Search Results (3,062)

Aquaculture output, sustainability, and profitability can be enhanced by using functional feed additives. The effect of supplementation with two different dietary levels of propylene glycol (PG) and essential oils (EOs) was evaluated in Nile tilapia. A total of 150 juvenile fish were randomly allocated into five groups. Growth performance, feed utilization, serum biochemistry, hematology, and gene expression were assessed. PG supplementation significantly improved growth performance, feed conversion, protein efficiency, and energy utilization. Both additives significantly reduced cortisol and glucose levels and altered liver enzymes and lipid profiles. PG improved immunological indices, while hematological responses were dose-dependent; both EOs and PG increased hematocrit, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH). Moreover, the high PG dose significantly increased platelet counts, reduced hemoglobin (Hb), and elevated hematocrit. Additionally, EOs significantly upregulated antifreeze protein IV (AFPIV) and fat metabolism-related genes in a dose-dependent manner, indicating a potential role in lipid mobilization and stress tolerance. Expression analysis of the immunoglobulin H (IGMH) gene revealed a significant increase in PG-supplemented groups, suggesting its immunostimulatory potential. Overall, PG enhanced immunity and growth performance, while EOs promoted AFPIV and fat metabolism gene expression. Therefore, PG and EO supplementation could serve as an effective functional strategy to enhance O. niloticus growth, stress adaptation, and immune resilience. Full article

The poultry sector plays a crucial role in global food production by meeting the growing demand for affordable, nutritious protein sources. However, it faces significant challenges in providing sustainable and cost-effective nutritional solutions that improve poultry health, performance, and product quality. Recent advancements in artificial intelligence (AI) have the potential to enhance poultry nutrition through the development of precise feeding strategies. AI helps monitor and optimize nutrient intake, thereby boosting feed efficiency, reducing waste, and lowering costs. This article examines how AI-driven innovations may advance the management of poultry feed ingredients, nutrient monitoring, and dietary formulations. By utilizing AI tools such as machine learning algorithms and real-time data analytics, poultry producers can track and assess the nutritional needs of individual birds. This allows for the development of more precise feed formulations tailored to the specific needs of different age groups, breeds, and environmental conditions. These AI technologies help select the best feed ingredients and enable precise adjustments to nutrient composition. This results in healthier birds, better feed conversion rates, and higher-quality poultry products. Additionally, AI advancements help reduce the environmental impact of poultry farming by reducing feed waste and resource consumption. This article highlights how AI-driven insights enhance decision-making, enabling the poultry industry to grow sustainably while promoting animal welfare, increasing efficiency, and producing high-quality poultry products that meet consumer expectations for both sustainability and nutritional value. Full article

Ginkgo biloba extract (GBE), obtained from dried Ginkgo biloba leaves, provides a natural option. GBE supplementation can increase livestock’s productivity through various biological functions, such as combating oxidative stress, reducing inflammation, optimizing gut microbiota, detoxifying intestinal toxins, and regulating immune responses. In this review, we utilized keywords such as “Ginkgo biloba extract” or “Ginkgo biloba extract” and “animal production” or “animal nutrition” to gather research on its various biological functions and the underlying mechanisms from databases such as Web of Science and PubMed, up to December 2025. Then, we systematically summarize the main bioactive components of GBE, its beneficial effects in livestock at different life stages and during different production cycles, and the related molecular pathways. Additionally, safety assessments and the potential applications were also discussed. This review highlights that GBE may be an effective plant-derived feed additive with multiple functions and strong potential to improve animal health, production efficiency, and product quality under intensive farming conditions. We hope that this review can stimulate broader discussions and better develop and utilize GBE as a feed additive in animal production. Full article

This study evaluated the effects of dietary glycerol fatty acid esters (GFAs) on growth performance, nutrient digestibility, nitrogen metabolism, jejunal microbiota, and intestinal transcriptome in Hu sheep. Thirty-six 4–5-month-old male Hu sheep were randomly assigned to three groups receiving a basal diet (GFA0%) or diets supplemented with 0.15% (GFA0.15%) or 0.20% (GFA0.20%) GFA for 52 days following a 7-day adaptation period. Growth performance parameters were unaffected (p > 0.05); however, feed-to-gain ratio decreased linearly and quadratically with increasing GFA levels (p < 0.001). GFA supplementation improved ether extract (EE) and neutral detergent fiber (NDF) digestibility (p < 0.05), neutral detergent fiber ADF digestibility showed a linear increase(linear = 0.025), significantly reduced fecal and urinary nitrogen excretion, and enhanced nitrogen utilization (p < 0.05). Jejunal microbiota analysis revealed significant genus-level separation among groups, with increased Bacillota abundance and the enrichment of Acetitomaculum and [Ruminococcus]_gauvreauii_group in the GFA0.20% group. Functional prediction indicated enhanced fiber degradation, nitrogen metabolism, and host interaction functions. Transcriptomic analysis showed dose-dependent gene regulation, with GFA0.15% primarily enriching immune-related pathways, while GFA0.20% additionally activated lipid and steroid metabolism pathways. Integrated microbiome-host analyses demonstrated coordinated regulation of nutrient metabolism and immune responses. Overall, dietary inclusion of 0.20% GFAs optimized feed efficiency, nutrient utilization, and intestinal metabolic-immune function in Hu sheep. Full article

With the rapid development of the livestock and poultry industry, the availability of feed resources in China has become a critical limiting factor, posing a significant challenge to the sustainable growth of animal husbandry. Black soldier fly (BSF) larvae are rich in protein, lipids, minerals, and trace elements and possess an essential amino acid profile comparable to that of fishmeal and soybean meal, which makes them a promising novel protein source for feed. This study aimed to investigate the effects of dietary BSF larvae protein supplementation on the growth performance, egg production, as well as meat and egg quality, blood biochemical parameters, and cecal microbiota diversity of Yunshang countryside chickens. The results showed that the inclusion of BSF larvae protein in the diet significantly reduced the feed-to-egg ratio and enhanced egg quality. Dietary supplementation with BSF larvae protein also effectively increased the abundance of dominant bacterial phyla and genera in the cecum, with the optimal inclusion level identified as 7.5%. Overall, the results demonstrate that BSF larvae can serve as a high-quality protein source in poultry production, thereby providing a scientific reference for the development and application of new feed resources and offering a theoretical basis for the utilization of BSF larvae as an alternative protein ingredient. Full article

Predicting tractor performance factors accurately is crucial for enhancing energy efficiency and assisting with the choice of machinery in agricultural operations. Using the Nebraska Tractor Test Laboratory (NTTL) identical data, this study uses artificial neural network (ANN) modeling to forecast important performance metrics of a front wheel assist (FWA) Massey Ferguson tractor. A feed-forward ANN model was developed and validated using reported data from official tractor tests. Performance indicators, such as drawbar pull (kN), drawbar power (kW), hourly fuel consumption rate (kg/h), drawbar specific fuel consumption (kg/kW·h), and drawbar specific volumetric fuel efficiency (kW/kg·h), were utilized as outputs and certain operational factors, the tractor characteristics variables as well as other variables were used as inputs. Statistical measures, including the coefficient of determination and error metrics from training and testing datasets, were used to assess the model’s performance. The results showed that the ANN model produced excellent generalization capabilities and good prediction performance by correctly capturing the nonlinear correlations between inputs and tractor performance indicators. The suggested strategy performed better than traditional regression-based techniques documented in the literature, especially when operation variables and tractor characteristics varied. The results show that combining NTTL data with ANN techniques offers a dependable and affordable method for predicting tractor performance indicators and evaluating energy efficiency. This eliminates the need for extensive experimental procedures and promotes data-driven decision-making in agricultural machinery management. Full article

The separation and purification of carbon dioxide (CO₂) from sour gas streams is critical for emission reduction and industrial reuse. This study presents a chemical absorption-based process simulation of CO₂ (carbon dioxide) and H₂S (hydrogen sulfide) separation using Aspen Plus V12.0, focusing on solvent-based treatment using an aqueous monoethanolamine (MEA) system selected based on industrial applicability and regeneration performance. The process was modeled for two gas streams originating from the Shurtan Gas Chemical Complex: a raw feed stream containing 3.42% CO₂ and 0.09% H₂S, and a treated dry gas containing 2.1% CO₂. The goal was to achieve high-purity CO₂ recovery (≥99.5%) with flow rates of 30 t/h and 20 t/h, respectively. Rate-based modeling was employed to simulate mass transfer and chemical kinetics in the absorber and regenerator columns. The simulation results indicated that at optimal solvent flow and absorber temperature (40–45 °C), over 98.6% CO₂ and 99.9% H₂S removal could be achieved. The specific energy requirement for solvent regeneration was estimated at 2.3 GJ per ton of CO₂, aligning with industrial efficiency benchmarks. The purified CO₂ is intended for use in the production of sodium carbonate (Na₂CO₃) at the Dehkanabad Potash Plant, which converts 20 t/h of CO₂ into 296,000 tons/year of calcined soda with 77% process efficiency. This approach enhances gas resource utilization while reducing atmospheric emissions. The model serves as a techno-economically viable foundation for scaling up CO₂ capture and utilization (CCU) in the Uzbek chemical industry. Full article

Artificial diets are increasingly utilized in sericulture, yet they often yield cocoons with properties that differ from those produced by mulberry leaf-fed silkworms. A reliable method to distinguish between these two types of cocoons remains lacking, compromising quality control in silk-related industries. In this study, we report a multidimensional analysis method integrating chemical, structural, and biomarker analyses to distinguish cocoons produced by mulberry leaf-fed (Mul-fed) silkworms from those produced by artificial diet-fed (Art-fed) silkworms. The SEM images showed that after the process of biomarker extraction, Mul-fed cocoons had a more complete morphological structure than Art-fed cocoons, and the sericin layer of Mul-fed cocoons was less damaged. Thermogravimetric and amino acid analyses revealed no significant differences between the two types of cocoons. Biomarker analyses via ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) revealed that quercitrin and quercetin were enriched in Mul-fed cocoons, while daidzein and genistein were enriched in cocoons produced by artificial diet-fed silkworms. Furthermore, materials extracted from Mul-fed cocoons demonstrated significantly superior bioactivity than those from Art-fed cocoons in in vitro assays. This study provides a reliable and accurate method for assessing cocoon quality and distinguishing cocoons from different feeding methods, laying a robust basis for quality evaluation and silk product development. Full article

Quinoa germ meal (QGM) is a protein-rich by-product with potential as an alternative protein source; however, its effects on growth performance and intestinal health in marine carnivorous fish remain unclear. Juvenile turbot (Scophthalmus maximus) were fed five isonitrogenous (45.6% crude protein) and isolipidic (9.8% crude lipid) diets for 8 weeks: a fishmeal-based control diet (C) and four experimental diets in which fishmeal was replaced with QGM at 10% (Q10), 20% (Q20), 30% (Q30), and 40% (Q40). Growth performance, muscle proximate composition, intestinal histomorphology, and intestinal transcriptomic profiles were analyzed. Growth performance parameters, including final body weight, weight gain rate, specific growth rate, daily feed intake, and condition factor, decreased significantly with increasing QGM inclusion levels compared with the control (p < 0.05). Feed conversion ratio increased significantly only when replacement exceeded 30% (p < 0.05), while survival rate was unaffected (p > 0.05). Muscle crude lipid content was significantly reduced in all QGM-fed groups (p < 0.05), whereas crude protein, moisture, and ash contents were unchanged. Intestinal mucosal fold height increased in the Q30 and Q40 groups, while submucosal width decreased in the Q40 group (p < 0.05). Transcriptomic analysis revealed a dose-dependent increase in differentially expressed genes, mainly enriched in ribosome-related pathways, linoleic acid metabolism, and protein digestion and absorption. High dietary inclusion of QGM (>30%) impaired growth performance in juvenile turbot, whereas low inclusion levels (≤20%) exerted minimal adverse effects. Quinoa germ meal represents a potential alternative protein source, yet its effective utilization requires further optimization to maintain growth performance. Full article

Drilling conditions serve as reliable indicators for evaluating drilling system performance. The recognition methodology forms a critical foundation for intelligent drilling and plays a significant role in preventing borehole accidents such as pipe sticking. This study proposes a recognition method for typical drilling conditions based on bit motion characteristics, developing intelligent algorithms that utilize fundamental motion parameters and drive signals as inputs to achieve accurate condition identification. Firstly, micro-drilling experiments were conducted under various pressure and rotational speed combinations. The variation characteristics of drilling speed, rotational speed, torque, and drilling pressure during the process were analyzed. Based on rotational and feed motion features at different stages, four typical drilling conditions were defined: stable drilling, slowing-down drilling, intermittent jamming, and pipe sticking. Subsequently, using actual coal mine drilling data, feature indicators for recognition models were selected through collective statistical characteristics and rolling statistical properties. Recognition models employing support vector machine (SVM), convolutional neural network (CNN), and long short-term memory (LSTM) networks were constructed and trained. Test results demonstrate that the LSTM-based model achieved the highest recognition accuracy (84% overall). It attained 100% accuracy in identifying slowing-down drilling and intermittent jamming conditions, though recognition accuracy for stable drilling remained relatively low. This research contributes to enhanced intelligent recognition of drilling conditions and provides theoretical support for advancing intelligent drilling technologies. Full article

Increasing constraints related to water consumption and operational complexity have intensified interest in dry coal beneficiation as an alternative to conventional wet cleaning, particularly for low-calorific coals used in thermal power plants. In this study, the performance of a gravity-based dry beneficiation process using an air table was experimentally investigated for run-of-mine coals from the Soma Coal Basin, utilized in the Soma A Thermal Power Plant. The coal was crushed to −10 mm and classified into three size fractions, 5–10 mm, 3–5 mm, and 1–3 mm, before beneficiation. A pilot-scale air table with a capacity of 10 t/h was employed, and operating parameters including table inclination, airflow rate, and vibration frequency were optimized for each size fraction. Clean coal yields of 86.8–88.7% were achieved, while the ash content was reduced from 32 to 35% in the feed to 27.8%–29.7% in the clean coal (dry basis), remaining within the acceptable ash limits of the boiler design. The reject fractions exhibited high ash contents of approximately 71%–72%, indicating effective de-stoning and removal of high-density gangue minerals. Low and consistent E_p values (0.05–0.06) together with nearly constant cut-point densities (D₅₀ ≈ 1.82%–1.83 g/cm³) demonstrated sharp and stable density-based separation. The dust fraction remained limited (1.4%–2.1%), confirming mechanically stable operation. The removal of approximately 10% of the feed as high-density reject was found to reduce coal milling energy demand and lower the energy consumption of ash handling and disposal systems. Overall, the results show that air table-based dry beneficiation enables water-free and energy-efficient pre-concentration of low-calorific coals, offering strong potential for application in water-scarce regions. Full article

The Three-Product Dense Medium Cyclone (TPDMC) has been widely applied in the coal preparation industry, yet the adaptive optimization of its parameters based on feed characteristics remains under-researched. This study utilizes a semi-industrial experimental platform with a JX300/240 TPDMC to investigate the influence of pump frequency (PF) and four second-stage structural parameters—cylindrical section length (L_2cy), overflow pipe insertion depth (Dep_2o), overflow pipe diameter (D_2o), and conical section length (L_2co)—on the separation performance of three feed materials with distinct washability characteristics. Experiments conducted with density tracer particles revealed a distinct hydrodynamic coupling effect: PF and D_2o were the only factors modulating inlet pressure (varying from 0.12 to 0.45 bar), which directly altered the clean coal yield. In contrast, L_2cy, Dep_2o, and L_2co primarily influenced the second-stage internal flow field and concentration effect, thereby affecting the yield and ash content of middling coal (gangue). To quantify feed-specific sensitivities, a new index, Near-Gravity-Range Material (NGRM), was proposed. Results demonstrated that Sample-3 exhibited the highest sensitivity to parameter variations, with its middling coal yield variation reaching 41.25% due to its high NGRM of 71%. Furthermore, statistical analyses were conducted to quantify the influence of each parameter on the heavy product partition ratio across different density fractions. Based on these findings, the following targeted optimization strategies are proposed: (1) for feeds rich in the 1.40–1.50 RD range, increasing PF or decreasing D_2o is recommended to enhance clean coal yield; (2) for materials dominated by the 1.7 ± 0.10 RD fraction, increasing D_2o, PF, or L_2cy maximizes middling coal recovery; and (3) for feeds high in the 1.90 ± 0.10 RD fraction, reducing Dep_2o, PF, L_2cy, or L_2co effectively minimizes middling coal contamination by high-density particles. Full article

Phosphorus enrichment remains a major driver of eutrophication in lake-feeding rivers, yet effective regulation is hindered by insufficient understanding of the spatiotemporal variability and dominant sources of total phosphorus (TP) at the basin scale. The Xiangjiang River, a major inflow to Dongting Lake, provides a representative system for examining TP dynamics in a human-impacted watershed. An interpretable association rule mining framework was applied to multi-source water quality, hydrological, agricultural, and socio-economic data (2020–2024) to characterize TP variation and quantify source contributions. TP concentrations exhibit pronounced seasonal and hydrological variability, with higher levels during spring and the flood season and lower levels during autumn and low-flow periods, together with a longitudinal increasing pattern from upstream to downstream. Quantitative source apportionment indicates that agricultural non-point sources dominate TP contributions at the basin scale, domestic sources provide a stable secondary contribution, and industrial sources exert localized influences. The spatial organization of source contributions closely corresponds to land-use patterns, with relatively consistent source structures among sites despite local heterogeneity. These results demonstrate the utility of an interpretable association rule mining framework for resolving TP source structures in heterogeneous river basins. The proposed framework offers a transferable approach for phosphorus source identification and supports basin-scale nutrient management and targeted control of agricultural non-point source pollution. Full article

Incorporating protein feed ingredients that exhibit strong attractiveness to the target fish species is an effective and sustainable feeding strategy to improve feed intake and enhance growth performance. An 8-week feeding experiment was performed to elucidate the manipulation impact of graded levels of jack mackerel meal (JMM) in diets replacing 10% fish meal (FM) with corn protein concentrate (CPC) on the growth, feed utilization, and blood chemistry of rockfish (Sebastes schlegeli), as well as to perform an economic analysis. A total of 450 fish were randomly assigned to 15 plastic tanks (30 juveniles/tank). Five experimental diets were formulated to be isoproteic (50.0% crude protein) and isolipidic (15.5% crude lipid). The control (Con) diet contained 55% FM. In the Con diet, 10% of the FM was substituted with CPC, and graded levels of JMM at 0%, 20%, 40%, and 60% were subsequently incorporated instead of the FM, referred to as CPCJ0, CPCJ20, CPCJ40, and CPCJ60, respectively. Triplicate groups of rockfish were carefully hand-fed the diets to satiation throughout the feeding experiment. The rockfish fed the CPCJ60 diet produced significantly higher weight gain (WG) (p < 0.002) and specific growth rate (SGR) (p < 0.003) than those fed the Con, CPCJ0, and CPCJ20 diets but showed no significant (p > 0.05) differences compared to those fed the CPCJ40 diet. Both the WG (Y = 0.9367X + 17.0500, p < 0.0001, Adjusted R² = 0.8468) and SGR (Y = 0.0005X + 0.0165, p < 0.0001, Adjusted R² = 0.8580) of the rockfish increased linearly with increased dietary JMM inclusion levels when 10% of the FM was replaced by CPC. The rockfish fed the CPCJ60 diet showed a significantly higher feed consumption (FC) (p < 0.03) compared to those fed the CPCJ0 diet. Among the dietary treatments, however, no significant (p > 0.05) differences were found in the feed utilization, proximate composition, amino and fatty acid profiles, and blood chemistry of the rockfish. The CPCJ60 diet resulted in the highest economic profit index (EPI) among the dietary treatments. Conclusively, JMM was found to be effective in improving the FC of rockfish fed the diets replacing 10% of the FM with CPC. Furthermore, the WG and SGR of the rockfish fed the diets replacing 10% of the FM with CPC improved linearly with elevated JMM inclusion. Thus, incorporating 60% of JMM into the diets substituting 10% of the FM with CPC was the most recommended strategy according to the growth performance and FC of the rockfish, providing the highest EPI for fish farmers. Full article

Taking a metal structural part blanking workshop as the application background, this study addresses the challenges of high material variety, long crane feeding travel caused by heterogeneous line-side storage layouts, and frequent machine stoppages due to the limited feeding capacity of a single overhead crane. To this end, an integrated machine–crane dual-resource scheduling model is developed by explicitly considering line-side storage locations. The objective is to minimize the maximum waiting time among all machine tools. Under constraints of material assignment, processing sequence, and the crane’s single-task execution and travel requirements, the storage positions of materials in line-side buffers are jointly optimized. To solve the problem, a genetic algorithm with fitness-value-based crossover is proposed, and a simulated-annealing acceptance criterion is embedded to suppress premature convergence and enhance the ability to escape local optima. Comparative experiments on randomly generated instances show that the proposed algorithm can significantly reduce the maximum waiting time and yield more stable results for medium- and large-scale cases. Furthermore, a simulation based on real production data from an industrial enterprise verifies that, under limited feeding capacity, the proposed method effectively shortens material-waiting time, improves equipment utilization, and enhances production efficiency, demonstrating its effectiveness. Full article