Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence.

This paper presents a simulation-based feasibility study of nonlinear model predictive control (NMPC) for process water quality management in the closed-loop water circuit of the Aktogai copper concentrator. Repeated water recirculation leads to the accumulation of total dissolved solids (TDSs), organic compounds (CODs), and suspended solids (TSSs), degrading flotation efficiency. A dynamic model of the process water pond with nine flows is combined with pseudo-first-order kinetics for H₂O₂ oxidation (k_ox = 0.003 L/(mg·h)) and polyaluminum chloride (PAC)/FeCl₃ coagulation (k_coag = 0.005 L/(mg·h)). The NMPC manipulates the treated-effluent flow Q₂ and the coagulant and oxidant doses over an 8 h prediction horizon, uses normalized one-sided soft constraints on water quality and a reagent cost, and is solved through sequential quadratic programming. The volume, TDSs, and TSSs are assumed to be measured online, whereas CODs are available only from laboratory analyses taken every 2–8 h with a 2 h delay. NMPC was compared with no-control, fixed dosing, a maximum-dilution policy, and a proportional–integral (PI) feedback dosing benchmark with the same manipulated variables. Under nominal disturbances, all strategies met the process limits. Under a simulated stress-test scenario (ore change and reagent overdosing), no control produced 26, 37, and 25 h of TDS, COD, and TSS violations in 72 h; maximum dilution alone removed the TDS and TSS violations but left 13 h of COD violations. With online measurements, a well-tuned PI controller performed almost as well as NMPC (1 h vs. 0 h of COD violations). With laboratory COD analyses, the PI controller incurred 4–11 h of COD violations, whereas NMPC, using a model-based COD estimate between analyses, incurred none, at the cost of 9–47% higher oxidant consumption; this advantage reflects the combined effect of model-based COD estimation and predictive optimization, not of predictive optimization alone. It persisted under up to 30% adverse parametric plant–model mismatch, and the results were insensitive to tenfold variations in the selected NMPC tuning weights. TDSs could be held below their limit only through hydraulic dilution and only while the thickener-overflow TDSs remained below about 2260 mg/L.

The aim of this work is to analyze and model the performance of the distillation column of a catalytic cracking plant using fuzzy initial information. A system of mathematical models of the studied columns was developed, and we discussed the issues of modeling the distillation column of a catalytic cracking plant operating in conditions of fuzzy initial information. The system of mathematical models of the studied columns was developed on the basis of statistical and fuzzy information. The mathematical models of columns K-1, K-2 and K-3 were identified with regression and fuzzy regression equations, i.e., combined models of the main columns of the catalytic cracking plant were constructed. The purpose of this study was to create an optimal control system for the distillation column of a catalytic cracking unit using a mathematical model of the process. The results obtained in this work can be helpful during the design, modernization and optimization of equipment and installations, especially those used in the chemical and petrochemical industries. They can be useful both from a scientific and practical point of view, and they are also significant in terms of environmental protection, economy and mechanical engineering.