This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations.

Nowadays, there is a growing need to develop appropriate methods to acquire, process and interpret large amounts of data in real time to assist the decision making process for optimal drinking water distribution systems management. The presented wireless AMR methodology, which was piloted in Talsi city, is designed as warning systems to prompt the appropriate response to eliminate or mitigate leaks, client's fraud and to ensure customers and water supply companies with reliable and timely data. One of the main benefits of the methodology presented here is that it enables operators to become more effective and efficient in emergency conditions.

With the global shift to electric vehicles, countries face unique challenges and opportunities shaped by their geographical and economic contexts. This paper presents a system that leverages smart transport technologies, the Internet of Things, and decision-making algorithms, such as PROMETHEE, to optimize charging stations and their positioning in diverse urban and rural settings. The system addresses key obstacles, including managing charging infrastructure, balancing energy consumption, and enhancing transport accessibility. By analyzing local conditions, the proposed solution incorporates innovative algorithms for electricity demand forecasting, charging station management, and integration with urban transport systems. This approach ensures a flexible, scalable, and sustainable electric vehicle management system that aligns with international standards and evolving technological trends.

The current control is becoming a challenging task for switched mode power supplies, while current sensorless control solutions can avoid the instantaneous current measurements that has been applied for mostly used power factor correction topologies. However, multilevel type of converters haven't been considered to use with current sensorless control (CSC). Hereby, the CSC is applied to three level neutral point clamped converter, where special volt-second balance is applied to inductor in order to keep average inductor's current value to track the reference signal during both discontinuous and continuous current modes. The explanation of post-fitting and pre-fitting current trajectories is done that are necessary for proper transition between voltage levels. Experimental results have confirmed the theoretical model.

This study presents a novel methodology for evaluating the quality of life in urban areas of Kazakhstan through the development of tailored sustainability indicators. While urban livability and sustainability are global concerns, few studies have focused on developing comprehensive, region-specific metrics for assessing these factors in Kazakhstan, a country with diverse urban environments and newly established administrative divisions. This research fills a critical gap by creating aggregated indicators that address economic, social, environmental, and infrastructural dimensions in a uniquely Kazakhstan context. The study goes beyond the existing literature by integrating local dynamics and challenges, offering a nuanced understanding of urban sustainability that reflects the country’s regional characteristics. The findings provide policymakers and urban planners with actionable insights to improve quality of life and make informed decisions. Moreover, the methodology is designed for ongoing application, allowing for continuous monitoring and adaptation to evolving urban development strategies across Kazakhstan. This research advances the field by offering a replicable framework that can be adapted to other regions with similar complexities.