Search Results (14,781)

To address the issue of negative damping instability easily induced by DC/DC converters under constant power load (CPL) in DC microgrids and to enhance the control robustness of the system under uncertainties such as parameter perturbations, this paper designs a controller based on the linear active disturbance rejection control (LADRC) theory. Firstly, by establishing an equivalent model of the DC microgrid with CPL, the intrinsic relationship between the equivalent incremental admittance of the hybrid load and the system damping is revealed. Subsequently, treating the nonlinear characteristics of the CPL and model parameter variations as external disturbances, the linear extended state observer (LESO) is employed to estimate and compensate for the total system disturbance in real time. This effectively eliminates the risk of negative damping instability caused by the CPL and enhances the system’s robustness against parameter variations. Then, theoretical analysis is conducted from three perspectives, the convergence of disturbance estimation error, the stability of the closed-loop system, and robustness against parameter variations, thereby ensuring the reliability of the proposed control strategy. Finally, the proposed control strategy is validated through simulations and experiments. The results confirm that, even in the presence of negative damping effects and parameter variations, the strategy can effectively maintain fast tracking and stable control of the output voltage. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

Estimating the adsorption data and understanding the adsorption behavior and mechanism of organic pollutants on nanoplastics are crucial for assessing their ecological risks. Herein, in silico techniques, i.e., grand canonical Monte Carlo simulations, density functional theory computations, and quantitative structure activity relationship (QSAR) modeling, were integrated to examine the adsorption of 39 representative aliphatic and aromatic compounds and nine emerging pollutants (brominated flame retardants and phosphorus flame retardants) onto 12 different nanoplastics under atmospheric conditions. Three QSAR models were constructed to predict the adsorption equilibrium constant (logK) for polyethylene, polyoxymethylene, and polyvinyl alcohol nanoplastics individually, along with 12 QSAR models for separately estimating adsorption capacities (C_m) on different nanoplastics. Furthermore, a novel multi-dimensional prediction model was developed, enabling simultaneous, high-throughput prediction of adsorption capacities across multiple nanoplastics and pollutants with a single input. These results revealed that van der Waals and electrostatic interactions serve as the primary driving forces for the adsorption. The novel multi-dimensional prediction model facilitates rapid and comprehensive assessment of pollutant–nanoplastic interactions with one-click, and paves the way for improved risk evaluations and advancing predictive environmental research. Full article

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article

Far-field narrow-band Direction-of-Arrival (DOA) estimation is a practical challenge in passive and active sonar applications. While the Conventional Beamformer (CBF) is a robust Maximum Likelihood Estimator (MLE), its precision is inherently constrained by the discrete scanning interval. To overcome this limitation, this paper proposes a novel Model Solution Algorithm (MSA estimator that leverages the exact theoretical beam pattern of the array to resolve the DOA. Unlike the classical Parabolic Interpolation Algorithm (PIA) estimator, which exhibits significant estimation bias due to polynomial approximation errors, the proposed MSA estimator numerically solves the deterministic beam pattern equation to eliminate such model mismatch. Quantitative simulation results demonstrate that the MSA estimator approaches the Cramér-Rao Lower Bound (CRLB) with a stable RMSE of approximately 0.12° under sensor position errors and a frequency-invariant precision of ~0.23°, significantly outperforming the PIA estimator, which suffers from systematic errors reaching 1.1° and 0.75°, respectively. Furthermore, the proposed method exhibits superior noise resilience by extending the operational range to −24 dB, surpassing the −15 dB breakdown threshold of Multiple Signal Classification (MUSIC). Additionally, complexity analysis and geometric evaluations confirm that the method retains a low computational burden suitable for real-time deployment and can be effectively generalized to arbitrary array geometries without accuracy loss. Full article

Reliable estimation of crop chlorophyll status, a key indicator of photosynthetic activity and nutritional condition, is essential for supporting informed field management decisions. Recently, unmanned aerial vehicle (UAV) remote sensing has attracted considerable attention in crop chlorophyll estimation. However, research on integrating spectral indices (SI) with texture and structural information derived from high-resolution UAV imagery to estimate cotton chlorophyll remains limited, and the relative contributions of these different types of features are still unclear. This study utilized multispectral UAV imagery of cotton during the flowering stage at flight altitudes of 60 m, 80 m, and 100 m, from which the features of 12 SI, eight texture indices (TI), and four structural indices (STI) were derived. The Soil–Plant Analyzer Development (SPAD) provides an indirect yet relatively reliable assessment of leaf chlorophyll status. Accordingly, the Boruta algorithm was subsequently employed to identify variables that contribute significantly to SPAD-based estimation. For each flight altitude, SPAD estimation models were constructed based on three distinct machine learning algorithms. Subsequently, the SHapley Additive exPlanations (SHAP) framework was applied to determine key variables influencing SPAD estimation and to examine how the contributions of the three index types varied across different UAV flight altitudes. The results showed that combining UAV-derived SI, TI, and STI enables accurate estimation of cotton SPAD values. SHAP analysis further revealed the three feature types’ relative contributions to the RF model predictions. Among them, SI had the highest average model-attributed importance (59.36%), followed by STI (23.38%) and TI (17.25%). Moreover, with increasing UAV altitude, the importance of SI gradually increased, with its contribution rising from 58.79% at 60 m to 63.06% at 100 m; in contrast, the contribution of TI showed a decreasing trend, dropping from 20.42% to 12.82%. This study reveals the contributions of spectral, texture, and structural features to cotton SPAD estimation at different UAV flight altitudes, providing a clearer understanding of the relative roles of different feature types in cotton SPAD estimation. Full article

The Hatu region in the Western Junggar, Xinjiang, is one of the most significant gold metallogenic concentration areas in China. Gold mineralization is primarily controlled by several parallel NE-trending strike-slip faults and Late Paleozoic granitic plutons, accompanied by multiple stages of hydrothermal activity. To enhance the objectivity and accuracy of mineral prospecting prediction, this study develops an integrated forecasting framework that combines multi-source remote sensing datasets with machine learning techniques. Alteration anomalies related to iron staining and hydroxyl-bearing minerals are extracted from ASTER data, alteration mineral mapping is performed using GF-5 hyperspectral imagery, and Landsat-9 data is used for structural interpretation to refine the regional metallogenic framework. On this basis, these multi-source remote sensing products are then integrated to delineate five prospective metallogenic areas (T1–T5). Subsequently, a Random Forest (RF) model optimized by the Grey Wolf Optimizer (GWO) algorithm is employed to quantitatively integrate key evidence layers, including alteration, structure, and geochemistry, for estimating mineralization probability. The results show that the GWO-RF model effectively concentrates anomalous areas and identifies two high-confidence targets, Y1 and Y2, both with mineralization probabilities exceeding 0.8. Among them, the Y1 target is associated with the Bieluagaxi pluton and exhibits strong montmorillonitization, chloritization, and iron-staining alteration, typical for magmatic–hydrothermal controlled mineralization. In contrast, the Y2 target is strictly controlled by the Anqi Fault and its subsidiary faults, primarily characterized by linear chloritization and iron-staining anomalies indicative of structure–hydrothermal mineralization. Field verification confirms the significant metallogenic potential of both Y1 and Y2, demonstrating the effectiveness of integrating multi-source remote sensing and machine learning for predicting orogenic gold systems. This approach not only deepens the understanding of the diverse gold mineralization processes in the Western Junggar but also provides a transferable methodology and case study for improving regional mineral exploration accuracy. Full article

Digitalization is increasingly central to economic growth strategies, yet robust macro-level evidence on the role of SME-led e-commerce remains limited. Drawing on the Resource-Based View, this study examines how SME digitalization, internet finance, and platform-based activities influence regional economic growth in China, and how these effects depend on digital infrastructure readiness (DIR). We construct an annual panel of 30 provincial-level regions in China over 2015–2024 and estimate dynamic relationships using two-step system GMM to address endogeneity and growth persistence. The results show that SME digitalization, supply-chain efficiency, mobile payment penetration, tech-driven employment growth, platform-economy contribution, and DIR all exert statistically significant positive effects on GDP growth. Quantitatively, a 10-percentage-point increase in SME digitalization is associated with approximately 0.3-percentage-point higher regional GDP growth, while a 10-point increase in DIR corresponds to about 0.4-percentage-point higher growth. Moderation analyses reveal that DIR significantly amplifies the growth effects of e-commerce expansion, mobile payments, and digital marketing, whereas its moderating role is weaker or insignificant for cross-border payments and supply-chain efficiency. These findings reconceptualize digitalization as a coordinated bundle of complementary resources and position DIR as a critical enabling capability for translating SME digital transformation into macroeconomic growth. The study offers policy-relevant evidence for targeting infrastructure investment and digital-economy strategies in emerging platform economies. Full article

Fibromyalgia is a chronic condition characterized by widespread pain, fatigue, and reduced quality of life. Exercise therapy, including Pilates, is commonly recommended; however, current reviews report inconsistent findings across specific modalities. This PRISMA 2020 systematic review and meta-analysis with a PROSPERO-registered protocol, designed as a focused update of post-2020 RCTs complementing prior comprehensive syntheses, evaluated Pilates-based interventions for pain and fibromyalgia impact (FIQ). HRQoL outcomes were synthesized narratively due to heterogeneity in measurement instruments, and all outcomes were extracted at the first post-intervention assessment (no pooled long-term data were available). Seven RCTs (6–12 weeks; 2–3 sessions/week) met eligibility criteria. Methodological quality was generally moderate (PEDro), and risk of bias was assessed using RoB 2. Certainty of evidence (GRADE) was rated very low for pain and low for FIQ. Among trials reporting adherence (4/7), values ranged from 68% to 92%; adverse event monitoring was inconsistent (systematically reported in 2/7), limiting tolerability conclusions. Between-group effects versus active comparators were small and non-significant for pain (pooled Hedges’ g = −0.10, 95% CI [−0.83, 0.63], p = 0.79; I² = 73%); this wide interval, spanning potential benefit to harm, precludes definitive conclusions. For FIQ, the primary (unadjusted) analysis was non-significant: pooled MD = −5.53 (95% CI [−11.96, 0.89], p = 0.09); sensitivity analysis using ANCOVA-adjusted estimates yielded MD = −6.71 (95% CI [−13.11, −0.30], p = 0.04). Both estimates remained below MCID thresholds and were sensitive to estimator choice. Absence of statistical significance does not demonstrate equivalence; non-inferiority designs with predefined margins would be required. Given very low (pain) to low (FIQ) certainty of evidence, adequately powered trials with standardized protocols and longer follow-up are needed to resolve uncertainty regarding Pilates’ comparative effectiveness within multimodal fibromyalgia management. Full article

Background and Objectives: Primary open-angle glaucoma (POAG) is one of the leading ocular diseases leading to irreversible blindness and is often asymptomatic until advanced cases. While intraocular pressure reduction remains the cornerstone of treatment, neuroprotective strategies targeting retinal ganglion cell metabolism are actively investigated. Niacinamide (nicotinamide, vitamin B3), a precursor of NAD+, has shown neuroprotective potential in preclinical models. This exploratory study evaluated the short-term functional, structural, and electrophysiological effects of oral niacinamide supplementation in POAG. Materials and Methods: In this interventional study, patients with POAG received oral niacinamide 500 mg daily for six months. Visual field (VF) global and localized sensitivity (Mean Deviation [MD], Pattern Standard Deviation [PSD]), Optic Coherence Tomography (OCT)-derived peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC), and Visual evoked potentials (VEP) latency parameters (P2 1.4 Hz, P100 1°, and P100 15′) were assessed at baseline and at six months. Because both eyes from some participants were included, primary longitudinal inference was based on clustered analyses using generalized estimating equations and linear mixed-effects models to account for inter-eye correlation. Eye-level paired analyses were used for exploratory comparison. Change–change relationships across modalities were explored using Spearman correlation. Results: After accounting for inter-eye correlation, no statistically significant change in MD was detected (mean ΔMD +0.43 dB; GEE p = 0.099; LME p = 0.101), and PSD remained stable. RNFL thickness showed a small decrease (−1.26 µm; GEE p = 0.046), while GCC did not change significantly. VEP P100 latencies remained stable, whereas P2 latency showed a small increase (+3.9 ms; GEE p = 0.039). Correlation analysis revealed a moderate association between changes in GCC and MD (ρ = 0.44), suggesting concordance between macular structural stability and global visual field performance. Conclusions: When inter-eye correlation is appropriately accounted for, six months of niacinamide supplementation in POAG is associated with overall functional, structural, and electrophysiological stability, without evidence of clinically meaningful improvement or progression. These findings support short-term safety and highlight the importance of clustered analytical approaches and macular-centered biomarkers in future glaucoma neuroprotection trials. Full article

Background: A major challenge in developing effective immunological damage-control therapies for traumatic hemorrhage (TH) is the lack of animal models that accurately reproduce the immune and pathophysiological responses observed in humans. In this study, we established a clinically relevant rat model that combines blast injury with hemorrhagic shock in a simulated prolonged damage control resuscitation environment. Methods: Male Sprague Dawley rats were anesthetized and subjected to moderate blast overpressure, followed by controlled hemorrhage equivalent to 40% of the estimated total blood volume. Animals then received hypotensive resuscitation with Plasma-Lyte A at twice the shed blood volume. Plasma-Lyte A was used in our study to correct hypovolemia and electrolyte imbalances, thereby helping to standardize the traumatic hemorrhage model. Results: Four of six rats in the blast-plus-hemorrhage (B + H) group survived the 25 h observation period. During resuscitation, mean arterial pressure remained markedly below baseline for at least 4 h. The B + H insult triggered a rapid innate immune response, characterized by elevated circulating HMGB1, terminal complement activation, and increased myeloperoxidase levels. Complement deposition (C4d, C5a, and C5b-9) was evident in lung tissue, accompanied by multi-organ histopathological injury, including pronounced inflammatory cell infiltration, hemorrhage, and cellular degeneration, apoptosis, or necrosis. Metabolic disturbances, including acidosis, hyperkalemia, and dilutional anemia, were also observed. Conclusions: Overall, this model reproduced key features of inflammation-driven multi-organ dysfunction syndrome seen in human polytrauma, supporting its utility for studying TH-related immunopathology and therapeutic interventions during prolonged damage control resuscitation. Full article

Large-scale cultural events generate substantial greenhouse gas emissions, raising increasing concerns regarding carbon neutrality. In Taiwan, long-standing forest conservation policies have largely restricted commercial logging since the early 1990s, resulting in extensive secondary forests where active management options are limited. Within this policy context, improved forest management (IFM) provides a potential pathway to enhance carbon sequestration while maintaining conservation objectives. This study evaluates the feasibility of using afforestation combined with IFM to offset the carbon emissions of the Taipei Biennial 2020, estimated at approximately 390 t CO₂-e. Carbon sequestration was assessed using the Verified Carbon Standard (VCS) methodology (VM0005 v1.2) under the principles of Measurement, Reporting, and Verification (MRV). A total area of 52.70 ha was assessed, with 10.11 ha designated as the project activity area. Over a 25-year period, projected CO₂ sequestration across four baseline scenarios ranged from 3816 to 4523 tons, indicating that event-related emissions could be offset within 8–9 years. Uncertainty remains due to hypothetical management assumptions, highlighting the need for continuous monitoring and adaptive management. Full article

Background/Objectives: The aim of this study was to translate the Dietary Fat and Free Sugar-Short Questionnaire (DFS) into Turkish (DFS-TR) and to establish its construct validity and reliability. Methods: Quota sampling was used to ensure demographic homogeneity across gender and age groups. Participant distribution by age categories was proportionally aligned with the demographic statistics of the adult Turkish population. The study comprised 314 participants aged 19–64 years (38.78 ± 12.10), of which 54.5% were female. The data collection form consisted of demographic characteristics, anthropometric measurements, information on eating habits, DFS-TR, the Food Frequency Questionnaire (FFQ), and the Power of Food Scale (PFS). Results: Test–retest reliability was confirmed in the 314 participants with a 4-week interval (r = 0.997, p < 0.01). The Cronbach α reliability coefficient was α = 0.777. Using the split-half method, the correlation coefficient between the two halves was 0.681, the Spearman–Brown coefficient was 0.811, and the Guttman coefficient was 0.809. Participants’ total DFS-TR scores and sub-dimension scores varied according to age, education level, income level, anthropometric characteristics, physical activity, and dietary habits, but did not vary according to gender or marital status. To investigate convergent validity, participants’ DFS-TR scores were correlated with the FFQ and PFS. DFS-TR scores showed a significant correlation with the percentage of energy from saturated fat and free sugar assessed by the FFQ. Positive relationships were found between DFS-TR scores and the total PFS score, particularly with the sub-dimension scores for food available and food present. Conclusions: The DFS-TR can be used as a reliable and valid measurement tool for estimating saturated fat and free sugar intake among Turkish adults. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

Ammonia (NH₃) is a key precursor to secondary particulate matter in Southeast Asia, yet Thailand has lacked a country-specific, policy-focused emission inventory. This study creates the first spatially gridded (12 × 12 km) and monthly resolved national NH₃ inventory for 2019, using detailed agricultural activity data, survey-based livestock management practices, and crop-specific fertilizer application profiles. Satellite-derived burned-area data were included to constrain emissions from open burning. National NH₃ emissions are estimated at 459.1 kt per year, with an overall uncertainty of ±15.3%. Agriculture accounts for 95.8% of total emissions. Livestock and manure management contribute 225.3 kt per year (49.1%), reflecting high densities of poultry, cattle, and pigs, as well as regional differences in manure handling and storage practices that enhance ammonia volatilization. Fertilizer-related emissions total 192.4 kt per year (41.9%), with seasonal peaks during primary planting cycles, in contrast to the more episodic biomass-burning emissions. Comparison with the global EDGARv8.1 inventory shows significant sectoral and temporal differences, including considerably higher livestock emissions and lower fertilizer emissions in this study, due to Thailand-specific emission factors and temporal emission allocation methods. These findings clarify the spatial and temporal drivers of NH₃ emissions in Thailand and offer actionable insights for targeted mitigation—notably improved manure management and optimized nitrogen use in regions where dry-season emissions coincide with severe PM_2.5 episodes. The THAI-NH₃ Inventory provides a strong foundation for chemical-transport modeling and evidence-based policymaking to reduce ammonia-related haze in Thailand. Full article