Search Results (5,833)

α-Glucosidase inhibitors play a role in postprandial blood glucose. However, synthetic hypoglycemic drugs often cause gastrointestinal discomfort and are unsuitable for use as food additives. Natural compounds from plants have therefore attracted interest as safer candidates for hypoglycemic functional foods. Vincoside lactam (VCS-LT), an indole alkaloid isolated from the leaves of Uncaria rhynchophylla (Miq.) Miq. ex Havil., is traditionally used in East Asian herbal teas and dietary supplements. We evaluated its carbohydrate-digestion inhibitory activity to provide experimental evidence for the development of herbal beverages and grain supplements with hypoglycemic properties. The in vitro assay used yeast-derived α-glucosidase, whereas the molecular docking targeted human maltase-glucoamylase (MGAM)—a distinction that is important for interpreting the respective findings. Molecular docking indicated the stable binding of VCS-LT to two catalytic domains of maltase-glucoamylase, a key intestinal enzyme involved in reducing postprandial glucose excursion after maltose loading following the consumption of high-carbohydrate staple foods. In a p-nitrophenyl-α-D-glucopyranoside-based assay using yeast-derived α-glucosidase, VCS-LT showed potent inhibitory activity. In alloxan-induced diabetic mice, oral VCS-LT reduced fasting glucose and attenuated postprandial spikes after maltose loading. In contrast to the gastrointestinal side effects commonly associated with synthetic α-glucosidase inhibitors such as acarbose, no gastrointestinal discomfort or other adverse effects were observed during the 12-day administration period. Collectively, our findings demonstrate that VCS-LT is a potent food-derived α-glucosidase inhibitor that effectively attenuates postprandial glucose excursion in diabetic mice, which supports its potential application as a functional food ingredient for daily dietary glycemic management. Nevertheless, our work remains at the proof-of-concept level, and future studies on processing stability, bioaccessibility, food-matrix interactions, sensory acceptability, and safety at food-use levels are essential before any commercial application can be envisioned. Full article

Substation-level carbon-emission factors (CEFs) are operationally relevant because substations concentrate transformer losses, auxiliary consumption, and sulfur hexafluoride (SF6) leakage at the interface between transmission and distribution. However, static or annual emission-factor methods average over heterogeneous operating regimes and cannot capture the pronounced non-stationarity of substation CEFs driven by seasonal loads, stochastic maintenance events, cooling-system switching, and extreme weather. To support high-frequency dynamic carbon tracing, dispatch optimization, and audit compliance, this study proposes a physics-guided fusion framework integrating a temporal foundation model, MOIRAI-2, with a Uniform Physics-Informed Neural Network (UPINN). A 15-dimensional physically constrained feature vector is constructed from IEEE C57.91 thermal-circuit equations and ideal-gas state equations, including transformer top-oil/hot-spot temperature, SF6 pressure/density estimation, and oil-forced/air-forced (OFAF) or oil-directed/water-forced (ODWF) cooling status. MOIRAI-2 uses Any-Variate Attention with binary attention bias to model intra-variate temporal dependencies and cross-variate physical couplings, whereas UPINN embeds thermal-balance, SF6 leakage-kinetics, and CEF conservation residuals as soft constraints. An adaptive gating network balances data-driven pattern recognition and physics-driven smoothness across steady-state, extreme-event, and maintenance regimes. Validation on a 220 kV substation dataset achieves a mean absolute error (MAE) of 0.352 gCO₂e/kWh, outperforming random forest (RF), gradient boosting machine (GBM), long short-term memory (LSTM), and a Pure Transformer by 24.0%, 19.1%, 23.0%, and 14.4%, respectively. Ablation studies show that the 15-dimensional physical-feature expansion improves accuracy by 8.8%, whereas physics-loss regularization reduces prediction variance by 37%. UPINN decomposition further indicates that transformer total loss, ambient temperature, and load factor dominate CEF dynamics, and rainfall cooling reduces CEF by 0.04 gCO₂e/kWh per 20 mm increment. The framework provides a physically consistent and intrinsically interpretable basis for dynamic substation carbon accounting and low-carbon operation. Full article

Combining biological treatment with electrochemical oxidation (EO) may provide an effective strategy for high-strength livestock wastewater by coupling biodegradable organic compounds removal with oxidation of more persistent contaminants. This study compared activated-sludge biological treatment (B) and boron-doped diamond EO in both sequences (EO + B and B + EO) using livestock wastewater with a chemical oxygen demand (COD) of approximately 16 g O₂ L⁻¹. B treatment was evaluated at different hydraulic retention times and EO at predefined COD removal targets. EO + B achieved 87–94% COD removal with lower specific energy consumption, whereas B + EO consistently achieved approximately 99% COD removal and greater removal of dissolved organic carbon, phosphorus, several metals, and solids. Lowering the organic load before EO did not reduce the stage-specific energy demand of EO polishing. Process order also altered nitrogen speciation: EO + B resulted in lower final ammonium and nitrate concentrations but nitrite accumulation, whereas B + EO promoted nitrate formation. Salmonella spp. and Escherichia coli were not detected after either sequence. B + EO provided deeper physicochemical polishing but promoted substantial chlorate and perchlorate formation, whereas EO + B was less energy demanding and avoided final oxychlorine accumulation. These distinct water-quality profiles support fit-for-purpose selection of treatment sequences according to the intended reuse application. Full article

The current study combines solar photovoltaic (PV) power, energy storage batteries and wind power to propose a novel and effective method for energy management and optimisation of hybrid renewable energy systems. Optimising the management and coordination of many energy sources is becoming essential, as the world moves towards sustainable energy choices. This study seeks to improve system performance, reliability and operating costs, using a unique hybrid control and power management paradigm, the Improved Human Evolutionary Optimisation (IHEO) algorithm, presented as a novel optimisation method that balances energy flow, generation, storage and consumption. This study shows that the proposed model greatly improves the efficiency of the operation of hybrid systems. The optimisation’s main objective is to reduce the overall cost of energy production while maintaining a smart energy management system. In this context, the cost function accounts for energy losses during electricity distribution as well as the generation costs of solar, wind, and battery storage. By minimising energy losses and optimising power flow between energy sources (wind, solar), storage (battery) and load demand, this can be used to assess system performance. The applied approach ensures system stability, optimises the use of renewable energy sources and reduces the power imbalance. The improved effectiveness of the IHEO algorithm in this study in minimising energy losses, lowering operating costs and enhancing overall system efficiency is demonstrated by thorough comparison with conventional particle swarm optimisation (PSO). Further to this, the integration of MPPT with PV systems and the IHEO algorithm enhances energy extraction efficiency by dynamically optimising power flow, ensuring maximum output from renewable sources under varying environmental conditions. Additionally, the BESS charging current ripple is also reduced from ±15 A to ±3 A using the model applied in this study, confirming smoother and safer battery charging operation. The key novelty lies in using IHEO for global exploration to find the best solution and PSO for local refinement to improve battery coordination with renewables and smooth DC-link regulation, which is then compared with conventional WOA. Full article

Residential photovoltaic (PV) and battery systems operating under time-of-use tariffs create genuine opportunities for demand-side management, yet comparative evidence across strategies under realistic operating constraints remains scarce, and most reported gains rely on forecasting or optimization frameworks that are difficult to deploy on existing residential hardware. Three deterministic, rule-based strategies, namely peak shaving, load shifting, and valley filling, were evaluated using a verified, energy-conserving MATLAB (Version: R2024b), hourly simulation for a typical European prosumer (6 kWp PV, 6 kWh battery, approximately 6000 kWh annual consumption), incorporating realistic battery constraints and actual three-tier tariff structures across 8760 operational hours. Baseline operation without demand management achieved 31.7% self-consumption, 85.4% grid dependence, and an annual cost of €872. Load shifting produced the strongest economic outcome, reducing costs to €720 (€152 savings), with 36.1% self-consumption and 70.1% grid dependency. Peak shaving saved €126 through automated battery control alone, reaching 35.2% self-consumption without requiring behavioral change. Valley filling prioritized grid stability, lowering power fluctuations by 31%, while achieving 33.6% self-consumption and €112 in savings. No single strategy optimized all objectives simultaneously, and strategy selection should depend on prosumer priorities; the rule-based framework further offers a deployable performance reference against which future forecast- and optimization-based controllers can be assessed. Full article

Background: Botulinum toxin type A (BoNT-A) has been proposed as an adjunct to orthognathic surgery because its temporary neuromuscular effects may reduce muscular loading during postoperative healing and potentially influence fixation stability, skeletal relapse, and postoperative recovery. This systematic review aimed to evaluate the available clinical evidence regarding the adjunctive use of BoNT-A in orthognathic surgery. Methods: A systematic literature search was conducted in PubMed, the Cochrane Library, and Google Scholar in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Clinical studies directly comparing orthognathic surgery with and without adjunctive BoNT-A administration and reporting outcomes related to mechanical stability, skeletal stability, or postoperative recovery were included. Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool for randomized controlled trials and ROBINS-I for the non-randomized study. Results: Three studies met the eligibility criteria, comprising two randomized controlled trials and one retrospective comparative study. Considerable heterogeneity was observed in surgical procedures, targeted muscles, BoNT-A doses, timing of administration, and evaluated outcomes. Individual studies reported associations between BoNT-A administration and a lower incidence of fixation plate fracture, reduced anteroposterior skeletal relapse at the Pogonion, or lower preanalgesic pain scores and postoperative opioid consumption. However, these findings were derived from different studies evaluating distinct interventions and outcomes and were not consistently demonstrated across comparable outcome measures. Conclusions: BoNT-A may represent a promising adjunct to orthognathic surgery, with potential benefits for fixation-related mechanical stability, selected parameters of skeletal relapse, and postoperative pain management. However, the limited number of studies, methodological limitations, and substantial clinical heterogeneity preclude definitive conclusions. Further well-designed randomized controlled trials with standardized injection protocols and longer follow-up are required before routine clinical use can be recommended. Full article

Accurate forecasting of energy consumption, renewable power output and utility expenditure is essential for sustainable planning of residential buildings and improving smart grid integration. This study presents several techniques such as random forest, gradient boosting regression, extreme gradient boosting, deep belief networks, random vector functional link, multi-layer perceptron and hybrid ensemble for forecasting of residential load demand, electricity bills, solar energy generation and solar irradiance. Electricity bills under Time-of-Use tariffs are introduced in the paper to accomplish realistic evaluation of economic implications and facilitation of optimized energy usage and cost savings using real-time residential energy data collected from Durban, South Africa. The performance of the forecasting model is assessed by root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²) and mean absolute scaled error (MASE). The outcomes of the study show that the hybrid ensemble model accomplished the highest forecasting accuracy of the electricity bill with MAE, RMSE, MSE, MASE and R² of 0.018126, 0.022961, 0.00052719, 0.30006 and 0.97978 when compared to other models. The findings of the research can be used as potential benchmarks for intelligent tariff forecasting, demand response planning, smart energy management and renewable energy integration in residential buildings. Full article

To enhance the fuel economy and operational adaptability of hybrid tractors under variable plowing loads, this paper proposes a slip-ratio-aware equivalent consumption minimization strategy (ECMS) calibrated via dynamic programming (DP). A resistance–slip ratio prediction model was first identified using plowing resistance and slip ratio data collected from soil-bin tests. The predicted slip ratio was integrated into the demand power model to quantify slip-induced traction losses. Offline DP was subsequently applied to generate globally optimized power split trajectories and establish a baseline equivalence-factor map indexed by plowing resistance level and battery state of charge (SOC). For real-time operation, the equivalence factor is dynamically adjusted via SOC feedback and normalized slip ratio deviation, enabling coordinated power distribution among the engine, MG1, and MG2. Powertrain bench tests were conducted by reproducing variable plowing loads using a dynamometer. The equivalent plowing resistance was calculated from measured load torque, and the corresponding slip ratio was estimated using the identified prediction model. Compared with A-ECMS, the proposed strategy reduced equivalent fuel consumption by 14.02% in simulation and 7.33% in bench tests. The proportion of engine operation in the high-efficiency region increased from 61% to 80% in simulation and from 65% to 77% in the bench test, while the corresponding proportion for the electric motors increased from 87% to 92% and from 88% to 90%, respectively. The SOC deviation decreased from 3.03% to 2.26% in simulation and from 3.07% to 2.43% in the bench test. These results demonstrate that the proposed strategy improves fuel economy, SOC regulation, and component operating efficiency under variable plowing loads. Full article

Accurate forecasting of electrical load at the substation level is essential for detecting grid losses that pose significant financial and safety challenges. This study investigates whether the standard correction method currently used by grid operators can be improved through the application of advanced forecasting models to granular, location-specific load data. Such improvements are particularly valuable for identifying abnormal consumption patterns indicative of grid losses due to electricity theft, defective meters, or cable damage. This paper evaluates a broad range of statistical and machine learning models—including ARIMAX, SARIMAX, Random Forests, Gradient Boosting Machines, Neural Networks, and Support Vector Regression—based on unique quarter-hourly datasets from several Dutch substations. Two hybrid approaches are proposed, combining the best-performing individual models through a stacked ensemble method and a simpler averaging strategy. The results show that incorporating lagged and additional exogenous variables, along with the application of various advanced models, significantly improves forecasting accuracy compared to the standard correction method, with the best hybrid model reducing MAE and RMSE by approximately 64.7% and 61.6%, respectively, relative to the current operational benchmark. This study demonstrates that substation-level, data-driven forecasting can strengthen the signals used to detect grid losses, offering practical implications for grid operators and policymakers. Full article

The transition to renewable energy is essential for mitigating greenhouse gas emissions and advancing carbon neutrality. Lignocellulosic biomass offers an abundant and sustainable feedstock for biofuel production, but its inherent recalcitrance demands effective pretreatment to enable enzymatic saccharification. This study evaluated five commercial organic acids typical of anaerobic digestion (AD) effluents, namely acetic, propionic, butyric, valeric, and caproic acids, for corncob pretreatment. Compared with untreated corncob, which gave a glucose yield of only 23%, all acid pretreatments substantially enhanced enzymatic hydrolysis. A strong correlation between xylan removal and total sugar yield (R² = 0.98) confirmed that hemicellulose solubilization is the primary factor governing digestibility in typical AD-derived organic acid pretreatment. Among the conditions tested, pretreatment with 2.5% butyric acid at 180 °C for 45 min was optimal, removing 91.84% xylan and 53.10% lignin while retaining 75.24% glucan, which led to a near-complete glucose release during subsequent enzymatic hydrolysis at 2% solid loading with cellulase (15 FPU/g substrate). Fermentation of the butyric acid-pretreated hydrolysate via separate hydrolysis and fermentation produced 43.32 g/L ethanol with ~99% glucose consumption. In this case, the final ethanol yield from consumed sugars was 79.8% of the theoretical yield. These findings demonstrate that typical AD-derived pure organic acids serve as effective pretreatment agents, offering a promising route for lignocellulose valorization and biofuel production within a circular biorefinery framework. Full article

Haul-road condition can affect traction demand and diesel-to-LNG substitution in mining trucks. We evaluated a 140-t truck at the Ekibastuz coal mine using 180 registered cycles (88 diesel-only [DOM], 92 dual-fuel [DGB]), 24 road segments, 36 defect events and 30 matched fuel-mode pairs. Mean ECM-reported substitution was 30.61% across DGB cycles. In matched pairs, diesel use declined from 34.00 to 23.76 L/cycle, a mean saving of 10.24 L/cycle (95% CI 9.53–10.95); calculated combustion-stage CO₂ declined by 7.10%. Total fuel energy, specific fuel-energy consumption and cycle time did not differ significantly (p > 0.30). Across Good-to-Poor road classes, engine load increased from 66.11% to 74.40% and substitution from 28.97% to 32.31%, while specific fuel-energy consumption increased from 5.91 to 6.72 MJ/(t·km). The load association persisted after temperature and wind adjustment and shift-clustered inference. Reference-state haul-road energy penalty was associated with rolling resistance and roughness, but remains dependent on a supplied reference input. The results distinguish diesel displacement from improved transport energy performance and are conditional on the cycle register. They do not establish causal road effects or a life-cycle climate benefit. Full article

Centralized sewer networks are rarely feasible for scattered mountain villages across China. Their construction costs stay high, and uneven terrain easily triggers pipe blockages and infiltration. Most existing rural wastewater treatment devices run on fixed operating schedules. They maintain full aeration even during low water inflow, wasting electricity and destabilizing effluent quality. This study reports a full-scale summer field assessment of an integrated attached-growth biofilm reactor deployed at the sewage treatment station serving Miaodong and Miaoxi Villages, Ruyang County, Henan Province, China. The system combines hydrolysis acidification, two-stage biological contact oxidation, sedimentation, post-sedimentation polishing, and a cloud-connected monitoring and control module. The control system adjusts influent pumping, aeration, internal reflux, and sludge discharge in response to measured hydraulic and dissolved-oxygen signals. The design treatment capacity was 850 m³ d⁻¹. During the 25-day monitoring period, the packing filling ratio was 70%, dissolved oxygen was maintained at 2.0–4.0 mg L⁻¹, and water temperature was 20 ± 5 °C. Average COD removal reached 91.1%, ammonium nitrogen (NH₄⁺-N) removal reached 88.9%, total nitrogen (TN) removal reached 83.5%, and total phosphorus (TP) removal achieved 81.7%. The average unit electricity consumption was 0.195 kWh·m⁻³. Because no fixed-frequency reference operation was conducted under identical influent and environmental conditions, the specific energy-saving contribution of the adaptive control module could not be quantitatively isolated. The reported value should therefore be interpreted as system-level field performance rather than as a verified percentage reduction attributable exclusively to intelligent control. The average TP concentration after polishing was 0.59 mg L⁻¹, exceeding the 0.5 mg L⁻¹ Class A limit of GB 18918-2002. The results characterize summer operation under the investigated loading and temperature conditions and should not be extrapolated directly to year-round compliance, winter operation, or heavy-rainfall events. Full article

Photonic computing systems require large numbers of accurate programmable voltages for photonic weight programming and device bias control. This paper presents a 128-channel digital-to-analog converter (DAC) implemented in a 250 nm BCD high-voltage CMOS process. A code-dependent per-channel auxiliary-DAC calibration scheme is proposed to compensate main-DAC conversion errors and channel-dependent offsets. In addition, a separated low-/high-voltage-domain driver and a stepwise multichannel update scheme are adopted to reduce static power and suppress update-induced disturbances. After calibration, the measured maximum absolute differential non-linearity (DNL) and integral non-linearity (INL) are 0.26 least significant bit (LSB) and 0.39 LSB, respectively, and the maximum deviation of voltage output (DVO) across 128 channels is 0.71 LSB. The DAC achieves rising/falling slew rates of 6.1/11.7 V/μs under an 8 V output swing. Under dynamic operation with 0.2 to 8.2 V sinusoidal outputs and a 10 kΩ load per channel, the total power consumption is 0.85 W. Thermo-optic phase-shifter measurements further verify programmable photonic phase tuning, demonstrating a scalable electrical control interface for thermo-optic phase-shifter-based photonic computing hardware. Full article

Aiming at the problems of high energy consumption, high carbon emissions from data centers and the difficulty of renewable energy accommodation in distribution networks driven by rapid growth in computing tasks, this paper proposes a collaborative trading method for data center–power grid–energy storage systems to improve spatiotemporal flexibility. Firstly, an integrated mechanism model including IT equipment, HVAC cooling systems, delay-tolerant batch tasks and UPS energy storage is established to quantify multi-dimensional internal flexible regulation potential. Secondly, an improved k-means algorithm is adopted for scenario reduction of wind–PV outputs, and a stochastic-robust collaborative trading optimization model considering carbon emission cost is constructed. Multiple practical constraints are incorporated, including power balance, power flow limits, nodal voltage bounds, task service latency and state of charge limits of energy storage. An improved particle swarm optimization with premature-convergence indicator is developed to solve this nonlinear, non-convex, mixed-variable problem. Simulations are carried out on a modified IEEE 33-node test system over a 24 h scheduling horizon. Numerical results demonstrate that compared with the conventional demand-response strategy, the proposed method reduces total operational cost by 10.7%, curtails wind–PV abandoned power, and achieves 28.6% peak-shaving ratio for data center load. Monte Carlo repeated experiments indicate that the improved Particle Swarm Optimization (PSO) reaches a 95% feasible solution rate with an average computation time of 26.8 s for day-ahead dispatch, which satisfies practical engineering requirements. Full article

Livestock carriers contribute to greenhouse gas emissions through the continued use of conventional fossil fuels. This study presents a theoretical, steady-state case study of replacing the Wärtsilä W7X35 diesel engine of the livestock carrier Motor Vessel MV Ganado Express with a Wärtsilä 46F methanol-fueled engine (7500 kW, operated at 81.2% load to deliver the required 6090 kW), using published specific fuel consumption values and emission factors. Fuel consumption and exhaust emissions were estimated with a bottom-up method over a 24 h sailing period, and the annual well-to-wake (WtW) greenhouse gas fuel intensity (GFI) and compliance costs were assessed for 2028–2035 against the two-tier targets of the IMO Net-Zero Framework approved at MEPC 83. Because methanol is a carbon-bearing fuel, tank-to-wake carbon dioxide emissions are nearly unchanged, declining by only 5.4%, whereas nitrogen oxide emissions fall by 73.2% and sulfur oxide emissions are eliminated. The decarbonization benefit arises instead on a well-to-wake basis: with renewable e-methanol, annual lifecycle emissions fall from 29,897 to 2291 tCO_2eq. Diesel operation exceeds both targets from 2028, at a compliance cost rising from USD 0.91 to 3.85 million per year, while e-methanol remains in direct compliance throughout the period. E-methanol nevertheless remains the more expensive option, the annual cost gap narrowing from USD 15.1 to 12.2 million by 2035. Full article