Search Results (1,262)

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions. Full article

This study investigates the synergistic effects of Cr–Zr and Mn–Zr additions on the microstructural evolution and mechanical properties of Al–6 wt.%Cu alloys. Alloys were designed with solute concentrations positioned below, near, and above their maximum solubility limits, and were evaluated under as-cast, T4, and T6 heat treatment conditions. Mechanical testing revealed distinct behavioral trends depending on the heat treatment: the T4 heat treatment condition generally exhibited superior hardness and yield strength, whereas the T6 heat treatment condition resulted in a slight reduction in hardness but facilitated a significant recovery in tensile strength and structural stability, particularly in alloys designed near the solubility limit. To elucidate the crystallographic origins of these mechanical variations, X-ray diffraction analysis was conducted to monitor changes in lattice parameters, dislocation density, and micro-strain. The results showed that T4 heat treatment induced lattice contraction and a decrease in dislocation density, suggesting that the high strength under T4 heat treatment conditions arises from lattice distortion caused by supersaturated solute atoms. Conversely, T6 aging led to lattice relaxation approaching that of pure aluminum, yet simultaneously triggered a re-accumulation of dislocation density and micro-strain due to the coherency strain fields surrounding precipitates, which effectively impede dislocation motion. Therefore, rather than proposing a single, definitive optimization condition, this study aims to secure foundational data regarding the correlation between these microstructural descriptors and mechanical behavior, providing a guideline for balancing the strengthening contributions in transition metal-modified Al–Cu alloys. Full article

Efficient thickening of unclassified tailings slurry (UTS) is critical for enhancing mine backfill efficiency and reducing operational costs. Ultrasonic technology has emerged as a promising approach to facilitating the solid–liquid separation process in such slurries. In this study, systematic experiments were conducted using a 20 kHz ultrasonic concentrator. The effects of ultrasonic treatment timing (applied at 0, 5, 10, 15, 20, 25, 30, and 35 min during free settling) and power (50 to 400 W in eight levels) were investigated by monitoring the solid–liquid interface settling velocity and underflow concentration. The key findings are as follows: Ultrasonic application at the 5 min mark yielded the optimal thickening performance, increasing the final mass concentration by 1.3% compared to free settling alone. The average settling velocity generally increased with ultrasonic power (with the exception of 50 W), and the final underflow concentration exhibited a steady rise. Notably, the 400 W treatment induced a significant settlement acceleration, attributed to the formation of drainage channels. Mechanistic analysis revealed that these drainage channels undergo a dynamic process of formation, expansion, contraction, and closure, driven by ultrasonically induced directional water migration, particle compaction, and energy boundary effects. This research not only enriches the theoretical framework of ultrasonic-assisted thickening but also provides practical insights for optimizing mine backfill operations. Full article

This paper investigates the optimal portfolio control problem under a stochastic volatility model, whose dynamics are governed by a highly nonlinear Hamilton–Jacobi–Bellman equation. We employ a separable value function and introduce a novel exponential approximation technique to simplify the nonlinear terms of the auxiliary function. The simplified HJB equation is solved numerically using the advanced Fréchet–Newton method, which is known for its rapid convergence properties. We rigorously analyze the numerical outcomes, demonstrating that the iterative sequence converges quickly to the trivial fixed point ($g^{*}=1$) under zero risk and zero excess return conditions. This convergence is mathematically justified through rigorous functional analysis, including the principles of contraction mapping and the Kantorovich theorem, which validate the stability and efficiency of the proposed numerical scheme. The results offer theoretical insight into the behavior of the HJB equation in simplified solution spaces. Full article

This study investigated the effects of 6 weeks of plyometric training (PT) performed on soft (unstable) and hard (stable) surfaces compared with conventional training on the balance, explosive power, and muscle strength of adolescent female basketball players. The participants were randomly assigned to three groups: soft-surface PT (n = 14), hard-surface PT (n = 14), and conventional training (n = 14). Performance outcomes included 30 m sprint time, vertical jump height, plantar flexion and dorsiflexion maximal voluntary isometric contraction (MVIC) torque, Y-balance dynamic balance, and center of pressure-based static balance. Ground reaction forces, MVIC torques, and balance parameters were measured using high-precision force sensors to ensure accurate quantification of biomechanical performance. Statistical analyses were performed using two-way repeated-measures ANOVA with post hoc comparisons to evaluate group × time interaction effects across all outcome variables. Results demonstrated that soft- and hard-surface PT significantly improved sprint performance, vertical jump height, and plantar flexion MVIC torque compared with conventional training, while dorsiflexion MVIC increased similarly across all the groups. Notably, soft-surface training elicited greater enhancements in vertical jump height, dynamic balance (posteromedial and posterolateral directions), and static balance under single- and double-leg eyes-closed conditions. The findings suggest that PT on an unstable surface provides unique advantages in optimizing neuromuscular control and postural stability beyond those achieved with stable-surface or conventional training. Thus, soft-surface PT may serve as an effective adjunct to traditional conditioning programs, enhancing sport-specific explosive power and balance. These results provide practical guidance for designing evidence-based and individualized training interventions to improve performance and reduce injury risk among adolescent female basketball athletes. Full article

Background: Spasticity is a complex and multifactorial condition resulting from upper motor neuron injury. It manifests through muscle contractions, pain, limited range of motion, and clonus, which significantly impair daily activities and quality of life. High-frequency spinal cord stimulation (HF SCS) has shown optimal results in treating chronic neuropathic pain, but its potential role in spasticity remains underexplored. This study aimed to evaluate the efficacy of HF SCS in patients with spasticity. Methods: From April 2021 to July 2024, six patients with spasticity from various etiologies underwent SCS implantation at our institution. Clinical evaluations including the use of the Visual Analog Scale (VAS), Douleur Neuropathique 4 (DN4), and the Ashworth score, as well as ambulation ability and clonus episodes, were performed preoperatively and at a minimum of six months post-surgery. Subjective assessments of motor function, including coordination, movement efficiency, and postural transitions, were also recorded. Results: The mean age of patients was 50.12 ± 9.41 years, with follow-up averaging 24.32 ± 10.83 months. Statistically significant improvements were observed in VAS (p = 0.0412) and DN4 (p = 0.0422) scores, alongside a reduction in clonus episodes. All patients reported subjective improvements in coordination, movement efficiency, and postural transitions. Ambulation remained stable or improved in all cases. No perioperative complications or sensory/motor side effects were noted. Conclusions: HF SCS offers a promising approach to managing spasticity, with improvements in motor function, ambulation, and postural transitions. These findings support further investigation into HF SCS for spasticity, with multicenter trials needed to optimize treatment protocols and identify the most responsive patient populations. Full article

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management. Full article

The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms, leaving retailers exposed to price volatility risks. In response, demand for risk management tools has grown significantly. Futures contracts serve as a core instrument for managing risks in the energy sector. This paper proposes a futures-based risk hedging model grounded in electricity price forecasting. A price prediction model is constructed using historical data from electricity markets and energy futures, with SHAP values used to analyze the transmission effects of energy futures prices on monthly electricity trading prices. The Monte Carlo simulation method, combined with a t-GARCH model, is applied to calculate CVaR and determine optimal portfolio weights for futures products. This approach captures the volatility clustering and fat-tailed characteristics typical of energy futures returns. To validate the model’s effectiveness, an empirical analysis is conducted using actual market data. By forecasting electricity price trends and formulating futures strategies, the study evaluates the hedging and profitability performance of futures trading under different market conditions. Results show that the proposed model effectively mitigates risks in volatile market environments. Full article

The precise identification of territorial space conflicts (TSCs) and their driving mechanisms is key to enhancing spatial security governance. Taking Jinan City as a case study, this research evaluates territorial space suitability across production, living, and ecological dimensions, proposes an empirical TSC identification model, and employs GeoDetector to analyze spatiotemporal evolution patterns and driving mechanisms. The results indicated that (1) from 2000 to 2020, significant spatial heterogeneity characterized the suitability of production–living–ecological spaces in Jinan City. High suitability zones of production and living space expanded in the northern plain along the Yellow River and central piedmont plain, respectively, while those of ecological space contracted in the southern mountainous and hilly areas. (2) Significant spatiotemporal variations in territorial space conflicts (TSCs) were observed in Jinan City over the past two decades. Intense conflicts dominated production–living and production–ecological space interactions, while moderate conflicts were prevalent in living–ecological and production–living–ecological space interactions. Production–living space conflict zones expanded, living–ecological space conflict zones contracted, and production–ecological and production–living–ecological space conflict zones showed consistent expansion trends. (3) The spatiotemporal evolution of territorial space conflicts is jointly driven by the natural environment, geographical location, social economy, and regional policies. The interaction of driving factors exhibited significant dual-factor and nonlineal enhancement effects. Finally, this study provides some scientific references for the comprehensive management and pattern optimization of territorial space in Jinan City. Full article

Addressing the issues of low positioning accuracy and poor robustness in shaft-rate magnetic fields, this study introduces the Improved Exponential Triangular Optimization Algorithm (IETO). By incorporating adaptive attenuation factors, dynamic population reduction, and intelligent boundary contraction strategies, it significantly enhances the global search capability and robustness. A magnetic dipole localization model is developed, and comparative simulations show that IETO achieves reliable accuracy and robustness under low signal-to-noise ratio (SNR) conditions, reducing localization error by 7.82% compared with the conventional Exponential Triangular Optimization Algorithm (ETO). The effects of base station deployment, number of stations, and sea depth on localization performance are further examined, and the capability of IETO for dynamic target tracking is verified. Preliminary sea trial results confirm the practical feasibility and engineering applicability of the proposed method. Full article

The optimization of enrichment zones for Co-rich crusts involves multiple factors such as crust thickness, elemental content, topography, slope, and biological distribution, making it a complicated research endeavor. Based on survey data from the contract area, the present study pioneers the integration of environmental factors into enrichment zone selection. It conducts a comprehensive analysis of resource, environment, and mining considerations, alongside optimization methodologies. Quantitative indicators for resource, environment, and mining are spatially correlated and assigned to corresponding grid cells. A weighted scoring method is proposed to compare enrichment zone selection results under different weightings, finally forming the optimal enrichment zone selection scheme. This scheme fully achieves the maximization of resource reserves, the protection of biological communities, and the safeguarding of future mineral development. It also provides technical reference for enrichment zone selection of deep-sea minerals such as polymetallic nodules and hydrothermal sulphides. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Background: Gestational diabetes mellitus (GDM) is associated with metabolic disturbance and subclinical cardiovascular changes during pregnancy and after birth. Optimal glycaemic control remains challenging for many patients despite existing management strategies. Ursodeoxycholic acid (UDCA) has shown potential metabolic effects, including enhanced insulin sensitivity and anti-inflammatory effects. Previously, we demonstrated that UDCA improves glycaemic control in women achieving higher circulating UDCA concentrations; however, its effect on maternal cardiac function remains unknown. The objective was to evaluate whether treatment with UDCA compared with placebo is associated with differences in maternal cardiac function in pregnancies complicated by GDM. Methods: In this randomized, placebo-controlled trial, 113 women with GDM were recruited, with 56 allocated to UDCA and 57 to placebo (IMIB-GU-2019-02, registration date: 17 June 2020; first participant enrolled: 3 March 2021). After measurement of maternal blood UDCA levels, 43 participants in the treatment group with levels ≥ 0.5 μmol/L were included in a per-protocol analysis. Participants had cardiac assessments at baseline, in the late third trimester (36 weeks) and postpartum. Detailed left ventricular systolic and diastolic functional indices were assessed using conventional pulse and tissue Doppler indices as well as strain imaging. Right ventricular systolic function was also assessed. Results: Baseline maternal characteristics and cardiac functional indices were comparable between the UDCA and placebo groups. In the third trimester, women treated with UDCA showed more negative left atrial strain during atrial contraction (LASct_AC) compared with placebo (p = 0.016), while no significant between-group differences were observed in conventional left ventricular systolic or diastolic parameters. In the postpartum period, UDCA treatment was associated with higher left atrial reservoir function, reflected by increased LASr_ED (p = 0.041) and LASr_AC (p = 0.036), as well as more negative left atrial conduit strain at end-diastole (LAScd_ED; p = 0.043). No consistent differences were observed in left ventricular systolic function, haemodynamic indices, or right ventricular functional parameters between the two groups. Conclusions: These findings are associated with small and time-dependent differences in reducing atrial dysfunction and improving cardiac efficiency during late pregnancy and postpartum. However, given the lack of long-term follow-up, further research is needed to determine the long-term cardiovascular relevance of UDCA in this population. Full article

South Africa’s manufacturing sector, a driving force for sustainable development, faces a profound challenge in decarbonizing without deindustrializing. This study provides an optimized, scenario-based assessment of the sector explicitly aligned with its Just Energy Transition Partnership (JETP) objectives. A novel framework is applied, integrating an extended Kaya–Logarithmic Mean Divisia Index (Kaya–LMDI) decomposition with scenario forecasting and Genetic Algorithm (GA) optimization. The decomposition disaggregates a conventional carbon intensity (CI) driver to include Electrification Share (ELE), Renewable Share (REN), and a newly defined Residual Carbon Factor (RCF) that captures direct fossil fuel use for industrial process heat. Historical analysis (2002–2022) shows that emissions growth was primarily driven by the RCF (224.1 MtCO₂, 160%) and Economic Activity (187.5 MtCO₂, 134%), partly offset by gains in Energy Intensity (−141.8 MtCO₂, 101.35%) and REN (−202.2 MtCO₂, −144.53%). Carbon emissions projections to 2040 reveal a critical sustainability trilemma: the Just Transition accelerated scenario (JTAS), despite achieving rapid renewable deployment, increases emissions by 469% as economic growth overwhelms decarbonization efforts. Conversely, the mathematically optimal (GA) pathway achieves a 90.8% reduction but only through structural contraction that implies socially unsustainable deindustrialization. This tension exposes the systemic limits of incremental decarbonization and underscores that a truly sustainable pathway requires transcending this binary choice by directly addressing the fossil fuel substrate of industrial production. Full article

Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods. Full article