Search Results (23,065)

This study evaluated the interaction between energy supplementation and pre-grazing sward height on grazing behavior, nutrient intake, digestion, and nitrogen metabolism of cattle. Eight rumen-cannulated Nellore steers (24 mo; 343 ± 7.4 kg BW) grazed palisade grass (Urochloa brizantha cv. Marandu) managed at 25 or 35 cm pre-grazing height until a 15 cm post-grazing target. Treatments were arranged in a 2 × 2 factorial structure: two sward heights and two supplementation levels (mineral supplement only or ground corn at 0.6% BW, DM basis). The experiment was conducted as two replicated 4 × 4 Latin squares, with eight animals, four treatments, and four experimental periods. An interaction (p = 0.02) showed that supplementation reduced grazing time only at 35 cm. Steers grazing 25 cm swards spent less time grazing, rested more, took fewer steps, had higher bite rates, and consumed more forage (p < 0.05). Supplementation reduced forage intake but increased total DM intake, improved forage NDF and total DM digestibility, increased microbial protein synthesis, enhanced nitrogen retention, reduced ruminal ammonia-N, urinary N losses, and the acetate:propionate ratio (p ≤ 0.05). Steers grazing 25 cm swards also exhibited greater ruminal ammonia-N, urinary N excretion, and nitrogen retention. Managing pastures at 25 cm improved forage harvesting efficiency, while energy supplementation enhanced rumen fermentation, nitrogen utilization, and overall grazing efficiency, with potential environmental benefits. Full article

Carbon pricing is a cornerstone of climate change mitigation; yet, the cost shocks it creates may destabilize the electricity demand and complicate the low–carbon transition. This paper examines how carbon price shocks propagate through production networks and affect electricity–growth resilience at the region–industry level in China. Carbon price shocks can travel through production networks and destabilize the electricity demand, making it important to understand how these shocks affect the resistance and recovery of electricity–consumption growth. Using monthly electricity–consumption data for 26 provinces from January 2023 to July 2025, we construct an electricity–growth resilience index that captures both dimensions. We combine carbon–market prices with a multi–regional input–output framework to measure the network–based exposure to carbon price shocks. The estimates show that network–transmitted carbon price shocks are associated with lower electricity–growth resilience and that the network channel is more important than direct local exposure after absorbing region–month and industry–month shocks. The association is weaker in industries with a stronger self–generation capacity and in regions with a higher clean–generation share, but stronger in electricity–surplus regions, regulated industries, electricity–dependent industries, and highly energy–intensive industries. Quantitatively, a one–unit increase in the network–based carbon price shock is associated with a 4.612–point reduction in the composite resilience index; the corresponding instrumental–variable estimate is −3.160. The forward– and backward–transmission coefficients are −4.672 and 3.986, respectively. These findings are relevant to the coordinated design of carbon–market and electricity–system policies. More broadly, the results suggest that expanding clean electricity generation and managing the cost transmission along production networks can help buffer transition costs, supporting an orderly low–carbon transition consistent with Sustainable Development Goals (SDGs), including SDG 7 (affordable and clean energy) and SDG 13 (climate action). Full article

Renewable-powered electric vehicle charging can support sustainable transport electrification by coupling low-carbon electricity generation with charging demand. Autonomous microgrids (MGs) combining wind, solar, and energy storage offer a pathway to this integration, including at locations with limited grid access. However, it can be challenging to monitor and manage the energy of standalone microgrids because they are time-varying and nonlinear. Solar and wind energy were the main sources of power for the microgrid. A microgrid’s primary load is thought to be an electric vehicle charging station (EVCS). The EVCS can charge quickly and uses a lot of power. Additionally, an energy storage system (ESS) is incorporated into the microgrid. This research evaluates a fractional-order-super-twisting sliding mode controller (FOSTSMC) for DC-bus regulation and ESS-assisted power balancing to support reliable renewable-powered fast EV charging. The FOSTSMC scheme includes three key parameters that are optimally tuned using the starfish optimization algorithm (SFOA). Regarding changes in wind velocity and solar irradiance, the response of the FOSTSMC was contrasted to that of a conventional proportional-integral (PI), super-twisting sliding mode controller (STSMC), and the fractional-order-PI (FOPI) regulators. MATLAB/Simulink (R2023a version 9.14) was used to simulate and model the MG. The findings show that the introduced FOSTSMC enhanced the MG’s transient response when compared to the other controllers. The proposed optimal FOSTSMC provides an improvement in the peak overshoot of 41.8% and 47.6% in the settling times over the best values of the other controllers. Moreover, simulation-based evaluation using NASA POWER weather profiles for solar irradiance and wind speed are applied to the proposed system to validate energy management effectiveness. Despite changes in wind velocity, solar intensity, and other parameters, the EVCS charging process and the DC-bus voltage tracked the set point with the least amount of disruption. To prove the effectiveness of SFOA, it is compared to particle swarm optimization (PSO). Full article

Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed fieldbus and wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main contribution is a conceptual decision framework derived from the literature synthesis, linking service adaptation, communication-path feasibility and coordination scope to the placement of sensing, processing and inference functions. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures. Full article

Regenerative braking recovers vehicle kinetic energy during deceleration, but realised battery-side recovery depends on braking demand and battery charge-acceptance conditions. This study characterises real-world regenerative braking behaviour and evaluates a dual-output event-level estimator using the public Real-World Electric Vehicle Data: Driving and Charging dataset. Negative-current intervals from eight drive records were segmented into 8181 valid events using hysteresis, short-gap bridging, acquisition-dropout splitting, and long-stretch exclusion. The events recovered 288.7 kWh and returned 683.4 Ah; the median event recovered 19.7 Wh over 3.6 s, and the maximum observed peak regenerative power was 224.1 kW. Event duration and peak current showed the strongest associations with recovered energy (Spearman rho = 0.802 and 0.871, respectively), while SoC, temperature, and voltage showed weak direct global associations. The maximum peak power decreased to 155.53 kW in the 90–100% SoC band, which indicates a constrained high-SoC charge-acceptance envelope. A 10-input, 12-hidden-neuron, two-output feedforward neural network (158 parameters) was optimised using HPWOA and benchmarked against PSO, WOA, and SFSA. On the reported held-out event set, HPWOA achieved R² = 0.9784 and RMSE = 1.839 Wh for recovered energy and R² = 0.9819 and RMSE = 4.891 kW for peak power. The framework is interpreted as retrospective event-level estimation rather than prebraking forecasting. Full article

Educational institutions in developing countries face increasing electricity demand, frequent power outages, and rising operational costs due to their heavy reliance on utility grids and diesel generators. This study evaluates a grid-connected hybrid energy system (HES) for the Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Pakistan, to enhance energy reliability, reduce costs, and mitigate carbon emissions. Two scenarios are considered: (i) the existing utility grid and diesel generator system and (ii) a proposed hybrid configuration integrating solar photovoltaic (PV), a battery energy storage system (BESS), and the utility grid. The system is modeled and optimized using HOMER Pro based on a detailed campus load profile developed from institutional data. The proposed HES achieves a Net Present Cost (NPC) of PKR 223 million and a Cost of Energy (COE) of PKR 13.06/kWh while reducing CO₂ emissions by approximately 70% compared with the existing system. Moreover, the investment analysis demonstrates strong financial viability, yielding an NPV of PKR 1855.56 million, an ROI of 179.63%, an IRR of 180.34%, and simple and discounted payback periods of 0.55 and 0.59 years, respectively. Sensitivity analysis further demonstrates that a 100 kWh BESS provides the preferred configuration based on the trade-off between economic performance and reliability under varying solar irradiance, inflation rates, and discount rates. The findings highlight the potential of HESs to support sustainable campus electrification and offer practical insights for energy planning in educational institutions across developing countries. Full article

This paper presents a method for the dynamic characterisation of load cells and force transducers designed to be straightforward to implement and based on instrumentation commonly used in structural dynamics testing. Impact hammers simultaneously provide an impulsive force to the sensor under test and measure the actual input to estimate the frequency response function. The bandwidth of the sensor is evaluated using an analytical single- or multi-degree-of-freedom frequency-domain model that is fitted to the experimental frequency response function using a Non-Linear Least-Squares approach. This paper provides guidelines for selecting the model complexity and the fitting frequency range and demonstrates the procedure on an experimental strain-gauge load cell. For the investigated transducer, the identified first natural frequency was 481.4 Hz, resulting in bandwidths of 47.9 Hz, 105.1 Hz, and 154.2 Hz for allowable deviations from the static sensitivity of 1%, 5%, and 10%, respectively. Moreover, for the single-degree-of-freedom case, the effect of masses added under operating conditions is modelled, with a worst-case relative error below 1% in the prediction of the natural frequency for the validation measurements, thus enabling the actual bandwidth to be estimated. Full article

Background/Objectives: Radiotherapy and docetaxel are mainstays of prostate cancer management, yet both are constrained-radiotherapy by normal-tissue tolerance and docetaxel by systemic toxicity and acquired resistance. Ultrasound-activated microbubbles (USMB) convert acoustic energy into spatially localized mechanical bioeffects that can potentiate both. This review examines USMB as a precision chemo- and radio-enhancer in prostate cancer. Methods: We conducted a scoping review with mechanistic and translational synthesis, informed by PRISMA-ScR reporting principles. We identified sources through a structured bibliographic search and supported it with source-level verification. Heterogeneity in model systems, acoustic parameters, treatment regimens, and outcomes precluded meaningful quantitative pooling; we therefore synthesized the evidence narratively. Results: Two distinct mechanisms recur: sonoporation-cavitation-driven permeabilization that increases intracellular docetaxel delivery and acid sphingomyelinase (ASMase)-mediated ceramide-driven endothelial perturbation that sensitizes tumor vasculature to radiation. In prostate models, the three modalities enhance one another, and the triple combination produces the largest reported increases in clonogenic death, apoptosis, and regression in selected immunodeficient PC3 models. These effects were achieved at reduced experimental drug and radiation doses, a potential dose-sparing benefit that remains a preclinical hypothesis. Human data, although still outside the prostate, provide early support for feasibility and acceptable safety in selected USMB-enhanced settings. Conclusions: The rationale is coherent and the preclinical prostate signal consistent, but the evidence remains predominantly preclinical, PC3-dominated, and heterogeneous. USMB is best understood not as a drug-delivery technique but as a tunable sensitization platform whose outcome depends on acoustic dose, vascular state, and sequencing; parameter standardization, prostate-specific delivery, and cautious early-phase trials remain prerequisites for translation. Full article

Accurate photovoltaic (PV) power forecasting is essential for enhancing grid stability, optimizing energy management, and facilitating the large-scale integration of renewable energy resources. Although deep learning techniques have demonstrated promising results in PV forecasting, their predictive performance is highly dependent on effective hyperparameter optimization. Furthermore, studies dedicated to field-deployed perovskite photovoltaic systems operating under semi-arid desert climatic conditions remain limited. To address this gap, this study proposes an Adaptive Hybrid Grey Wolf Optimizer–Sparrow Search Algorithm (AH-GWOSSA) deep learning framework for single-step-ahead (5 min lead time) and multi-step power forecasting of a perovskite PV system installed in Mosul, Iraq. A real-world dataset comprising 21,456 valid daytime observations (filtered for solar irradiance > 5.0 W/m²) was collected between November 2025 and April 2026 and partitioned strictly chronologically (70% training, 10% validation, 20% held-out testing). The proposed framework was benchmarked against Persistence, an unoptimized Base LSTM, Standard GRU, CNN-LSTM, standalone GWO, SSA, Random Search, and an ablation Fixed-Weight Hybrid under an equivalent evaluation budget of 160 candidate network trainings. Over 30 independent optimization runs, AH-GWOSSA achieved the lowest mean RMSE of 15.55 W (std. 0.34 W), an MAE of 8.79 W (std. 0.25 W), and the highest mean R² of 0.885 (std. 0.008). Non-parametric Wilcoxon signed-rank testing across 30 paired run-wise RMSE values confirmed a statistically significant improvement over the unoptimized Base LSTM ($W=78.0$, $p=1.8\times {10}^{-6}$ at $\alpha =0.01$). Multi-horizon evaluations (5, 15, 30, 60, and 120 min) demonstrated consistent superiority over Persistence and Base LSTM, with RMSE improvements of up to 39.8% at the 30 min horizon. The findings demonstrate that adaptive hybrid metaheuristic optimization provides a competitive framework for ultra-short-term perovskite PV forecasting, with RMSE improvements of up to 39.8% over Persistence at the 30 min horizon, offering a solid foundation for future smart-grid and battery storage management applications. Full article

Energy management in ship integrated power systems (IPSs) requires real-time dispatch of diesel generators, battery storage, and shore power under strict operational constraints. Existing deep reinforcement learning (DRL) approaches are economically competitive but cannot guarantee that device constraints are satisfied during training or deployment. This paper proposes MA-SRL, a safe reinforcement learning framework for ship IPSs that couples an execution-layer Safe Projection Layer (SPL) with a training-stage Lyapunov-based policy update. The SPL projects each raw action onto the feasible set of the current mode before execution, whenever that set is non-empty, and quantifies the departure as a constraint-cost signal that drives a Mode-Dependent Constrained Markov Decision Process (MD-CMDP), making feasibility observable to the learner. The Lyapunov update is designed to control the expected discounted constraint cost through a budget condition during training. Under the nominal scenario, this signal drives the raw policy close to the feasible set, lowering constraint violation by 2.2–6.0× and cost by 7.7–16.9% over DRL baselines, with the lowest constraint violation retained under storm conditions. The same architecture and settings are replicated on a second vessel, plant, route, and operating profile, attaining the lowest cost and constraint violation of all compared methods, and sustaining the lowest distance shortfall and unmet load under a single-generator-loss contingency. Full article

Sustainable smart factories require energy management that conserves industrial demand and respects receiver capacity across production campaigns. This retrospective study assessed whether a scenario framework could reduce energy above a fixed daily threshold, exceedance days, maximum daily grid energy, and purchased grid energy without deleting demand. The dataset contained 435 daily records from eight campaigns, including 423 finite positive observations used for scenario evaluation, three products, six metered energy sources, and 10,440 hourly weather records. Weather-derived photovoltaic availability was coupled with daily battery-grid accounting. Data from 2019–2024 supported development, data from 2025 supported temporal validation, and data from 2026 supported chronological testing. Five scenarios compared the baseline (S1) with deterministic tuning (S2), demand-side management (S3), a genetic algorithm (GA; S4), and particle swarm optimization (PSO; S5) under identical objectives and constraints. Across all data, S2, S4, and S5 reduced above-threshold energy by 6.9%, left exceedance days and maximum daily grid energy unchanged, and increased purchased grid energy by 0.1%. Their 2026 reduction was 1.0%. Accepted transfers represented 0.50–0.68% of demand, retained unplaced requests at source, met the three-day limit, and caused no receiver violations. GA and PSO returned identical objectives and outcomes across 30 seeded runs each, providing scenario-based evidence under aggregate daily assumptions. Full article

With the rapid electrification of transportation, electric vehicles (EVs) have become an increasingly important component of sustainable mobility, making the efficient operation and management of charging infrastructure increasingly critical. Accurately forecasting future charging service states is therefore essential for charging resource scheduling, energy management, and intelligent EV charging operations. However, existing EV charging forecasting studies mainly focus on predicting isolated indicators. Such formulations overlook the inherently multi-dimensional nature of charging services, where different indicators describe complementary aspects of future operational conditions. As a result, single-indicator forecasting provides only a partial characterization of charging states and limits its effectiveness for downstream tasks such as risk awareness and intelligent charging management. To address this limitation, this paper reformulates EV charging forecasting as a service-state forecasting problem and proposes EleState, a framework designed to learn and forecast multi-dimensional charging service states. EleState constructs comprehensive service-state representations by jointly modeling temporal evolution, inter-state dependency, and spatial interaction among charging stations. Based on the learned representations, EleState forecasts three key service-state dimensions, including occupancy, charging duration, and charging volume, enabling a more complete characterization of future charging conditions. Extensive experiments on the UrbanEV dataset demonstrate that EleState achieves the best target-wise MAE and RMSE across all three service-state dimensions, reducing target-wise MAE by 1.4–2.4% and RMSE by 1.2–3.2% compared with the strongest baseline. Furthermore, EleState provides stronger operational risk awareness than conventional load-oriented forecasting and maintains robust performance across different forecasting horizons. Overall, this work extends EV charging forecasting beyond conventional single-indicator prediction toward comprehensive service-state forecasting, providing a more effective foundation for intelligent EV charging management. Full article

Accurate battery models must generalize to unseen operating routes while supporting terminal-voltage prediction and recursive state-of-charge (SOC) estimation. This study evaluates whether physics-guided residual learning improves complete-route generalization compared with increasing equivalent-circuit-model (ECM) order or using direct data-driven voltage predictors. Seven open-loop voltage models and five matched extended Kalman filter (EKF) observers were assessed using 82 real-world electric-vehicle routes (119,720 synchronized observations) and only electrical and thermal battery-management-system signals. Sixty-six routes were used for development, and 16 formed a held-out route set. The residual 1RC M6 model achieved the lowest test path root mean square error (RMSE) voltage (0.797 V), reducing the error of the 2RC M3 physical model by 20.6%, while M7 achieved the (0.797 V), reducing the error of the physical 2RC model M3 by 20.6%, while M7 achieved the lowest median absolute route-energy error (0.091%). Development-only repeated grouped cross-validation yielded lower mean fold RMSE for M6 and M7 than for M3 in all 15 repeat fold combinations. For SOC estimation, the 1RC-residual observer S4 achieved the lowest mean and median RMSE relative to BMS-reported SOC (0.797 and 0.660 percentage points, respectively). The observer-family effect was significant (Friedman ${\chi }^2=14.15$, $p=0.0068$), although no planned paired comparison remained significant after Holm correction. The results support physics-guided residual learning as a promising single-vehicle across-route strategy, with route-dependent benefits and increased computational cost. Full article

This study develops a techno-economic sizing and energy-management framework based on the Hippopotamus Optimization Algorithm (HOA) for hydrogen-integrated autonomous Hybrid Renewable Energy Systems (HRES) for rural electrification. A representative remote community in Tabuk, Saudi Arabia, is investigated using 11 years of NASA POWER satellite-derived meteorological data. The modelled community is constructed from a synthesised connected-load inventory representing approximately 600 households and 3000 residents; accordingly, the results represent a simulation-based planning case study rather than a validated design for a specific settlement. Seven configurations combining photovoltaic generation, wind turbines, battery storage, hydrogen production and storage, fuel cells, and diesel generation are evaluated considering Total Net Present Cost, CO₂ emissions, and Loss of Power Supply Probability (LPSP), with a Demand Response Management System (DRMS) incorporated into the framework. The three objectives are combined using a weighted-sum scalar formulation, complemented by a hard-constrained formulation for reliability. HOA is benchmarked against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Walrus Optimizer (WO), and Osprey Optimization Algorithm (OOA) under a common budget of 10,000 objective-function evaluations per run and 10 independent runs. Under the constrained formulation, six of the seven configurations satisfy LPSP ≤ 5% within the investigated sizing bounds, with costs of energy (COE) ranging from $0.1046/kWh for PV/wind/battery to $0.1357/kWh for wind/battery/diesel; the fully renewable PV/wind/hydrogen configuration is feasible at $0.1195/kWh. Only the wind-free configuration fails to satisfy both imposed constraints because of the 30% diesel-energy limit rather than reliability. Evaluation over the eleven individual meteorological years shows that all designs violate the 5% reliability criterion in every year, reaching 2.0–2.9 times the design LPSP because hour-of-year averaging removes prolonged low-resource periods. Re-optimization against the worst observed year increases COE by 33–63% and storage capacity by factors of three to five, with hydrogen storage in the fully renewable configuration increasing from 10 to 75.4 kg. Sensitivity analysis identifies wind availability as the dominant economic parameter, with a 20% wind-speed reduction increasing COE by 36.1%. The DRMS reduces the peak-to-average ratio by 20.0% for the assumed evening-peaking profile, whereas no reduction is obtained for an afternoon-peaking profile consistent with measured Saudi residential demand. These findings demonstrate that meteorological and demand-profile representation materially affects autonomous HRES sizing and should be explicitly considered when interpreting techno-economic optimization results. Full article

The increasing specialization of crop and livestock production has disrupted traditional nutrient cycling in agricultural systems, creating a dual challenge of excessive dependence on synthetic fertilizers and inefficient utilization of livestock manure. Crop–livestock circular agriculture offers a promising pathway to address these interconnected problems by recoupling animal production, manure management, and crop cultivation within an integrated nutrient-recycling framework. This review critically synthesizes recent advances in crop–livestock circular agriculture, with particular emphasis on manure valorization, nutrient recovery and reuse, enabling treatment technologies, crop–livestock nutrient matching, and regionally adapted implementation models. Current evidence demonstrates that appropriately managed manure recycling can partially substitute synthetic fertilizers, improve soil fertility and structure, enhance nutrient-use efficiency, and reduce nutrient losses and associated environmental pressures. Technologies including solid–liquid separation, aerobic composting, anaerobic digestion, and emerging resource-recovery approaches further expand the potential for converting livestock waste into fertilizers, energy, and other value-added agricultural inputs. However, the environmental and agronomic benefits of these systems depend strongly on balancing manure-derived nutrient supply with crop demand and local land carrying capacity. This requirement is particularly important in the hilly agricultural regions of Southwest China, where fragmented farmland, dispersed livestock production, complex terrain, and high transportation costs constrain the direct adoption of large-scale centralized models. Locally adapted strategies integrating decentralized manure treatment, nearby land application, and coordinated regional nutrient allocation may therefore provide more practical solutions. Despite substantial progress, broader implementation remains limited by spatial mismatches between manure production and cropland demand, insufficient technological adaptation, economic constraints, and a lack of long-term system-level assessments. Future research should move beyond individual waste-treatment technologies toward integrated crop–livestock management that combines nutrient budgeting, precision manure application, resource recovery, digital decision support, and region-specific governance. Such advances are essential for transforming livestock manure from an environmental liability into a strategic nutrient resource and for accelerating the transition toward resource-efficient, low-impact, and resilient agricultural systems. Full article