Quantifying the importance of nodes in complex networks is known as the problem of identifying influential nodes and is considered a critical aspect in interacting with these networks. This problem has many applications such as controlling rumors, sickness spreading, and viral marketing, where its importance has been understood by the research society in the last decade. This paper proposes a new semi-local centrality to identify influential nodes in complex networks based on the theory of Local Average Shortest Path with extended Neighborhood concept (LASPN). LASPN focuses on a distributed technique to extract the subgraph associated with each node and apply the average shortest path theory to it. We use the extended neighborhood concept to find the nearest neighbors of each node with low complexity, where this can lead to high efficiency in dealing with large-scale networks. In addition to applying relative changes in the average shortest path, the proposed metric considers the importance of the node itself as well as its nearest neighbors in ranking the nodes. Evaluation of the proposed centrality metric has been done through numerical simulations on several real-world networks. The results based on Kendall's $$\tau$$τ coefficient under the SIR infection spreading model show that LASPN improves the performance by 2.7% compared to the best available equivalent method.

This study evaluated the effects of flavonoids from mulberry leaves (FML) on plasma biochemical indices, serum activities of lipid metabolism-related enzymes, fat morphology, fatty acid composition, and lipid metabolism in different adipose tissues of finishing pigs. We used 120 Chinese hybrid barrows of Berkshire and Bama mini-pigs with an average initial body weight of 45.11 ± 4.23 kg. The pigs were randomly assigned to five treatment groups and fed a control diet based on corn, soybean meal, and wheat bran or a control diet supplemented with 0.02%, 0.04%, 0.08%, or 0.16% FML. Each experimental group had six replicates (pens), with four pigs per pen. After a 7-d adaptation period, the feeding trial was conducted for 58 d. Blood and adipose tissue samples were collected from 30 pigs (one pig per pen) at the end of the test. The results showed that FML supplementation significantly decreased the feed intake to body gain ratio, the plasma concentrations of total cholesterol and free fatty acids, and the serum activity of 3-hydroxy-3-methylglutaryl coenzyme A reductase (linear or quadratic effects, P < 0.05), and decreased the plasma triglyceride concentration (quadratic, P = 0.07). Increasing FML supplementation increased the average daily gain and serum activities of lipoprotein lipase (linear and quadratic effects, P < 0.05) and adipose triglyceride lipase (linear, P < 0.05). Dietary FML supplementation decreased the adipocyte area in the dorsal subcutaneous adipose (DSA) tissue of finishing pigs (linear, P = 0.05) and increased the adipocyte area in the visceral adipose tissue (quadratic, P < 0.01). Increasing FML supplementation decreased the C20:1 content in DSA, abdominal subcutaneous adipose, and visceral adipose tissues of finishing pigs (P < 0.05) and increased the C18:3n3 and n-3 PUFA contents (P < 0.05). The lipid metabolism genes were regulated by the PPARγ-LXRα-ABCA1 signaling pathway, and their expressions differed in different adipose tissues. These findings suggest that FML improved growth performance, regulated lipid metabolism, inhibited fat production, and improved fatty acid distribution in the adipose tissue of finishing pigs, thereby improving pig fat's nutritional quality and health value.

Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of duck body dimensions and weight. A dataset of 1023 Linwu ducks, comprising over 5000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 5.73% and an R² of 0.953 across seven morphometric parameters describing body dimensions, and an MAPE of 10.49% with an R² of 0.952 for body weight, indicating robust and consistent predictive performance across both structural and mass-related phenotypes. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance.

The intensive use of antibiotics in juvenile livestock and aquaculture has led to serious public health risks such as drug residues, bacterial resistance, and environmental pollution, creating an urgent need for safe and efficient antibiotic alternatives. Flavonoids from mulberry leaves (FML), as typical natural polyphenolic compounds, exhibit strong antioxidant, anti-inflammatory, immunomodulatory, intestinal microbiota-regulating, and metabolic-improving activities, showing great potential in promoting juvenile animal health. This review sorts out global published literature concerning FML biological functions and animal feeding trials to clarify its multi-target regulatory mechanisms and practical application value in young animals. We integrated research data covering weaned piglets, calves, juvenile fish and broiler chicks, summarized core signaling pathways mediated by FML, and consolidated in vivo phenotypic effects observed across different juvenile animal models. Mechanistic analysis demonstrates that FML activates the Nrf2 pathway to alleviate oxidative damage, inhibits NF-κB/TLR4/MAPK inflammatory cascades, stabilizes intestinal barrier integrity and microbiota homeostasis, and regulates IRS-1/PI3K/GLUT4 and PPARα pathways to optimize glucose and lipid metabolism. Feeding trials further confirm that FML supplementation effectively mitigates weaning stress, lowers diarrhea rates, enhances non-specific immunity and improves overall growth performance of juvenile animals. Nevertheless, widespread application of FML is still constrained by unresolved issues including inconsistent optimal dosages across species, low in vivo bioavailability, and insufficient large-scale farm verification data. This paper summarizes these critical limitations and proposes feasible directions for subsequent mechanistic and applied research. Overall, this review offers comprehensive theoretical references for the development and popularization of FML as green feed additives within sustainable livestock and aquaculture systems.

Dietary flavonoids are increasingly investigated in swine nutrition, but how supplementation level, intervention duration, and growth stage are associated with growth and antioxidant responses remains unclear. This PRISMA 2020 systematic review and meta-analysis evaluated dietary flavonoid supplementation in pigs. Twenty-eight randomized controlled trials were synthesized using three-level random-effects models to account for dependent effect sizes. Flavonoid supplementation was associated with increased average daily gain (ADG; SMD = 0.719, 95% CI: 0.428 to 1.009; $I^2=51.85\%$) and a lower feed-to-gain ratio (F/G; SMD = −0.708, 95% CI: −0.929 to −0.487; $I^2=32.25\%$), whereas the association with average daily feed intake (ADFI) was not statistically significant (SMD = 0.217, 95% CI: −0.101 to 0.535; $I^2=59.30\%$). Supplementation was also associated with increased superoxide dismutase (SMD = 0.907) and glutathione peroxidase activities (SMD = 0.666) and reduced malondialdehyde concentrations (SMD = −0.989), although heterogeneity was substantial for these outcomes ($I^2=96.47$–$97.83\%$). The certainty of evidence was assessed as low for ADG and moderate for ADFI, F/G, and antioxidant outcomes under the adapted GRADE framework. Exploratory stage-specific dose–time meta-regression identified potential relationships between supplementation intensity and ADG in weaned pigs (35 effect sizes from 14 studies) and finishing pigs (12 effect sizes from four studies). In finishing pigs, intervention duration was associated with growth responses. However, this subgroup contained few independent studies, and intervention duration covaried with age, physiological stage, flavonoid source, dose, health status, and basal diet. This association therefore does not establish an independent causal effect of intervention duration.

With China’s rapid economic development, the demand for natural resources is increasing, and resource and environmental issues have become serious constraints to sustainable economic development, so it is especially important to adopt green GDP instead of traditional GDP to measure a country’s sustainable development. Based on the resource and environment perspective, this paper accounts for the green GDP of each province in mainland China from 1997 to 2020 by constructing a green GDP accounting system, and divides provinces according to energy use efficiency, and establishes a panel vector autoregressive model (PVAR) between green GDP and environmental and economic indicators in energy-efficient, medium-efficient and inefficient regions based on the panel data of each province for empirical research. It was found that the effects of each variable on green GDP differed among the divided regions. In the energy-inefficient and energy-medium zones, both the total consumption of CO2 clean mechanism trade and the total consumption of solid waste recycling have positive effects on green GDP development, while in the energy-efficient zone, only the total consumption of solid waste recycling has a positive effect on green GDP, while the total consumption of energy has a negative effect. To achieve sustainable green GDP development, we need to take comprehensive measures, including improving CO2 clean mechanism trading, enhancing solid waste recycling and sustainable disposal, reducing reliance on electricity and coal, finding sustainable alternatives to crude oil, and integrating natural gas resources with renewable energy. Achieving a long-term driving effect on green GDP.