Search Results (3,793)

The preservation of rooster semen quality during short-term liquid storage remains a challenge in poultry reproductive biotechnology because sperm cells rapidly lose functional competence under ambient conditions. This deterioration is largely associated with oxidative stress and lipid peroxidation of sperm membranes, which are particularly vulnerable in avian species due to their high polyunsaturated fatty acid content and limited cytoplasmic antioxidant defenses. Selenium is an essential trace element involved in cellular antioxidant protection through its incorporation into several selenoproteins that regulate redox balance and protect cellular structures from oxidative injury. The present study evaluated the effects of selenium methionine supplementation on rooster semen quality during liquid storage at 25 °C. Semen was diluted using a standard poultry semen extender composed of sodium glutamate, glucose, potassium acetate, magnesium acetate, and potassium citrate. Selenium methionine was incorporated into the semen extender at concentrations of 0.5%, 1%, and 2% (w/v) at the time of semen dilution prior to storage. Semen quality was assessed at 0, 4, 8, 12, and 24 h of storage. Functional parameters, including total sperm motility, sperm viability, and dead sperm percentage, together with kinematic variables (VSL, VCL, VAP, ALH, LIN, and STR), were analyzed using computer-assisted sperm analysis (CASA). Structural integrity was evaluated through acrosome and plasma membrane integrity tests, while sperm physiological status and apoptotic progression were assessed using Annexin V-FITC/propidium iodide flow cytometry. Significant effects of storage time, selenium methionine concentration, and their interaction were observed for multiple semen quality parameters (p < 0.05). Among the tested concentrations, supplementation with 0.5% selenium methionine consistently produced the most favorable results, maintaining higher sperm motility, viability, and membrane integrity while reducing dead sperm percentage and apoptotic progression during storage, with protective effects particularly evident at 8, 12, and 24 h compared with the control and higher concentrations. Polynomial contrast analysis indicated predominantly non-linear dose–response relationships, with quadratic and cubic components providing the best model fit (R² = 0.90–0.99; p < 0.0001), suggesting a hormetic antioxidant effect. Overall, these findings indicate that selenium methionine supplementation in semen extender improves the stability of rooster semen during short-term liquid storage at ambient temperature, with 0.5% showing the most consistent protective effects among the concentrations evaluated. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

This paper proposes an energy-based training assistance controller for a unicycle riding simulator inspired by Human Adaptive Mechatronics (HAM). We focus on sagittal plane (pitch) balance for beginners and derive a simplified longitudinal plane unicycle model, where pedaling is represented as an action–reaction torque between the wheel and the rider–saddle body. After time normalization, the saddle dynamics is expressed in a form suitable for energy analysis. Using the natural Hamiltonian of the uncontrolled system, we design a minimum-intervention pumping–damping controller that modifies the energy flow only when necessary. The assistance is smoothly activated outside a training core region defined by a saddle-angle bound: a damping term suppresses excessive motion, and a pumping term prevents trapping in a tilted posture when the energy becomes too small. The proposed framework offers physically interpretable, localized assistance while preserving the natural unicycle dynamics required for skill learning. Full article

Cancer center infusion departments are often challenged with scheduling a large number of patients while having a limited number of nurses available to administer the infusions. Cancer patients have different acuity levels depending on many factors, such as treatment plans, drug side effects, and health status. Thus, several factors need to be considered when assigning patients to nurses, as unbalanced nurse-to-patient assignments affect patient flow and nurse workload. This study introduces a mixed-integer programming model for nurse–patient assignments that minimizes patient wait times while ensuring workload balance among oncology nurses, while addressing the limited attention in existing studies to jointly modeling patient acuity and nurse continuity. The model also explores the effects of maintaining nurse continuity for patients desiring the same nurse throughout their treatments. Because the mixed-integer programming model can become difficult to solve when there are many cancer patients, an alternative nurse–patient assignment heuristic is proposed and evaluated. Numerical examples based on data from a regional cancer center compare the effectiveness and performance of the exact and heuristic methods. The results show that patient wait time and workload variation among nurses increase when there is a stronger requirement to maintain nurse continuity, which could negatively affect both patient and nurse satisfaction. This study provides valuable insights into the nurse–patient assignment problem and helps cancer infusion centers determine the impacts of maintaining different levels of nurse continuity in their settings. Full article

Polycystic ovary syndrome (PCOS) is associated with reproductive, metabolic, and inflammatory disturbances. Alterations in T-cell subpopulations—particularly increased T helper 17 cells (Th17) and decreased regulatory T cells (Treg)—have been reported in PCOS; however, data on normoandrogenic phenotype D remain limited. Hypoxia-inducible factor 1α (HIF-1α), a key regulator of hypoxic response, also influences immune and metabolic processes and may affect the Treg/Th17 balance. To assess Treg and Th17 abundance, HIF-1α expression within these cells, and their ratios in women with phenotype D PCOS compared with healthy controls. The study included 49 women with phenotype D PCOS and 40 controls comparable in terms of age and BMI. Anthropometric, hormonal, metabolic, and inflammatory parameters were evaluated. Peripheral T-cell subsets and intracellular HIF-1α expression were analyzed by multiparameter flow cytometry. Absolute numbers of Treg and Th17 cells did not differ between groups. However, PCOS patients showed significantly higher Treg/Th17 and HIF-1α-positive Treg/HIF-1α-positive Th17 ratios. HIF-1α-positive Treg cells correlated positively with adiposity and insulin resistance markers; however, after False Discovery Rate (FDR) correction, correlations no longer remained statistically significant. Despite normoandrogenemia, PCOS patients exhibited higher hs-CRP levels. Phenotype D PCOS is characterized by altered immune cell ratios rather than absolute T-cell differences, suggesting distinct immunological features and persistent low-grade inflammation. Full article

Membrane technology is a highly efficient, cost-effective, and chemical-free process, leading to its widespread application across various fields. However, the high capital cost of traditional ceramic benchmarks remains a barrier. This study addresses this challenge by engineering a low-cost, waste-derived geopolymeric membrane functionalized with a silver molybdate (Ag₂MoO₄) catalytic coating for the removal of trimethoprim (TMP), a persistent emerging contaminant. Systematic filtration assays for the removal of TMP (100 mg·L⁻¹, pH 4) revealed the role of the Ag₂MoO₄ layer as a performance intensifier, yielding a 26% increase in initial permeate flux and a 33% improvement in the selectivity compared to the pristine support, while maintaining robust rejection efficiency. Comprehensive characterization attributes these enhancements to synergistic effects between increased surface hydrophilicity and favorable solute–catalyst interfacial interactions. Furthermore, a fouling analysis using Hermia’s models indicated the simultaneous operation of multiple blocking mechanisms, a phenomenon linked to the non-uniform nature of the coating and subsequent formation of preferential flow paths. Overall, the incorporation of the silver molybdate coating effectively improved the membrane’s flux performance and selectivity. These findings demonstrate that integrating catalytic coatings onto waste-based geopolymer frameworks provides a scalable, circular-economy-aligned strategy for advanced wastewater treatment, balancing high-flux performance with the efficient removal of recalcitrant pharmaceuticals. Full article

As a primary associated by-product of coal mining, the comprehensive utilization of coal gangue has become a core pathway for the green transformation of the energy system and the establishment of a resource recycling system. The fully mechanized top-coal caving mining method used in China lacks a quality linkage mechanism between underground matched mining and surface coal blending, resulting in significant fluctuations in coal quality, larger volumes of gangue brought to the surface, and low utilization rates of coal washing by-products. In this paper, we propose a reverse decision-making method for whole-lifecycle coal quality control and construct an ash content tracing and regulation model to coordinate coal blending and matched mining in fully mechanized caving faces. In the coal blending stage, under the constraints of calorific value balance and cost minimization, the method transforms low-calorific-value by-products, such as middlings and fine gangue, into valuable resources. In the matched mining stage, a reverse tracking model based on the surface–underground ash content balance is proposed, grounded in material flow analysis theory. The model formulates correlation equations among face length, the low calorific value of raw coal, daily advance per cycle, and caved gangue volume. It further proposes a reverse coal quality tracing theory that links commercial coal sales targets with caving process parameters. The study clarifies the deep coordination mechanism between underground matched mining and surface coal blending. The results demonstrate that the proposed method systematically establishes a closed-loop pathway integrating underground gangue reduction at the source and surface fine gangue blending. The implementation has yielded direct economic benefits totaling RMB 65.31 million, increased commercial blended coal output by 104.5 thousand tons, and reduced gangue emissions by 258.5 thousand tons. This study provides a reference for the reduction, resource utilization, and recycling of coal gangue. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

Scour has been a topic of significant concern among coastal geotechnical engineers in recent years. The Shields number serves as a crucial parameter for erosion calculations, reflecting the balance between sediment particle conditions and hydrodynamic forces, derived from the mechanics of sediment particle equilibrium. Seepage flow, a common phenomenon driven by pressure in soil, further influences the movement of sediment particles. Building upon the classical three-dimensional two-slope angle erosion model, this study incorporates the vertical seepage force. It comprehensively considers slope angles, sediment response angles, incident current angles, and vertical seepage intensities to adjust the Shields number for sediment particles on slopes. The calculation encompasses both transverse and longitudinal slope configurations. Based on the derived formula and parametric analysis, the study draws the following conclusions: 1. The modified Shields number (${\theta }_{cr}/{\theta }_{cr0}$) decreases non-linearly with the increase of slope angle; 2. ${\theta }_{cr}/{\theta }_{cr0}$ is central and has axial symmetry about 180° incident current angles for transverse and longitudinal slopes, respectively; 3. ${\theta }_{cr}/{\theta }_{cr0}$ increases non-linearly with the increase of soil angle of response; 4. ${\theta }_{cr}/{\theta }_{cr0}$ decreases linearly with the increase of seepage intensity; 5. There exists an approximately zero ${\theta }_{cr}/{\theta }_{cr0}$ area when the response angle approaches the slope angle, and the area increases non-linearly as the seepage intensity becomes greater. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

This study investigated the effects of four LED light qualities, red+blue+far-red (WRS-LED), blue+red (BR-LED), blue (B-LED), and red (R-LED), and exogenous L-glutamic acid at 10 ppm on the growth and quality of red mustard spinach (Brassica rapa var. perviridis) cultivated in a plant factory using a recirculating deep-flow hydroponic system. Plants were exposed to four LED light quality treatments at 180 ± 10 μmol·m⁻²·s⁻¹ PPFD for 28 days after transplanting. L-glutamic acid at 10 ppm was applied once to the recirculating nutrient solution 15 days after transplanting, resulting in 13 days of exposure prior to final harvest on day 28. All growth and quality parameters were measured at the final harvest after 28 days of cultivation. WRS-LED promoted the greatest biomass production. Additionally, vitamin C content, DPPH radical scavenging activity, and total phenolic content were highest under BR-LED and B-LED conditions. Notably, under B-LED, L-glutamic acid treatment increased total phenolic content to approximately twice that of the control. Leaf redness, expressed as Hunter a* values, was observed exclusively under BR-LED. Principal component analysis revealed that LED light quality was the primary determinant of treatment responses, with growth-related traits associated with WRS-LED and R-LED, and quality-related traits with B-LED and BR-LED. Overall, BR-LED combined with L-glutamic acid represents the most suitable treatment for red mustard spinach cultivation in plant factories, achieving a favorable balance between growth and nutritional quality. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

This article details a matrix-based mathematical method to calculate power flows and transmission losses in an electric grid specifically attributable to low-carbon and renewable energy sources (LCRES) (wind, solar, nuclear). The goal is to improve the transparency and reliability of Guarantees of Origin (GO) certificates. Current GO schemes rely on contractual accounting and neglect physical power losses, undermining consumers’ confidence that they receive “clean” energy. The method uses steady-state power flow analysis to derive a power-loss distribution coefficient matrix. This matrix accurately allocates grid losses back to the LCRES generating nodes, complying strictly with electrical engineering principles. It accommodates both time-varying renewable output and stable nuclear generation. The results offer highly accurate loss-attribution data, supporting more verifiable GOs, ensuring fair compensation for losses, and enhancing energy balance accuracy in hybrid power systems. Full article

Agriculture in Xinjiang, a region in arid northwest China, is almost entirely dependent on irrigation, leading to significant supply–demand contradictions. This study addresses the spatial and temporal mismatches between water supply and demand, and the resulting conflicts in crop water supply. Using the primary irrigation cycle of Wutai branch canal as a case study, we developed a two-level optimal water allocation model based on large-scale system optimization. For the lateral canal water distribution, a model minimizing the sum of squares of the water shortage rate was solved using the Sequential Quadratic Programming (SQP) algorithm. For the drip irrigation systems, water distribution time was incorporated as a second objective, and the resulting bi-objective model was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Compared to actual distribution processes, our results show that (1) 74% of the distribution canals and pipelines achieved over 90% of their design flow rate, fully utilizing flow capacity and reducing the overall distribution time of the branch canal by 4.68 h. (2) The overall water shortage rate was reduced by 1.59% compared to the actual rate, with a more balanced water allocation among users. These results demonstrate that the model can effectively coordinate water distribution in a multi-level canal system, enhance the fairness of water use, and provide a valuable reference for single-event water distribution in water-scarce areas. Full article