Urban vertical farming has emerged as a sustainable and innovative approach to addressing the increasing global demand for food in rapidly growing and densely populated cities, where traditional agriculture faces significant challenges due to space and resource constraints. A primary issue in these systems is the efficient management of critical resources, particularly water and energy, which are essential for maintaining high crop productivity and environmental sustainability. This study introduces, develops, and evaluates a mathematical model that integrates Internet of Things (IoT) technology to optimize water and energy usage in a hydroponic vertical farming setup. The model utilizes real-time environmental data collected from IoT sensors to dynamically adjust irrigation and energy consumption, ensuring minimal waste while sustaining optimal conditions for plant growth. Extensive simulations conducted using Python demonstrate substantial improvements in Water Use Efficiency (WUE) and significant energy savings, validating the model’s effectiveness. The study also presents practical case studies from regions like Singapore, Qatar, and Malaysia, showcasing how the integration of renewable energy sources, such as solar photovoltaic panels, with advanced smart irrigation technologies can lead to up to 50% growth rate improvements. Despite existing challenges, such as high initial capital investments, technical complexities, and the need for continuous maintenance, the findings indicate that modular and scalable system designs offer a promising path forward. Future research should aim to reduce overall costs and enhance system adaptability for various urban environments. Ultimately, this research provides a scalable and efficient framework for advancing urban agriculture, with the potential to contribute significantly to global food security and promote the sustainability of urban ecosystems.

This study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. Maintaining precise microclimate control is essential, as even minor deviations can significantly affect the yield and product quality, but traditional monitoring methods fail to adapt promptly to changing conditions. To overcome this limitation, an automated monitoring system integrating machine learning methods XGBoost 3.0.0, principal component analysis (PCA), and fuzzy logic was developed. The model continuously identifies the deviations in environmental parameters and recommends corrective actions to stabilize the growth conditions. Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. Real-time data collection was facilitated through IoT sensors transmitting readings via Wi-Fi every 5 s to a local server, accumulating approximately 17,280 records per day. The analysis highlighted air humidity, solution humidity, and temperature as critical influencing factors. This research confirms the developed system’s effectiveness in intelligent hydroponic monitoring, with future work aimed at integrating IoT and IIoT technologies for scalable management across diverse crops.

The migration from fifth generation (5G) to sixth generation (6G) mobile networks is driven by the Massive Internet of Things (IoT), which targets device densities of up to ten million units per square kilometer. Two scaling limits govern such systems: a data-volume limit, beyond which batch analytics must give way to distributed streaming, and a network-capacity limit, beyond which the radio access network can no longer carry the aggregate uplink. These limits have so far been treated independently, and no empirical anchor exists for Central Asia. This study makes three contributions. First, it derives a closed-form, dimensionless separation ratio that couples the two breakpoints through shared system parameters and determines which limit binds first. Second, the model is instantiated using 145 regulated 5G New Radio measurements from the commercial Tele2 network in Astana, Kazakhstan, across five load scenarios spanning 13 to 100,000 emulated devices. Third, the closed-form structure of the ratio suggests the splitting is a general trait of uplink-bound IoT domains, though this study empirically instantiates only the vertical-farming case. The instantiated model yields a separation ratio of about thirty-two, with breakpoints at twenty-eight nodes for network capacity and eight hundred eighty for data volume. The measured round-trip time of 124.8 milliseconds and the measured uplink of 0.19 megabits per second miss the International Mobile Telecommunications-2030 (IMT-2030) targets by factors of 1248 and about 5.3 × 10⁵, respectively. Because the network-capacity limit binds first, the analytics regime required for dense deployment is unreachable on 5G. For the measured single-operator deployment, 6G is a precondition rather than an enhancement; generalization to other operators and sites requires further measurement.

The growing demand for customized pet diets highlights the shortcomings of commercial dog foods designed for all breeds, especially when it comes to addressing breed-specific diseases, metabolic disorders, and health risks. This research presents the development and evaluation of a hybrid system for formulating wet canine food recipes. The system combines data on ingredients, veterinary feeds, and breed-related diseases; the architecture includes a recommendation module for ingredient selection and a linear programming block for recipe optimization, considering veterinary nutrient restrictions. The evaluation of the system included automatic classification of foods by specialization, visual analysis of recipe clustering, and comparison of formulas obtained by different models. The average precision of label recovery was 85.4% for TF-IDF and 88.2% for the E5 model. A comparison of ingredient extraction methods showed that machine learning produces more stable recipes, while the statistical approach provides greater variability. The developed system demonstrates potential for automating recipe creation, filling in missing data, and developing veterinary decision support platforms aimed at personalized diet selection based on the physiological needs of animals.