

Pancreatic lipase (PL) inhibition is a promising dietary strategy for obesity management. In this study, the inhibitory mechanisms and structural basis of polyphenols extracted from different sugarcane fractions were investigated using in vitro enzyme assays, spectroscopy, and molecular docking analyses. PL inhibitory activity was evaluated using p-nitrophenyl laurate (pNPL) as the substrate, with all assays performed in triplicate and results statistically analyzed. Among the extracts, sugarcane peel polyphenols (SP) exhibited the strongest inhibition, with a half-maximal inhibitory concentration (IC₅₀) of 31.56 mg/mL, significantly lower than that of sugarcane juice polyphenols (SJ, 55.86 mg/mL) and sugarcane bagasse polyphenols (SB, 65.31 mg/mL). Enzyme kinetic analyses revealed a reversible mixed-type inhibition mechanism. In contrast to crude extracts, individual phenolic monomers showed substantially lower IC₅₀ values (0.13–1.33 mg/mL), highlighting the intrinsic dilution. Compositional analysis identified ferulic acid, gallic acid, chlorogenic acid, and schaftoside as key contributors to PL inhibition. Fourier transform infrared (FTIR) and fluorescence spectroscopy demonstrated that polyphenols altered PL secondary structure by modulating α-helix and β-sheet contents and perturbed the microenvironment of tryptophan (Trp) and tyrosine (Tyr) residues. Molecular docking further indicated that these compounds bind within or near the substrate-binding channel via hydrogen bonding and hydrophobic interactions, engaging critical residues including Ser152, His263, and Phe77, and potentially influencing conformational elements involved in active-site accessibility. Collectively, these results suggest that sugarcane, particularly its peel, represents a valuable natural source of PL inhibitors. Despite the relatively high IC₅₀ values of crude extracts, their inhibitory activity arises from multicomponent contributions and supports their potential application as dietary modulators of fat digestion rather than as pharmaceutical lipase inhibitors. Full article

Multi-radar fusion is fundamental for robust, all-weather perception for diverse applications. However, current fusion paradigms face structural and computational bottlenecks. Traditional statistical frameworks suffer from an explosion of dimensional calculation, where computational complexity scales with the number of active sensor nodes. Concurrently, existing statistical and deep learning fusion models exhibit systemic brittleness; their rigid topological binding to predefined sensor counts leads to a drop in performance during sensor dropouts. Furthermore, generic attention mechanisms suffer a phenomenological mismatch with radar signals, neglecting the spatial features of radar targets and leading to false alarms. To overcome these limitations, we propose RadarsBEV, a scalable end-to-end multi-radar detection framework. By decoupling per-sensor feature extraction from the central spatial fusion process, RadarsBEV achieves permutation invariance. This design breaks the scalability limit and enables graceful degradation utilizing residual nodes without system downtime. Crucially, we introduce a physics-aware Gaussian cross-attention mechanism. By guiding sparse feature sampling through predicted two-dimensional Gaussian target geometry, this mechanism decouples attention weights from clutter signal. Extensive experiments on high-fidelity simulations and real-world datasets demonstrate that RadarsBEV achieves better detection performance. Notably, the framework exhibits robust configuration zero-shot generalization, adapting to entirely unseen spatial layouts and degraded operational environments without fine-tuning. Full article

Background/Objectives: The optimal management of anterior cruciate ligament (ACL) rupture remains debated, especially regarding long-term outcomes after early ACL reconstruction (ACLR) versus rehabilitation-first with optional delayed ACLR. The interpretation of randomized evidence is complicated by frequent treatment crossover. This review synthesized evidence from randomized controlled trial (RCT) cohorts comparing surgical versus rehabilitation-first management strategies across available follow-up durations. Methods: A structured review based on a systematic literature search and narrative synthesis was conducted, with study identification and reporting guided by PRISMA 2020. MEDLINE (via PubMed) and Google Scholar were searched in February 2026 for English-language human RCTs (2000–2026) comparing early ACLR plus rehabilitation with rehabilitation-first management allowing delayed ACLR for persistent instability. A linked-report PubMed search using the KANON trial registration number (ISRCTN84752559) was additionally performed to identify cohort-derived follow-up publications. Reports were grouped by underlying RCT cohort. Data were extracted on crossover, follow-up, and clinical outcomes. Risk of bias for primary RCT reports was assessed with Cochrane RoB 2. Results: Twenty-seven reports representing three RCT cohorts (KANON, COMPARE, ACL SNNAP) were included; six index reports were prioritized for synthesis. In acute ACL rupture (KANON, COMPARE), early ACLR did not show a consistent long-term superiority in patient-reported outcomes versus rehabilitation-first with optional delayed ACLR, although COMPARE reported a statistically significant 2-year subjective functional difference favoring early ACLR; early ACLR more consistently improved mechanical stability and reduced instability episodes. Crossover from rehabilitation to delayed ACLR was common. In non-acute ACL injury with persistent symptomatic instability (ACL SNNAP), surgery-first improved 18-month patient-reported outcomes. Meniscal procedure rates and osteoarthritis-related outcomes did not consistently favor early ACLR. Conclusions: In acute ACL rupture, rehabilitation-first with timely access to delayed ACLR appears to provide long-term patient-reported outcomes comparable to an early ACLR strategy in many patients, while early ACLR more consistently improves knee stability. In non-acute symptomatic ACL deficiency, a surgery-first strategy appears more effective in the mid-term. These randomized trials should be interpreted as comparisons of management strategies rather than of “pure” operative versus nonoperative treatment approaches. Full article

Recently, two novel extensions of the Weibull distribution have been introduced through Manly’s exponential transformation, offering a flexible mechanism for modeling skewness, tail behavior, and complex hazard rate structures. In this study, we develop a comprehensive theoretical and inferential framework for one of these models, referred to as the Baker–T1 distribution, to establish it as a mature and practically viable lifetime model for reliability and survival analysis. While the Baker–T1 model exhibits remarkable flexibility in capturing skewness, tail behavior, and complex hazard rate shapes, its statistical properties and practical performance have not yet been systematically investigated. To bridge this gap, we derive a wide range of fundamental distributional characteristics, including reliability measures, hazard and reversed-hazard functions, quantiles, moments, skewness, kurtosis, dispersion indices, and order statistics, establishing the model’s analytical tractability and structural richness. An extensive inferential framework is introduced by implementing eight classical estimation techniques, and their finite-sample behavior is rigorously examined through a large-scale Monte Carlo simulation study under diverse parameter configurations. The practical relevance of the Baker–T1 model is further demonstrated using two genuine datasets from biomedical and engineering domains, where it consistently outperforms thirteen competing lifetime distributions according to likelihood-based and information-theoretic criteria. Full article

Background: Trace elements function as essential micronutrients involved in oxidative balance, mitochondrial activity, and cardiovascular metabolism. Cigarette smoking represents a significant source of toxic metals and may disrupt systemic trace element homeostasis. Alterations in micronutrient and metal balance may contribute to oxidative stress, endothelial dysfunction, and myocardial remodeling, which are central mechanisms in the pathogenesis of heart failure with preserved ejection fraction (HFpEF). This study aimed to investigate whether smokers with HFpEF exhibit distinct hair trace element profiles compared with smokers without HFpEF. Methods: In this prospective pilot study, scalp hair samples were collected from adults undergoing clinical evaluation for suspected cardiovascular disease. Trace element concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS). Participants were first stratified according to smoking status and subsequently, within the smoker subgroup, according to HFpEF diagnosis based on the Heart Failure Association Pre-test assessment, Echocardiography and natriuretic peptide score (HFA-PEFF) algorithm. Differences in trace element concentrations were analyzed using appropriate statistical tests, with multiple-comparison correction using the Benjamini–Hochberg false discovery rate (FDR). Active smoking was defined as ≥10 cigarettes per day for at least 1 year, and cumulative exposure was quantified in pack-years. Results: Fifty-eight participants were included, including 27 active smokers. In unadjusted analyses, several trace elements differed between smokers with HFpEF and those without HFpEF, including vanadium, lithium, aluminum, and copper. However, after FDR correction, only copper remained significantly elevated in smokers with HFpEF (q = 0.004). Hair copper concentrations were markedly higher in the HFpEF group compared with smokers without HFpEF. These differences were observed alongside echocardiographic features consistent with diastolic dysfunction and structural cardiac remodeling. Conclusions: In this hypothesis-generating pilot study, smokers with HFpEF demonstrated elevated hair copper concentrations, suggesting disturbances in trace element and micronutrient homeostasis. Altered copper metabolism may reflect oxidative stress-related cardiometabolic remodeling associated with HFpEF. These findings raise the hypothesis that cardiometabolic phenotype, rather than smoking exposure alone, may modulate trace element homeostasis in HFpEF; however, causal relationships cannot be established. Full article

The high-quality development of the aquaculture industry is of great significance for ensuring food security and promoting regional economies, and improving the technological level is an important driving force for breaking through industry bottlenecks and enhancing core competitiveness. This study aims to deeply explore the multi-dimensional driving mechanism and enhancement path of aquaculture competitiveness. Based on the theoretical framework of Porter’s Diamond Model, it uses panel data from 30 provinces in the “China Fishery Statistical Yearbook” from 2014 to 2025 to empirically test the impact of technological level, resource endowment, policy support, related industries, and market scale on industry competitiveness using a partial least squares structural equation model. The empirical results show that technological level and resource endowment not only directly drive the development of supporting industries and market expansion, but also enhance overall efficiency through mediation transmission mechanisms. Policy support can stimulate market demand. Related industries play an important role in enhancing competitiveness. Full article

Background: Adverse pregnancy outcomes such as preeclampsia (PE), preterm birth, low birth weight (LBW), small for gestational age (SGA), and stillbirth are major contributors to maternal and neonatal morbidity and mortality. Elevated maternal homocysteine (Hcy) levels, influenced by genetic, dietary, and lifestyle factors, have been increasingly associated with placental dysfunction and adverse pregnancy outcomes. This review aims to evaluate the link between hyperhomocysteinemia and pregnancy complications to inform clinical practice. Methods: A comprehensive search of PubMed, Scopus, Web of Science, and Cochrane Central Library was conducted up to December 2024. Observational studies assessing maternal Hcy levels in relation to pregnancy complications were included. Heterogeneity was measured using the I² statistic, and a random-effects model using the DerSimonian–Laird method was applied to account for study variability. Effect sizes were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results: Thirteen studies were included in this meta-analysis. Elevated maternal Hcy was significantly associated with: PE (OR: 2.49; 95% CI: 1.41–4.40; I² = 96.03%; n = 9), preterm birth (OR: 4.01; 95% CI: 1.84–8.72; I² = 91.08%; n = 6), fetal loss (OR: 1.76; 95% CI: 1.22–2.52; I² = 41.47%; n = 6), SGA (OR: 1.69; 95% CI: 1.35–2.11; I² = 0.00%; n = 3), and LBW (OR: 2.46; 95% CI: 1.37–4.43; I² = 77.71%; n = 3). Conclusions: This review highlights a significant association between elevated maternal Hcy levels and various pregnancy complications. However, given the substantial heterogeneity and reliance on observational evidence, these findings should be interpreted with caution. Future well-designed prospective cohort studies with standardized definitions of hyperhomocysteinemia, consistent timing of exposure assessment across pregnancy trimesters, and adjustment for key confounders are needed to better clarify these associations and underlying mechanisms. Full article