MEF University

Varicellovirus bovinealpha1 (BoAHV-1) is among the most important infectious agents affecting cattle health and productivity. This study aimed to determine the seroprevalence of BoAHV-1 infection in Türkiye and estimate its economic impact using a meta-analytical approach. A total of 39 studies comprising data from 5,219 animals were included in the meta-analysis. Three analytical groups were established based on sample size and study scale: the small-scale group (SSG), large-scale group (LSG), and herd-level group (HLG). Random effects models were used for prevalence estimation, and heterogeneity was assessed using the Q, I2, and τ2 statistics. Publication bias was evaluated via Egger’s regression and Kendall’s Tau tests. The pooled prevalence was estimated at 0.320 for the SSG group, 0.427 for the LSG group, and 0.356 for the HLG group. The highest level of heterogeneity was identified in the LSG group (I2 = 99.76%). The economic impact of BoAHV-1 was evaluated under two distinct scenarios—one incorporating abortion-related losses and the other excluding them. Principal components of loss included reduced milk production, live weight reduction, delayed onset of first calving, prolonged calving intervals, and losses attributable to aborted calves. The average total economic loss per infected animal was estimated to range from 285.18 to 383.21 USD. These findings suggest a dynamic epidemiological pattern and a potentially high economic burden of BoAHV-1 infection in Türkiye, highlighting the need for effective herd-level control and prevention measures.

Abstract Prior research highlights the independent roles of neighborhood and psychosocial risk and protective factors for accelerated cognitive decline. However, the combined role of risk and protective neighborhood and psychosocial factors for cognitive aging among middle-aged and older Latinos in the U.S. remains unclear. Utilizing data from Latinos aged 50 and older (n = 2,321) in the Health and Retirement Study (2006 - 2016), this study aimed to examine the role of neighborhood (perceived physical disorder, neighborhood social cohesion) and psychosocial (loneliness) factors in shaping baseline cognitive function and change in cognitive function. Linear mixed models were used to estimate baseline and rate of change in cognitive function, adjusting for sociodemographic factors. Neighborhood social cohesion was positively associated with baseline cognitive function (p < 0.001), while perceived neighborhood social disorder and loneliness were both negatively associated with baseline cognitive function (p < 0.001). However, none of these factors were significantly associated with cognitive trajectories over time. These findings highlight the role of psychosocial and neighborhood risk and protective factors in baseline cognitive function among Latino adults. To promote healthy aging, health promotion efforts should consider implementing culturally relevant strategies to strengthen neighborhood social cohesion and reduce loneliness.

In this paper, we address a charger-assisted unmanned aerial vehicle (UAV) application in which the UAV must follow a long, predefined route composed of an ordered sequence of points of interest. To support the UAV's limited flight range, an unmanned ground vehicle (UGV) serves as a mobile charger and can travel to designated charging locations to recharge the UAV's battery. Our objective is to generate a rendezvous schedule-that is, to determine where (locations) and when (times) the UAV and UGV should meet for recharging. The schedule is subject to the constraint that recharging may only occur at predefined charging sites, and that the UAV's flight time between consecutive charges must not exceed its maximum range. We formulate the scheduling problem as a minimum-time optimal control problem, and demonstrate how its specific properties can be exploited to enable efficient solution via dynamic programming. The resulting algorithms are shown to be suitable for real-time applications across a wide variety of mission scenarios, accounting for factors such as landing type (docked or dockless), muling (enabled or disabled), charging time variability (fixed or varying), and final location constraints. Our approach scales effectively to large missions while ensuring mission time optimality.

This study presents a scalable multimodal Artificial Intelligence (AI) and Machine Learning (ML) framework designed to enhance decision making in the fashion industry. The proposed system integrates garment segmentation, multimodal feature extraction, and similarity recommendation into a unified pipeline. Using Segformer for segmentation, along with the convolutional neural network (CNN)-based feature extraction models ResNet152V2 and Xception, and the transformer-based vision-language model LLaVA, the framework generates visual and semantic embeddings of garments. These representations are processed through similarity detection using OpenAI embedding models and stored in the Pinecone vector database for efficient retrieval. Real-time similarity scoring is enabled through FastAPI endpoints, offering interactive search capabilities. Preliminary results demonstrate the system's strong ability to identify conceptually and visually similar items across a large catalog, providing actionable insights for designers. This framework lays the groundwork for intelligent, interpretable, and production-ready AI systems in the fashion industry.