Search Results (890)

The integration of renewable energy sources has become a strategic necessity for sustainable energy management and supply security. This study evaluates the performance of eight metaheuristic optimization algorithms in scheduling a renewable-based smart grid system that integrates solar, wind, and battery storage for a factory in İzmir, Türkiye. The algorithms considered include classical approaches—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), the Whale Optimization Algorithm (WOA), Krill Herd Optimization (KOA), and the Ivy Algorithm (IVY)—alongside hybrid methods, namely KOA–WOA, WOA–PSO, and Gradient-Assisted PSO (GD-PSO). The optimization objectives were minimizing operational energy cost, maximizing renewable utilization, and reducing dependence on grid power, evaluated over a 7-day dataset in MATLAB. The results showed that hybrid algorithms, particularly GD-PSO and WOA–PSO, consistently achieved the lowest average costs with strong stability, while classical methods such as ACO and IVY exhibited higher costs and variability. Statistical analyses confirmed the robustness of these findings, highlighting the effectiveness of hybridization in improving smart grid energy optimization. Full article

Vaccination has resulted in substantial public health benefits for human populations worldwide since it was first introduced more than a century ago. This article presents an overview of the history of vaccine development, its implementation, and price setting, the latter mainly from a developed world perspective. It considers potential issues and challenges. Over time, vaccine development and production has evolved to a market-driven approach, conducted largely by private commercial entities. The complex processes of identifying potential vaccine targets and developing and producing vaccines at scale have now become more efficient. However, vaccine pricing is an emerging concern. The elements that maximize the overall health benefit of vaccination include high volume, high coverage, and rapid initial implementation to achieve the high coverage with the vaccine as quickly as possible. It therefore requires substantial initial investment. Consequently, the price set for the vaccine should be reasonable to avoid limiting the coverage given the available budget. Suboptimal coverage leads to suboptimal benefit if herd protection is not fully achieved. This may disappoint health authorities and may result in program discontinuation. Conventional cost-effectiveness analysis is therefore not ideally suited to vaccine price setting, as it is based on the concept of ‘more for more’, i.e., higher health gain achieved at a higher reimbursement cost that does not account for limited budgets. Constrained optimization (CO) combines value assessment with constrained budget allocation into one analysis method and may therefore be the better option for vaccine pricing. Full article

Deep learning models have achieved significant progress in developing self-driving algorithms. Despite their advantages, these algorithms typically require substantial amounts of data for effective training. Critical driving data, in particular, is essential for enhancing training efficiency and ensuring driving safety. However, existing methods for identifying critical data often rely on human prior knowledge or are disconnected from the training of self-driving algorithms. In this paper, we introduce a novel data distillation technique designed to autonomously identify critical data for training self-driving algorithms. We conducted experiments with both numerical simulations and the NGSIM dataset, which consists of real-world car trajectories on highway US-101, to validate our approach. In the numerical experiments, the distillation method achieved a test root mean squared error of 1.933 using only 200 distilled training data samples, demonstrating a significant improvement in data efficiency compared to the 1.872 test error obtained with 20,000 randomly sampled training samples. The distilled critical data represents only 1% of the original dataset, optimizing data usage and significantly enhancing computational efficiency. For real-world NGSIM data, we demonstrate the performance of the proposed method in scenarios with extremely sparse data availability and show that our proposed data distillation method outperforms other sampling baselines, including Herding and K-centering. These experimental results highlight the capability of the proposed method to autonomously identify critical data without relying on human prior knowledge. Full article

Postpartum uterine diseases such as metritis and endometritis impair reproductive performance and cause substantial economic losses in dairy cows worldwide. The multifactorial etiology, involving polymicrobial infections and complex host immune responses, poses diagnostic and therapeutic challenges. Traditional treatments rely on antibiotics, e.g., cephalosporins like ceftiofur and cephapirin, with broad-spectrum efficacy. However, emerging antimicrobial resistance, biofilm formation by pathogens such as Trueperella pyogenes, Fusobacterium necrophorum, and Escherichia coli, and bacterial virulence factors have reduced effectiveness of conventional therapies. Advances in systems biology, particularly proteomics, metabolomics, and microRNA (miRNA) profiling, have provided unprecedented insights into the molecular mechanisms underpinning uterine disease pathophysiology. Proteomic analyses reveal dynamic changes in inflammatory proteins and immune pathways, whereas metabolomics highlight shifts in energy metabolism and bacterial–host interactions. Furthermore, miRNAs have critical roles in post-transcriptional gene regulation affecting immune modulation, inflammation, and tissue repair, and also in modulating neutrophil function and inflammatory signaling. Uterine inflammation not only disrupts local tissue homeostasis but also compromises early embryo development by altering endometrial receptivity, cytokine milieu, and oocyte quality. Integration of multi-omics approaches, combined with improved diagnostics and adjunct therapies—including micronutrient supplementation and immunomodulators—offers promising avenues for enhancing disease management and fertility in dairy herds. This review synthesizes current knowledge on proteomics, metabolomics, and miRNAs in postpartum uterine diseases and highlights future directions for research and clinical applications. Full article

Early and accurate prediction of mastitis is crucial in effective herd management and minimizing economic losses. This study investigated the effects of farm-specific factors on the accuracy of mastitis predictions by applying machine learning (ML) models to an automated milking system (AMS) and farm management data. We analyzed a large dataset consisting of 5.88 million observations over the period of 2019–2024 from four dairy farms in Germany. Six ML algorithms were applied to predict mastitis occurrence, with a focus on understanding how farm-specific factors like herd size, management practices, and farm environment may influence prediction accuracy. For training and testing on combined data, the accuracy, sensitivity and specificity ranged between 83 and 92%, 78 and 93% and 83 and 92%, respectively, with an area under curve (AUC) between 91 and 96%. However, under mixed-to-individual farm effects analysis, results exposed weaknesses in the generalization. Models adapted well to internal patterns when analyzing each individual farm separately, reaching very high AUCs of up to 98%, but the results were significantly different again when analyzed with a leave-one-out approach. The analysis determined that data from each farm carries variable underlying patterns, suggesting that a tailored approach to each farm’s unique characteristics might improve mastitis prediction through ML. Full article

A physics-based vehicle–track coupled dynamic model embedding a hydraulic electromechanical regenerative damper (HERD) is developed to quantify electrical power recovery and wear depth in high-speed service. The HERD subsystem resolves compressible hydraulics, hydraulic rectification, line losses, a hydraulic motor with a permanent-magnet generator, an accumulator, and a controllable; co-simulation links SIMPACK with MATLAB/Simulink. Wheel–rail contact is computed with Hertz theory and FASTSIM, and wear depth is advanced with the Archard law using a pressure–velocity coefficient map. Both HERD power regeneration and wear depth predictions have been validated against independent measurements of regenerated power and wear degradation in previous studies. Parametric studies over speed, curve radius, mileage and braking show that increasing speed raises input and output power while recovery efficiency remains 49–50%, with instantaneous electrical peaks up to 425 W and weak sensitivity to curvature and mileage. Under braking from 350 to 150 km/h, force transients are bounded and do not change the lateral wear pattern. Installing HERD lowers peak wear in the wheel tread region; combining HERD with flexible wheelsets further reduces wear depth and slows down degradation relative to rigid wheelsets and matches measured wear more closely. The HERD electrical load provides a physically grounded tuning parameter that sets hydraulic back pressure and effective damping, which improves model accuracy and supports calibration and updating of digital twins for maintenance planning. Full article

Porcine circovirus 2 (PCV2) is still infecting pigs after almost 20 years of massive vaccination all over the world. Vaccines are highly effective at counteracting the clinical signs of systemic disease caused by PCV2 and can significantly reduce the number of subclinically infected pigs. However, current vaccination programs based on one single dose in piglets are insufficient to prevent infection in a proportion of animals. Moreover, systematic vaccination of the herd changes viral epidemiology and, consequently, can cause modifications in infection timing. Such a scenario may prompt intrauterine and piglet early infections, thus facilitating viral circulation even before vaccination takes place. Considering the demonstrated high vaccine efficacy, it would be legitimate to explore the possibility of eliminating PCV2 from swine herds, but only one attempt to eliminate the virus from a herd has been published so far. The present speculative review evaluates the existing scientific literature regarding the feasibility of getting rid of this virus under commercial farm conditions. The use of PCV2 vaccination in all swine populations within a herd and the implementation of regional or national control programs are foreseen as compulsory for the eventual successful elimination of this endemic viral infection. Full article

Given the demand for sustainable and cost-effective manure management in livestock systems, this study evaluated the economic feasibility of cattle manure treatment via composting and anaerobic digestion (AD) under different configurations. Five scenarios were compared: composting without solid–liquid separation, AD without separation at 20- and 30-day hydraulic retention times (HRTs), and combined systems with separation, composting the solid fraction and digesting the liquid. The analysis was based on a 200-cow herd and experimental data, with 15-year projected cash flows. Economic indicators included net present value (NPV), internal rate of return (IRR), discounted payback period (DPP), benefit–cost ratio (B/C), modified internal rate of return (MIRR), uniform annual equivalent (UAE), and profitability index (PI), supported by sensitivity analysis and Monte Carlo simulation. All scenarios were viable and posed low risk. Energy and fertilizer value were key drivers. The scenario 30-day HRT without separation had the best financial performance (NPV = 53,407.15 USD; IRR = 15.54%; DPP = 7.33 years; B/C = 1.57; MIRR = 9.28%; UAE = 5654.48 USD; PI = 1.66) and is recommended for capitalized farms seeking higher returns. Composting had lower returns (NPV = 9832.06 USD) but required the lowest investment, remaining a cost-effective alternative for smallholders. Full article

Understanding generational differences in herding behavior is crucial for policymakers, financial educators, and market regulators, particularly in emerging markets where retail investor participation is rapidly growing. This study investigates the influence of herding behavior on investment decision-making among Generations X, Y, and Z in Indonesia, as well as the moderating role of investment experience. Using a multi-group structural equation modeling (SEM) approach with data from 1293 retail investors, the research compares behavioral tendencies across cohorts. Results reveal that herding behavior has a positive and significant impact on investment decision-making in all generations, with the strongest effect observed in Generation X, followed by Generation Z and Generation Y. Investment experience significantly weakens herding behavior’s influence for Generation X but shows no significant moderating effect for Generations Y and Z, suggesting that psychological and social influences, particularly from digital platforms, may outweigh experiential learning in younger cohorts. These findings align with behavioral finance theory, which explains herding as a cognitive and emotional bias heightened by market uncertainty. The results provide practical implications for designing targeted financial education programs and regulatory measures to promote independent decision-making and reduce susceptibility to biased market information, especially among younger generations in digitally driven investment environments. Full article

Paratuberculosis or Johne’s disease is caused by Mycobacterium avium subsp. paratuberculosis (MAP). The disease is characterized by a chronic and incurable enteritis in ruminants and it is responsible for significant economic losses, also raising concerns about food safety and animal welfare. Effective control is hindered by diagnostic limitations, long incubation periods, and the environmental resistance of the pathogen. This study aimed to reduce the apparent prevalence of paratuberculosis in a single intensive dairy herd through an integrated approach that combines diagnostics and management strategies. All cows over 24 months of age were tested using both fecal PCR and ELISA serology. Digital PCR (dPCR) was used to quantify MAP shedding in fecal-positive animals, enabling prioritization for removal based on environmental contamination risk. Integrating diagnostic tools allowed the precise identification and quantification of high-risk animals. Meanwhile, structural improvements and biosecurity measures were implemented on the farm. Preliminary outcomes suggest a marked reduction in herd-level MAP prevalence, lowering the seroprevalence from 7.6% to 4.5% and the fecal PCR prevalence from 6.5% to 2.8%. This case highlights the effectiveness of combining laboratory testing (serology and molecular diagnostics) and targeted changes in farm management to control paratuberculosis in high-density dairy systems. Full article

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role of FOMO, with both direct and indirect impacts on gold investment decisions. Notably, both loss aversion and herd behavior positively influence FOMO, thereby indirectly encouraging relatively hasty and inadequately considered investment decisions. The study also finds that FOMO has a negative relationship with anticipated regret but is positively correlated with subjective expected pleasure. Furthermore, as determined through Multi-Group Analysis (MGA), psychological messages featuring “self-decision” or “risk warning” demonstrate a significant moderating role, potentially reducing or enhancing the influence of FOMO on investment decisions. These findings contribute to enriching behavioral finance theory and provide an empirical basis for developing effective risk management policies and gold market regulation aimed at mitigating the negative impacts of FOMO. Full article

Assessing the externalities of nature conservation policies, conceived as unintended socio-economic and ecological effects, is essential for evaluating societal costs and improving conservation strategies. This is particularly relevant in the case of wolf conservation and its interaction with traditional pastoralism, an animal farming system that provides valuable ecosystem services but is rapidly declining across Europe. We used structured questionnaires with Spanish herders to evaluate the environmental and societal impacts of livestock-guarding dogs (LGDs) as a measure to prevent wolf attacks. On average, farms with 750 sheep employed five mastiffs and three sheepdogs, with LGD numbers increasing in wolf-abundant areas. The number of mastiffs rose proportionally with herd size (0.6 mastiffs per 100 sheep), whereas sheepdog numbers plateaued. The estimated annual cost per LGD was €364, with LGDs contributing approximately 7% of a farm’s carbon emissions. Sixty-one percent of herders reported minor societal conflicts involving LGDs, primarily dogs chasing pedestrians or cyclists, while 1% involved minor bites. The likelihood of societal conflict increased with the number of LGDs on a farm. Additionally, each LGD caused an estimated 0.71 wildlife fatalities per year, mostly involving small mammals such as rabbits, hares, and young ungulates. These findings highlight the need for urgent coexistence strategies to balance effective wolf conservation with the sustainability of traditional pastoralism. Without targeted intervention, the continued expansion of Spain’s wolf population may increase costs to herders and foster growing public opposition to wolf conservation efforts. Full article

Accurate re-identification of individual cows is crucial for effective herd management in precision cattle farming. However, this task is challenging in real-world scenarios due to variability in cow appearances and environmental conditions as well as the limited number of reference images available for re-identification. This paper addresses the problem of cow re-identification under open-set and few-shot conditions, where the system must recognize previously unseen individuals with limited annotated data. Metric learning was used to train a neural network for re-identification and its performance was evaluated using K-nearest neighbors (KNN). The neural network is applied to two datasets: OpenSetCows2020 and MultiCamCows2024 captured on different farms. Four testing variants are proposed that resemble different real-life situations: initial deployment, barn change, addition of new cows and cross-farm generalization. The results show that the applied model achieves >90% accuracy with 10 reference images on the same-farm dataset, while cross-farm performance either requires 60 or more reference images to reach similar levels or remains below 63% across metrics. The proposed framework directly addresses the challenges in real-world cattle farming, and allows for a more in-depth analysis of the characteristics and applicability of re-identification methods from a practical perspective than existing evaluation metrics. Full article

Tall fescue toxicosis, caused by ingestion of endophyte-infected tall fescue (Lolium arundinaceum), impairs growth and reproduction in beef cattle and results in over USD 3 billion annual loss to the U.S. livestock industry. While the effects on host metabolism and rumen function have been described, the impact on the rectal microbiome remains poorly understood. In this study, we performed whole-genome shotgun metagenomic sequencing on fecal samples collected before and after a 30-day toxic fescue seed supplementation from eight pregnant Angus × Simmental cows and heifers. We generated 157 Gbp of sequencing data in 16 metagenomes, and assembled 13.1 Gbp de novo microbial contigs, identifying 22 million non-redundant microbial genes from the cattle rectum microbiome. Fescue toxicosis significantly reduced alpha diversity (p < 0.01) and altered beta diversity (PERMANOVA p < 0.01), indicating microbial dysbiosis. We discovered significant enrichment of 31 bacterial species post-treatment, including multiple core rumen taxa. Ruminococcaceae bacterium P7 showed an average of 16-fold increase in fecal abundance (p < 0.01), making it the top-featured species in linear discriminant analysis. Functional pathway analysis revealed a shift from energy metabolism to antimicrobial resistance and DNA replication following toxic seed consumption. Comparative analysis showed increased representation of core rumen taxa in rectal microbiota post-treatment, suggesting disrupted rumen function. These findings demonstrate that fescue toxicosis alters both the composition and functional landscape of the hindgut microbiota. Ruminococcaceae bacterium P7 emerges as a promising biomarker for monitoring fescue toxicosis through non-invasive fecal sampling, with potential applications in herd-level diagnostics and mitigation strategies. Full article

Prepartum vaccination of dairy cows against newborn calf diarrhea protects calves during the first weeks of life via the colostrum. Vaccination may also induce non-specific effects (NSEs) beyond antibody production, altering the disease susceptibility and productivity of the vaccinated mother. This retrospective study analyzed herd records and on-site survey data from 73,378 dairy cows on 20 German farms using linear mixed-effects models and random forest algorithms. Management practices and milk yield showed stronger associations with outcomes than vaccination. However, the cows vaccinated with non-live vaccines had increased odds of retained placenta and metritis (OR: 1.5–1.7), as well as endometritis (OR: 3–6), and were 20–24% less likely to conceive than non-vaccinated cows. Among non-live vaccinated cows, those vaccinated 2.5–4 weeks before calving had an 8% higher non-return rate compared to those vaccinated 6–8 weeks prior. Multiparous cows receiving live vaccine components were 1.9 times more likely to conceive, compared to non-live vaccinated multiparous cows. These findings suggest potential NSE of prepartum vaccination on uterine health and fertility. However, this study’s retrospective design limits causal interpretation, and the benefits in calves may outweigh possible adverse effects. Further research should clarify the mechanisms and optimize vaccine timing and composition. Full article