Search Results (8,325)

To develop sustainable strategies for mitigating ruminal methanogenesis and improving nitrogen efficiency in dairy systems, this study investigated how low-dose tannic acid (T), tea polyphenols (TP), and their combination (T+TP; 50:50) modulate rumen microbiota and function. A sample of Holstein cows were given four dietary treatments: (1) control (basal diet); (2) T (basal diet + 0.4% DM tannic acid); (3) TP (basal diet + 0.4% DM tea polyphenols); and (4) T+TP (basal diet + 0.2% DM tannic acid + 0.2% DM tea polyphenols). We comprehensively analyzed rumen fermentation, methane production, nutrient digestibility, milk parameters, and microbiota dynamics. Compared with the control group, all diets supplemented with additives significantly reduced enteric methane production (13.68% for T, 11.40% for TP, and 10.89% for T+TP) and significantly increased milk protein yield. The crude protein digestibility significantly increased in the T group versus control. The results did not impair rumen health or fiber digestion. Critically, microbiota analysis revealed treatment-specific modulation: the T group showed decreased Ruminococcus flavefaciens abundance, while all tannin treatments reduced abundances of Ruminococcus albus and total methanogens. These microbial shifts corresponded with functional outcomes—most notably, the T+TP synergy drove the largest reductions in rumen ammonia-N (34.5%) and milk urea nitrogen (21.1%). Supplementation at 0.4% DM, particularly the T+TP combination, effectively enhances nitrogen efficiency and milk protein synthesis while reducing methane emissions through targeted modulation of key rumen microbiota populations, suggesting potential sustainability benefits linked to altered rumen fermentation. Full article

The Thomas–Fermi model and its quantum and exchange corrections with mathematic manipulations and numerical approaches are primarily investigated. The reduced ideal electron chemical potential is adopted as the initial value for the iterative solution of the Thomas–Fermi model. A new transformation for the quantum and exchange equations is proposed to apply the boundary conditions easily. Both the Thomas–Fermi equation and correction equations are solved with the Runge–Kutta algorithm. The mathematical difficulties in controlling the computing accuracy of the equations containing the Fermi–Dirac integral are settled. The equation of state, based on the Thomas–Fermi model with its quantum and exchange corrections, is constructed and compared with relevant data. Full article

This study aimed to assess whether dietary supplementation with probiotics could alleviate intestinal injury in lipopolysaccharide (LPS)-challenged piglets. Healthy weaned piglets were randomly allocated to four individual groups (n = 6): (1) a control group; (2) an LPS group; (3) an LPS + Lactobacillus group; and (4) an LPS + Bacillus group. The control and LPS groups received a basal diet, while the probiotic groups were provided with the same basal diet supplemented with 6 × 10⁶ cfu/g of Lactobacillus casei (L. casei) or a combination of Bacillus subtilis (B. subtilis) and Bacillus licheniformis (B. licheniformis) at a dosage of 3 × 10⁶ cfu/g, respectively. On day 31 of the trial, overnight-fasted piglets were killed following the administration of either LPS or 0.9% NaCl solution. Blood samples and intestinal tissues were obtained for further analysis several hours later. The results indicate that dietary supplementation with probiotics significantly exhibited health-promoting effects compared with the control group and effectively reduced LPS-induced histomorphological damage to the small intestine, impairments in barrier function, and dysregulated immune responses via modulation of enzyme activity and the expression of relevant genes, such as nuclear factor-kappa B (NF-κB), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), claudin-1, nuclear-associatedantigenki-67 (Ki-67), and β-defensins-1 (pBD-1). Collectively, these results suggest that dietary supplementation with probiotics could alleviate LPS-induced intestinal injury by enhancing the immunity and anti-inflammatory responses in piglets. Our research provides a theoretical basis for the rational application of probiotics in the future. Full article

Due to global warming, extreme temperature events have become increasingly prevalent, posing significant threats to both socioeconomic development and human safety. While previous studies have extensively examined the influence of individual climatic circulation systems on extreme temperature, the combined effects of multiple concurrent circulation patterns remain poorly understood. Using daily temperature data from 29 meteorological stations in Chongqing (1960–2019), this study employs linear trend analysis, correlation analysis, and random forest (RF) models to analyze spatiotemporal variations in the intensity and frequency of extreme temperature. We selected 21 climate indicators from three categories—atmospheric circulation, sea surface temperature (SST), and sea-level pressure (SLP)—to identify the primary drivers of extreme temperatures and quantify their respective contributions. The key findings are as follows: (1) All extreme intensity indices exhibited an increasing trend, with the TXx (annual maximum daily maximum temperature) showing the higher trend (0.03 °C/year). The northeastern region experienced the most pronounced increases. (2) Frequency indices also displayed an upward trend. This was particularly evident for the TD35 (number of days with maximum temperature ≥35 °C), which increased at an average rate of 0.16 days/year, most notably in the northeast. (3) The Western Pacific Subtropical High Ridge Position Index (GX) and Asia Polar Vortex Area Index (APV) were the dominant climate factors driving intensity indices, with cumulative contributions of 26.0% to 33.4%, while the Western Pacific Warm Pool Strength Index (WPWPS), Asia Polar Vortex Area Index (APV), North Atlantic Subtropical High Intensity Index (NASH), and Indian Ocean Warm Pool Strength Index (IOWP) were the dominant climate factors influencing frequency indices, with cumulative contributions of 46.4 to 49.5%. The explanatory power of these indices varies spatially across stations, and the RF model effectively identifies key circulation factors at each station. In the future, more attention should be paid to urban planning adaptations, particularly green infrastructure and land use optimization, along with targeted heat mitigation strategies, such as early warning systems and public health interventions, to strengthen urban resilience against escalating extreme temperatures. Full article

Mycoplasmal pneumonia of sheep (MPS), caused by Mesomycoplasma (Mycoplasma) ovipneumoniae, profoundly impacts ovine productivity and survival. Although gut–lung microbiota interactions are increasingly recognized in respiratory diseases, whether similar crosstalk occurs between the lung and rumen microbiota in MPS-affected sheep remains unknown. To investigate alterations in the lung and rumen microbiota of sheep with MPS, the crosstalk between these microbial communities, and their impacts on growth phenotypes. From a cohort of 414 naturally infected six-month-old male Hu sheep, we selected 10 individuals with severe pulmonary pathology and 10 healthy controls for detailed phenotypic and microbiome analyses. Assessment of 359 phenotypic traits revealed that MPS significantly impairs feed efficiency and growth rate (p < 0.05). Through 16S rRNA gene sequencing, we found that MPS significantly altered the pulmonary microbiota community structure (p < 0.01), with a noticeable impact on the rumen microbiota composition (p = 0.059). Succinivibrionaceae_UCG-001 was significantly depleted in both the rumen and lungs of diseased sheep (p < 0.05) and strongly associated with reduced average daily feed intake (p < 0.05). In addition, pulmonary Pasteurella and ruminal Succinivibrionaceae_UCG-002 were significantly enriched in MPS-affected sheep, showed a strong positive correlation (p < 0.05), and were both negatively associated with feed efficiency (p < 0.05). Notably, Pasteurella multocida subsp. gallicida may act as a keystone species influencing feed efficiency. These findings point to a previously unrecognized rumen-lung microbial axis that may modulate host productivity in sheep affected by MPS. This work provides new insights into the pathogenesis of MPS and offers potential targets for therapeutic intervention and management. Full article

Climate-driven hydrological extremes and anthropogenic interventions are increasingly altering streamflow regimes worldwide. While prior studies have explored climate or regulation effects separately, few have integrated multiple teleconnection indices and reservoir chronologies within a cross-basin comparative framework. This study addresses this gap by assessing long-term streamflow nonstationarity and its drivers at two key stations—Khabarovsk on the Amur River and Datong on the Yangtze River—representing distinct hydroclimatic settings. We utilized monthly discharge records, meteorological data, and large-scale climate indices to apply trend analysis, wavelet transform, percentile-based extreme diagnostics, lagged random forest regression, and slope-based attribution. The results show that Khabarovsk experienced an increase in winter baseflow from 513 to 1335 m³/s and a notable reduction in seasonal discharge contrast, primarily driven by temperature and cold-region reservoir regulation. In contrast, Datong displayed increased discharge extremes, with flood discharges increasing by +71.9 m³/s/year, equivalent to approximately 0.12% of the mean flood discharge annually, and low discharges by +24.2 m³/s/year in recent decades, shaped by both climate variability and large-scale hydropower infrastructure. Random forest models identified temperature and precipitation as short-term drivers, with ENSO-related indices showing lagged impacts on streamflow variability. Attribution analysis indicated that Khabarovsk is primarily shaped by cold-region reservoir operations in conjunction with temperature-driven snowmelt dynamics, while Datong reflects a combined influence of both climate variability and regulation. These insights may provide guidance for climate-responsive reservoir scheduling and basin-specific regulation strategies, supporting the development of integrated frameworks for adaptive water management under climate change. Full article

This study proposed a mechanochemical activation strategy using ethanol-diisopropanolamine (EDIPA) to improve the grindability and hydration reactivity of basic oxygen furnace slag (BOFS), aiming for its large-scale industrial utilization. The incorporation of EDIPA significantly refined the particle size distribution and reduced the repose angle. As a result, the compressive strength of BOFS paste increased by 25.4 MPa at 28 d with only 0.08 wt.% EDIPA. Conductivity tests demonstrated that EDIPA strongly complexes with Ca²⁺, Al³⁺, and Fe³⁺, facilitating the dissolution of active mineral phases, such as C₁₂A₇ and C₂F, and accelerating hydration reactions. XRD and TG analyses confirmed that the incorporation of EDIPA facilitated the formation of Mc (C₄(A,F)ČH₁₁) and increased the content of C-S-H, both of which contributed to microstructural densification. Microstructural observations further revealed that EDIPA refined Ca(OH)₂ crystals, increasing their specific surface area from 4.7 m²/g to 35.2 m²/g. The combined effect of crystal refinement and enhanced hydration product formation resulted in reduced porosity and improved mechanical properties. Overall, the results demonstrated that EDIPA provided an economical, effective, and scalable means of activating BOFS, thereby promoting its high-value utilization in low-carbon construction materials. Full article

In the era of Industry 5.0, future wireless networks are increasingly shifting from traditional symmetric architectures toward heterogeneous and asymmetric paradigms, driven by the demand for diversified and dynamic services. This architectural evolution gives rise to complex and asymmetric flows, such as the coexistence of periodic and burst flows with varying latency, jitter, and deadline constraints, posing new challenges for deterministic transmission. Traditional time-sensitive networking (TSN) is well-suited for periodic flows but lacks the flexibility to effectively handle dynamic, asymmetric traffi. To address this limitation, we propose a two-stage asymmetric flow scheduling framework with dynamic deadline control, termed A-TSN. In the first stage, we design a Deep Q-Network-based Dynamic Injection Time Slot algorithm (DQN-DITS) to optimize slot allocation for periodic flows under varying network loads. In the second stage, we introduce the Dynamic Deadline Online (DDO) scheduling algorithm, which enables real-time scheduling for asymmetric flows while satisfying flow deadlines and capacity constraints. Simulation results demonstrate that our approach significantly reduces end-to-end latency, improves scheduling efficiency, and enhances adaptability to high-volume asymmetric traffic, offering a scalable solution for future deterministic wireless networks. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

Herein, the energy dispersion relationship and the density of states of triangular and rectangular moiré patterns are investigated using a tight binding model. Their characteristics of Hofstadter butterflies under different magnetic fields are also examined. The results indicate that, by analyzing different moiré superlattices, Hofstadter butterflies arising from different moiré pattern structures are obtained, exhibiting considerable fractal characteristics and self-similarities. Moreover, it is also observed that under an alternating magnetic field, the redistribution of electronic states leads to a significant change in the density of states curve, and the Van Hove peak changes with the increase in magnetic field intensity. This study enriches the understanding of the electronic behavior of moiré systems, but it also provides multiple potential application directions for future technological development. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV), an enveloped single-stranded positive-sense RNA virus, poses a significant threat to global swine production. Despite the availability of modified live virus and inactivated vaccines, their limited efficacy and safety concerns highlight the urgent need for novel antiviral therapeutics. This study aimed to investigate the molecular mechanisms by which lycopene inhibits PRRSV replication. Initial assessments confirmed that lycopene did not adversely affect cellular viability, cell cycle progression, or apoptosis. Using fluorescence microscopy, flow cytometry, immunoblotting, quantitative real-time PCR (qRT-PCR), and viral titration assays, lycopene was shown to exhibit potent antiviral activity against PRRSV. Mechanistic studies revealed that lycopene suppresses reactive oxygen species (ROS) production, which is critical for PRRSV proliferation. Additionally, lycopene attenuated PRRSV-induced inflammatory responses, as demonstrated by immunoblotting, ELISA, and qRT-PCR assays. These findings suggest that lycopene inhibits PRRSV replication by modulating ROS levels and mitigating inflammation, offering a promising avenue for the development of antiviral therapeutics. This study provides new insights and strategies for combating PRRSV infections, emphasizing the potential of lycopene as a safe and effective antiviral agent. Full article

Black tea is widely consumed worldwide, and its characteristic taste and color result from fermentation, where polyphenols are enzymatically oxidized to generate major pigments, including theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs). This study investigated the effects of increased oxygen treatment during fermentation on the flavor attributes and chemical properties of Congou black tea. Fresh tea leaves (variety “Fuyun 6”) were subjected to four oxygen treatments: 0 h (CK), 1 h (TY-1h), 2 h (TY-2h), and 3 h (TY-3h), with oxygen supplied at 8.0 L/min. Sensory evaluation revealed that oxygen-treated samples exhibited tighter and deeper-colored leaves, a redder liquor, fuller taste, and a sweeter fragrance compared with CK. Chromatic analysis showed significant increases in redness (a*) and luminance (L*), alongside reduced yellowness (b*), indicating enhanced liquor color. Chemical analyses demonstrated elevated levels of TFs, TRs, and TBs in oxygen treatments, with TRs showing the most pronounced increase. Non-targeted metabolomics identified 2318 non-volatile and 761 volatile metabolites, highlighting upregulated flavonoids, phenolic acids, and lipids, and downregulated catechins and tannins, which collectively contributed to improved taste and aroma. Optimal results were achieved with 2–3 h of oxygen treatment, balancing pigment formation and sensory quality. These findings can provide a scientific basis for optimizing oxygen conditions in black tea fermentation to improve product quality. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Sunflower (Helianthus annuus L.) is an important oilseed crop in Northwest China, exhibiting resistance to salt and drought. Mining its excellent tolerance genes can be used for breeding. However, the current platforms for identifying gene function in sunflower is inadequate. The transient transformation system, which can rapidly validate gene function, shows promising prospects in research. In this study, we established an efficient transient expression transformation system for sunflower using three methods: Agrobacterium-mediated infiltration, injection, and ultrasonic-vacuum. The detailed procedures were as follows: Agrobacterium GV3101 carrying a GUS reporter gene on the pBI121 vector with an OD₆₀₀ of 0.8 as the bacterial suspension and 0.02% Silwet L-77 as the surfactant were utilized in all three approaches. For the infiltration method, seedlings grown hydroponically for 3 days were immersed in a bacterial suspension containing 0.02% Silwet L-77 for 2 h; for the injection method, the same solution was injected into the cotyledons of seedlings grown in soil for 4 to 6 days. Subsequently, the seedlings were cultured in the dark at room temperature for three days; for the ultrasonic-vacuum method, seedlings cultured in Petri dishes for 3 days were first subjected to ultrasonication at 40 kHz for 1 min, followed by vacuum infiltration at 0.05 kPa for 5–10 min. Agrobacterium-mediated transient transformation efficiency achieved by the three methods exceeded 90%, with gene expression being sustained for at least 6 days. Next, we employed the infiltration-based sunflower transient transformation technology with the Arabidopsis stable transformation platform to confirm salt and drought stress tolerance of candidate gene HaNAC76 from sunflower responding to various abiotic stresses. Altogether, this study successfully established an Agrobacterium-mediated transient transformation system for sunflower using these three methods, which can rapidly identify gene function and explore the molecular mechanisms underlying sunflower’s resistance traits. Full article