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Background: The Ironman 70.3 race is also called a half Ironman, and consists of 1.9 km of swimming, 90.1 km of cycling, and 21.1 km of running. The authors provide practical insights that may be useful for medical support in future events by summarizing the process and results of on-scene medical care. Methods: The medical post was established at the transition area between the cycling and running courses, which was close to the finish line, and staffed with the headquarters team comprised of an emergency physician, an EMT, two nurses, and an ambulance with a driver. The other five ambulances were located throughout the course. The medical staff identified participants according to their numbers when providing medical support, and described complaints, treatment provided, and disposition. When treating non-participants, gender and age were recorded instead of numbers. The treatment records were analyzed after the race. Results: The medical team treated a total of 187 participants. One suffered cramps in the calf muscles during the swimming part of the course. Nineteen were treated for injuries suffered during the cycling race. A total of 159 were treated for injuries on the running course. Five casualties, all of which occurred during the cycling race, required transport to hospital. Conclusions: Medical directors preparing medical support during a triathlon event should expect severe injuries in the cycling course. In hot climates, staff may also suffer from heat injuries as well as runners, and proper attention should be paid to these risks.

Weaning is a major stressor for pigs, often increasing agonistic behaviors such as aggression, ear biting, and tail biting, which can impair growth and welfare. This study evaluated the combined effect of rubber sticks and Italian ryegrass hay as environmental enrichment (EE) on growth performance, agonistic behavior, ear and tail biting lesion development, fecal consistency, and blood biochemical parameters. A total of 64 pigs (8 pigs × 4 pens × 2 groups) at 7 weeks of age were assigned to control (without EE) and treatment (with EE) groups for four weeks. Pens were the experimental unit for growth, fecal scores, lesion scoring, and behavioral outcomes. Growth and fecal consistency were measured weekly, while ear and tail lesions were scored at the end. Agonistic behavior was quantified using overhead RGB cameras and a YOLOv8-based AI system with high accuracy, mAP50 = 0.953, validated against manual observations, with behavioral outputs aggregated at the pen level from a single representative pen per group. Combined EE reduced lesion severity, lowered free fatty acids, improved fecal consistency, and decreased agonistic behavior, without affecting growth. AI-based monitoring offers a promising tool for quantifying social stress, although further studies with greater pen-level replication are warranted.

Respiratory diseases remain one of the most significant health challenges in modern swine production, leading to substantial economic losses, compromised animal welfare, and increased antimicrobial use. In recent years, advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled the development of non-invasive, continuous monitoring systems based on pig vocalizations. Among these, audio-based technologies have emerged as especially promising tools for early detection and monitoring of respiratory disorders under real farm conditions. This review provides a comprehensive synthesis of AI-driven audio classification approaches applied to pig farming, with focus on respiratory health and welfare monitoring. First, the biological and acoustic foundations of pig vocalizations and their relevance to health and welfare assessment are outlined. The review then systematically examines sound acquisition technologies, feature engineering strategies, machine learning and deep learning models, and evaluation methodologies reported in the literature. Commercially available systems and recent advances in real-time, edge, and on-farm deployment are also discussed. Finally, key challenges related to data scarcity, generalization, environmental noise, and practical deployment are identified, and emerging opportunities for future research including multimodal sensing, standardized datasets, and explainable AI are highlighted. This review aims to provide researchers, engineers, and industry stakeholders with a consolidated reference to guide the development and adoption of robust AI-based acoustic monitoring systems for respiratory health management in swine.

Rearing density influences pig productivity and welfare, but its behavioral and physio-logical effects remain unclear. This study evaluated how increasing space allowance from 0.57 to 0.97 m2/pig affects growth, agonistic behavior, and stress in growing pigs. Seventy-six 12-week-old pigs were allocated to high or low rearing density (HRD: 12 pigs/pen, n = 4 pens; LRD: 7 pigs/pen, n = 4 pens) for 28 days by varying pig numbers within identical pens. Growth performance was recorded weekly, while agonistic behavior was continuously monitored using RGB cameras and detected with a YOLOv8-based model (overall mAP50 = 0.953; aggression = 0.960, ear biting = 0.927, tail biting = 0.972). Ear base temperature was measured at baseline and twice weekly, lesion scores were assessed at trial completion, and blood biochemical parameters were also assessed. Pigs under LRD exhibited higher (p < 0.01) body weight, daily gain, and feed intake, with a lower feed conversion ratio than HRD pigs. Increased space allowance reduced (p < 0.05) agonistic behavior, lesion scores, plasma glucose, free fatty acids, cortisol, and ear base temperature. These findings indicate that increased space allowance improves growth and welfare and demonstrate the value of AI-based behavioral monitoring in pig production systems.

In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

Respiratory diseases and welfare impairments impose substantial economic and ethical burdens on modern broiler production, driven by high stocking density, rapid pathogen transmission, and limited sensitivity of conventional monitoring methods. Because respiratory pathology and stress directly alter vocal behavior, acoustic monitoring has emerged as a promising non-invasive approach for continuous flock-level surveillance. This review synthesizes recent advances in audio classification and artificial intelligence for monitoring respiratory health and welfare in broiler chickens. We have reviewed the anatomical basis of sound production, characterized key vocal categories relevant to health and welfare, and summarized recording strategies, datasets, acoustic features, machine-learning and deep-learning models, and evaluation metrics used in poultry sound analysis. Evidence from experimental and commercial settings demonstrates that AI-based acoustic systems can detect respiratory sounds, stress, and welfare changes with high accuracy, often enabling earlier intervention than traditional methods. Finally, we discuss current limitations, including background noise, data imbalance, limited multi-farm validation, and challenges in interpretability and deployment, and outline future directions for scalable, robust, and practical sound-based monitoring systems in broiler production.

The importance of backfat thickness in sows lies in its correlation with nutritional status, reproductive performance, and overall health. Identifying the optimum backfat thickness is crucial for determining the ideal energy reserves needed to support successful reproduction and lactation. This research aimed to determine optimal backfat thickness (BFT) of sows in relation to reproductive and lactation performance. In this study, 32 lactating sows were housed in a controlled environment and assigned to four groups based on their BFT before farrowing: <17.00 mm, 17.00–17.99 mm, 18.00–18.99 mm, and ≥19.00 mm. The data were analyzed with One-way analysis of variance, and the association between backfat thickness and sow reproductive performance was examined through Spearman’s correlation analysis using SAS software. The results revealed no significant difference between the groups in total born, total born alive, and litter size weaned, but the piglets’ survival rate during the lactation period is lower from sows with BFT < 17.00. Moreover, piglet birth weight and body weight at Day 3 were significantly lower in sows with BFT < 17.00 mm. The BFT of sows at weaning showed significant differences among the groups associated with the backfat thickness before farrowing. No significant difference was found in the duration of farrowing. The return-to-estrus interval was longer in sows with <17.00 mm BFT than in those with 17.00–17.99 mm, 18.00–18.99 mm, and ≥19.00 mm backfat thickness, with estrus intervals of 7.17, 6.25, 5.31, and 5 days after weaning, respectively. Numerically, calculated milk yield (MY) is lowest in sows with BFT < 17.00, and the highest MY was obtained from sows with BFT 18.00–18.99 mm. In conclusion, sows with at least 17.00 mm BFT before farrowing are ideal for increasing the lifetime productivity of sows. This study provides valuable insights into the importance of sow management during gestation for subsequent reproductive success.

The late gestation period is crucial for fetal growth and development, impacting swine enterprises’ profitability. Various nutritional strategies have been explored to enhance reproductive performance in sows, but findings regarding birth weight and litter size have been inconsistent. This study investigated the effects of increased feeding allowance during the late gestation period on the reproductive performance and farrowing behavior of primiparous and multiparous sows. A total of 28 sows (Landrace × Yorkshire) were used in this experiment, and fed 2.50 kg/d or 3.50 kg/d from 84 days of gestation until farrowing. Farrowing behavior was monitored using a DeepEyesTM M3SEN camera. The data were analyzed using the 2 × 2 factorial within Statistical Analysis System (SAS, 2011, Version 9.3) software. The results indicated that regardless of the parity number, sows fed a high diet exhibited a numerical increase in the total number of born piglets and a significant increase in milk yield (p = 0.014) and piglet birthweight (p = 0.023). Backfat thickness loss was significantly higher in sows with a 2.50 kg feeding allowance (p = 0.022), and the total number of piglets born, live births, and litter size were numerically higher in sows fed 3.50 kg per day. Moreover, stillborn piglets, mortality rate, and re-estrus days were numerically lower in sows with a high feeding allowance. The diet and parity did not individually affect the average duration of farrowing and farrowing intervals. However, the duration of postural changes in sows after farrowing was significantly reduced (p = 0.012). The principal component analysis revealed 81.40% and 80.70% differences upon partial least-squares discriminant analysis. Therefore, increasing feeding allowance during the late gestation period, regardless of parity, could positively influence sows’ reproductive performance and piglets’ growth performance during the lactation phase.

Agonistic behaviors such as aggression, ear biting, and tail biting remain major challenges for pig welfare, particularly during the weaning and growing periods. Computer vision (CV) technologies are emerging as scalable tools for non-invasive monitoring of these behaviors. This systematic review summarizes recent advances in CV-based detection of agonistic behaviors in pigs and identifies factors influencing their reliability and commercial adoption. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a structured search of Scopus, Web of Science, and PubMed identified 42 eligible studies. Most studies employ deep learning approaches, including you only look once (YOLO)-based detectors and spatio-temporal models, achieving detection accuracy of up to 97% for behaviors such as head knocking, head-to-body pushing, and tail biting, typically evaluated under controlled conditions using mAP@0.5. Three key findings emerged: rapid progress in deep learning-based detection; methodological heterogeneity in behavioral definitions, validation strategies, and annotation protocols; and a gap between high detection accuracy and demonstrated improvements in welfare or productivity. Progress is limited by scarce cross-farm validation, inconsistent bout definitions, reliance on manual annotations, and weak integration with physiological and production indicators. Future research should prioritize standardized behavioral definitions, multimodal integration, predictive modeling, and rigorous external validation.

Background Respiratory disease is an economically important disease in the swine industry. Housing air quality control is crucial for maintaining the respiratory health of pigs. However, maintaining air quality is a limitation of current housing systems. This study evaluated the growth and health parameters of pigs raised under different environmental conditions and identified key environmental variables that determine respiratory health. Eighty (Largewhite × Landrace) × Duroc crossed growing pigs (31.71 ± 0.53 kg) were equally distributed into two identical climate-controlled houses with distinct environmental conditions (CON = normal conditions and TRT = poor conditions). Two-sample tests were performed to compare the means of the groups, and a random forest algorithm was used to identify the importance scores of the environmental variables to respiratory health. Results Pigs in the TRT group were significantly exposed to high temperatures (28.44 vs 22.78 °C, p  < 0.001), humidity (88.27 vs 61.86%, p  < 0.001), CO 2 (2,739.93 vs 847.91 ppm, p  < 0.001), NH 3 (20.53 vs 8.18 ppm, p  < 0.001), and H 2 S (14.28 vs 6.70 ppm, p  < 0.001). Chronic exposure to these factors significantly reduced daily feed intake (1.82 vs 2.32 kg, p  = 0.002), resulting in a significant reduction in average daily gain (0.72 vs 0.92 kg, p  = 0.026), increased oxidative stress index (3.24 vs 1.43, p  = 0.001), reduced cortisol levels (2.23 vs 4.07 mmol/L, p  = 0.034), and deteriorated respiratory health status (74.41 vs 97.55, p  < 0.001). Furthermore, a random forest model identified Min CO 2 , Min NH 3 , and Avg CO 2 as the best predictors of respiratory health, and CO 2 was strongly correlated with NH 3 and H 2 S concentrations. Conclusions These findings emphasize the critical importance of proper environmental management in pig farming and suggest that regular monitoring and control of either CO 2 or NH 3 , facilitated by environmental sensors and integration into intelligent systems, can serve as an effective strategy for improving respiratory health management in pigs.