Abstract:Background: This study explores the integration of digital forensic technologies – including online trial broadcasting – into Kazakhstan’s criminal justice system as a key driver of legality, rights protection, and judicial transparency. Drawing on Kazakhstan’s criminal procedure legislation and recent digital transformation initiatives, the authors examine the procedural framework governing digital evidence and prosecutorial responsibilities in a digital environment. Through comparative analysis with advanced international practices, the study identifies effective models for managing electronic evidence, incorporating artificial intelligence (AI), and conducting online trial broadcasts. The article reviews scholarly literature on digital forensics, AI applications in criminal justice, and the challenges of live-streaming court proceedings. Empirical data from Kazakhstan’s judiciary highlight both achievements and persistent legal gaps, particularly in regulating online broadcasts of high-profile criminal cases. Special attention is given to international approaches that successfully balance transparency, data privacy, and procedural fairness – serving as benchmarks for Kazakhstan’s ongoing reforms. From the analyzed sources, the authors extract key theoretical and practical insights to shape a comprehensive prosecutorial model tailored to Kazakhstan’s digital realities. This model aims to safeguard individual rights and public interests, ensure the admissibility and integrity of digital evidence, enhance prosecutorial decision-making through AI tools, and promote judicial openness via regulated online broadcasting. Strategic recommendations are proposed for legislative reform, technological integration, and capacity-building to strengthen prosecutorial effectiveness in Kazakhstan’s digital judicial transformation. Methods: To achieve the research objectives, the authors employed a multi-methodological approach, including: − Descriptive legal analysis to examine Kazakhstan’s legislative framework on digital and forensic technologies in criminal procedure, with emphasis on prosecutorial roles and the regulation of electronic evidence and online broadcasting. − Comparative legal analysis to identify and assess international best practices in digital evidence management, AI integration, and online trial broadcasting, evaluating their relevance to Kazakhstan’s legal and technological context. − Legal modeling to propose scenarios for integrating advanced forensic tools and AI systems into prosecutorial procedures, aiming to optimize evidence handling, enhance transparency, and uphold legal safeguards. − Empirical case studies of Kazakhstan’s judicial practices involving digital evidence and online broadcasts, revealing legislative gaps, procedural challenges, and technological limitations. This comprehensive methodology supports the development of targeted recommendations for reform and modernization within Kazakhstan’s criminal justice system. Results and Conclusion: the study recommends establishing a national digital evidence management platform incorporating AI and blockchain verification, alongside legal procedures for their use and unified cybersecurity standards. Forecasts suggest these measures could reduce criminal case durations by 20–25%, improve evidence analysis accuracy to 90%, and enhance public trust in the justice system. Adapting global best practices to Kazakhstan’s context provides a strategic foundation for modernizing criminal justice and reinforcing the role of public prosecutors.
Abstract:Studying the atmospheric environmental effects of the spatial structure of green innovation in urban agglomerations is important for the governance of atmospheric pollution in urban agglomerations. Based on panel data from 19 urban agglomerations in China from 2008 to 2022, we used a fixed-effects model to analyze the impact of the spatial structure of green innovation in urban agglomerations on atmospheric pollution. The research findings were as follows: First, a polycentric spatial structure of green innovation in urban agglomerations tended to have a more inhibitory effect than a monocentric structure on atmospheric pollution. Second, the atmospheric environmental effects of the spatial structure of green innovation were heterogeneous across different urban agglomerations. Third, upgrading the industrial structure proved to be the path through which the spatial structure of green innovation in urban agglomerations influenced atmospheric pollution.
Abstract:The increasing prevalence of water scarcity has emphasized the urgent need for effective management of water distribution systems, particularly in urban areas. To achieve rational management of facilities and ensure long-term customer comfort, water utilities on the worldwide are seeking solutions to find a good compromise to avoid the major problem of leaks in distribution networks. Several techniques exist to address this problem, including pressure modeling of distribution networks, which has become a widely used technique for reducing leak rates and extending the lifespan of installations. This work consists of proposing a methodology for optimizing leaks in a drinking water supply network in Bologhine city using hydraulic models and stochastic methods. Stochastic model namely BAT algorithm was employed to simulate various scenarios of demand fluctuations and pressure changes, allowing us to assess the risk and occurrence of leaks under different operational conditions. The optimization of pressure control was achieved by implementing a strategic pressure management system that dynamically adjusts pressure settings based on real-time demand data and identified vulnerabilities within the network. By employing these methodologies, cities like Bologhine can enhance their infrastructure resilience, reduce water loss, and promote sustainability in urban water systems.
Abstract:The current research examined the catalytic efficiency of ash derived from date palm fronds for the production of biodiesel by the ethyl transesterification of waste cooking oil. The optimal conditions for synthesizing the catalyst were determined by subjecting date palm fronds to various calcination temperatures (ranging from 300 to 700 ◦C). The catalyst produced was subjected to characterization using thermogravimetric analysis (TG-DTG), X-ray diffraction (XRD), BET, and scanning electron microscopy (SEM). The ideal parameters were as follows: a temperature of 85 ◦C, a molar ratio of alcohol to oil of 9:1, a catalyst concentration of 7% (w/w), and a reaction period of 1.5 hour. These conditions led to the formation of biodiesel containing 81% ester content. The reusability of the catalyst was evidenced by its sustained catalytic performance, achieving an ester content exceeding 78% during the initial two reaction cycles. A sustainable and effective catalyst for biodiesel manufacture, the synthesis of a catalyst from date palm fronds has numerous benefits: it is low cost, readily synthesizable, and derived from generally accessible biomass waste.
Abstract:This research aimed to investigates the recycling of date seeds, which is a real practical example of the sustainable and integrated use of renewable material resources, providing innovative and sustainable solutions that can contribute to reducing environmental waste and maximizing the use of available natural resources. Highlighting the role of date seeds in supporting local industries as sustainable and safe alternative to cosmetics and pharmaceuticals. It illustrates the economical and medicinal value of date seeds waste as a natural source of active compounds. Date seeds oil was extracted, and it applications in cosmetics and nutrition were studied along with phytochemical screening tests, antioxidant and approximate analysis. Three types of extracts were prepared: methanol, aqueous, and oily. The aqueous extract was found to be rich in flavonoids (+4), tannins (+3), terpenoids (+3), saponins (+2) and absent of alkaloids (-). The approximate analysis showed moisture content (11.70%), ether extract (5.23%), fiber (75.3%), protein (8.7%), and ash (37.5%). The radical's percentage inhibition (PI) of DPPH was significantly obtained by dose IC50 = 1000 µl. Date seeds aqueous extract, with IC50 (80%) have values of 38.3, 78.9, 90.1, and 95.2 mg/mL, which contribute to its strong DPPH radical scavenging activity. Date seed oil was found to contain a high percentage of saturated and unsaturated fatty acids, phenolic compounds, and antioxidant properties, which are used in many industries, such as cosmetics (skin and hair cream, soap, eyeliner, glycerin, and coarse and smooth sandpaper), nutrition, date kernel molasses and raw material for animal feeds.
Abstract:Background: The study analyzes the mechanisms for protecting public law interests in criminal cases involving the return of property assets, as a factor in ensuring the *accessibility and effectiveness of justice, through traditional methods and tactics of financial investigations, as well as using the capabilities of artificial intelligence technology. The authors rely on the provisions of the criminal procedure legislation of the Republic of Kazakhstan, which reflect the types and features of financial investigations, with an emphasis on the impact on the effectiveness and accessibility of justice. By identifying the mandatory features and elements of a financial investigation under the legislation of Kazakhstan, parallels are drawn with other types of financial investigations provided for in developed Western countries. The directions of using artificial intelligence tools in financial investigation are proposed. The works of scientists devoted to the analysis of the effectiveness of the institute of financial investigation in the return of illegally acquired assets are considered. The authors present in the article practical and empirical data available on the territory of the Republic of Kazakhstan in the context of the issue under consideration. Special attention is paid to international experience in implementing measures aimed at developing financial investigations, as well as criminal procedure standards and the experience of countries such as the Netherlands and Estonia. From all the works of scientists, a selection of those provisions has been made that, according to the authors, contribute to the formation and improvement of the Kazakh model of the institution of a full-fledged financial investigation, which allows protecting public interests in the context of ensuring the effectiveness and accessibility of justice. Metods: The authors used a number of methods of scientific knowledge to achieve their goals and solve research problems, in particular, such methods as legal analysis (descriptive), comparative legal analysis, the method of legal modeling, as well as the method of comparative research. The analysis of the criminal procedure legislation of the Republic of Kazakhstan and institutional mechanisms for asset recovery was used to identify the specifics of the financial investigation in the context of the Kazakh experience. This analysis allows us to identify the features of financial investigations in the Republic of Kazakhstan, compare them with best practices, predict and suggest ways to introduce artificial intelligence technologies into these processes. The comparative legal analysis made it possible to compare models and procedures for the use of financial investigations in Western countries and in Kazakhstan, to formulate proposals for possible extrapolation. Attention is drawn to the doctrinal analysis of the concept of financial investigation, the author's approach to its understanding is highlighted in accordance with the FATF recommendations, which is of particular value for Kazakhstan. The formulated author's position can be adapted to improve the legal system of Kazakhstan, including as one of the factors ensuring the effectiveness and accessibility of justice through the protection of public interests. Specific cases have been studied within the framework of the procedure for the return of illegally acquired property, considered by the authorized bodies of the Republic of Kazakhstan, an analysis of legislation has been carried out, which has revealed the main trends in law enforcement practice that affect the effectiveness and accessibility of justice through the prism of ensuring public interests. A comprehensive study of the problem at various levels revealed the imperfection of the current model of the Kazakh institute of financial investigation and legal protection of public interests in the framework of the return of illegally acquired assets through the prism of ensuring the effectiveness and accessibility of justice. Results and Conclusion: Creating conditions for a fair system for the return of illegally acquired assets (including using traditional investigative techniques and tactics, as well as using artificial intelligence technologies), ensuring effective and public financial investigations are among the factors ensuring the protection of public interests not only of the state, but also of society as a whole, and this Ultimately, it is one of the factors important in ensuring the effectiveness and accessibility of justice in cases of this category. The creation of an effective tool for the protection of public law interests makes it possible to provide a correct legal assessment of the activities of law enforcement agencies in returning illegally acquired assets to the country.
Abstract:This study addresses issues of slow detection and reduced accuracy in existing YOLO models for construction sites by proposing AL-YOLO, an improved lightweight helmet detection model. First, AL-YOLO introduces Partial Convolution on top of the YOLOv8 model, which reduces the number of parameters of the model and allows the network to obtain more features containing detailed information. Secondly, the SimAM attention mechanism is introduced to enhance the algorithm's ability to extract high-level semantic information and enable the algorithm to find the strongest correlation between the similarity of features. Finally, considering the category imbalance and model regression bias in the helmet wear detection dataset, the Wise-IoU loss function is introduced to improve the accuracy of bounding box selection and localization speed. The experimental results show that: on the public dataset SHWD, AL-YOLO improves the detection speed of AL-YOLO model by 18.2% and the detection accuracy by 1.8%comparedwith the YOLOv8 model.
Abstract:Intelligent fault detection in photovoltaic (PV) systems plays a critical role in ensuring sustainable energy production and system reliability. This can be done using infrared thermal imagery from the Infrared Solar Modules dataset. In realworld, imbalanced dataset of low-resolution images may exist. This work applies artificial intelligence and image processing techniques to classify anomalies in solar panels. To address the class imbalance challenge, a convolutional neural network (CNN)-based model was developed alongside a comprehensive preprocessing pipeline, including data augmentation, class weighting, and undersampling of dominant classes. Model performance was evaluated using loss, accuracy, AUC-PR, F1-score, and G-Mean to ensure a robust and fair assessment. The results demonstrate that targeted preprocessing significantly improved generalization and minority class recognition, reducing false positives and negatives and enabling more balanced predictions. Nonetheless, persistent misclassifications highlight the need for further dataset enrichment, advanced architectures, and real-time integration. The findings reinforce the value of AI-driven diagnostics for PV monitoring while underscoring the importance of data quality and tailored pre-processing strategies.
Abstract:Background: Neonatal organ injuries significantly exacerbate the burden of neonatal death and disability. This study investigates sex-specific risk factors and patterns of neonatal organ injuries, taking into account both clinical and maternal factors. Methods: We conducted a retrospective analysis of 155 mother-infant pairs admitted to Sichuan Provincial Maternity and Child Health Hospital (2020-2022). Univariate and multivariate analyses, including restricted cubic spline models, were performed to identify risk factors. Sex-specific subgroup analyses and correlation studies of organ injuries were also conducted. Results: Key results revealed distinct sex-based predictors. For male neonates, gestational age (OR=1.41, 95% CI: 1.13-1.87, P=0.007) and birth weight (OR=1.00, 95% CI: 1.00-1.00, P=0.008) were significantly associated with organ injury risk. For female neonates, initial C-reactive protein levels showed a significant association (OR=1.14, 95% CI: 1.01- 1.31, P=0.045). Strong correlations were found between various types of brain injuries, with cerebral infarction showing high co-occurrence with hemorrhagic injuries (OR range: 60.77-117.81, P<0.001). Conclusion: Our findings highlight the importance of sex-specific approaches in neonatal care and risk assessment. The complex interplay between different organ injuries, particularly in the brain, underscores the need for comprehensive monitoring strategies in neonatal intensive care units. These results provide a foundation for developing targeted preventive measures and individualized care protocols.
Abstract:Background: Colorectal cancer (CRC) is a major global health problem. In the Western Pacific Region, its incidence is rising, especially among the elderly. Metabolic risk factors like high fasting plasma glucose (FPG) contribute to CRC burden, yet no study has explored this in the western pacific region's elderly. Also, the relationship between CRC and human resources for health (HRH) in this region is unknown. Methods: Analyzed CRC burden due to high FPG in 31 western pacific region countries by Global Burden of Disease Study 2021, including trends, age-specific trends, SDI correlations, decomposition analysis, and HRH-burden relationship. Results: In 2021, China had the highest CRC burden related to high FPG, Tokelau the lowest. From 1990-2021, the burden increased significantly in most countries. In most countries, the burden of disease is highest in people aged 60-69 years, with the most rapid increase in those aged 95 years and older. The correlation with SDI weakened over time. Population growth was the main driver of burden increase. Overall HRH density showed a positive correlation with CRC burden, with the greatest associations found in “Aides & Emergency Medical Workers” and “Nursing & Midwifery Professionals.” Conclusions: The CRC burden due to high FPG in the western pacific region's elderly has increased. Factors like population growth, epidemiological changes, and aging play roles. The complex relationships with SDI and HRH offer insights for public health policies. These findings underscore the urgent need for integrated public health strategies targeting metabolic risk factor control in the elderly, alongside critical evaluation and optimization of health workforce deployment and roles to effectively address the rising CRC burden in the region.