Search Results (1,453)

The rapid development of renewable energy generators (REGs) has increased the uncertainties and security risks in power systems. Furthermore, extreme weather conditions impose higher demands on the secure operation range of power systems. Energy storage systems (ESSs), with fast power regulation capability, can smooth fluctuations of REGs and mitigate risks of power deficits and power flow violations under extreme events. To this end, this paper proposes an ESS siting and sizing model that considers the economic efficiency, security, and resilience requirements. First, to overcome drawbacks of existing ESS planning methods that ignore the resilience requirement under extreme events and the strong nonlinearity of power flow entropy indicator reflecting system security margins, the loading rate balance (LRB) indicator is developed to describe the safety and resilience of transmission grid and is incorporated into the ESS planning model in a first-order dispersion form to keep the optimization model linear. Second, a coordinated ESS planning and dispatch optimization model is formulated to minimize the equivalent daily planning cost, daily dispatch cost, and LRB, subject to secure operation constraints of the power system under renewable generation uncertainties. Third, a sample average approximation -based chance-constrained approach is proposed in the ESS planning model to characterize the uncertainties of wind and solar power to avoid distributional dependence and the curse of dimensionality. Detailed simulations validate the effectiveness of the proposed ESS planning method in terms of improving economic efficiency while ensuring system security and resilience. Full article

Efficient and sustainable management of urban water resources is challenged by increasing water demand and water losses in distribution networks. Developing a Standard Water Balance (SWB) at the District Metered Area (DMA) scale is an effective method for quantifying water losses and supporting Non-Revenue Water (NRW) reduction strategies. This study applies a monthly SWB to a pilot DMA in Antalya, Türkiye. System input volumes were obtained from SCADA-based flow measurements, while billed authorized consumption was calculated using customer billing records from the local water utility. The results show distinct monthly variations in system input volume, with higher water losses observed during periods of increased demand. These findings demonstrate that a monthly, DMA-based SWB is a practical decision-support tool for sustainable water loss management. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article

Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup. Full article

Background/Objective: Blood flow restriction (BFR) training has emerged as a promising adjunct to rehabilitation following anterior cruciate ligament reconstruction (ACLR), but its effectiveness across postoperative outcomes remains uncertain. To systematically evaluate and quantify the effects of BFR training on rehabilitation outcomes after ACLR. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines. Ten electronic databases were searched from inception to December 2025. Eligible studies included randomized and non-randomized clinical trials investigating BFR after ACLR. Risk of bias was assessed using RoB 2 and ROBINS-I. Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using random-effects models, and certainty of evidence was evaluated using GRADE. Results: Seventeen studies involving 643 participants were included, of which 12 contributed to the meta-analysis. Most studies combined low-load resistance training with BFR at 40–80% limb occlusion pressure (LOP), initiated within 0–12 weeks after surgery and continued for 2–16 weeks. Compared with conventional rehabilitation, BFR significantly improved quadriceps strength (n = 9; SMD = 0.77, 95% CI 0.42–1.13; p < 0.0001; moderate-certainty evidence) and functional recovery (n = 6; SMD = 1.47, 95% CI 0.08–2.87; p = 0.04; I² = 91%; low-certainty evidence). Larger effects were observed at occlusion pressures >80% LOP. No serious BFR-related adverse events were reported. No significant effects were found for balance (n = 5; SMD = 0.22, 95% CI −0.96 to 1.40; p = 0.72) or pain (n = 5; SMD = 0.82, 95% CI −0.28 to 1.92; p = 0.14), both supported by very low-certainty evidence. Conclusions: Moderate-certainty evidence supports BFR training for improving quadriceps strength after ACLR. Evidence for functional recovery is limited by substantial heterogeneity, while effects on pain, postural balance, and muscle morphology remain inconclusive. Findings regarding optimal occlusion pressure should be considered exploratory pending confirmation in future trials. Full article

Background: Type II workplace violence by patients, relatives, or visitors is an occupational health and patient-safety concern in primary care. Cranston Ridge Medical Clinic (CRMC), a single urban family medicine and walk-in primary care clinic in Calgary, Alberta, plans to implement a universal, risk-informed workplace-safety bundle that is based on observable behaviour, situational risk, and documented safety concerns rather than demographic profiling. Methods: This article describes a single-site internal quality improvement and workplace-safety evaluation protocol. The comparison is CRMC usual practice during the pre-implementation baseline period; there is no concurrent external control group. The planned evaluation will use aggregate, de-identified operational data from a 12-month pre-implementation baseline, a four-week implementation period, and 12 months of post-implementation monitoring. All clinic staff will receive workplace-safety training as part of routine implementation. No staff, patients, or visitors will be recruited as research participants, and the evaluation will not use individual-level staff survey, interview, or focus-group data. Patient/visitor information will be used only as aggregate operational monitoring data when needed to assess safety, access, patient flow, and complaints. Intervention and analysis: The bundle includes worksite analysis, staff training, a brief arrival safety screen, a response algorithm, standardized reporting, monthly safety huddles, and post-incident support. The primary metric will be the Type II workplace-violence incident rate per 1000 clinic visits. Planned analyses include run charts, pre–post rate ratios, and Poisson or negative binomial segmented regression if monthly counts are sufficient. Implementation learning will be summarized from routine training records, safety-huddle summaries, post-incident debrief themes, and other aggregate de-identified operational indicators. Expected contribution: The protocol contributes a transparent, equity-sensitive, and operationally feasible model for balancing staff safety with patient access in primary care. Full article

Corporate balance sheets report aggregate equity and liability totals but conceal the internal allocation of financing sources across asset categories—an identification problem that conventional econometric methods cannot resolve without additional parametric assumptions. This paper develops a maximum entropy (ME) panel estimator to recover two latent scalar parameters: x ∈ (0,1), the share of equity capital directed toward long-term asset financing, and y ∈ (0,1), the corresponding debt allocation share. Grounded in maximum entropy principle, the estimator selects the unique parameter vector that satisfies the mean-level balance-sheet constraint while maximising joint Shannon entropy—the least-biassed solution consistent with observable data. The closed-form logistic representation yields a scalar Lagrange multiplier λ*, interpreted as a financing pressure index, recoverable via bisection in at most 21 iterations at tolerance ε = 10⁻⁵. Building on the ME estimates, we introduce a continuous matching alignment index M* = x* − y* that measures the degree of compliance with the financial matching principle along a continuous spectrum rather than as a binary categorisation. Applied to a ten-firm, cross-sectoral panel spanning Technology, Finance, Energy, and Automotive sectors over an observation window spanning 2001 to 2025 (with firm-specific subperiods reflecting differences in IPO dates and data availability), the framework reveals substantial heterogeneity in latent financing flows: equity allocation shares range from 30.1% (NVIDIA) to 75.1% (ExxonMobil), while debt allocation shares span 37.1% to 77.5%. Across the panel, only Meta exhibits substantial positive matching alignment, while Microsoft, ExxonMobil, Apple, and Tesla show only very slight differences that fall within the neutral band, and the remaining firms show varying degrees of structural departure from the matching benchmark; the thresholds used to summarise these descriptive labels are interpretive aids rather than re-imposed binary criteria, and the substantive ranking of firms along M* does not depend on the specific threshold values adopted. The ME solution’s entropy H(x*, y*) and the normalised diversification index D(x*, y*) describe allocation balance under the estimator’s information–theoretic criterion rather than independently observed firm complexity; in the present sample, the cross-firm ordering of these values is not recovered by firm size, leverage, or sector classification alone. These findings, based on a ten-firm case-study panel with time-invariant allocation parameters, should be interpreted as descriptive patterns of the present sample rather than statistically validated regularities. They provide a theoretically rigorous and computationally tractable identification of unobservable corporate financing flows, with potential implications for capital structure theory, financial risk assessment, and balance sheet analysis that would benefit from validation on larger and more representative samples in future work. Full article

Background/Objectives: Obesity is a major public health problem associated with chronic inflammation and functional alterations in multiple organs and systems. Few studies have examined colostrum from obese mothers, particularly with respect to macrophage function, enzyme and cytokine concentrations, and the role of melatonin in immune modulation. This study aimed to evaluate melatonin levels and their effects on macrophage polarization, cytokine concentrations, nitric oxide synthase [iNOS], and arginase in colostrum from obese mothers. Colostrum samples were collected from eutrophic mothers [BMI: 18.5–24.9 kg/m²] and obese mothers [BMI: ≥30 kg/m²]. Methods: Macrophages were isolated by density gradient and treated with melatonin. The expression of M1 and M2 macrophages and cytokine concentrations were assessed by flow cytometry, while melatonin levels in colostrum supernatants, iNOS, and arginase in cell lysates were determined by ELISA. Results: An endogenous increase in melatonin was also observed in the colostrum of obese mothers. Maternal obesity has been shown to reduce M1 and M2 macrophage expression, increase nitric oxide synthase [NOS] activity, and elevate interleukin-6 [IL-6] and interleukin-17 [IL-17] levels. However, melatonin treatment restored M1 and M2 macrophage levels and reduced inducible nitric oxide synthase [iNOS] and arginase production to levels similar to those observed in mothers of healthy weight. Conclusions: these findings suggest that maternal obesity creates a pro-inflammatory environment in colostrum, characterized by altered macrophage polarization, altered cytokine secretion, and an imbalance in the enzymatic activities of iNOS and arginase within the L-arginine metabolic pathway. Both natural and supplemental melatonin exhibited immunomodulatory, antioxidant, and anti-inflammatory effects, helping to restore immune balance in colostrum. These results emphasize the potential benefits of melatonin as an immunometabolic modulator and its contribution to understanding immunometabolic regulation in obese mothers. Full article

Background/Objectives: Endodontic perforation repair requires biomaterials that balance sealing ability with minimal cellular injury. AH Plus (epoxy resin-based) remains widely used despite cytotoxicity concerns. CeraSeal (calcium silicate-based bioceramic) is a potentially more biocompatible alternative. However, comparative data on sealer-induced cytotoxicity and inflammatory responses remain limited. This study compared the cytotoxicity and inflammatory profiles of CeraSeal and AH Plus using in vitro and in vivo approaches. Methods: Human periodontal ligament stem cells (hPDLSCs) were exposed to sealer extracts (1:4 AH Plus, 1:8 CeraSeal) for 120 h. Cell death was assessed by MTT, Live/Dead, LDH release, and Annexin V/PI flow cytometry. Oxidative stress was quantified via ROS generation (DCFH-DA). In a rat furcation perforation model (n = 8 teeth/group), inflammatory markers (TNF-α, IL-1β, CD68), osteogenic activity (ALP), and osteoclasts (TRAP) were evaluated. Results: AH Plus was associated with significantly greater necrotic cell death (357.6 ± 47.6% LDH release vs. CeraSeal 128.8 ± 37.5%; p = 0.0079) and reduced hPDLSC viability at all time points (p < 0.0001). ROS generation was comparable between sealers (~32–35%, p > 0.05). In vivo, IL-1β was higher in AH Plus-treated tissues (52.25 vs. 24.88 cells/mm²; p = 0.0002), while TNF-α and CD68 were greater in CeraSeal (p ≤ 0.0011). ALP was higher in AH Plus (median 6.15 vs. 3.68; p = 0.0002), with no difference in TRAP-positive osteoclasts. Morphometric analysis showed superior cellular preservation with CeraSeal (p = 0.0079), while inflammatory infiltration was higher in CeraSeal (p = 0.0002). Conclusions: AH Plus was associated with a necrotic-inflammatory profile with elevated IL-1β and higher ALP expression. CeraSeal demonstrated better cellular preservation, lower LDH release, and a distinct inflammatory signature (higher TNF-α and CD68). These findings establish comparative response profiles for the two sealers and support CeraSeal as a potentially biocompatible alternative, though further mechanistic studies are warranted. Full article

Rural sustainability planning requires spatially explicit methods that integrate agricultural resource bases, ecological condition, low-carbon feasibility, community implementation support, and cultural landscape values. Although the Circular and Ecological Sphere (CES) concept offers an integrative framework for rural transition, empirical CES studies remain largely qualitative or policy-oriented. This study develops a FarmMap-integrated Python-GIS workflow for proxy-based CES-oriented spatial prioritization in Yangpyeong-gun, a peri-rural county on the eastern fringe of the Seoul metropolitan region in Korea. Public spatial and administrative datasets were integrated into thirteen indicators grouped under five CES-relevant axes. The model does not measure realized circular material flows, governance quality, resident participation, or carbon emission reduction directly; instead, it identifies where CES-relevant spatial potentials co-occur. An axis-balanced entropy model assigned equal total weight to each axis while applying entropy weighting within axes. Robustness was tested through equal-weight, axis-emphasis, raw entropy diagnostic, Monte Carlo perturbation, and spatial-scale sensitivity analyses using 100 m diagnostic, 500 m, and eup/myeon supports. The final 250 m priority surface identified the top fifth of analyzed Yangpyeong-gun area as very-high relative priority and remained stable across weighting and spatial-support diagnostics. Rural-experience villages and village enterprises had significantly higher CES scores than random background locations. The results demonstrate a reproducible first-stage spatial screening workflow for CES-oriented rural planning while clarifying the limits of proxy-based circularity, governance, and low-carbon indicators. Full article

Background/Objectives: The Doppler perfusion index (DPI) quantifies the ratio of arterial to total hepatic blood flow and reflects hepatic hemodynamic balance. Its clinical applicability is limited by insufficient standardization and the absence of clearly defined physiological reference conditions. This study aimed to establish an upper physiological reference limit for the DPI and to assess its dependence on standardized physiological conditions in healthy adults. Methods: In this prospective study, 44 healthy adults underwent Doppler ultrasonography under standardized conditions (fasting/resting, post-exercise, postprandial). Volumetric blood flow was measured in the portal vein and via the proper hepatic artery and, where feasible, the common hepatic artery. The DPI was calculated as the ratio of arterial to total hepatic inflow. Nonparametric statistical methods were applied. Results: After exclusion of participants with non-standard hepatic arterial anatomy, 39 individuals were analyzed. The DPI varied across physiological conditions, reflecting changes in the relative contributions of arterial and portal venous inflow. Under fasting/resting conditions, values based on the proper hepatic artery showed low variability (mean 0.242 ± 0.057) and normal distribution (Shapiro–Wilk p = 0.625). The empirically derived 90th percentile was 0.30. Measurements based on the common hepatic artery were higher and more variable. Conclusions: The DPI is a physiologically dynamic parameter whose clinical use requires standardized measurement conditions. Under defined protocols, a value of approximately 0.30 may be considered an upper physiological reference limit. Standardization of acquisition and use of the proper hepatic artery enable reproducible and interpretable measurements. This provides a methodological basis for further clinical applications, including oncological contexts in which functional alterations of hepatic perfusion may be relevant. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

The increasing flow of documents in digital libraries, archives and electronic document management systems makes the standardization, adaptation and automation of the process of creating metadata an urgent scientific problem. Metadata directly affects the efficiency of document search, identification, semantic interpretation, long-term storage and intersystem exchange. However, while standardized description based on MARC21, a flexible approach to creating a dynamic field, and intelligent methods based on deep learning, cover these requirements separately, the issue of their full integration into a single methodological system has not been sufficiently resolved. In this study, an integrated hybrid model for describing electronic documents based on standardized, flexible, and intelligent metadata was proposed. A mixed electronic document corpus of 1500 documents was formed for evaluation. The corpus consisted of books, dissertations, scientific articles, archival documents, and heterogeneous electronic documents, with 300 samples selected from each group. Key metadata elements for each document were manually identified and used as ground truth. According to experimental results, the MARC21-based constructor achieved 96.8% structural compatibility and 95.6% metadata completeness, but the average description time was 6.8 min. The dynamic field approach achieved 93.4% structural compatibility and 94.1% metadata completeness, and reduced the description time to 4.1 min. The deep learning-based intelligent module achieved a structural matching score of 91.7%, a metadata extraction score of 93.8% F1, and reduced the processing time to 1.9 min. The proposed hybrid model achieved a structural matching score of 95.9%, a metadata F1 score of 95.1%, and an average description time of 2.3 min. The results showed that the hybrid model is a balanced solution between metadata quality, flexibility, and automation. Full article

Non-invasive fetal electrocardiogram (FECG) extraction from maternal abdominal ECG (AECG) is crucial for prenatal monitoring but remains challenging due to strong interference from maternal ECG (MECG), baseline drift, and noise. We propose an FECG extraction method based on minimal channel attention (MCA) and flow matching (FM), learning a deterministic mapping from AECG to FECG via a probabilistic path. To balance the preservation of physiological signals and separation of interference, we employ bridge variance scheduling for the diffusion process. Target matching loss is introduced to regress the FECG directly, enhancing training stability and waveform fidelity. For feature selection, a minimal channel attention module with global average pooling and a single linear layer is embedded after feature extraction, capturing cross-channel dependencies with minimal parameters. Enhanced residual connections are incorporated to retain underlying features and optimize gradient flow in deep networks. Experiments on two public datasets (ADDB and BDDB) with a leave-one-out cross-validation strategy show that our method achieves average Pearson correlation coefficients (PCCs) of 0.94 ± 0.050 on ADDB and 0.91 ± 0.122 on BDDB, demonstrating robust performance across diverse real-world recording conditions. The method balances high accuracy with efficient feature extraction, offering a reliable solution for non-invasive fetal heart health monitoring. Full article