Animals

Dogs increasingly function as relational beings, shaping their guardians’ emotional well-being and daily routines. Consequently, dog owner education has expanded beyond behaviour-focused training toward integrative approaches that address the emotional, relational, and cognitive dimensions of the human–dog relationship. Despite this shift, international comparative research on the organisation and institutionalisation of dog owner education remains limited. The study applies a qualitative exploratory comparative case study to examine systems in the United States, the United Kingdom, Germany, Japan, and Australia, identifying national patterns and shared components to inform context-appropriate frameworks, particularly in South Korea. The study examines legal and policy documents, institutional guidelines, and standardised education programmes that function as national or de facto standards using document and content analysis. It integrates within- and cross-case comparisons and interprets findings through a framework drawing on human–animal interaction, attachment, canine behaviour, and cognitive–behavioural coaching theories. The analysis reveals five shared components of behaviour change: guardian responsibility and animal welfare, science-based positive reinforcement, early socialisation and prevention, a balance between standardisation and individualisation, and guardians’ emotional and relational engagement. These findings suggest that dog owner education functions as an integrated system that supports responsible guardianship and stable human–dog relationships across sociocultural contexts.

In intensive cage rearing systems, accurate dead hen detection remains difficult due to complex environments, severe occlusion, and the high visual similarity between dead hens and live hens in a prone posture. To address these issues, this study proposes a dead hen identification method based on a Spatial-Temporal Graph Convolutional Network (STGCN). Unlike conventional static image-based approaches, the proposed method introduces temporal information to enable dynamic spatial-temporal modeling of hen health states. First, a multimodal fusion algorithm is applied to visible light and thermal infrared images to strengthen multimodal feature representation. Then, an improved YOLOv7-Pose algorithm is used to extract the skeletal keypoints of individual hens, and the ByteTrack algorithm is employed for multi-object tracking. Based on these results, spatial-temporal graph-structured data of hens are constructed by integrating spatial and temporal dimensions. Finally, a spatial-temporal graph convolution model is used to identify dead hens by learning spatial-temporal dependency features from skeleton sequences. Experimental results show that the improved YOLOv7-Pose model achieves an average precision (AP) of 92.8% in keypoint detection. Based on the constructed spatial-temporal graph data, the dead hen identification model reaches an overall classification accuracy of 99.0%, with an accuracy of 98.9% for the dead hen category. These results demonstrate that the proposed method effectively reduces interference caused by feeder occlusion and ambiguous visual features. By using dynamic spatial-temporal information, the method substantially improves robustness and accuracy of dead hen detection in complex cage rearing environments, providing a new technical route for intelligent monitoring of poultry health status.

This retrospective study describes the national histopathology caseload of feline tumours submitted to a Portuguese diagnostic laboratory over a five-year period. A total of 1904 histopathology-confirmed neoplasms were analysed by biological behaviour, anatomical location, and demographic/geographical variables. Malignant tumours predominated (77.4%), whereas 22.6% were benign. Tumours most commonly involved the mammary gland (44.8%) and cutaneous/soft tissues (42.4%), together accounting for 87.2% of cases; all other sites were individually uncommon (≤5.6%). The most frequent malignant tumour types were mammary carcinoma (38.3%), fibrosarcoma (8.0%), squamous cell carcinoma (6.4%), and mast cell tumour (4.8%). Cats with malignant tumours were older than those with benign lesions (p < 0.001), and females comprised most submissions (69.3%), largely driven by mammary neoplasia. Multiple, histologically distinct tumours were identified in 8.3% of cats and were more frequent in older females (p = 0.001). Domestic Shorthairs comprised the vast majority of cases, and no significant associations were detected between breed (including pure breeds) or geographical location and tumour occurrence or biological behaviour (p > 0.05). These findings highlight a sustained predominance of malignant disease in Portuguese cats, concentrated in mammary and cutaneous/soft-tissue sites, supporting a low threshold for biopsy in older cats and systematic mammary screening in females, and continued registry-based surveillance to monitor temporal changes in tumour patterns.