Volume 7.
Issue 05.

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.

This article studies methods of regeneration and their impact on the filtering capabilities of perlite. It was found that increasing alkali concentration up to 40% at various ratios enhances the purification of vegetable oil; however, with further regeneration, the effectiveness decreases. Increasing acid concentration raises the amount of distilled water required, leading to higher costs and inefficient water usage. Regenerated perlite after winterization filters saturated fatty acids because, at lower temperatures, waxy substances and saturated fatty acids begin to crystallize and precipitate with the filtering agent. As a result, the content of unsaturated fatty acids increases in the system.

This article examines modern approaches to the personalization of multimodal content in e-commerce, driven by the growing complexity of user requests and the evident need to adapt recommendations to diverse data formats and content modality. The relevance of this topic is underscored by the increasing volume of  information associated with the article, including text, images, video, and audio, which necessitates the application of specialized methods for precise customization and improved personalization. The purpose of the study is to develop original proposals for optimizing recommendation algorithms based on multimodal information, enabling the consideration of both context and individual user preferences. The research reveals contradictions in the literature—while many studies focus on specific aspects of personalization, such as textual data or visual elements, integrative approaches to the analyzed content are insufficiently addressed. The author proposes solutions combining deep learning methods and behavioral model analysis to achieve more accurate results in predicting audience interests. The materials presented in this work will be useful for e-commerce professionals, developers of recommendation systems, and researchers focused on evaluating behavioral patterns.

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.

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.

The rapid integration of automated driving functionalities into modern vehicles represents a profound transformation in the automotive domain, with Automated Lane Keeping Systems (ALKS) emerging as one of the most prominent and commercially deployed forms of conditional automation. This transformation is not solely technological; it is deeply regulatory, organizational, and methodological in nature. Automated systems increasingly rely on artificial intelligence–driven perception, decision-making, and control mechanisms, which challenge traditional paradigms of functional safety, software lifecycle governance, cybersecurity assurance, and regulatory compliance. This research article presents a comprehensive and theoretically grounded examination of ALKS within the context of international regulatory frameworks, safety standards, and software engineering processes. Drawing strictly on authoritative standards and scholarly references, the study explores the alignment and tensions between UNECE vehicle regulations, ISO and IEC lifecycle standards, Automotive SPICE process maturity models, AUTOSAR adaptive architectures, MISRA coding compliance, and emerging requirements for software updates and cybersecurity management. Using a qualitative, standards-based analytical methodology, the article synthesizes regulatory intent, engineering practices, and safety assurance mechanisms to illuminate how automated lane keeping can be introduced safely into mixed traffic environments. The findings emphasize that compliance alone is insufficient; instead, a systems-thinking approach that integrates safety, cybersecurity, software evolution, and artificial intelligence governance across the vehicle lifecycle is required. The discussion highlights limitations in current frameworks when confronted with learning-enabled systems and outlines future research directions aimed at harmonizing functional safety with adaptive intelligence. This work contributes a holistic academic perspective that supports regulators, automotive manufacturers, and researchers in advancing safer, more resilient automated driving systems.

The pollution of groundwater sources is a major issue globally, particularly for developing countries where the uncontrolled exploitation of natural mineral resources and human activities could lead to the pollution of water resources. The objective of this study was to examine the quality of groundwater in selected mining communities in the Central and Western regions of Ghana. A total of fifty (50) water samples collected from boreholes and wells in five mining communities; Ayanfuri, Abenabena, Nkonya, Forbinso, and Gyamang were analyzed for trace metals and physicochemical properties. The Water Quality Index (WQI) method was used to classify the various samples. Respondents expressed concerns about the smell and salty taste of groundwater. There was also high arsenic (1.028 mg/l and 1.048 mg/L), iron (0.303 mg/L and 0.304 mg/l), and cadmium (0.189 mg/l and 0.191 mg/l) pollution in the study area which requires urgent attention due to the potential adverse human health effects associated with exposure to high levels of these trace metals. The study revealed high turbidity in some groundwater samples in the study area making them unhealthy for domestic use. In the  j WQI classification, all the groundwater samples apart from Nkonya (Nk-w1, Nk-w2, and Nk-site w) which were considered poor water were classified as good water. There is a need to control the levels of arsenic, iron, cadmium, and turbidity levels in groundwater in the study area, particularly in Nkonya. Community residents should be educated on the effects of groundwater pollution.  

This article provides an in-depth study of the chemical and mineralogical composition of kaolin raw material from the Karnab deposit, its industrial applications, and beneficiation methods. The article describes effective techniques for removing harmful impurities present in Karnab kaolin, such as iron and titanium oxides, as well as alkali and alkaline earth metal oxides. The kaolin is bleached and its physical and optical properties are improved through the use of various chemical reagents (e.g., sulfuric, hydrochloric, and organic acids), flotation, hydrothermal, and autoclave processing technologies.

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.