Бұл мақалада білім берудегі цифрлық егіз (ЦЕ) технологияларының қазіргі жағдайы мен даму тенденцияларына шолу жасалады. Ол сондай-ақ университеттердің кибернетикалық білім беру жүйелерінде (КБЖУ) олардың киберқауіпсіздігін (КҚ) қамтамасыз ету мәселелерін қарастырады. Ол ДТ оқытуды дербестендірудің, оқу үдерісінің интерактивтілігін арттырудың, білім берудің қолжетімділігін кеңейтудің және теңшелетін білім беру жүйелерін синтездеудің маңызды құралы ретінде қызмет ететінін көрсетеді. Ғылыми жарияланымдар мен қолданбалы мысалдарды талдау негізінде мақалада виртуалды зертханаларды, VR/AR платформаларын, интерактивті тренажерларды және студенттің цифрлық профильдерін қоса алғанда, білім беру тәжірибесінде қолданылатын ДТ санаттары талданады. Зерттеу ДТ-дағы деректерді қорғауға, құпия ақпараттың ағып кету қаупіне және ДТ үшін кешенді киберқауіпсіздік шараларын енгізу қажеттілігіне бағытталған. КБЖУ ішіндегі сандық егіздің құрылымы сипатталған және қорғанысты қажет ететін негізгі ЦЕ құрамдастары бөлектелген. Ақпаратты қорғаудың ауыспалы әдістерін біріктіретін көп циклды ЦЕ қауіпсіздік жүйелерін жасау қажет деген қорытынды жасалды.

Objective and reproducible diagnosis of attention-deficit/hyperactivity disorder (ADHD) remains challenging because of the limited availability of reliable electroencephalography (EEG) biomarkers and the high variability of neural signals. This study proposes a computational framework for ADHD detection based on dynamic functional connectivity and network-index modeling. Multichannel EEG recordings were transformed into temporal connectivity graphs using sliding-window correlations of band-limited amplitude envelopes. Several network-index models were evaluated, including linear, graph-based, recurrent, and hybrid spatio-temporal approaches. The proposed Hybrid Spatio-Temporal Graph Transformer demonstrated moderate, yet reproducible, subject-level classification performance. On the independent test set, the model achieved an accuracy of 63.16%, a balanced accuracy of 62.22%, a sensitivity of 80.00%, a specificity of 44.44%, an F1-score of 69.57%, and an AUC-ROC of 0.7444. Additional analysis of the derived network index demonstrated moderate intergroup separability, with a mean index shift of 1.16, Cohen’s d = 0.73, Pearson’s r = 0.36, and distribution overlap = 0.72. These findings suggest that the proposed framework captures informative spatio-temporal EEG connectivity patterns associated with ADHD; however, the model’s diagnostic applicability should be considered preliminary and requires validation in larger independent cohorts.

This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance variables. The model incorporates spatially consistent data partitioning, leakage-controlled model development, and probabilistic forecasting to improve the robustness and transferability of groundwater potential assessment. A total of 2402 spatial observations, including 601 groundwater-related locations and 1801 spatially filtered pseudo-absence samples, were used to evaluate eleven machine learning and deep learning models. The proposed hybrid framework achieved the best overall validation results with ROC-AUC of 0.9319, PR-AUC of 0.8363, F1-measure of 0.8226, balanced accuracy of 0.8847, and MCC of 0.7613. Independent spatial block testing further demonstrated strong generalization ability, showing ROC-AUC of 0.9535, PR-AUC of 0.9002, F1-measure of 0.7983, balanced accuracy of 0.8594, and MCC of 0.7336. Comparative experiments demonstrated that the proposed framework remains competitive with state-of-the-art machine learning and deep learning approaches while providing robust probabilistic estimates in spatially separated validation. The resulting groundwater potential maps identify areas with environmental conditions similar to known groundwater observations and provide a reliable basis for prioritizing hydrogeological studies, groundwater exploration, and regional water resources planning in data-poor settings.

This study presents NORIA, an availability-aware multitask learning framework for clinical prediction from incomplete electronic health records (EHRs). We analyzed a public dataset of 77,724 patients and 107 numerical features. To prevent temporal leakage, predictors were grouped by availability, and training-only feature selection identified three baseline and two first-year variables. NORIA jointly predicts baseline and updated glycemic control, an HbA1c change index, and 21 medication labels using shared representations, masked training, and stage-specific prediction updates. Comparators included classical machine learning, gradient boosting, deep tabular models, and multitask architectures (MMoE and PLE). On the internal test set, NORIA achieved AUROCs of 0.8554 and 0.8888 for baseline and updated glycemic control, respectively, and medication macro-AP of 0.1854 versus 0.1879 for CatBoost. The predefined medication improvement criterion was not met. With 20% of baseline features artificially masked, updated-control AUROC declined by 0.0271 for NORIA, versus 0.0510 for CatBoost and 0.0542 for FT-Transformer. Ablation analyses indicated that masked training provided the clearest robustness benefit. NORIA thus demonstrated competitive accuracy and robustness in selected simulated missing-data scenarios, without establishing overall superiority.