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.
Abstract:Background: Heart failure (HF) is a leading cause of morbidity and mortality in intensive care units (ICUs). While echocardiography is routinely used for prognosis, critical predictors distinguishing survivors from non-survivors remain poorly defined across diverse clinical settings. Machine learning (ML) offers potential to improve risk stratification by identifying key mortality determinants. Methods: In this retrospective study, we analyzed data from two large ICU databases: MIMIC-IV and eICU. Adult patients (>18 years) with HF who underwent echocardiographic evaluation during their ICU stay were included. After feature selection using the Boruta algorithm, we developed and compared 17 ML models to predict in-hospital mortality, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR) and others. Models were internally validated on a test subset of MIMIC-IV data and externally validated on the eICU cohort. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was applied to identify and rank key mortality predictors. Results: A total of 3,485 patients were included, with 2,738 from the MIMIC-IV database and 747 from the eICU database. Thirty-two potential predictors were identified through screening using the Boruta algorithm. Receiver operating characteristic (ROC) curves analysis showed that the RF model consistently demonstrated superior performance across all validation sets and achieved the highest AUC (0.940) in the combined MIMIC-IV and eICU datasets. SHAP algorithm analysis revealed that the clinical features with the greatest impact on model outputs were SAPS II score, OASIS score, SOFA score, age, white blood cell count within the first 24 hours, and first 24-hour blood urea nitrogen. Clinical interpretability analysis indicated that the positive contributions (SHAP values) of these features were 0.115, 0.090, 0.084, 0.046, 0.035, and 0.015, respectively, demonstrating the model's good granularity and transparency in individualized prediction. Conclusions: This study compared 17 prediction models, among which the RF model demonstrated the best predictive performance for mortality in HF patients within the ICU, and may assist clinicians in identifying and managing high-risk HF patients.
Abstract:Background: Periodontal disease is a widespread and severe public health issue globally. Using the latest data from Global Burden of Disease Study (GBD) 2021, this study conducted a systematic analysis of the burden of periodontal disease in China and worldwide from 1990 to 2021. Methods: Data on number and age-standardized rates (ASR) of periodontal disease were extracted from GBD 2021 and stratified analyzed by gender, age group, and geographic location (China and globally). Temporal trends were assessed using joinpoint regression analyses, while the Bayesian age-period-cohort model with nested Laplace approximation was applied to predict the disease burden by 2035. Results: Compared to 1990, the incidence number of periodontal disease in China and globally increased by over 70% in 2021, while prevalence and disability-adjusted life years numbers increased by more than 90%. The ASR of periodontal disease burden decreased in China but increased globally. In terms of age distribution, the burden of periodontal disease initially increased and then declined with age; from 1990 to 2021, the age peak of certain burden-related indicators shifted to older age groups. Concerning gender distribution, the burden was higher among younger and middle-aged men, whereas in women, the burden among middle-aged and elderly populations gradually surpassed that of men. By 2035, the burden of periodontal disease is expected to increase further. Conclusion: Our findings indicate that the burden of periodontal disease continues to rise, which is associated with global population growth, lifestyle and dietary changes, and aging.
Abstract:Background: Asthma is a common chronic inflammatory airway disease. This study assessed the disease burden of asthma in China and globally from 1990 to 2021 and predicted future trends, aiming to provide a evidence for prevention and control strategies. Methods: Using the GBD database, a descriptive analysis of asthma burden data was conducted in China and globally, covering the changing trends of incidence, prevalence, mortality and disability-adjusted life years (DALYs), as well as the distribution characteristics in terms of age and gender. Joinpoint regression analysis was employed to further explore the epidemiological characteristics, and eight machine learning algorithms were fitted to predict the future trends. Results: From 1990 to 2021, the overall burden in both China and globally decreased. However, the global number of deaths increased by 16.51%. In terms of age, children, adolescents, and the elderly (≥60 years) all faced an extremely high burden. Regarding gender, the burden on males in most age groups in China was higher than that on females, while the pattern was reversed globally. Among the eight algorithms, the Autoregressive Integrated Moving Average algorithm performed the best. Predictions show that by 2050, the age-standardized rate of incidence, prevalence, and DALYs of asthma globally will show an upward trend, while the age-standardized mortality rate will decline. In China, the situation is contrary to that of the global trend. Conclusion: Over the past 32 years, although the asthma burden in China and globally has declined, the burden remains substantial. This study recommends strengthening attention to asthma management in children, adolescents, the elderly, and women during critical life stages, optimizing healthcare resources, enhancing international cooperation, and increasing investment in increasing investment in resource-limited regions to alleviate the asthma burden.
Abstract:This study aimed to assess the potential of Commiphora Myrrha (C. Myrrha) extracts (methanol, aqueous, and petroleum ether) as well as their chemical characteristics and medicinal applications. Also, to evaluate antimicrobial, anti-inflammatory, antioxidant, and anti-cancer activity. The chemical structure of C. Myrrha extracts constituents were compounds such as gamma-Terpinene, heerboresene, campesterol, keto steroids, βsitosterol, 3-epi-α-amyrin, α-amyrone, commiferin, commiphorinic acid, and different commiphoric acids. Methanol extract of C. Myrrha showed higher activity than aqueous and oil against microbial organisms. It shows zone inhibition against Candida albicans (15 mm), Aspergillus niger (16 mm), Staphylococcus aureus (15 mm), and Pseudomonas aeruginosa (15 mm). The reason is the presence of phenolic, germacrene D, and pcymene compounds. It has been noticed that phytochemical screening tests of methanolic and aqueous extracts of C. Myrrha are rich in terpenoids and tannins (+3), flavonoids, saponins, and steroids (+2), and alkaloids and cyanogenic glycosides in both extracts are not present. The approximate analysis shows that the methanolic and aqueous extracts demonstrate moisture content (25.33-75.35%), ash (2.20-3.30%), crude protein (8.01- 8.30%), ether extract (3.02-5.35%), crude fibre (10.09-11.00%), and none free extract (4.50-3.50%), respectively. The standard inhibitory activity against colon carcinoma cells was detected with different concentrations (100, 50, 25, 12.5, 6.25, & 3.125 µl/100 µl) with IC50 = 13.19±0.63 µl/100 µl. methanolic and aqueous extracts of C. Myrrha in concentrations (100, 50 µl) induce apoptosis (99.03, 93.86), respectively, and inhibit the proliferation and migration of gastric cancer cells through down-regulating cyclooxygenase-2 expression. The reason is the presence of the furano-sesquiterpene compound. in which, the inhibition percentage of the heat-induced hemolysis of RBCs membrane in the case of aqueous extract is low (58.2%), as for methanol extract is high (95.8%) compared to the standard drug Aspirin (97.4%). Antioxidant activity of aqueous and methanol extracts of C. Myrrha against DPPH radical scavenging activity obtained by dose sample with IC50 = µl/ 1000 µl, were (78.5%) and (98.2%), respectively.
Abstract:Background: Acute respiratory failure (ARF) is a common and severe condition in the intensive care unit (ICU) with a poor prognosis. Transthoracic echocardiography (TTE), a noninvasive and reproducible imaging modality, offers unique advantages for assessing cardiac function and hemodynamic status. However, its impact on outcomes in ARF patients remains unclear. Methods: In this retrospective study using the MIMIC-III database, 3,697 adult patients with ARF were analyzed. Patients were stratified into a TTE group (n = 1,996) and a non-TTE group (n = 1,701) based on whether TTE was performed. To reduce baseline imbalances, we employed propensity score matching (PSM), multivariable logistic regression, inverse probability of treatment weighting (IPTW), and doubly robust estimation to evaluate the association between TTE use and 28-day mortality, as well as secondary outcomes such as duration of mechanical ventilation, vasopressor use, and fluid management. Sensitivity analyses were also conducted. Results: Prior to matching, the TTE group exhibited significantly higher Simplified Acute Physiology Score (SAPS) and Sequential Organ Failure Assessment (SOFA) scores, as well as higher proportions of patients receiving mechanical ventilation and vasopressors, indicating more severe illness compared to the non-TTE group. After PSM, the groups were well-balanced in terms of age, gender, service unit, and key interventions (standardized mean differences < 0.2). Across all statistical models, TTE use was consistently associated with a significant reduction in 28-day mortality (e.g., OR = 0.67, 95% CI: 0.55–0.81, p < 0.001 in the PSM analysis; OR = 0.56, 95% CI: 0.39–0.79, p < 0.001 in the multivariable logistic regression). Sensitivity analyses confirmed these findings. Conclusion: The use of TTE in ARF patients appears to be independently associated with a significant reduction in 28-day mortality, potentially by enabling more precise and timely therapeutic interventions through comprehensive hemodynamic assessment. Future prospective, multicenter studies are warranted to validate these findings and further explore the role of TTE in optimizing clinical management and resource allocation in this high-risk population.