Healthcare IT underwent a revolution through artificial intelligence (AI) together with high-performance computing which particularly enhances diagnostics along with intelligent data processing operations. The use of AI-powered computing racks delivers exceptional speed alongside efficiency for handling large-scale medical data which leads to faster diagnoses and real-time patient observation and precise medical treatments. This paper studies how AI-powered computing racks redefine healthcare IT operations through their ability to boost computational power and generate more accurate diagnoses along with optimizing data management systems in hospital facilities and research facilities. The research uses actual medical studies together with machine learning methods and high-performance computing models to analyze how AI-powered racks affect medical IT infrastructure. It follows a quantitative data-oriented methodology. The study explores methods that these systems apply to maximize medical imaging analysis and electronic health records management while implementing advanced AI-based protection protocols to meet requirements from HIPAA and GDPR. AI-powered computing racks decrease diagnostic process durations by 40% while raising medical image precision to 30% and improving healthcare IT operational effectiveness by 45% compared to standard computing hardware solutions. The racks incorporate AI cybersecurity tools that both find irregularities and shield data infrastructure from cyber dangers to maintain secure database operations. The study enhances AI in healthcare IT knowledge while developing guidelines for hospital and research facility integration of AI-powered computing racks. This study introduces novel research through its real-time data processing system design along with deployment potential which leads to better healthcare operational efficiency and improved patient results.

This article provides an in-depth study of food safety criteria in the processes of production, storage, and distribution of flour and flour products in the regions of Uzbekistan. The research analyzes the requirements of national ( O'zDSt , GOST ) and international (ISO, HACCP) standards, identifying key differences between them.

The study revealed that standards applied in Uzbekistan are mainly based on organoleptic and physicochemical indicators, whereas international standards focus on ensuring safety and quality control at all stages of the production process. The automated necessity of introducing modern technologies and monitoring systems in production, storage, and transportation processes has been substantiated.

As a result, specific recommendations were developed to align Uzbekistan's flour industry with international standards, including technological modernization, certification, strengthening sanitary and hygiene requirements, and enhancing export potential. These proposals aim to improve the quality of local flour products and ensure their competitiveness in the global market.

Seed priming, a pre-sowing treatment, enhances germination and seedling establishment in various crops, particularly cereals. This review examines the role of chemical priming agents in modulating germination responses in cereal crops. We explore the physiological and biochemical mechanisms involved, focusing on how specific chemical compounds improve germination rate, uniformity, and stress tolerance. The impact of chemical priming on crop establishment under both optimal and adverse environmental conditions is discussed, along with its implications for agricultural productivity.

Striga hermonthica remains one of the main constraints to pearl millet production in Côte D’ivoire. The aim of the study was to identify the most effective modes and periods of application of local products such as compost and Parkia biglobosa fruit powder against its infestation. Thus, a study was carried out using a susceptible pearl millet variety to Striga hermonthica. The experimental design was randomized complete block design with 16 treatments in three replications. Principal component analysis (PCA) and ascending hierarchical classification (AHC) revealed four groups of treatments. The first group, comprising four treatments (T1, T2, T3 and T4), was characterized by good development and a high number of Striga hermonthica plants per plot (36.01 cm in height and 36.86 plants). Group 2, comprising three treatments (T8, T9 and T13), was characterized by late emergence (65.96 days after sowing) and a low number of Striga hermonthica plants per plot (15.92 plants). Group 3 was made up of six treatments (T0, T5, T6, T11, T12 and T14) which showed no particular effect on the variables studied. Group 4, comprising three treatments (T7, T10 and T15), was characterized by good millet ear characteristics (12.80 g per 1 ear and 7.27 g grain weight per ear). Thus, the treatments in groups 2 and 4 proved to be more effective in managing Striga hermonthica infestation in millet.