Detecting botnets in IoT environments is difficult because most intrusion detection systems treat network events as independent observations. In practice, infections spread through device relationships and evolve through distinct temporal phases. A system that ignores either aspect will miss important patterns. This paper explores a hybrid architecture combining Graph Neural Networks with Long Short-Term Memory networks to capture both structural and temporal dynamics. The GNN component models behavioral similarity between traffic flows in feature space, while the LSTM tracks how patterns change as attacks progress. The two components are trained jointly so that relational context is preserved during temporal learning. We evaluated the approach on two datasets with different characteristics. N-BaIoT contains traffic from nine devices infected with Mirai and BASHLITE, while CICIoT2023 covers 105 devices across 33 attack types. On N-BaIoT, the model achieved 99.88% accuracy with F1 of 0.9988 and Brier score of 0.0015. Cross-validation on CICIoT2023 yielded 99.73% accuracy with Brier score of 0.0030. The low Brier scores suggest that probability outputs are reasonably well calibrated for risk-based decision making. Consistent performance across both datasets provides some evidence that the architecture generalizes beyond a single benchmark setting.
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.
Viral and fungal diseases of greenhouse strawberries lead to significant crop losses, while traditional uniform spraying schemes do not account for the actual distribution of infection foci or changes in the microclimate. This paper proposes an integrated system for greenhouse farms that combines a stochastic SIRS model of the epidemic process with a microclimate-dependent infection coefficient beta eff(t), a computer vision module based on a lightweight YOLOv10n detector, and a mobile sprayer robot. For three sets of parameters corresponding to moderate infection, outbreak, and suppression scenarios, ensemble simulations are performed (100 realizations per scenario). The results show that the maximum number of infected plants reaches approximately 690 out of 1000 in the outbreak scenario and only about 28 out of 1000 in the suppression scenario, reflecting the effect of timely microclimate correction and local spraying. The YOLOv10n detector is used as a sensor to determine the proportion of affected plants I(0)/N and provides automatic formation of the initial conditions of the population model. The resulting forecasts then serve as the basis for selecting one of three operating modes for the spraying robot (observation, microclimate correction, local treatment). Unlike existing works that consider disease detection, epidemiological models, or robotic spraying separately, this paper proposes a unified closed-loop scheme of "computer vision-stochastic model-mobile robot," linking detection quality with epidemic process forecasting and treatment strategy. In this study, the feasibility of the proposed system was examined through numerical simulations, detector-level performance evaluation, and offline image-based integrated validation of the detector-to-decision workflow. Full closed-loop experiments in a real greenhouse environment are planned for future work.
This study evaluates the effectiveness of natural zeolite (Shankhanai deposit, Kazakhstan) as a functional hydroponic substrate compared to a commercial foamed-glass control (GrowPlant). Using the Nutrient Film Technique (NFT), we assessed the growth and metabolic responses of Medicago sativa L. and three cultivars of Lactuca sativa L. Brunauer-Emmett-Teller (BET) analysis confirmed that zeolite (particle size 3.70 ± 1.20 mm) possesses a high specific surface area (21.80 m2/g), significantly exceeding the control (0.49 m2/g). This structure ensured superior moisture retention and cation exchange, even after a moderate decrease in surface area to 16.66 m2/g post-cultivation due to organic pore-filling. In M. sativa experiments, zeolite increased seedling viability and promoted a more branched root system compared to the artificial substrate. Gas chromatography-mass spectrometry (GC-MS) metabolic profiling of L. sativa revealed a significant substrate-driven reprogramming: zeolite increased the relative proportion of fatty acids and their derivatives (up to +51.27% in May King variety roots), suggesting membrane-protective adaptation. Genotype-specific responses were observed, with the Yeralash cultivar showing increased polyol synthesis (+2.93%) for osmoregulation. The results demonstrate that natural zeolite is an efficient, stable substrate for intensive hydroponics, optimizing root development and physiological stability through enhanced nutrient and water management.
контексте трансформации международных отношений возрастает влияние дискурсивных и восприимчивых факторов на формирование внешней политики. Национальный имидж, будучи комплексом внешних представлений, не только отражает позицию государства в системе международных отношений, но и определяет восприятие его политики другими странами. Средства массовой информации выступают ключевыми акторами в конструировании данного имиджа и продвижении внешнеполитических нарративов. В фокусе данного исследования – анализ материалов о Казахстане, опубликованных в китайских изданиях «Жэньминь жибао», «Синьхуа мэйжи дяньсюнь» и «Хуаньцю шибао» в период с 2013 по 2025 год. Цель работы заключается в том, чтобы выявить роль ведущих китайских СМИ в формировании имиджа Казахстана в условиях процесса углубления стратегического партнёрства между двумя странами и оценить последствия этого процесса для двухсторонних отношений. Проведённый контент и дискурс-анализ демонстрирует, что китайские медиа формируют целостный имидж Казахстана как стабильного, предсказуемого и стратегически значимого партнёра. Основной нарратив выстраивается вокруг тем высокоуровневого взаимодействия, стратегической сопряженности и конкретных достижений в различных сферах сотрудничества. Подобная репрезентация не только фиксирует позитивную динамику отношений, но и активно формирует восприятие казахстанского партнёра в Китае, укрепляя общественную поддержку сотрудничества и политическое доверие между сторонами. Таким образом, исследование вносит вклад в изучение взаимосвязи между медиадискурсом и внешней политикой. Полученные результаты раскрывают механизм, посредством которого медийная репрезентация легитимирует и продвигает повестку двухстороннего сотрудничества, что способствует углублению понимания как теории международных коммуникаций, так и практики публичной дипломатии в конкретном региональном контексте.
