This study examines the application and implementation of energy-efficient measures and strategies for use in buildings aimed at achieving the goal of virtually zero energy consumption by buildings. In particular, the aim of the study is to introduce integrated solar devices into the building, functioning as a functional component of the structure for heat storage, in order to increase the flexibility of the structure while maintaining comfortable conditions inside. This article discusses an experimental comparison of flat and tubular solar collectors in the southern and northern regions of Kazakhstan. Compared to glass (S = 3), a tubular solar collector has a coefficient of thermal conductivity that is two times lower for single-layer elements and 2.5 times lower for double-layer elements. This means that less heat is lost. A flat solar collector was also developed, a heat-insulating translucent double-glazed window with reduced pressure, and the coolant is made of thin-walled corrugated stainless-steel pipe. In this collector, there is a constant circulation of heat, which increases the efficiency of heat transfer by eliminating additional partitions between the panel and thermal insulation. A calculation was carried out that allowed us to determine the productivity and useful gain for two collectors. The annual increase in the useful energy of flat solar collectors is about 12780000 kJ/hour and 20100000 kJ/hour for tubular solar collectors; therefore, the annual increase in the useful energy of evacuated tubular collectors is 20% more than that of flat plate collectors in the northern region.

This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.

The development of a wearable sensor device integrated into the internet of things (IoT) infrastructure is presented, with functionality aimed at continuous measurement of the user's physiological parameters and their intelligent processing for real-time stress level assessment. The system enables continuous monitoring of physiological parameters, allowing early detection of stress signals and supporting adaptive behavioral responses. The hardware platform is designed to consolidate various biomedical sensors, enabling continuous acquisition and intelligent processing of physiological data in real time. During testing, heart rate (HR) ranged from 68 to 89 beats per minute (bpm), respiratory rate varied from 11 to 15 breaths per minute, and skin conductivity ranged from 63 to 77 µS. Acquired physiological data were uploaded to a cloud-based infrastructure to enable advanced processing and analysis. The system achieved an overall stress detection accuracy of 87%, and signal stability remained high even under changing conditions. The proposed wearable solution demonstrates strong potential for use in healthcare, education, and occupational environments. It also offers scalability through the integration of intelligent algorithms and additional sensor modules.

This paper analyzes the dynamics of cyberattacks and fraud techniques using machine learning algorithms for Industrial Internet of Things (IIoT) systems. Special attention is paid to information security issues of digital twins within the framework of the Industry 4.0 concept. Various machine learning techniques, such as logistic regression, random forest, and the nearest neighbor method, are considered for classifying attacks in IIoT systems, taking into account the problem of data imbalance inherent in rare attacks. The use of data balancing and cross-validation methods can improve the accuracy of the models and minimize the number of false positives. The results demonstrate that the random forest model outperforms other methods in terms of accuracy, confirming its effectiveness for IIoT security and digital twin protection. Additionally, feature selection and optimization techniques were explored to enhance model performance further. The study also highlights the importance of real-time threat detection, which is crucial for maintaining the integrity and resilience of IIoT environments. Future research is planned to investigate more sophisticated methods, including deep learning, to improve attack detection and classification in the context of Industry 4.0. The integration of federated learning and adaptive AI-driven security measures may also offer promising solutions to emerging cyber threats.

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems.

This paper presents an architecture design for a patient monitoring system integrated with Internet of Things (IoT) technology to detect and quantify patient stress levels. Research in remote patient prediction systems is considered one of the most important areas at present. This technology offers the potential to improve stress assessment, provide interventional treatment, and provide personalized stress management techniques. A Raspberry Pi microcontroller was used as a key controller. The unit is equipped with electroencephalography sensors, electrocardiogram sensors, glucose sensors, and electromyography sensors to record physiological signals indicative of stress, such as cardiac activity and human brain activity, a method for monitoring blood glucose levels in diabetic patients and measuring electrical activity. Muscles are collected from these four sensors and transmit information via communication channels (Wi-Fi, USB). The information obtained is transferred to a storage database, where patient data is securely stored. In the storage database, interaction between the patient and the doctor occurs via a 4G communication channel. Data is transmitted via a 4G communication channel from the storage database to the doctor’s personal computer. From the doctor’s personal computer, data is transferred to the doctor’s control panel, and from there the data is transferred to a web server, where all data is processed and the patient is monitored. In the course of research, it was found that the proposed device has 95% reliability in measuring cardiac activity and human brain activity, a method for monitoring blood glucose levels in patients with diabetes and measuring the electrical activity of muscles.