Search Results (267)

Heat stress represents a pervasive welfare challenge for beef cattle and other species in tropical and subtropical regions. While its physiological and production impacts are well-documented, quantitative measures of the welfare impact of heat stress remain absent. This study provides the first quantification of the welfare impact of heat stress in beef cattle (mostly Nelore), estimated as cumulative time in thermal discomfort of four intensities (Annoying, Hurtful, Disabling, Excruciating) using the Welfare Footprint Framework. We analyzed climate data from 636 locations over five years across major beef production areas in Brazil, Argentina, Colombia, Paraguay, and Uruguay. Daily heat stress episodes and chronic heat stress exposure were assessed, respectively, using Comprehensive Climate Index (CCI) levels and the Annual Thermal Load metric, which sums daily excesses above a threshold of thermal comfort (CCI = 30 °C) throughout the year, classifying locations into five risk categories. Welfare impacts were estimated for thirteen heat stress scenarios modeled by considering each CCI level within each thermal risk category. Beef cattle in moderate-risk regions were estimated to experience primarily mild thermal discomfort for an average of 5 h daily. This duration increased to an average of 7 h daily in high-risk areas, of which 4.5 h in moderate to intense thermal discomfort (Hurtful or higher). Very high-risk regions reached 10 h of daily thermal discomfort, while extreme-risk regions showed beef cattle facing heat stress for over 11 h on 307 days annually, including over 3 h per day under severe thermoregulatory effort. Overall, 65% of animals were in regions of high thermal risk or above, experiencing between 280 and 2800 h annually in moderate to intense thermal discomfort—a magnitude that places heat stress among the most significant welfare challenges in animal production. Shade provision reduced time in severe discomfort of Disabling intensity by 85% (from 578 to 83 h annually), with economic returns of US$12–16 per animal and payback periods of approximately 16 months. By quantifying welfare impacts as cumulative time in thermal discomfort, shade provision emerges as one of the most effective welfare interventions available for beef cattle, and likely other grazing ruminants, in tropical and subtropical regions. Full article

Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students’ understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. Full article

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO₂ emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO₂ emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks. Full article

This study evaluates the impact of high ambient temperature (HT) on milk production in Tunisian local goats using both fixed and random regression models with quadratic and cubic Legendre polynomials. Daily minimum (Tmin), maximum (Tmax), and average (Tavg) temperatures were tested as heat load indicators, measured on the milking day and averaged over the 1–3 preceding days. The deviance information criterion (DIC) consistently showed that models including temperature effects provided a better fit than a baseline model without heat load. Cubic polynomials showed superior accuracy compared with quadratic models, even if the differences were relatively small. The best model was obtained with Tavg on the milking day, followed closely by Tmax averaged across one or two preceding days. The population response showed a thermoneutral plateau at lower temperatures, followed by declines beyond the HT thresholds. For Tmax, moderate and severe thresholds were detected at 20–23 °C and 25–27 °C, respectively, while for Tavg, thresholds occurred at 11–13 °C and 16–19 °C. Milk losses ranged from 22 to 85 g/°C depending on the temperature indicator, representing an average 4–5% decline in daily yield per degree above thermoneutrality. High variability in individual responses was observed. Some goats maintained stable production, while others showed steep declines under HT, with slope differences reaching over 150 g/°C. Correlations of milk yield across contrasting thermal environments were low, indicating that animal ranking changes with temperature. High-producing goats are more affected by heat, showing the need for a balance between production and heat tolerance. Full article

This study evaluated the effects of including additional roughage and the timing of roughage addition on rumen temperature (T_RUM), rumen pH, dry matter intake as a percentage of live weight (DMILW), water consumption as a percentage of live weight (WILW) and average daily gain of grain-fed steers exposed to a simulated heat wave. A total of forty-eight Black Angus steers (539.53 ± 4.95 kg) were housed within climate control rooms for 21 days and exposed to a 5-day simulated heat wave. Steers were randomly allocated into four cohorts with 12 steers/cohort, and then allocated to one of three dietary treatments: Treatment 1 (T1) were fed a finisher diet for the 21 days; Treatment 2 (T2) transitioned from the finisher diet to a heat load diet on d 9 and fed the heat load diet until d 14; and Treatment 3 (T3) transitioned from the finisher diet to the heat load diet on d 7 and fed the heat load diet until d 14. On d 15, T2 and T3 transitioned back to the finisher diet. The study was categorised into five phases consisting of (i) Phase I, d 0–6 (Temperature Humidity Index, THI 65 to 78); (ii) Phase II, d 7–8 (THI 65 to 78); (iii) Phase III, d 9–11 (THI 83 to 90); (iv) Phase IV, d 12–13 (THI 78 to 85); and (v) Phase V, d 14–20 (THI 65 to 78). During the heat wave challenge in Phase III, all Treatments exhibited lower DMILW (p < 0.0001), greater T_RUM and rumen pH (p < 0.0001), lower ranges in T_RUM and rumen pH (p ≤ 0.0005) and altered diurnal T_RUM and rumen pH rhythms. Average daily gain was not influenced by Treatments (p ≥ 0.98). Overall, these results suggest that nutritional management remains an important consideration to reduce the impact of hot climatic conditions on the rumen environment during heat wave and post-heat wave conditions. Full article

Given the sustainable increase in kitchen waste production, the treatment of organic waste is quite important for both alleviating environmental risks and recovering biomass energy. Anaerobic digestion (AD) could achieve the goals of both organic stabilization and the green energy production of biogas. However, AD conducted at a high organic loading rate can easily suffer from low treatment efficiency due to the accumulation of volatile fatty acids and an imbalance in the microbial community. This study investigated the functional microbial enhancement strategy for enhancing AD performance. The results suggested that adding 10 g of granular sludge every 5 days could enhance AD efficiency. In that case, the daily average methane production rate was increased by 43.21% compared to that in the control group, and the pH and ammonia nitrogen concentration were maintained at the optimal level. Humic acid production was strengthened; it served as an electron shuttle, which facilitated direct interspecies electron transfer. Both Cloacimonadota and Methanobacterium were enriched in the system inoculated with the granular sludge. Metabolomics indicated that the acetyl–CoA conversion was strengthened, and that energy metabolism (complex I and archaeal ATPase) was also enhanced. The granular sludge inoculation also activated the archaeal genetic information processing system. This technology could promote the generation of green energy, which is more conducive to sustainable resource development. This study provides the theoretical basis for a microbial enhancement strategy that can enhance kitchen waste AD. Full article

This study explored the efficacy of dietary Fagopyrum dibotrys extract (FDE) in mitigating Escherichia coli O157 (E. coli) infections in broilers. A total of 240 one-day-old male Shengze 901 broilers were randomly allocated to four groups (with 10 broilers per group): CON (basal diet), COLI (basal diet + E. coli challenge), FDE (basal diet + 500 mg/kg FDE), and FDEC (basal diet + 500 mg/kg FDE + E. coli challenge). The results showed that E. coli challenge reduced the average daily gain (ADG) and average daily feed intake (ADFI), increased the feed conversion ratio (FCR) and cecal E. coli load, impaired the intestinal mucosa, and induced intestinal inflammatory responses (p < 0.05). FDE supplementation improved growth performance, increased duodenal villus height and villus/crypt ratio; reduced serum interleukin (IL)-1β, tumor necrosis factor-α (TNF-α), diamine oxidase (DAO), and endotoxin levels; and lowered cecal E. coli counts (p < 0.05). Molecularly, FDE supplementation upregulated Occludin, Claudin-1, and ZO-1 gene expressions, and downregulated jejunal TLR4 and MyD88 mRNA levels. Microbiome analysis revealed that FDE increased the relative abundance of Faecalibacterium and alleviated the E. coli-induced reduction in Clostridia_UCG-014. In conclusion, dietary supplementation with 500 mg/kg FDE could mitigate colibacillosis-related intestinal damage and inflammatory responses. Full article

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (${\mathrm{M}}_{\mathrm{A}\mathrm{P}\mathrm{E}}$) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs. Full article

The rapid integration of artificial intelligence (AI) into daily life highlights the need for culturally adapted tools to assess public attitudes. This study translated, culturally adapted, and validated the Japanese version of the General Attitudes toward Artificial Intelligence Scale (GAAIS-J) to enable cross-cultural research. The original GAAIS was translated, back-translated, and reviewed by experts for cultural and linguistic equivalence. A web-based survey of 3689 Japanese was conducted. Confirmatory factor analysis supported a two-factor model—positive and negative attitudes—after removing one low-loading item and allowing select item covariances, yielding acceptable fit indices. Reliability was high (ω = 0.94, α = 0.92), and average variance extracted values indicated satisfactory convergent validity. Construct validity was demonstrated through correlations with AI trust/distrust, generative AI usage frequency, socioeconomic status, gender, and Big Five traits. Positive attitudes were linked to higher trust, income, and openness; negative attitudes correlated with older age, lower education, and lower agreeableness. The GAAIS-J is a reliable, valid instrument for assessing AI attitudes in Japan and supporting international comparisons. Full article

Phase change materials (PCMs) integrated into building envelopes can store and release latent heat, reducing indoor temperature fluctuations and shifting thermal peaks. This study quantifies the time lag and comfort impact of PCM plaster under free-running conditions using two identical, instrumented model houses in Bácsalmás, Hungary. One house served as a reference, while the other was retrofitted with interior PCM plaster panels on four walls (51.2 kg paraffin, ≈8.12 MJ latent heat capacity). The temperatures of the walls, indoor air, and outdoor environment were monitored every five minutes for 105 spring/summer days. Daily peak times were extracted using moving-average smoothing, and time lags between exterior and interior wall peaks were computed. The PCM house exhibited roughly double the average lag compared with the reference (≈200 vs. ≈100 min), with lag distributions well described by lognormal fits. Comfort evaluation based on exceeded degree-hours (EDH) relative to the adaptive comfort range (EN 16798-1) revealed that larger peak-time lags correlated with lower overheating. Results confirm that PCM plaster significantly delays and attenuates daily temperature peaks, extends comfort periods, and supports passive strategies such as night ventilation and demand-side load shifting in lightweight buildings. Full article

The adult pig intestinal microbiota boosts piglet intestinal and microbiome development, thereby improving growth. However, the functional bacteria, metabolites, and their region-specific intestinal roles remain to be characterized. Administration of adult colon microbiota (CM; devoid of metabolites) to piglets promoted intestinal development post-weaning, as indicated by increased intestinal mucosal weight, villus-to-crypt ratio of the ileum (p < 0.05), and stimulated mucin secretion (p < 0.05). This effect was potentially mediated by modulating beneficial microbiota, including ASV50_Prevotella 7, ASV52_Prevotella 1, and ASV81_Coprococcus 1. Adult colon-derived microbiota was found to preferentially colonize the piglet colon, supported by significantly higher bacterial loads in colonic contents. Piglets receiving adult colon supernatant (CS; without bacterial cells) showed improved feed efficiency (FE; p < 0.05), with numerically higher body weight (BW) and average daily gain (ADG) compared to the control (CON) group. Additionally, CS transplantation (CST) promoted intestinal development, potentially by modulating abundances of beneficial bacteria species, including ASV95_Turicibacter, and ASV109_Ruminococcaceae, which correlated with increased production of antioxidant and anti-inflammatory chemicals, including protocatechuic acid (PCA, p < 0.01). Adult colon-derived microbiota and metabolites enhanced intestinal development in piglets. CS supplementation improved growth and immunity, mitigating post-weaning stress potentially through enriching growth-linked bacteria (e.g., Turicibacter and Ruminococcaceae) and metabolites production (e.g., prephenate and PCA). These findings highlight these functional microbiota and metabolites as promising direct-fed microbial or metabolite additives for piglet growth and intestinal health post-weaning. Full article

Nano- and microplastics (NMPs) in waterways reflect the impact of anthropogenic activities. This study examined spatial variations in the presence and types of NMPs in Galveston Bay (Texas, USA) surface waters and eastern oysters (Crassostrea virginica). The results reveal most MPs carried by surface waters are fibers > films > fragments. Up to 200 MPs were present in individual oysters [=1.88 (± 0.22 SE) per g wet weight]. Oyster health, based on condition index, varied spatially, but was not correlated with MP load. Based on attenuated total reflectance—Fourier-transform infrared spectroscopy, polyamide and polypropylene were frequently found in waters in the upper bay while ethylene propylene and polyethylene terephthalate were more common in the lower parts of the bay. Pyrolysis–gas chromatography–mass spectrometry revealed a very large range in concentrations of NMPs, from 28 to 10,925 µg ∑NMP/g wet weight (or 172 to 67,783 µg ∑NMP/g dry weight) in oysters. This chemical analysis revealed four main types of plastics present in oysters regardless of location: polypropylene, nylon 66, polyethylene and styrene butadiene rubber. Based on this finding, the average daily intake of NMPs estimated for adult humans is 0.85 ± 0.45 mg NMPs/Kg of body weight/day or a yearly intake of 310 ± 164 mg NMPs/Kg of body weight/year. These findings reveal higher body burdens of plastics in oysters are revealed by the chemical analysis relative to the traditional approach; this is not unexpected given the higher sensitivity and selectivity of mass spectrometry and inclusion of the nanoplastic particle range (i.e., <1 mm) in the sample preparation and analysis. Full article

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

This paper investigates the factors influencing the power consumption of electric vehicle (EV) charging infrastructure and develops a methodology for determining the design electrical loads of EV charging stations (EVCSs). A comprehensive review of existing research on demand factor (DF) calculations for EVCSs is presented, highlighting discrepancies in current approaches and identifying key influencing factors. To address these gaps, a simulation model was developed in Python 3.11.9, generating minute-by-minute power consumption profiles based on EVCS parameters, EV fleet characteristics, and charging behavior patterns. In contrast with state-of-the-art methods that often provide limited reference values or scenario-specific analyses, this study quantifies the influence of key factors and demonstrates that the average number of daily charging sessions, EVCS power rating, and the number of charging ports are the most significant determinants of DF. For instance, increasing the number of sessions from 0.5 to 4 per day raises DF by 2.4 times, while higher EVCS power ratings reduce DF by 32–56%. This study proposes a practical generalized algorithm for calculating DF homogeneous and heterogeneous EVCS groups. The proposed model demonstrates superior accuracy (MAPE = 6.01%, R² = 0.987) compared with existing SOTA approaches, which, when applied to our dataset, yielded significantly higher errors (MAPE of 50.36–67.72%). The derived expressions enable efficient planning of distribution networks, minimizing overestimation of design loads and associated infrastructure costs. This work contributes to the field by quantifying the impact of behavioral and technical factors on EVCS power consumption, offering a robust tool for grid planners and policymakers to optimize EV charging infrastructure deployment. Full article