Search Results (483)

Helminth parasites of wild animals represent a major threat to the health of these animals, leading to significant losses in performance, health, and zoonotic implications. In some zoos, anthelmintics have traditionally been used to control these parasites, many of which are also zoonotic. Other actions, such as the removal of organic waste, have also been adopted. Few or no control measures are applied to free-ranging wild animals. Helminthophagous fungi are a promising biological alternative. When animals ingest fungal spores, they are excreted in their feces, where they trap and destroy helminth larvae and eggs, preventing and reducing the parasite load in the environment. Another alternative is to administer fungi by spraying them directly into the environment. This review aims to examine the use of helminthophagous fungi in the control of helminthiases in wild animals, highlighting their potential to minimize dependence on chemical treatments and promote sustainable animal breeding and production. There are many challenges to making this viable, such as environmental variability, stability of formulations, and acceptance of this new technology. These fungi have been shown to reduce parasite burdens in wild animals by up to 75% and can be administered through the animals’ feeding troughs. To date, evidence shows that helminthophagous fungi can reliably curb environmental parasite loads for extended periods, offering a sustainable alternative to repeated anthelmintic dosing. Their use has been linked to tangible gains in body condition, weight, and overall welfare in various captive and free-ranging wildlife species. Full article

Public–private partnership (PPP) logistics infrastructure projects have become increasingly prevalent globally. Consequently, the effective management of these projects as their concession periods expire presents a crucial challenge for governments, vital for the sustainable management of PPP logistics infrastructure. This study addresses this challenge by focusing on the pivotal post-concession decision: whether the government should self-operate the mature logistics infrastructure or re-delegate its management to a private entity. Our theoretical model, built on a principal–agent framework, first establishes a social welfare baseline under government self-operation and then analyzes delegated operation under symmetric information, identifying efficiency frontiers. Under symmetric information, we find that government self-operation is more advantageous when its own operational efficiency is sufficiently high, irrespective of the private enterprise’s efficiency; conversely, delegating to an efficient private enterprise is optimal only when government operational efficiency is low. We also demonstrate that if the government can directly specify the demand quantity and service level and delegates operation via a fixed fee, the enterprise can be incentivized to align with the social optimum. However, under asymmetric information, potential welfare gains from delegation are inevitably offset by informational rent and output distortion. We further uncover non-monotonic impacts of parameters like the proportion of low-cost firms on social welfare loss and demonstrate how information asymmetry can indirectly compromise the long-term resilience of the infrastructure. Ultimately, our work asserts that delegation is only superior if its potential efficiency gains sufficiently offset the inherent losses stemming from information asymmetry. Full article

During commercial pig production, weaning is a major stressor that disrupts the gut microbiome, compromises intestinal barrier integrity, and increases the susceptibility of piglets to pathogens. This often results in post-weaning diarrhoea (PWD), leading to growth retardation, morbidity, and economic loss. This study investigated the effects of dietary xylo-oligosaccharide (XOS) supplementation on the growth performance and gut health of 216 piglets with naturally occurring PWD. Piglets received either 0 (CON), 50 (XOS-50), or 500 (XOS-500) mg XOS/kg feed from weaning at 28 days of age (d1) for 54 days. XOS-500 significantly improved body weight at d22 and d54, but had no effect on average daily gain, daily feed intake (DFI), or feed conversion ratio. The intestinal microbiota alpha-diversity was unaffected by XOS, though jejunal beta diversity differed between CON and XOS-500 groups at d22. Jejunal Chao richness correlated positively with d54 body weight, while ileal Chao richness correlated negatively with DFI. Salmonella was present in all diet groups but did not differ in abundance; however, the levels were negatively correlated with alpha diversity. XOSs increased Lactobacillus (d22, d54) and Clostridium_XI (d22), while reducing Veillonellaceae spp. (d22). XOSs reduced jejunal goblet cell (GC) density at d22 but increased duodenal and jejunal GCs and reduced duodenal crypt depth at d54. XOSs upregulated the genes for the tight junction proteins CLDN2, CLDN3, ALPI, and ZO-1, while downregulating the cytokine IL-8. These findings highlight XOSs’ potential to improve growth and gut health in weaning piglets with naturally occurring PWD, to maintain productivity and enhance welfare. Full article

Ornamental fish shipped by road or rail may spend days in transit without food, leading to a reduction in somatic growth after transportation and during acclimatization. In the present study, a time-series (0, 2, 4, 6, 8, 10, and 12 days) experiment was conducted to investigate the growth recovery of male Siamese fighting fish (Betta splendens, 1.56 ± 0.02 g body weight, n = 15 per group) transported by road for two days. Biometric changes, nesting activity, skin pigmentation, digestive enzyme activity, muscle quality, and whole-body composition, were compared across all fish groups. The recovery in growth, as indicated by final body weight, increased with post-transportation time (p < 0.05), causing a significant reversal of weight loss with a proportionally stable condition factor from day 8 until the end of observation (p > 0.05). During this time period, the fish exhibited similar bubble-nest building activity to the control group that was not transported (p > 0.05). Color parameters, digestive enzyme activities, muscle quality, and whole-body composition of fish 8 days after shipping were comparable to the control fish group (p > 0.05). Our findings indicate that an 8-day recovery time is an appropriate protocol for Siamese fighting fish acclimatization following overland shipping. Full article

Small ruminant lentiviruses (SRLVs) are widespread and have a long co-evolutionary history with their hosts, namely sheep and goats. These viruses induce insidious pathologies, causing significant financial losses and animal welfare issues for the affected flocks. In Switzerland, in the 1980s, an eradication campaign was launched targeting these viruses, exclusively in goats, eliminating the virulent SRLV-B strains from the goat population, in which SRLV-B-induced arthritis was prevalent. Nevertheless, although they do not seem to induce clinical diseases, SRLV-A strains continue to circulate in Swiss goats. For this study, we contacted farmers who had animals testing positive for these strains during the census from 2011 to 2012 and visited six of these flocks, conducting serological, virological, and clinical analyses of the animals. We confirmed the absence of SRLV-B; however, we have detected SRLV-A in these flocks. Positive and negative animals lived in close contact for ten years and, except for a small flock of 13 animals, 7 of which tested positive, the transmission of these viruses proved inefficient. None of the positive animals showed any pathology attributable to SRLV infection. These encouraging results allowed us to formulate recommendations for the continued surveillance of these viruses in the Swiss goat population. Full article

This study employs a dynamic stochastic general equilibrium model with Bayesian estimation to rigorously evaluate China’s macroeconomic responses to cost-push, monetary policy, and foreign income shocks. This analysis leverages quarterly data from 2000 to 2024, focusing on critical variables such as the output gap, inflation, interest rates, exchange rates, consumption, investment, and employment. The results demonstrate significant social welfare losses primarily arising from persistent inflation and output volatility due to domestic structural rigidities and global market dependencies. Monetary policy interventions effectively moderate short-term volatility but induce welfare costs if overly restrictive. The findings underscore the necessity of targeted structural reforms to enhance economic flexibility, balanced monetary policy to mitigate aggressive interventions, and diversified economic strategies to reduce external vulnerability. These insights contribute novel policy perspectives for enhancing China’s macroeconomic stability and resilience. Full article

The identification and prediction of losses, along with environmental and behavioral analyses and animal welfare monitoring, are key drivers for the use of technologies in poultry farming which help characterize the productive environment. Among these technologies, robotics emerges as a facilitator as it provides space for the use of several computing tools for capture, analysis and prediction. This study presents the full methodology for building a robot (so called RobôFrango) to its application in poultry farming. The construction method was based on evolutionary prototyping that allowed knowing and testing each physical component (electronic and mechanical) for assembling the robotic structure. This approach made it possible to identify the most suitable components for the broiler production system. The results presented motors, wheels, chassis, batteries and sensors that proved to be the most adaptable to the adversities existing in poultry farms. Validation of the final constructed structure was carried out through practical execution of the robot, seeking to understand how each component behaved in a commercial broiler aviary. It was concluded that it was possible to identify the best electronic and physical equipment for building a robotic prototype to work in poultry farms, and that a final product was generated. Full article

Accurate behavioral monitoring of silkworms (Bombyx mori) during the fourth instar development is crucial for enhancing productivity and welfare in sericulture operations. Current manual observation paradigms face critical limitations in temporal resolution, inter-observer variability, and scalability. This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. Methodologically, Res2Net blocks are first integrated into the backbone network to enable hierarchical residual connections, expanding receptive fields and improving multi-scale feature representation. Second, standard convolutional layers are replaced with distribution shifting convolution (DSConv), leveraging dynamic sparsity and quantization mechanisms to reduce computational complexity. Additionally, the minimum point distance intersection over union (MPDIoU) loss function is proposed to enhance bounding box regression efficiency, mitigating challenges posed by overlapping targets and positional deviations. Experimental results demonstrate that RDM-YOLO achieves 99% mAP@0.5 accuracy and 150 FPS inference speed on the datasets, significantly outperforming baseline YOLOv5s while reducing the model parameters by 24%. Specifically designed for deployment on resource-constrained devices, the model ensures real-time monitoring capabilities in practical sericulture environments. Full article

This study evaluated the impact of transporting Nile tilapia at stocking densities of 250 kg/m³ and 500 kg/m³ for 1 h, with post-transport resting periods of 0, 2, 4, and 6 h, on biochemical parameters and fillet quality. A 2 × 4 factorial design was employed for the experiment, with 15 repetitions per treatment. The density of 500 kg/m³ resulted in a longer time to rigor mortis after 4 h of rest, while shorter rigor times were observed at 0 and 2 h. Fillets taken from fish transported at 250 kg/m³ for 4 h exhibited greater intensities of red and yellow color. The highest weight loss during cooking occurred in fish transported at 500 kg/m³ without rest. High-density stocking increased the pH of the fillets, reduced color intensity, and increased weight loss and drip loss. Resting periods of 4 and 6 h resulted in firmer fillets with improved water retention. Fish rested for 6 h at 250 kg/m³ recovered glycogen and glucose levels, indicating restored homeostasis. In contrast, fish subjected to high-density transport showed impaired metabolic recovery and compromised fillet quality. These results support the use of resting periods to improve fish welfare and product quality in aquaculture systems. Full article

The rapid development of unmanned aerial vehicles (UAVs) has had a significant impact on the growth of the economic, industrial, and social welfare of society. The possibility of reaching places that are difficult and dangerous for humans to access with minimal use of third-party resources increases the efficiency and quality of maintenance of construction structures, agriculture, and exploration, which are carried out with the help of drones with a predetermined trajectory. The widespread use of UAVs has caused problems with the control of the drones’ correctness following a given route, which leads to emergencies and accidents. Therefore, UAV monitoring with video cameras is of great importance. In this paper, we propose a Yolov12 architecture with positive–negative pulse-based optimization algorithms to solve the problem of drone detection on video data. Self-attention-based mechanisms in transformer neural networks (NNs) improved the quality of drone detection on video. The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. The proposed approach improved object detection accuracy by 2.8 percentage points compared to known state-of-the-art analogs. Full article

Animal production is a core sector to solve the increasing food demand worldwide, with productivity severely affected by climate change. Experts are predicting huge global productive losses in animal-derived products. Moreover, productive loss affects the economy, and the US dairy industry has reported losses of 1.5 billion dollars annually due to climate change. Beef and dairy production are based on cow reproduction and fertility is a key indicator of productivity. However, under heat stress (HS), several physiological modifications decrease cows’ fertility. Lower levels of estradiol, progesterone, and epidermal growth factor lead to undetectable ovulations, an inability to maintain the embryo and the pregnancy, or increased cortisol levels, inducing immunosuppression and, consequently, puerperal diseases delaying new pregnancies. The welfare of cows under HS, especially those raised on pasture, is a huge concern. Considering the impact of ambient-temperature-induced HS, developing strategies to improve fertility—namely through the selection of thermotolerant breeds allied to environmental management measures—can improve cattle production efficiency and reduce resource use, thereby reducing the carbon footprint. This review focuses on the effects of HS on female fertility, from parturition until the new conception, and on the role of heat shock proteins during this period. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

The breeding of sika deer has significant economic value in China. However, the traditional management methods have problems such as low efficiency, easy triggering of strong stress responses, and damage to animal welfare. Therefore, the development of non-contact, automated, and precise monitoring and management technologies has become an urgent need for the sustainable development of this industry. In response to this demand, this study designed a model MFW-YOLO based on YOLO11, aiming to achieve precise detection of specific body parts of sika deer in a real breeding environment. Improvements include: designing a lightweight and efficient hybrid backbone network, MobileNetV4HybridSmall; The multi-scale fast pyramid pooling module (SPPFMscale) is proposed. The WIoU v3 loss function is used to replace the default loss function. To verify the effectiveness of the method, we constructed a sika deer dataset containing 1025 images, covering five categories. The experimental results show that the improved model performs well. Its mAP50 and MAP50-95 reached 91.9% and 64.5%, respectively. This model also demonstrates outstanding efficiency. The number of parameters is only 62% (5.9 million) of the original model, the computational load is 60% (12.8 GFLOPs) of the original model, and the average inference time is as low as 3.8 ms. This work provides strong algorithmic support for achieving non-contact intelligent monitoring of sika deer, assisting in automated management (deer antler collection and preparation), and improving animal welfare, demonstrating the application potential of deep learning technology in modern precision animal husbandry. Full article

The adoption of green building materials (GBMs) has become increasingly important in reducing carbon emissions and realizing zero-carbon cities. Although some scholars have investigated the decision-making of GBMs adoption in markets, they mainly focused on the impact factors of GBMs adoption without considering consumers’ multi-channel purchasing behavior. Thus motivated, this paper aims to develop a theoretical game model incorporating consumers’ multi-channel purchasing behavior and study the optimal sales strategies of GBMs manufacturers and retailers in markets for promoting GBMs adoption. To do this, not only the equilibrium outcome on sales strategy is examined, but also the effects of different GBMs sales strategies on urban environments and social welfare are theoretically verified. It is found that (1) the equilibrium sales strategy relies on two core parameters, namely matching rate and online return cost. Only when the matching rate is low and the online return cost is at a medium level can the GBMs manufacturer and retailer achieve a strategic consensus, and the equilibrium sales strategy is S (i.e., selling GBMs through the online channel, offline channel, and store-to-online channel). (2) When pursuing total profits of manufacturers and retailers in GBMs markets, the S sales strategy is 100% superior to the D sales strategy (i.e., selling GBMs only through online and offline channels). This is because the introduction of a store-to-online channel can reduce online return losses by providing consumers with physical experiences. (3) When pursuing social welfare (refers to public benefits including consumer surplus, urban environmental impacts, and others), the D sales strategy is optimal if the matching rate is relatively large and the return cost is low. (4) Under certain conditions, governments should incentivize GBMs manufacturers and retailers to adopt the D sales strategy through regulatory instruments, so as to achieve a balance between economic benefits and social benefits. Full article

Accurate classification of poultry behavior is critical for assessing welfare and health, yet most existing methods predict behavior categories without providing explanations for the image content. This study introduces the PBC-Transformer model, a novel model that integrates image captioning techniques to enhance poultry behavior classification, mimicking expert assessment processes. The model employs a multi-head concentrated attention mechanism, Head Spatial Position Coding (HSPC), to enhance spatial information; a learnable sparse mechanism (LSM) and RNorm function to reduce noise and strengthen feature correlation; and a depth-wise separable convolutional network for improved local feature extraction. Furthermore, a multi-level attention differentiator dynamically selects image regions for precise behavior descriptions. To balance caption generation with classification, we introduce the ICL-Loss function, which adaptively adjusts loss weights. Extensive experiments on the PBC-CapLabels dataset demonstrate that PBC-Transformer outperforms 13 commonly used classification models, improving accuracy by 15% and achieving the highest scores across image captioning metrics: Bleu4 (0.498), RougeL (0.794), Meteor (0.393), and Spice (0.613). Full article