Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-dimensional target–horizon problem covering three generation families and five forecast horizons: H1, H3, H6, H12, and H24. A leakage-safe data construction protocol generates causal predictors from meteorological observations, generation history, calendar cycles, lagged and rolling statistics, ramp descriptors, and physics-informed transformations. PI-BHTF partitions the 422-dimensional predictor space into solar, wind, temporal-calendar, and cross-context components, encodes them through parallel nonlinear branches, and combines the representations using cross-energy gated fusion. Its neural core uses a heteroscedastic predictive head and Monte Carlo dropout to distinguish input-dependent aleatoric uncertainty from epistemic variability, whereas the final hybrid forecasts are calibrated using residual quantiles computed from a chronologically held-out validation segment. Across three prespecified random seeds, the full PI-BHTF achieved a mean MAE of 0.0797 ± 0.0007 on the internal chronological test and 0.0686 ± 0.0006 on locked external SCADA validation. Performance and calibration varied substantially across target–horizon tasks, including marked short-horizon external undercoverage for wind generation. Component-wise ablation supported semantic feature partitioning and the heteroscedastic head, whereas the physics-informed features, cross-energy gate, and physics-consistency loss did not independently reduce aggregate MAE. PI-BHTF should therefore be interpreted as a reproducibly competitive framework that balances multi-horizon accuracy, structured representation, uncertainty estimation, and external transferability rather than as a universally dominant model.

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R² = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection.

Starting with the history of computers and the process of mass computerization, the rapid development of the Internet and the possibilities of mobile communication form the basis of digitalization, which is the core of basic research and multilateral discussions as a scientific concept in Industry 4.0. New tools and digital technologies, such as educational collaboration platforms, help educational organizations automate internal processes, simplify everyday tasks, and improve communication between participants in the educational process. These changes change the behavior and daily activities of all subjects. It also allows teachers to focus only on the students and not on the preparation and maintenance of day-to-day paperwork. In particular, it implements effective learning technologies to ensure that knowledge is available anytime, anywhere. It cannot be said that learning technologies are fundamentally new, we can say that traditional learning technologies are being improved and updated due to the pedagogical potential of modern digital technologies. The article contains theoretical studies on the importance of digital education, legal documents adopted for the development and informatization of education in the Republic of Kazakhstan, the elimination of information inequality and digitalization. The digital transformation of education has led to the study and emergence of new areas of traditional scientific disciplines focused on digital technologies, and has contributed to the development and improvement of the components of the methodological system of education. The impact of digital technologies on learning was described using the SAMR model. Our experience in improving the independent work of students and the methodological possibilities of using the chatbot service in teaching were demonstrated. The work was supported financially by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (grant no. AP09259047).

Academic writing is undoubtedly the most important skill in academic situations. It is also the most difficult skill to learn for most students. However, they offer students the chance to write, edit and publish their work for an audience, blogs allow students to engage in literacy activities without anxiety. Using the writing practice they have acquired as a prerequisite for being able to interpret the thoughts of their peers in a healthy way, they can find their writing style or adjust it according to the audience they have. Students' capacity to comprehend and speak clearly is then developed through blogging. As a result of the Internet, blogs began to be used as a means of communication. They formed the basis of a very large body of work on rhetoric, writing and the consequences of communication. The use of weblogs as a forum for unrestricted discussion in the classroom has also been used by teachers. The purpose of the recent research study was examining how Web blogging affects students' ability to write academically. Overall 52 students took part from the International University of Tourism and Hospitality in Turkestan. From 1st and 2nd years participated 32 female and 20 male students took part on this research project. The descriptive research design and Quantitative research method was used. Survey was conducted among participants. Results calculated by using using The Statistical Package for the Social Sciences 23.0 version. According to the descriptive statistics students attitudes showed positive results towards use of WeB-Blogs on developing academic writing skills of students. Mann-Whitney U-test used to calculate differences between two independent variables (course and gender differences). There was no significant difference between gender and years of study at the university. The research study concluded as proving positive effect of blogging on evolving academic writing of students.