Search Results (397)

Very little is known about goose-beaked whales (Ziphius cavirostris) and Sowerby’s beaked whales (Mesoplodon bidens), especially off the western coast of Ireland, due to their elusive behaviors. This study aimed to characterize the acoustics of these beaked whales and investigate whether temporal patterns may affect their occurrences. Using passive acoustic monitoring (PAM), beaked whale bioacoustic clicks were manually analyzed, revealing different click frequency ranges than expected. Double clicks and echoes produced by both beaked whale species were also present, which have previously been infrequently observed in these species. The occurrence of beaked whales and the presence of double clicks and echoes were further investigated, along with how the diel cycle may affect these click characteristics. Hourly presence of goose-beaked whale double clicks and echoes was found to have significance for both day and night. There was no significance found for Sowerby’s beaked whale double clicks and echoes for day and night, along with the hourly occurrences of both beaked whales and the occurrence of other beaked whales. These findings highlight the need for future research on PAM and beaked whale acoustics, which could aid in better monitoring of their presence to address the impacts of human activities. Full article

The dynamic metabolic landscape underlying goose meat quality deterioration during refrigerated storage remains incompletely elucidated. Here, ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based widely targeted metabolomics was employed to characterize metabolic profiling in refrigerated goose meat. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed 211 differential metabolites, while random forest regression (RFR) identified 30 candidate biomarkers. Seven metabolites, including xanthine, oxidized glutathione, and inosine 5′-monophosphate, exhibited significant correlations with total volatile basic nitrogen (TVB-N). By integrating potential biomarkers, metabolic pathways involving purines, amino acids, and sugars were identified as underlying mechanisms of goose meat spoilage. Notably, through comprehensive analysis of time-dependent correlations between physicochemical properties and metabolic profiles, a temporal threshold for quality deterioration in refrigerated goose meat was identified as day 5. These findings deepen our understanding of metabolite variations in refrigerated goose meat and provide a basis for optimizing storage protocols. The identified biomarkers may enable rapid detection kits and smart packaging systems for poultry industry applications. Full article

In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms. Full article

Digital substation technology adhering to the IEC 61850 standard has provided several opportunities and flexibility for the rapid growth and complexity of the present and future electrical grid. The communication infrastructure allows complete interoperability between legacy and modern devices. The emergence of 5G wireless communication and its utilization in substation operation presents significant advantages in terms of cost and scalability, while also introducing challenges. This paper identifies research gaps in the literature and offers valuable insights for future analysis by providing a simulation study using an empirical latency dataset of a 5G network to illustrate three aspects of substation operational challenges: coordination of protection schemes, sequential reception of packet data streams, and time synchronization processes. The findings show a mean latency of 8.5 ms for the 5G network, which is significantly higher than that of a wired Ethernet network. The results also indicate that the high latency and jitter compromise the selectivity of protection schemes. The variability in latency disrupts the sequence of arriving data packets such that the packet buffering and processing delay increases from around 1.5 ms to 11.0 ms and the buffer size would need to increase by 6 to 10 times to handle out-of-sequence packets. Additionally, a time synchronization success rate of 14.3% within a 0.1 ms accuracy range found in this study indicates that the IEEE 1588 protocol is severely affected by the latency fluctuations. Full article

Zoning is vital for balancing biodiversity conservation and sustainable development in protected areas, yet traditional approaches often lead to ecological overprotection and social conflict. This study introduces an integrative modeling framework to optimize zoning strategies in the Longbao Reserve on the Qinghai–Tibet Plateau. We employed MaxEnt and Random Forest algorithms to evaluate habitat suitability for two flagship species: the bar-headed goose (Anser indicus) and the black-necked crane (Grus nigricollis). Results showed that 7.9% of the reserve comprised highly suitable habitats, mainly in the southeast, characterized by wetlands, water proximity, and low human disturbance. Land use and June NDVI emerged as key predictors, contributing over 30% and 35% to model performance, respectively. Based on habitat suitability and current zoning mismatches, we propose a revised four-tier zoning scheme: Core Habitat Conservation (16.9%), Ecological Rehabilitation (7.2%), Ecological Management (53.5%), and Sustainable Utilization Zones (22.4%). This refined framework aligns conservation priorities with local development needs and offers a scalable approach to adaptive protected area management. Full article

Migratory flyways sustain waterbird populations by linking critical habitats across their annual cycle. However, stage-specific impacts of climate change on these habitats remain poorly understood. We integrated species distribution models with annual migration data from 30 Greater White-fronted Geese (Anser albifrons frontalis) to assess changes in habitat suitability, distributional shifts, and suitability fluctuations across breeding, stopover, and wintering stages under mid-century (2040–2060) climate scenarios. Suitability fluctuations were quantified as the coefficient of variation ($C_V$) in habitat suitability between current and future projections. Projected habitat responses varied markedly across stages: breeding areas contracted by 29.9%, wintering areas expanded by 62.7%, and stopover sites showed minimal net change. Centroids of all habitats are projected to shift northward by mean distances of 125–492 km under future climate scenarios. Breeding habitats exhibited the greatest suitability fluctuations ($C_V=3$0–45; ~50% area affected under SSP585), followed by stopover and wintering grounds ($C_V$ ≈ 11), with 35.8% and 23.3% of their areas falling within high-fluctuation zones. These findings highlight the urgent need to prioritize breeding habitats, implement stage-specific conservation strategies, and enhance international cooperation to ensure the protection of waterbirds along the East Asian Flyway. Full article

Subclinical mastitis in dairy cows poses a significant challenge to the dairy industry, leading to reduced milk yield, altered milk composition, compromised animal health, and substantial economic losses for dairy farmers. A model based on the XGBoost algorithm, optimized with an Improved GOOSE Optimization Algorithm (IGOOSE), is presented in this work as an innovative approach for predicting subclinical mastitis in order to overcome these problems. The Dairy Herd Improvement (DHI) records of 4154 cows served as the model’s original foundation. A total of 3232 samples with 21 characteristics made up the final dataset, following extensive data cleaning and preprocessing. To overcome the shortcomings of the original GOOSE algorithm in intricate, high-dimensional problem spaces, three significant enhancements were made. First, an elite inverse strategy was implemented to improve population initialization, enhancing the algorithm’s balance between global exploration and local exploitation. Second, an adaptive nonlinear control factor was added to increase the algorithm’s stability and convergence speed. Lastly, a golden sine strategy was adopted to reduce the risk of premature convergence to suboptimal solutions. According to experimental results, the IGOOSE-XGBoost model works better than other models in predicting subclinical mastitis, especially when it comes to recognizing somatic cell scores, which are important markers of the illness. This study provides a strong predictive framework for managing the health of dairy cows, allowing for the prompt identification and treatment of subclinical mastitis, which enhances the efficiency and quality of milk supply. Full article

The purpose of this study was to determine the effect of breed and sex (3 × 2) on the basic chemical composition, concentration of some minerals, and physicochemical properties of edible giblets of farm geese. The study material consisted of edible giblets (livers, gizzards, hearts) obtained from 42 geese from three Polish native breeds (Rypin, Suwałki, Kartuzy) at 220 weeks of age. Edible giblets were obtained during goose evisceration from seven males and seven females of each breed. Each bird was an experimental unit. Goose breed and sex had a significant effect on the chemical composition and physicochemical properties of the edible giblets. Rypin geese had higher (p < 0.05) intramuscular fat content in the gizzard and heart, as well as higher protein content in the heart and lower water content in the gizzard, compared to Kartuzy and Suwałki geese. Kartuzy geese, in turn, had higher content of water in the heart, and higher concentrations of phosphorus, calcium, iron, manganese, sodium, and chromium in the liver, compared to Rypin and Suwałki geese. In turn, Suwałki geese had higher concentrations of phosphorus in the gizzard, and potassium, phosphorus, copper, and iron in the heart compared to the hearts of Rypin and Suwałki geese, while Kartuzy and Suwałki geese higher concentrations of sodium, magnesium, zinc, and manganese in hearts than the hearts of Rypin geese. In these studies, the highest lightness (L*) was observed in the liver and heart of Rypin geese, the lowest yellowness (b*) was observed in the gizzard of Suwałki geese, and the highest pH₂₄ and EC₂₄ were observed in the heart of Kartuzy geese. Regardless of breed, males had higher protein, collagen, and intramuscular fat contents in the heart, a higher water content in the gizzard, higher concentrations of potassium, and sodium in the liver and gizzard, copper in the heart and liver, and phosphorus in the gizzard, and less water in the heart and zinc in the liver, as well as higher (p < 0.05) concentrations of iron in the liver and heart compared with females. The breed by sex interaction was significant for intramuscular fat and water content in the gizzard and heart, and protein content in the heart. Significant differences were also noted for EC₂₄ in the liver and heart, yellowness of the gizzard, and concentrations of most labeled minerals in edible giblets. The obtained results indicate that the nutritional value and suitability of edible goose giblets for the poultry industry vary depending on breed and sex. Due to the limited research on the chemical composition and physicochemical properties of goose giblets, further research in this area is necessary in the future. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

The aim of this study was to evaluate the influence of genotype and diet on geese from crossbreeding meat lines Tapphorn (T) and Eskildsen (E). This study was conducted on 240 crossbred geese assigned to two dietary groups: an SBM diet group fed a standard soybean-based diet and an LPS diet group fed a yellow lupin-based diet. Birds were reared under identical management conditions and slaughtered at 17 weeks of age. The following traits were recorded: meat colour (CIELab), pH₂₄, cooking loss, breast and thigh muscle texture (shear force and energy), and sensory traits. The results showed a significant effect of both genotype and diet on meat quality. The LPS diet lowered shear force and energy (by ~11%, p < 0.001), reduced cooking loss in breast muscles (by ~5%, p < 0.001), and improved the juiciness and flavour of thigh muscles. The ET genotype positively influenced the meat colour intensity (lower L*, higher a*), while the lupin-based diet improved technological parameters, especially the water-holding capacity. The results confirm that replacing soybean meal with yellow lupin protein is an effective nutritional strategy that can improve goose meat quality and sustainability without compromising the sensory quality. These outcomes support developing soy-free feeding strategies in goose production to meet consumer expectations and reduce reliance on imported feed. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

Anseriformes (waterfowl) and Charadriiformes (shorebirds) are well-recognized natural reservoirs of low pathogenic (LP) influenza A viruses (IAVs). Historically, LP IAVs circulate among healthy individuals during seasonal, and often transcontinental, migrations. However, following the introduction of clade 2.3.4.4b highly pathogenic (HP) A/Goose/Guangdong/1/1996 lineage H5 IAV to North America in 2021, countless wild birds succumbed to fatal infections across the Western Hemisphere. Due to their small size and cryptic plumage patterns, opportunities for carcass recovery and postmortem evaluation in sanderlings (Calidris alba) and other shorebirds are rare. A multispecies mortality event in coastal Virginia, USA, in March–April 2024 included sanderlings among other wild bird species. Nine sanderlings underwent postmortem evaluation and clade 2.3.4.4b H5 IAV RNA was detected in pooled oropharyngeal-cloacal swabs from 11/11 individuals by real-time reverse transcription polymerase chain reaction. Histopathology was similar to that in waterfowl and included necrosis in the pancreas and brain and less commonly in the gonad, adrenal gland, spleen, liver, and intestine. Immunohistochemistry revealed IAV antigen labeling in necrotic neurons of the brain (neurotropism) and epithelial cells of the pancreas, gonad, and adrenal gland (epitheliotropism). Describing HP IAV-attributed pathology in shorebirds is key to understanding ecoepidemiology and population health threats in order to further document and compare pathogenesis among avian species. Full article

With increasing attention to sustainability and energy efficiency in transportation systems, advanced intelligent algorithms provide promising solutions for optimizing urban rail transit operations. This study addresses the challenge of optimizing train operation plans for urban rail transit systems characterized by spatiotemporal passenger flow imbalance. By exploring a combined short-turning and unpaired train operation mode, a three-objective optimization model was established, aiming to minimize operational costs, reduce passenger waiting times, and enhance load balancing. To effectively solve this complex problem, an Improved GOOSE (IGOOSE) algorithm incorporating elite opposition-based learning, probabilistic exploration based on elite solutions, and golden-sine mutation strategies were developed, significantly enhancing global search capability and solution robustness. A case study based on real operational data adjusted for confidentiality was conducted, and comparative analyses with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) demonstrated the superiority of IGOOSE. Furthermore, an ablation study validated the effectiveness of each enhancement strategy within the IGOOSE algorithm. The optimized operation planning model reduced passenger waiting times by approximately 12.72%, improved load balancing by approximately 39.30%, and decreased the overall optimization objective by approximately 10.25%, highlighting its effectiveness. These findings provide valuable insights for urban rail transit operation management and indicate directions for future research, underscoring the significant potential for energy savings and emission reductions toward sustainable urban development. Full article

The novel goose parvovirus (NGPV) and the novel duck reovirus (NDRV) are pathogens that can substantially affect the growth and development of ducklings, causing considerable economic losses to duck farms. Therefore, a timely, rapid, accurate, and high-throughput diagnosis and identification of viral infections are critical for preventing the spread of epidemics. In this study, a TaqMan probe-based duplex one-step RT-qPCR was established for the simultaneous detection and qualitative and quantitative identification of the two viruses. It demonstrated greater sensitivity than conventional PCR, detecting as low as 2.42 copies/μL of NGPV genome and 70.1 copies/μL of NDRV genome. Additionally, it exhibited remarkable specificity, responding exclusively to the nucleic acids of target pathogens. It also demonstrated excellent reproducibility and availability, particularly in clinical settings, with a coinfection detection rate of 13.3%, contributing to the development of NGPV- and NDRV-testing technologies. Full article

As a crucial characteristic waterfowl breed, the egg-laying performance of Lion-Headed Geese serves as a core indicator for precision breeding. Under large-scale flat rearing and selection practices, high phenotypic similarity among individuals within the same pedigree coupled with traditional manual observation and existing automation systems relying on fixed nesting boxes or RFID tags has posed challenges in achieving accurate goose–egg matching in dynamic environments, leading to inefficient individual selection. To address this, this study proposes YOLO-Goose, an improved YOLOv8s-based method, which designs five high-contrast neck rings (DoubleBar, Circle, Dot, Fence, Cylindrical) as individual identifiers. The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. Experimental results show that the model achieves an F1 score of 93.8% and mAP50 of 96.4% on the self-built dataset, representing increases of 10.1% and 5% compared to the original YOLOv8s, with a 27.1% reduction in computational load. The dynamic matching algorithm, incorporating spatiotemporal trajectories and egg positional data, achieves a 95% matching rate, a 94.7% matching accuracy, and a 5.3% mismatching rate. Through lightweight deployment using TensorRT, the inference speed is enhanced by 1.4 times compared to PyTorch-1.12.1, with detection results uploaded to a cloud database in real time. This solution overcomes the technical bottleneck of individual selection in flat rearing environments, providing an innovative computer-vision-based approach for precision breeding of pedigree Lion-Headed Geese and offering significant engineering value for advancing intelligent waterfowl breeding. Full article