Search Results (13)

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article

Background: Accurate estrus identification in dairy cows is essential for enhancing reproductive efficiency and economic performance. The dispersed nature of estrus data and individual cow differences pose significant challenges for accurate identification. Methods: This study gathered cow estrus data from 812 literature sources using Python 3.9 crawler technology. The data were then preprocessed using CiteSpace 6.4. We constructed a knowledge graph depicting physiological, behavioral, and appearance changes during estrus through entity and relationship extraction. To uncover potential relationships within the graph, we applied and compared two association rule algorithms: FP-Growth and Apriori. We utilized Boolean functions derived from association rule learning to validate the ability of the rules to identify normal estrus. Additionally, we employed an enhanced Iforest-OCSVM anomaly detection model to assess the performance of the association rules in detecting abnormal estrus. Furthermore, we optimized the Incremental FP-Growth Algorithm for Dynamic Knowledge Expansion. Results: Based on the initial knowledge graph with 86 entities and 9 relationships, mining added 17 new strong association relationships marked by ‘with’, enhancing its completeness and providing deeper insights into estrus behaviors and physiological changes. Furthermore, these strong association rules exhibited notable effectiveness in both normal and abnormal estrus detection, validating their robustness in practical applications. The algorithm’s optimization bolstered its scalability, making it more adaptable to future data expansions and complex knowledge integrations. Conclusions: By constructing a knowledge graph that integrates physiological, behavioral, and appearance changes during estrus, we established a comprehensive framework for understanding cow estrus. Association rule mining, particularly with the FP-Growth algorithm, added 17 new strong association relationships to the graph, enriching its content and offering deeper insights into estrus behaviors and physiological changes. The strong association rules derived from FP-Growth demonstrated notable effectiveness in both normal and abnormal estrus detection, validating their robustness and practical utility in enhancing estrus identification accuracy, and providing a robust foundation for future multi-dimensional estrus research. Full article

Inspired by the effects of solar or UV radiation on the decay of airborne bacteria during their transport, this study investigated the effect of UVA on the decay of airborne bacteria from cattle houses and analyzed the potential use of UVA to reduce indoor airborne bacteria under laboratory conditions. Airborne bacteria from the cattle source were generated and released into a small-scale test chamber (1.5 m³) with different strategies according to the different objectives in decay tests and simulated sterilization tests. Increasing with the UVA radiation gradients (0, 500, 1000, 1500 μW cm⁻²), the average decay rate of total curable airborne bacteria ranged from 2.7% to 61.6% in decay tests. Under the combination of different UVA radiation intensities (2000 μW cm⁻² in maximum) and radiation durations (60 min in maximum), simulated sterilization tests were conducted to examine the potential use of UVA radiation for air sterilization in animal houses. With the dynamic inactive rate (DIR) ranging from 17.2% to 62.4%, we proved that UVA may be an alternative way to reduce the indoor airborne bacteria in cattle houses if applied properly. Similar effects would be achieved using either a high radiation intensity with a short radiation duration or a low radiation intensity with a long radiation duration. Full article

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. Full article

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition. Full article

The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months’ laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. Full article

Many expression data showed miRNAs have a potential function on regulating gonadal differentiation in animals, but their function is rarely studied in vivo, especially in chickens. Using the comprehensive expression profiles analysis, the specific male-biased miR-2954, which is significantly higher expressed in male embryos and gonads at all detected stages, was firstly screened during the early stages of chicken embryogenesis and gonadogenesis. In sex-reversed female gonads treated with aromatase inhibitors, the expression of miR-2954 was increased, which was consistent with the up-regulation of DMRT1 and SOX9. The injection of vivo-morpholino of miR-2954 significantly inhibited the expression of miR-2954 in chicken embryos, and the down-regulation of miR-2954 decreased the expression of testis-associated genes DMRT1 and SOX9, while the expression of ovary-associated genes and the gonadal morphology did not change obviously. These results confirm that miR-2954 coincides with testicular differentiation in chicken embryos, but whether it might be an upstream cell autonomous factor to sex development in birds still need to be further determined. Full article

Surface adsorption and dissociation processes can have a decisive impact on the catalytic properties of metal alloys. We have used density functional theory to investigate the adsorption and dissociation of O₂ on Cu-skin Cu₃Au(111) surface. The calculated results show that the b-f(h)-b adsorption configuration is the most energetically favorable on the Cu-skin Cu₃Au(111) surface. For O₂ dissociation, there are two thermodynamically favorable dissociation paths. One path is from b-f-b to two O atoms in hcp sites, and the other path is from b-h-b to two O atoms in fcc sites. Moreover, the stability of O₂ adsorption is higher and the dissociation energy barrier of the adsorbed O₂ is lower as compared to those on the Cu(111) surface. This theoretical work provides valuable guidance for the practical application of Cu-Au alloys as highly efficient CO oxidation catalysts. Full article

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R², RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake. Full article

High fructose corn syrup (HFCS) is widely used as sweetener in processed foods and soft drinks in the United States, largely substituting sucrose (SUC). The orexigenic hormone ghrelin promotes obesity and insulin resistance; ghrelin responds differently to HFCS and SUC ingestion. Here we investigated the roles of ghrelin in HFCS- and SUC-induced adiposity and insulin resistance. To mimic soft drinks, 10-week-old male wild-type (WT) and ghrelin knockout (Ghrelin^−/−) mice were subjected to ad lib. regular chow diet supplemented with either water (RD), 8% HFCS (HFCS), or 10% sucrose (SUC). We found that SUC-feeding induced more robust increases in body weight and body fat than HFCS-feeding. Comparing to SUC-fed mice, HFCS-fed mice showed lower body weight but higher circulating glucose and insulin levels. Interestingly, we also found that ghrelin deletion exacerbates HFCS-induced adiposity and inflammation in adipose tissues, as well as whole-body insulin resistance. Our findings suggest that HFCS and SUC have differential effects on lipid metabolism: while sucrose promotes obesogenesis, HFCS primarily enhances inflammation and insulin resistance, and ghrelin confers protective effects for these metabolic dysfunctions. Full article

Segregation at metal alloy surfaces is an important issue because many electrochemical and catalytic properties are directly correlated to the surface composition. We have performed density functional theory calculations for Mo segregation in MoNi(111) in the presence of chemisorbed atomic oxygen. In particular, the coverage dependence and possible adsorption-induced segregation phenomena are addressed by investigating segregation energies of the Mo atom in MoNi(111). The theoretical calculated results show that the Mo atom prefers to be embedded in the bulk for the clean MoNi(111), while it segregates to the top-most layer when the oxygen coverage is thicker than 1/9 monolayer (ML). Furthermore, we analyze the densities of states for the clean and oxygen-chemisorbed MoNi(111), and see a strong covalent bonding between Mo d-band states and O p-states. The present study provides valuable insight for exploring practical applications of Ni-based alloys as hydrogen evolution electrodes. Full article