Baku Higher Oil School

Load balancing is a widely adopted strategy in modern distributed systems because it distributes workloads across servers, mitigating overload and improving overall performance. However, the rapid growth of such systems has created a need for more adaptive strategies to ensure optimal utilization and responsiveness of resources. Traditional algorithms such as Round Robin (RR) and Weighted Round Robin (WRR) assign requests without considering server states or request characteristics. We implement a machine learning (ML)–based predictive load balancer, forecasting the latency of a request based on the request itself and container parameters, specifically the average latency of the last 50 requests and the count of active requests, and evaluate it against RR and WRR. For the experiment, synthetic data were generated to replicate real-world requests by creating random URL and method combinations, attaching a task size in Million Instructions (MI), and distributing them among three containers with varying resources according to the load balancing strategies described above. Under the conditions tested, the ML approach achieved the worst performance, trailing both RR and WRR in terms of throughput and average latency, although the model accuracy was sufficiently high (R2 = 0.8+). Post hoc analysis indicates that limited and occasionally stale runtime features caused the load balancer to direct all requests to a single container until the next statistics update, since that container was considered the ‘best’ during that interval.

Sustainable irrigation is a critical component of modern agriculture, particularly in the face of increasing water scarcity, climate variability, and the demand for higher crop productivity. In recent years, the convergence of Internet of Things (IoT) technologies with Artificial Intelligence (AI) has enabled the development of intelligent irrigation systems that optimize water usage while minimizing environmental impact. Among various wireless communication protocols, LoRaWAN (Long Range Wide Area Network) has emerged as a preferred solution due to its long-range communication, low power consumption, and scalability across large agricultural fields. This review paper provides a comprehensive overview of AI-driven LoRaWAN-based frameworks applied in agricultural irrigation. We analyze the fundamental architecture of such systems, their key components—including sensors, gateways, and cloud services—and discuss how AI models are employed to interpret environmental data and make autonomous irrigation decisions. Furthermore, real-world case studies are examined to highlight practical applications, while existing challenges such as connectivity limitations, data reliability, energy efficiency, and system integration are critically discussed. Finally, the paper outlines current research gaps and suggests future directions to advance the development of intelligent, sustainable, and autonomous irrigation systems through the integration of AI and LoRaWAN technologies.

The problem of homoglyph attacks has become significant nowadays. Due to the increasing range of characters in modern encoding systems, similar symbols are being added. This tendency causes problems in distinguishing characters, which hackers exploit in spoofing attacks. In this research, an attempt is made to examine the problem of homoglyph attacks and provide an overview of practical solutions, such as different algorithms. Additionally, a solution to address the issue of spoofing attacks using the Levenshtein edit distance algorithm, widely used in spell checking, will be provided. The proposed solution, which utilizes a backend application written in the Java programming language with the Spring technology stack, can detect homoglyphs and attempt to replace them with normal characters. Additionally, a desktop client for the application is also presented.

Abstract Integrated Disease Management (IDM) is associated with various factors such as plant protection, private sector investment, trade and export, land use, education and IDM strategy awareness. Integrated plant disease management (IDM) is a holistic approach which combines cultural biological, physical and chemical and host resistance strategies to prevent, suppress and manage disease in sustainable manner. IDM aims to minimize reliance on chemical pesticides, minimize environmental risks, maintain soil health, enhance biodiversity and ensure long term crop productivity. Present study was conducted to analyze response of farmers from Koregoan tehsil to Integrated Disease management (IDM).

This study explores the theoretical foundations of artificial intelligence (AI) and big data, focusing on their role in the Fourth Industrial Revolution and sustainable development. Despite growing recognition of big data’s transformative potential, there is limited understanding of its specific impact on decision-making and societal transformation towards achieving sustainable development goals (SDGs). The research aims to fill this gap by analyzing how big data can support e-government initiatives and development objectives. Using a descriptive and analytical methodology, including a case study approach, the study examines the primary techniques and projects that facilitate large-scale data analysis in digital transformation. Results reveal that big data plays a critical role in monitoring progress, informing decisions, and driving social change aligned with SDGs. These findings contribute to a better understanding of big data’s value in modern governance and sustainable development efforts.

Objective: This study explores the family planning counselling that adolescents receive during pregnancy care, with the aim of identifying those factors that make counselling effective or ineffective. Methods: The study population consisted of ten adolescents who had recently given birth and ten health care providers. Adolescents were recruited using posters; providers, by email. Individual semistructured interviews were conducted and analyzed in keeping with grounded theory. Analysis consisted of open coding (identification of initial concepts), axial coding (drawing connections between repetitive and similar concepts), and selective coding (clustering of categories to identify themes). Results: The adolescent participants desired detailed information about pregnancy and motherhood, were challenged by the disruptive effects of pregnancy, and were strongly reliant on nonmedical support people. Our participants described that the adolescent-provider relationship was strengthened when providers sought to empower adolescents and to provide a balance of support and independence. Judgment and bias interfered with relationship building. Postpartum continuity with the pregnancy care provider and a positive relationship with the family physician were both highly desirable yet highly variable. In general, the adolescents found family planning counselling an awkward and intimate undertaking and preferred providers to slowly progress towards explicit counselling. The adolescents who opted not to initiate postpartum contraception still found the counselling process informative and beneficial. Conclusion: Effective family planning counselling is not information driven but rather gradual, individualized, and context focused. This article has been peer reviewed.

This study explores the use of artificial intelligence (AI) in e-government applications, focusing on the various phases of e-government expansion and advancement. The frameworks include providing information, enabling interaction, and facilitating transactions. The main source of improvement is the integration of AI into government services, enabling computer systems to learn, reason, and make human-like decisions. The use of generator AI is expected to result in more intelligent, precise, and efficient approaches, but it is essential for organizations to formulate plans that align with advancements and consequences of intelligent technology. The goal is to achieve development goals that enable the government to adopt smart generators in its applications.

Soft robotics has emerged as a rapidly growing field, offering remarkable adaptability and dexterity in various applications such as medical devices, structural engineering, automotive systems, and aerospace industries. However, the precise control over soft structures has been a longstanding challenge, necessitating the development of variable stiffness mechanisms. This research explores novel designs that integrate granular jamming and laminar jamming structures to achieve variable stiffness in robots. The main objective is to contribute to the expanding knowledge in soft robotics and propose a methodology for analyzing soft robot prototypes. Specifically, the purpose of the research is to pave the way for the creation of versatile soft machines that can emulate the capabilities of traditional rigid robots. As a practical application, we present a test-case prototype for the arm of a soft robotic nurse for assisting patients, highlighting the potential of these innovative designs. The research covers the manual control for conducting the experiments and provides an experimental comparative analysis for 2 different skeleton designs.

En el Distrito Central del Municipio de Vinto (Cochabamba- Bolivia), hay sectores que desconocen su calidad del agua para consumo humano, que se traduce en un riesgo para la salud; generar esta información es difícil, sobre todo para sistemas autogestionados, que son responsables del manejo y dotación. Para apoyar a estos sistemas autogestionados se determinó el nivel de riesgo de contaminación y la calidad del agua en Organizaciones Territoriales de Base (OTB) del Distrito Central de Vinto con pozos autoadministrados. A través de inspecciones sanitarias se evaluó el nivel de riesgo de las 5 OTB identificadas, presentando riesgo alto-muy alto (Alto Cercado, 1.er y 2.o pozo de Cercado, Rodriguez) y riesgo bajo (Campos Verdes y Albina Patiño). Al análisis de calidad de agua, solo el pozo de Campos Verdes es apta para consumo humano (NB-512), las demás exceden al menos un parámetro (coliformes totales, turbiedad, color, hierro total, manganeso o nitrógeno amoniacal). Además, se encontró que no existe una relación estadísticamente significativa entre el nivel de riesgo de contaminación y la calidad del agua de los pozos, por tanto, además de realizar inspecciones sanitarias es necesario realizar análisis de calidad del agua para garantizar un consumo seguro.