Search Results (666)

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

Monitoring the activity index of animals is crucial for assessing their welfare and behavior patterns. However, traditional methods for calculating the activity index, such as pixel intensity differencing of entire frames, are found to suffer from significant interference and noise, leading to inaccurate results. These classical approaches also do not support group or individual tracking in a user-friendly way, and no open-access platform exists for non-technical researchers. This study introduces an open-source web-based platform that allows researchers to calculate the activity index from top-view videos by selecting individual or group animals. It integrates Segment Anything Model2 (SAM2), a promptable deep learning segmentation model, to track animals without additional training or annotation. The platform accurately tracked Cobb 500 male broilers from weeks 1 to 7 with a 100% success rate, IoU of 92.21% ± 0.012, precision of 93.87% ± 0.019, recall of 98.15% ± 0.011, and F1 score of 95.94% ± 0.006, based on 1157 chickens. Statistical analysis showed that tracking 80% of birds in week 1, 60% in week 4, and 40% in week 7 was sufficient (r ≥ 0.90; p ≤ 0.048) to represent the group activity in respective ages. This platform offers a practical, accessible solution for activity tracking, supporting animal behavior analytics with minimal effort. Full article

Dexamethasone, widely used as an exogenous glucocorticoid in clinical and animal practice, has recently been recognized as an environmental contaminant of concern. Existing evidence documents its ability to induce persistent dyslipidemia in adult offspring. In this study, plasma cholesterol levels in male rats exposed to dexamethasone prenatally (PDE) were increased. Meanwhile, developmental tracking revealed a reduction in hepatic low-density lipoprotein receptor (LDLR) promoter H3K27 acetylation (H3K27ac) and corresponding transcriptional activity across gestational-to-postnatal stages. Mechanistic investigations established glucocorticoid receptor/histone deacetylase2 (GR/HDAC2) axis-mediated epigenetic programming of LDLR through H3K27ac modulation in PDE offspring, potentiating susceptibility to hypercholesterolemia. Additionally, in peripheral blood mononuclear cells (PBMC) of PDE male adult offspring, LDLR H3K27ac level and expression were also decreased and positively correlated with those in the liver. Clinical studies further substantiated that male newborns prenatally treated with dexamethasone exhibited increased serum cholesterol levels and consistent reductions in LDLR H3K27ac levels and corresponding transcriptional activity in PBMC. This study establishes a complete evidence chain linking PDE with epigenetic programming and cholesterol metabolic dysfunction, proposing PBMC epigenetic biomarkers as a novel non-invasive monitoring tool for assessing the developmental toxicity of chemical exposures during pregnancy. This has significant implications for improving environmental health risk assessment systems. Full article

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

This paper explores responses to online coverage of an avian tracking project. Researchers attached novel trackers to a small group of wild magpies (Gymnorhina tibicen). These were subsequently removed by conspecifics, an example of ‘rescue behavior’ that was recounted in several media outlets. Online comments on three articles, from across the political spectrum (the Conversation, UK Guardian, and UK Daily Mail), were selected for thematic analysis. The resulting 680 comments were analyzed qualitatively and quantitatively to uncover predominant themes and the overall balance of positive and negative sentiments expressed about this tagging project or wildlife tagging generally. Topics occurring most frequently were themed into three interrelated areas: (1) sharing personal feelings and experiences, (2) comparing the merits of different species, and (3) sharing knowledge and opinion. Twenty-one percent (21%) of respondents expressed an opinion on the ethics of wildlife tagging. In the Daily Mail and Guardian, this opinion was more likely to be negative towards the use of tags. Opinion was more balanced for readers of the Conversation’s article. Willingness to comment on online news is low, and readers of this story were not asked directly for their opinion. Nevertheless, the data here illustrate some public perceptions of wildlife tagging, and there was a clear negative reaction from many responders. Widening the means through which people can engage with animal science has the potential to advance discussions around research ethics and animal welfare. Reactions to this story expose important questions for scientists seeking to engage with, and convince, the public of the merits of their work. Full article

Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement. Full article

The blue-eared pheasant (Crossoptilon auritum), a Near Threatened (NT) species endemic to China, is primarily distributed across the northeastern region of the Qinghai–Tibetan Plateau. To bridge the fine-scale spatiotemporal gap in blue-eared pheasant behavioral ecology, this study combines satellite telemetry, movement modeling, and field-based habitat assessments (vegetation, topography, human disturbance). This multidisciplinary approach reveals detailed patterns of their behavior throughout the breeding season. Using satellite-tracking data from six individuals (five males tracked at 4 h intervals; one female tracked hourly) in Wanglang National Nature Reserve (WLNNR), Sichuan Province during breeding seasons 2018–2019, we quantified their home ranges via Kernel Density Estimation (KDE) and examined the female movement patterns using a Hidden Markov Model (HMM). The results indicated male core (50% KDE: 21.93 ± 16.54 ha) and total (95% KDE: 158.30 ± 109.30 ha) home ranges, with spatial overlap among individuals but no significant temporal variation in home range size. Habitat selection analysis indicated that the blue-eared pheasants favored shrub-dominated areas at higher elevations (steep southeast-facing slopes), regions distant from human disturbance, and with abundant animal trails. We found that their movement patterns differed between sexes: the males exhibited higher daytime activity yet slower movement speeds, while the female remained predominantly near nests, making brief excursions before returning promptly. These results enhance our understanding of the movement ecology of blue-eared pheasants by revealing fine-scale breeding-season behaviors and habitat preferences through satellite-tracking. Such detailed insights provide an essential foundation for developing targeted conservation strategies, particularly regarding effective habitat management and zoning of human activities within the species’ range. Full article

The valorization of agro-industrial by-products for their use in animal feed leads to a reduction in inputs, creating the opportunity to optimize the sustainability of the agri-food chain, a priority of the SDG 2030 strategy; it also leads to a reduction in production costs. The objective of this study was to examine the changes that occur during the silage process of the pomegranate varieties Mollar de Elche (PDO) and Wonderful in terms of their nutritional and antioxidant characteristics for subsequent use in ruminant feed. Microsilos were created with the by-products of these two different pomegranate varieties. Two different microsilos for each variety were monitored on days 0 (raw material), 14, 35, 60, and 180. The variables studied included microbiology tracks, fermentation products, pH, dry matter (DM), macronutrient composition, organic acid and sugar contents, and antioxidant activity. The results show that, for both varieties, the silage process was successful; the stability of the fermentation process was determined by day 35, and its viability was ensured for a minimum period of 6 months. Furthermore, the nutritional characteristics of the raw material were preserved in the ensiled product. An evaluation of the total phenols and antioxidant capacity (ABTS and DPPH) showed that they remained stable throughout the monitoring period, despite the decrease in bioactive compounds (total phenols) at the end of the study period. It was concluded that silage is an effective preservation method for the by-products of Mollar de Elche and Wonderful pomegranate varieties, and its outcome presents valuable potential as a sustainable nutritional resource for ruminants. Full article

Distributed acoustic sensing (DAS) is a highly effective method of monitoring all kinds of intrusions on railway tracks. These intrusions represent a real problem in the railway sector, as they can lead to human deaths or damage to railway tracks, and these intrusions may be human or animal. A fiber was deployed along 12 km of track in a railway test center, enabling us to acquire data day and night. A data acquisition campaign was carried out in April 2023 to capture the signatures of several scenarios (walking, digging, falling rocks, etc.) in order to train machine learning models and prevent any intrusion by detecting and classify these intrusion. The study shows the diversity of signals that fiber can acquire in the rail sector and the machine learning model performance. Signals associated with the presence of animals are also presented. Full article

Many viruses mutate rapidly to adapt to host defenses, and for some of these viruses, the result is long-term infection in individual hosts. The work described here examines the infection and long-term maintenance of a newly identified virus, equine parvovirus-hepatitis (EqPV-H), in an individual horse. This description is possible because of a hypervariable region in the capsid gene; sequence variants were tracked by high-throughput sequencing of serum samples taken over a 16-year period. The data support the hypothesis that EqPV-H infection resulted in a sequence variant bottleneck. The continuing infection evolved into a complex viral population showing a pattern of emergence, dominance, and recession with replacement. This is the first temporal description of the capsid gene evolution of EqPV-H in a single animal. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

Immersive learning has been recognized as a promising paradigm for enhancing educational experiences through the integration of VR. We propose an architecture for intelligent tutoring in immersive VR environments that employs LLM-based non-playable characters. Key system capabilities are identified, including natural language understanding, real-time adaptive dialogue, and multimodal interaction through hand tracking, gaze detection, and haptic feedback. The system synchronizes speech output with NPC animations, enhancing both interactional realism and cognitive immersion. This design demonstrates that AI-driven VR interactions can significantly improve learner engagement. System performance was generally stable; however, minor latency was observed during speech processing, indicating areas for technical refinement. Overall, this research highlights the transformative potential of VR in education and emphasizes the importance of ongoing optimization to maximize its effectiveness in immersive learning contexts. Full article

Understanding how piglets move around sows during posture changes is crucial for their safety and healthy growth. Automated monitoring can reduce farm labor and help prevent accidents like piglet crushing. Current methods (called Joint Detection-and-Tracking-based, abbreviated as JDT-based) struggle with problems like misidentifying piglets or losing track of them due to crowding, occlusion, and shape changes. To solve this, we developed MSHMTracker, a smarter tracking system that introduces a motion-status hierarchical architecture to significantly improve tracking performance by adapting to piglets’ motion statuses. In MSHMTracker, a score- and time-driven hierarchical matching mechanism (STHM) was used to establish the spatio-temporal association by the motion status, helping maintain accurate tracking even in challenging conditions. Finally, piglet group aggregation or dispersion behaviors in response to sow posture changes were identified based on the tracked trajectory information. Tested on 100 videos (30,000+ images), our method achieved 93.8% tracking accuracy (MOTA) and 92.9% identity consistency (IDF1). It outperformed six popular tracking systems (e.g., DeepSort, FairMot). The mean accuracy of behavior recognition was 87.5%. In addition, the correlations (0.6 and 0.82) between piglet stress responses and sow posture changes were explored. This research showed that piglet movements are closely related to sow behavior, offering insights into sow–piglet relationships. This work has the potential to reduce farmers’ labor and improve the productivity of animal husbandry. Full article