Search Results (51)

Wireless sensor networks (WSNs) are foundational to modern smart environments, supporting applications ranging from healthcare and precision agriculture to industrial control and disaster response. Despite their potential, WSNs remain constrained by a limited battery life, packet loss, variable throughput, latency, and security vulnerabilities. This paper extends Horse Herd Leadership Optimization (HHLO), a bio-inspired metaheuristic modeling herd leadership, synchronization, and exploration to drive energy-aware clustering and trust-aware routing. HHLO rotates cluster-head leadership in order to balance load, injects chaotic exploration in order to avoid premature convergence, and incorporates a continuously updated node trust score directly into the routing cost in order to exclude unreliable or malicious nodes. Extensive MATLAB simulations with 1000 nodes deployed over a 1000 m × 1000 m² field for 400 rounds, under both static and mobile settings, demonstrate HHLO’s effectiveness. Compared to baseline approaches, HHLO achieves residual energy improvement of 12–21%, throughput gains of 14–23%, Packet Delivery Ratio (PDR) increase of 6–12%, and network lifetime extension of 18–32%; it also achieves an energy balance factor (EBF) of 0.91 and a trust balance factor (TBF) of 0.88, reduces end-to-end latency by 8–10%, and reduces control overhead ratio (COR) by 10–12%. These improvements result from HHLO’s joint optimization of energy, congestion, mobility, and trust, yielding longer-lived and more reliable networks. By unifying security and optimization within a single framework, HHLO advances the development of sustainable, resilient, and environmentally conscious WSNs for next-generation IoT deployments. Full article

Streptococcus agalactiae is a major cause of bovine mastitis, which affects the quality and yield of milk. The main strategy for controlling this pathogen on dairy farms is the use of antibiotics. This study investigated the clonality, serotype distribution, antimicrobial susceptibility, and presence of resistance and virulence genes in 46 S. agalactiae isolates obtained from raw bovine milk in northeastern Brazil. Capsular types were determined using multiplex PCR and antibiotic susceptibility profiles were determined using disc diffusion or the gradient strip method. Clonal diversity was evaluated via pulsed-field gel electrophoresis. Eight isolates were sequenced using short- and long-read methods. There was high overall genetic diversity, whereas the resistance and virulence profiles were largely homogeneous within herds. Tetracycline and macrolide resistance was frequent and mediated by tetO and ermB and less frequently by tetM. Genome analysis demonstrated that resistance genes are present in mobile genetic elements that are also present in human isolates, and phylogenomic analyses identified ST-103 as the predominant and multi-host-adapted lineage, whereas ST-91 clustered with the bovine-adapted lineage. These findings expand the molecular epidemiology of S. agalactiae in dairy farms of a region in northeast Brazil and highlight the importance of surveillance strategies for guiding mastitis control and mitigating the spread of antimicrobial resistance. Full article

This paper explores the application of mobile sensor networks in cow herds, focusing on the challenge of achieving local communication under minimal computational constraints such as restricted locality, limited memory, and implicit coordination. To address this, we propose a high connectivity based sensor network scheme that enables individual sensors to self-organize and dynamically adapt to topological variations caused by cow movements. In this scheme, each sensor acquires local distribution data from neighboring sensors, identifies those with high connectivity, and forms a local network with a star topology. The overlap of these local networks results in a globally interconnected mesh topology. Furthermore, information exchanged through broadcasting and overhearing allows each sensor to incrementally update and adapt to dynamic changes in its local network. To validate the proposed scheme, a custom wireless sensor tag was developed and mounted on the necks of individual cows for experimental testing. Furthermore, large-scale simulations were performed to evaluate performance in herd environments. Both experimental and simulation results confirmed that the scheme effectively maintains network coverage and connectivity under dynamic herd conditions. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

Human papillomavirus (HPV) infection remains a critical public health challenge in China, particularly in Guangdong Province, where HPV-52, 16, and 58 genotypes predominate, and male infection rates exceed 40%. Despite the successful implementation of a government-funded school-based program that has achieved 88% HPV vaccine coverage among adolescent girls, several persistent barriers, including genotype mismatch (the free HPV vaccine covers < 50% of high-risk local strains), regional disparities (80% vs. 60% for first-dose coverage), and exclusion of males, thwart progress toward herd immunity. Financial sustainability risks pose an even more significant threat to the expansion of HPV vaccination programs, especially in Guangdong province where annual expenditures exceed CNY 200 million. This review delves into Guangdong’s pioneering efforts and proposes practical solutions: accelerating domestic multivalent HPV vaccine development, adopting gender-neutral vaccination policies, and leveraging mobile clinics for remote populations. These strategies not only provide a roadmap for China but also serve as valuable insight for other LMICs striving to overcome HPV-related inequalities. Full article

With the rise of smart agriculture and the expansion of pig farming, pig aggressive behavior recognition is crucial for maintaining herd health and improving farming efficiency. The differences in background and light variation in different barns can lead to the missed detection and false detection of pig aggressive behaviors. Therefore, we propose a deep learning-based pig aggressive behavior recognition model, in order to improve the adaptability of the model in complex pig environments. This model, combined with MobileNetV2 and Autoformer, can effectively extract local detail features of pig aggression and temporal correlation information of video frame sequences. Both Convolutional Block Attention Module (CBAM) and Advanced Filtering Feature Fusion Pyramid Network (HS-FPN) are integrated into the lightweight convolutional network MobileNetV2, which can more accurately capture key visual features of pig aggression and enhance the ability to detect small targets. We extract temporal correlation information between consecutive frames by the improved Autoformer. The Gate Attention Unit (GAU) is embedded into the Autoformer encoder in order to focus on important features of pig aggression while reducing computational latency. Experimental validation was implemented on public datasets, and the results showed that the classification recall, precision, accuracy, and F1-score of the model proposed in this paper reach 98.08%, 94.44%, 96.23%, and 96.23%, and the parameter quantity is optimized to 10.41 M. Compared with MobileNetV2-LSTM and MobileNetV2-GRU, the accuracy has been improved by 3.5% and 3.0%, respectively. Therefore, this model achieves a balance between recognition accuracy and computational complexity and is more suitable for automatic pig aggression recognition in practical farming scenarios, providing data support for scientific feeding and management strategies in pig farming. Full article

Extreme climate events are increasing in severity and frequency and affecting the livelihood of pastoralists. Understanding these impacts is crucial for developing effective management strategies. Thus, this study examines the effects of drought on livestock production and market dynamics in semi-arid Ethiopia and explores the adaptation strategies employed by Borana pastoralists. Both the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to calculate indicators of drought severity between 1993 and 2022. Surveys were also conducted in 244 selected households. In addition, focus group discussions and field observations were conducted to investigate the adaptation practices of Borana pastoralists to drought. A line graph was used to illustrate the relationship between the Standardized Precipitation Index (SPI) and livestock market prices. The study found extreme drought in 1985, 2000, and 2011, with the most severe to moderate dryness occurring in the Arero, Elwaya, Dubuluk, Guchi, and Yabelo areas. The study found that severe droughts are increasing, affecting pastoralists’ livelihoods. The recurring drought led to a shortage of feed and water, which resulted in the starvation and death of livestock and jeopardized the livelihoods of pastoralists. In addition, the decline in milk production and falling market prices are said to have had a negative impact. Diversification of livelihood sources, mobility of livestock to seek out forage and water resources, and diversification of herd composition to take advantage of varying drought tolerance have been the usual long-term adaptation strategies of Borana pastoralists. Given the multiple negative impacts of climate change, development interventions in pastoral and agro-pastoral areas of Ethiopia should focus on proactive measures to reduce the impacts of climate change on livestock production. Full article

Introduction: Streptococcus uberis is a poorly controlled cause of bovine intramammary infections and a common motivation for the use antibiotics in dairy farms worldwide. Therefore, studying the genomic characteristics of this pathogen is fundamental to understand its complex epidemiology and behavior against antimicrobials. Methods: A comparative genomic analysis of 10 S. uberis strains was performed and their antimicrobial susceptibility was assessed. Results: Ten different novel sequence types were found, and genes (tetM, tetO, patB, lnuC, lnuA, lsaE, ermB, ANT(6)-la) and mobile genetic elements previously associated with antimicrobial resistance (repUS43, ISSag2, and ISEnfa4) and virulence (315.2 phage) were detected. Additionally, our strains had the highest relative rate of recombination to mutation (8.3) compared to other S. uberis strains isolated from different continents (America: 7.7, Asia: 2.9, Europe: 5.4, and Oceania: 6.6). Most of the strains (80%) tested showed phenotypic resistance to clindamycin and 70% exhibited intermediate susceptibility to penicillin. Conclusions: The high heterogeneity of strains observed and the presence of genetic factors linked to antimicrobial resistance represent a challenge for the implementation and surveillance of measures focused on the control and elimination of this pathogen. Full article

In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state changes and how temperature affects them. By combining the resulting dataset with satellite-based land-use data, we show the distribution of activities across landscapes and seasons and within a day. We accurately reproduced key aspects of cattle mobility and characterised rarely documented features of Sahel agropastoral practices, such as transhumance phases, nocturnal grazing and in-field rainy season paddocking. These results suggest that our methodology, which we make available, could be valuable in addressing issues related to the future of Sahelian pastoralism. Full article

Background: Lameness in cattle negatively affects welfare and productivity. Early identification of lameness allows for prompt treatment, and mobility scoring allows for herd-level prevalence data to be monitored. The reliability of a four-point mobility scoring system was investigated when used by beef farmers and veterinary surgeons. Methods: An online questionnaire that contained forty video clips of beef cattle was created for mobility scoring performed by farmers and vets. Results: The Fleiss kappa coefficient for inter-observer agreement across all 81 respondents and all videos was 0.34, which showed fair agreement. Beef farmers generally had lower agreement than vets (0.29 vs. 0.38). Vets had significantly higher inter-observer reliability compared to beef farmers (p = 0.035). Overall, Cohen’s kappa coefficient for intra-observer agreement across all respondents varied from 0.085 (slight agreement) to 0.871 (almost perfect agreement). Limitations: The survey was only available online, which may have limited distribution and engagement. The recruitment of participants was not specific to differing levels of previous experience in mobility scoring. The mobility scoring was not performed in person, which could be more reflective of clinical application. Conclusions: The application of a four-point mobility scoring system for beef cattle had fair inter-observer reliability and a wide range of intra-observer reliability, but this is poorer than previously reported. This presents a challenge for the identification of lame beef cattle at both the individual and herd levels. Full article

Caribou (Rangifer tarandus) undergo exceptionally large, annual synchronized migrations of thousands of kilometers, triggered by their shared environmental stimuli. The proximate triggers of those migrations remain mysterious, though snow characteristics play an important role due to their influence on the mechanics of locomotion. We investigate whether the snow melt–refreeze status relates to caribou movement, using previously collected Global Positioning System (GPS) caribou collar data. We analyzed 117 individual female caribou with >30,000 observations between 2007 and 2016 from the Bathurst herd in Northern Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snow pack melt characteristics derived from 37 GHz vertically polarized (37V GHz) Calibrated Enhanced-Resolution Brightness Temperatures (CETB) at 3.125 km resolution. The timing of migration for Bathurst caribou generally tracked the snowmelt onset. The start of migration was closely linked to the main melt onset in the wintering areas, occurring on average 2.6 days later (range −1.9 to 8.4, se 0.28, n = 10). The weighted linear regression was also highly significant (p-value = 0.002, $R^2=0.717$). The relationship between migration arrival times and the main melt onset on the calving grounds ($R^2$ = 0.688, p-value = 0.003), however, had a considerably more variable lag (mean 13.3 d, se 0.67, range 3.1–20.4). No migrations ended before the main melt onset at the calving grounds. Thawing conditions may provide a trigger for migration or favorable conditions that increase animal mobility, and suggest that the snow properties are more important than snow presence. Further work is needed to understand how widespread this is and why there is such a relationship. Full article

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60–100%), and specificities (95–100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are <100%. This effect increases with more frequent scoring. Lameness prevalence was the only parameter that could be estimated with reasonable accuracy when simulating quarterly mobility scoring. These findings can help inform mobility scoring practices and the interpretation of mobility scoring data. Full article

This longitudinal study aimed to quantify the effects of dietary supplementation of a direct-fed microbial (DFM) consisting of three lactobacilli isolates on milk yield, milk fat and protein yields, somatic cell count (SCC), and liveweight in a single dairy herd in Australia. A total of 150 dairy cows were randomly selected based on parity and days in milk and divided into two groups: control (n = 75) and DFM treatment (n = 75). Throughout the study, the two groups of cows were housed separately in a dry lot yard, and each group had their own feeding area. For the DFM treatment group, selected cows in mid-lactation were supplemented with 10 mL/cow/day of the DFM via top dressing of the feed for the remainder of the lactation and through the dry period, extending into subsequent lactation. The control group had no supplementation. The milk yield and liveweight were recorded daily. Milk samples were collected every two months for milk component analysis (fat, protein, and somatic cell count [SCC]). The DFM-treated cows gained more liveweight across the study (19.40 kg, 95% CI 0.44 kg; 38.30 kg, p = 0.05) compared to the control cows. In the second production year, the DFM-treated cows mobilized more liveweight (−6.06 kg, 95% CI −10.49 kg; −1.61 kg, p = 0.01) and produced more milk (0.39 L/d 95% CI 0.10; 0.89, p = 0.05). Over a full lactation, DFM cows yielded at least 258 L (95% CI 252 L; 265 L) more milk than controls. No significant differences were found in fat and protein yield or SCC. This study suggests that consistent and ongoing supplementation with a Lacticaseibacillus- and Lentilactobacillus-based DFM could have a positive effect on milk production, but further research is needed to understand the underlying mechanism. Full article

This review paper provides an in-depth analysis of three critical metabolic diseases affecting dairy cattle such as subacute ruminal acidosis (SARA), ketosis, and hypocalcemia. SARA represents a disorder of ruminal fermentation that is characterized by extended periods of depressed ruminal pH below 5.5–5.6. In the long term, dairy herds experiencing SARA usually exhibit secondary signs of the disease, such as episodes of laminitis, weight loss and poor body condition despite adequate energy intake, and unexplained abscesses usually 3–6 months after an episode of SARA. Depressed milk-fat content is commonly used as a diagnostic tool for SARA. A normal milk-fat test in Holstein dairy cows is >4%, so a milk-fat test of <3% can indicate SARA. However, bulk tank testing of milk fat is inappropriate to diagnose SARA at the herd level, so when >4 cows out of 12 and <60 days in milk are suspected to have SARA it can be considered that the herd has a problem. The rapid or abrupt introduction of fresh cows to high-concentrate diets is the most common cause of SARA. Changes in ruminal bacterial populations when exposed to higher concentrate rations require at least about 3 weeks, and it is recommended that concentrate levels increase by no more than 400 g/day during this period to avoid SARA. Ketosis, a prevalent metabolic disorder in dairy cattle, is scrutinized with a focus on its etiological factors and the physiological changes leading to elevated ketone bodies. In total mix ration-fed herds, an increased risk of mastitis and reduced fertility are usually the first clinical signs of ketosis. All dairy cows in early lactation are at risk of ketosis, with most cases occurring in the first 2–4 weeks of lactation. Cows with a body condition score ≥3.75 on a 5-point scale at calving are at a greater risk of ketosis than those with lower body condition scores. The determination of serum or whole blood acetone, acetoacetate, beta-hydroxybutyrate (BHB) concentration, non-esterified fatty acids (NEFA), and liver biopsies is considered the best way to detect and monitor subclinical ketosis, while urine or milk cowside tests can also be used in on-farm monitoring programs. Concentrations >1.0 mmol/L or 1.4 mmol/L blood or serum BHB are considered diagnostic of subclinical ketosis. The standard threshold used for blood is 1.2 mmol/L, which corresponds to thresholds of 100 mcmol/L for milk and 15 mg/dL for urine. Oral administration of propylene glycol (250–400 g, every 24 h for 3–5 days) is the standard and most efficacious treatment, as well as additional therapy with bolus glucose treatment. Hypocalcemia is a disease of adult dairy cows in which acute hypocalcemia causes acute to peracute, afebrile, flaccid paralysis that occurs most commonly at or soon after parturition. Dairy cows are at considerable risk for hypocalcemia at the onset of lactation, when daily calcium excretion suddenly increases from about 10 g to 30 g per day. Cows with hypocalcemia have a more profound decrease in blood calcium concentration—typically below 5.5 mg/dL. The prevention of parturient paresis has been historically approached by feeding cows low-calcium diets during the dry period. Negative calcium balance triggers calcium mobilization before calving and better equips the cow to respond to the massive calcium needs at the onset of lactation. Calcium intake must be limited to <20 g per day for calcium restriction to be effective. The most practical and proven method for monitoring hypocalcemia is by feeding cows an acidogenic diet for ~3 weeks before calving. Throughout the review, emphasis is placed on the importance of early diagnosis and proactive management strategies to mitigate the impact of these metabolic diseases on dairy cattle health and productivity. The comprehensive nature of this paper aims to serve as a valuable resource for veterinarians, researchers, and dairy farmers seeking a deeper understanding of these prevalent metabolic disorders in dairy cattle. Full article

Peste des petits ruminants (PPR) is a highly contagious viral disease and one of the deadliest affecting wild goats, sheep, and small ruminants; however, goats are generally more sensitive. The causative agent is the Peste des Petits Ruminants virus (PPRV), which is a single-stranded RNA virus of negative polarity belonging to the Paramyxoviridae family. In February 2020, an active outbreak of PPR was reported in a herd of a transhumant farmer in the village of Gainth Pathé (department of Kounguel, Kaffrine region, Senegal). Of the ten swabs collected from the goats, eight returned a positive result through a quantitative real-time PCR. The sample that yielded the strongest signal from the quantitative real-time PCR was further analyzed with a conventional PCR amplification and direct amplicon sequencing. A phylogenetic analysis showed that the sequence of the PPR virus obtained belonged to lineage IV. These results confirm those found in the countries bordering Senegal and reinforce the hypothesis of the importance of animal mobility between these neighboring countries in the control of PPRV. In perspective, following the discovery of this lineage IV in Senegal, a study on its dispersion is underway throughout the national territory. The results that will emerge from this study, associated with detailed data on animal movements and epidemiological data, will provide appropriate and effective information to improve PPR surveillance and control strategies with a view to its eradication. Full article