Turan University

Automated road damage detection has become a critical component of intelligent transportation systems, enabling timely infrastructure maintenance and enhanced traffic safety. However, detecting pavement defects such as cracks, potholes, and surface degradation remains challenging due to significant scale variation, irregular geometries, illumination changes, and class imbalance. This study proposes a real-time Multi-Scale Feature Pyramid YOLO architecture designed to achieve accurate and deployment-efficient multi-class road damage detection. The framework integrates hierarchical feature extraction with bidirectional multi-scale fusion to enhance sensitivity to both small and large defects. A decoupled detection head is employed to improve classification-localization balance, while focal loss and small-object emphasis mechanisms address class imbalance and fine-grained crack detection challenges. Comprehensive experiments conducted on a multi-class road damage dataset demonstrate that the proposed model achieves a mAP@0.5 of 0.68 and a recall of 0.81, outperforming several representative real-time detection approaches. Precision-recall analysis, confusion matrix evaluation, and ablation studies confirm the effectiveness of multi-scale feature aggregation and targeted optimization strategies. Qualitative results further illustrate robust detection performance under diverse environmental conditions. The proposed framework provides a practical trade-off between accuracy and computational efficiency, making it suitable for real-world deployment in intelligent road condition monitoring systems.

Depression and suicidality in older adults are major public health concerns worldwide. These phenomena are strongly influenced by social and quality of life factors. The aim of the study was to characterize depressive and suicidal symptoms in older people in the context of quality of life. In this study 76 people aged 60–74 years were examined using interviews and questionnaires, namely: the Mannheim inventory of living conditions in old age (Mannheimer Inventar der Lebensverhältnisse im Alter, MILVA), the geriatric depression scale (GDS-15), Beck hopelessness scale (BHS) and the suicide crisis inventory revised (SCI-2). Data analysis was performed using statistical methods: regression analysis, Student’s T‑test, Pearson correlation. Older people with suicidal thoughts have a lower quality of life, namely by scales of communication and finances, with higher scores of depression and hopelessness. A negative relationship was found between social factors and indicators of a suicidal crisis. Regression analysis showed that activity and communication significantly influence the level of depression in older adults, while communication and financial stability influence the severity of suicidal crisis. Improving the quality of life of older people, social support, increasing activity and communication can help to maintain well-being as well as to prevent depression and suicidal behavior in old age.

Non-invasive blood glucose monitoring remains a major challenge in biomedical sensing due to strong light scattering in biological tissues, physiological variability, and limited signal stability of existing optical methods. Near-infrared (NIR) spectroscopy has attracted significant interest as a promising approach for continuous and painless glucose monitoring; however, many reported systems remain confined to laboratory conditions and lack sufficient experimental validation. In this study, a compact multispectral non-invasive sensor system based on NIR spectroscopy is developed and experimentally validated. A mathematical model of optical absorption in biological tissues, based on the Beer-Lambert law and implemented in the MATLAB/Simulink environment, was used to identify wavelength regions exhibiting favorable sensitivity-stability trade-offs. Based on simulation results, four operating wavelengths (940, 1050, 1200, and 1350 nm) were selected for sensor implementation. The proposed system integrates near-infrared light-emitting diodes, a photodiode with low-noise amplification, an analog-to-digital conversion stage, and a microcontroller-based data acquisition unit. Experimental validation was performed under both in vitro measurements using aqueous glucose solutions and in vivo measurements conducted on the human earlobe in a transmission configuration. The results demonstrate a strong correlation between optical signal attenuation and glucose concentration (r > 0.95), with a relative measurement deviation not exceeding 5% under controlled experimental conditions. The highest sensitivity was observed at 940 nm, while longer wavelengths (1200-1350 nm) provided enhanced signal stability. Digital signal processing enabled noise reduction of approximately 25-30%, improving measurement reproducibility. Overall, the results confirm the feasibility of the proposed multispectral NIR-based sensor as a proof-of-concept platform for non-invasive glucose monitoring and provide abasis for further optimization and extended experimental and preclinical validation studies.

IntroductionThis article examines how parliamentary discourse on artificial intelligence is transformed into legal and regulatory frameworks across different political contexts. It focuses on five jurisdictions—the European Union, the United States, Brazil, Kazakhstan, and the United Kingdom—where recent AI-related legislative and parliamentary developments provide a comparative basis for analysis.MethodsThe study applies a hybrid qualitative design combining a structured comparative review of academic literature, legal acts, and policy documents with a pilot critical discourse analysis of five selected parliamentary episodes from 2023 to 2026. The analysis is based on securitization theory and the concept of digital sovereignty as a discursive project.ResultsThe study identifies distinct semantic cores in each jurisdiction: “risk–fundamental rights” in the European Union, “barriers–dominance” in the United States, “inequality–high risk” in Brazil, “national code–partnership sovereignty” in Kazakhstan, and “dependency lock-in–delayed reflection” in the United Kingdom. The findings show that H2 and H3 are supported, while H1 is not confirmed in its original formulation and requires revision.DiscussionThe article contributes to AI governance studies by proposing an updated five-part typology of AI regulation. It introduces the concept of “sovereignty through partnership” as an alternative to technological autarky and one-sided regulatory borrowing, and conceptualizes “delayed reflection” as a regulatory pattern among established democracies that recognize infrastructural dependency only after it has already emerged.

PurposeIn today's dynamic business environment, Sustainable Human Resource Management has become a key factor in enhancing employee productivity. This study examines the impact of Sustainable Human Resource Management practices, including Care of employees, Flexibility, Care of Environment, Equity and Diversity, and Employee Development and Training, on employee productivity in Kazakhstan's quasi-governmental sector. Special attention is given to the moderating role of employee engagement, which may strengthen or weaken the influence of these human resources initiatives on productivity.Design/methodology/approachThe empirical analysis is based on a survey of 1,068 employees from four Kazakhstani quasi-governmental organizations. The data were analyzed using correlation and regression analysis.FindingsThe results confirmed a significant positive relationship between Care of employees, Care of Environment, and Equity and Diversity with employee productivity, whereas no significant relationship was found between Flexibility, Employee Development and Training, and productivity. Additionally, employee engagement negatively moderated the relationship between Care of employees, Care of Environment, Equity and Diversity, and productivity, but no significant moderating effect was observed for Flexibility and Employee Development and Training.Practical implicationsThese findings contribute to the existing literature on Sustainable Human Resource Management and productivity and have practical implications for human resources professionals in the quasi-governmental sector. The study highlights the importance of adapting human resources strategies based on employee engagement levels to enhance productivity and achieve sustainable organizational growth.Originality/valueThe novelty of this study lies in the use of a contextually adapted combination of indicators reflecting sustainable human resources practices and an analysis of their impact on employee productivity. Furthermore, academic literature lacks research on this topic within the Kazakhstani context, particularly in the quasi-governmental sector. This study thus provides a significant and timely contribution to the advancement of human resources practices in this sector.