РИЗИК ФАКТОРИ ЗА НАРУШЕНА ПЛОДНОСТ КАЈ ВИСОКОМЛЕЧНИТЕ КРАВИ

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of dairy cows has declined during the past 3 decades. Traits such as first-service conception rate and the lenght of the service period are influenced by numerous explanatory factors common to specific farms or individual cows level A one years retrospective study was carried out to identify risk factors affecting the reproductive performance of black-white lactating cows. The data for reproductive, health and production events were obtained from farm reproductive board. The first breeding of haifers was at average on 479.48±78.521 days of age, and the first calving was at average on 756.99±78.661 days of age. The first service conception rate was 97.30%. The average number of days in lactation to conception was 112.10±56.262. The annual calving rate was 47.42%, the high calving rate was evidented in the winter calving season (57.73%), and the low was evidented in the summer calving season (42.80%). The average lenght of calving interval was 437.35±101.94 days. The average length of the pregnancy was 276.96±27.920 days. The average lenght of the lactation was 320.54±61.698 days. The univariate GLM was used to analyze risk factors responsible for reproductive efficiency. Among the risk factors that were found to affect the lenght of the service period, statistical significance at level p<0,001 showed the lenght of lactation. The cow parity and season of calving did not show statistical significant influence on lenght of the service period

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living