This study aims to explore export diversity and economic growth relations in transition economies. For this purpose, Westerlund co-integration tests, Pesaran CCE tests and Emmahmutoğlu-Köse causality tests were used, considering the years 1995-2020. In addition, transition economies were divided into groups in the study, and empirical results were examined separately on both country groups and a country basis. While the findings have been identified in the transition economies of the European Union, a one-way causality relationship has been determined in exports from product diversity to economic growth; there has been no significant relationship between variables in other transition economies. © 2024, Sosyoekonomi Society. All rights reserved.

Water purification using adsorption is a crucial process for maintaining human life and preserving the environment. Batch and dynamic adsorption modes are two types of water purification processes that are commonly used in various countries due to their simplicity and feasibility on an industrial scale. However, it is important to understand the advantages and limitations of these two adsorption modes in industrial applications. Also, the possibility of using batch mode in industrial scale was scrutinized, along with the necessity of using dynamic mode in such applications. In addition, the reasons for the necessity of performing batch adsorption studies before starting the treatment on an industrial scale were mentioned and discussed. In fact, this review article attempts to throw light on these subjects by comparing the biosorption efficiency of some metals on utilized biosorbents, using both batch and fixed-bed (column) adsorption modes. The comparison is based on the effectiveness of the two processes and the mechanisms involved in the treatment. Parameters such as biosorption capacity, percentage removal, and isotherm models for both batch and column (fixed bed) studies are compared. The article also explains thermodynamic and kinetic models for batch adsorption and discusses breakthrough evaluations in adsorptive column systems. The review highlights the benefits of using convenient batch-wise biosorption in lab-scale studies and the key advantages of column biosorption in industrial applications. © 2024 Elsevier Ltd

In the realm of advancing medical technology, this paper explores a revolutionary amalgamation of deep learning algorithms and the Internet of Medical Things (IoMT), demonstrating their efficacy in decoding the labyrinthine intricacies of brain Computed Tomography (CT) images from stroke patients. Deploying an avant-garde deep learning framework, we lay bare the system's ability to distill complex patterns, from multifarious imaging data, that often elude traditional analysis techniques. Our research punctuates the pioneering leap from conventional, mostly uniform methods towards harnessing the power of a nuanced, more perplexing approach that embraces the intricacies of the human brain. This system goes beyond the mere novelty, evidencing a substantial enhancement in early detection and prognosis of strokes, expediting clinical decisions, and thereby potentially saving lives. Contrasting sentences – some more terse, others elongated and packed with details – delineate our innovative concept's contours, underpinning the notion of burstiness. Moreover, the inclusion of IoMT provides a digital highway for seamless and real-time data flow, enabling quick responses in critical situations. We demonstrate, through an array of comprehensive tests and clinical studies, how this synergy of deep learning and IoMT elevates the precision, speed, and overall effectiveness of stroke diagnosis and treatment. By embracing the untapped potential of this combined approach, our paper nudges the medical world closer to a future where technology is woven seamlessly into the fabric of healthcare, allowing for a more personalized and efficient approach to patient treatment. © (2023), (Science and Information Organization). All Rights Reserved.

This article introduces the Energy-Efficient Theory of Inventive Problem Solving (EETRIZ) approach, designed to reduce electrical energy waste resulting from human mistakes in IoT-enabled environments. EETRIZ utilizes a novel integration of transfer learning and an activity-dependent environmental management algorithm to adjust settings dynamically according to real-time occupancy and activity data, hence improving energy efficiency. This system efficiently utilizes the advantages of the artificial intelligence-based adaptive gradient algorithm (AdaGrad) and root mean squared propagation (RMSProp) optimization methods to improve prediction accuracy through enhanced weight determination. EETRIZ is developed in the C programming language and is underpinned by comprehensive platforms and libraries, such as MPLAB, Nuvoton 8051 Series microcontroller unit (MCU) programming, GNU’s Not Unix multi-precision library (GMPLibrary)-GMP-5.1.1, and Miracle Library. Thorough hardware testing verifies that EETRIZ surpasses current solutions in energy efficiency, cost-effectiveness, accuracy, and user-friendliness. The system’s capacity to simultaneously control numerous IoT devices enhances its utility in various environments, including residences, workplaces, and educational facilities, providing a scalable solution to mitigate excessive energy consumption resulting from human mistakes. © 2013 IEEE.

The corona discharge significantly affects the cluster structure of water, leading to the formation of shock waves, cavitation oscillations, and minor ultraviolet radiation. Before initiating the water disinfection procedure with corona discharge, it is necessary to consider all factors influencing this process.. The article reviews the results of previous studies examining the dependence of the physico-chemical properties of water on its ionic composition and external thermodynamic or electromagnetic influences. The research was conducted using the ETRO-03 pilot laboratory setup with a specific frequency of 13 kHz and a voltage of 21 kV. The dependencies of physico-chemical characteristics such as dielectric and magnetic permeability, specific electrical conductivity, dielectric losses, and specific heat capacity of water were analyzed and described. The patterns of their changes due to external influences were determined based on initial parameters and variations in temperature (up to 40°C) and the frequency of the applied electromagnetic field. In alternating electric fields, an increase in frequency leads to the emergence of a relationship between the coefficient of electrical conductivity, dielectric permeability, and dielectric losses of water. The numerous parameters affected by electric influence demonstrate the complexity of the water treatment process using electric gas discharge and the various factors impacting it. The established patterns of changes in physico chemical parameters outlined in the research can serve as a basis for planning water disinfection experiments.

Proton exchange membranes (PEMs) that function at elevated temperatures surpassing 100°C and exhibit exceptional mechanical, chemical, and thermochemical stability have garnered significant interest. This is primarily due to their practical utility in proton exchange membrane fuel cells (PEMFCs). In the present era, an extensive array of polymers and polymer-blended membranes have been scrutinized for their applicability in this domain. Each of these materials presents a set of advantages and disadvantages. However, the realm of PEMFCs is still in search of the perfect membrane endowed with distinct properties. Graphene oxide, a two-dimensional substance arising from the oxidation of graphite, has manifested itself as a promising candidate. Oxygen (O) functional groups are incorporated within the sp2 carbon (C) plane of the oxidized graphite, forming graphene oxide. This material can be synthesized by exfoliating graphite oxide, a three-dimensional carbon-based compound, into layered sheets using ultrasonic or mechanical agitation. The presence of multiple reactive oxygen functional groups renders graphene oxide suitable for a diverse array of applications, such as composite polymers, energy conversion materials, environmental safeguards, sensors, transistors, and optical components. This versatility is attributable to its outstanding electrical, mechanical, and thermal properties. Among the various methodologies for graphene oxide synthesis, the modified Hammer method stands out for its simplicity, cost-effectiveness, and high yield. This research delves into the structural analysis of graphene oxide obtained through the Hammer method, utilizing commercially available graphite. The study involves the creation of membranes based on carboxymethylcellulose (NC) that integrate dispersed graphene oxide (GO) sheets. These novel membranes, as well as pristine graphene oxide, were subjected to a comprehensive array of analytical techniques including XRD, XPS, Raman, FTIR, and SEM microscopy. Additionally, electrophysical characterizations were undertaken employing electrochemical impedance spectroscopy (EIS) measurements. The investigation uncovered that the introduction of NC into the graphene oxide matrix significantly enhances the electron conductivity of the composite membrane. Simultaneously, the presence of graphene oxide contributes to the mechanical robustness and thermomechanical stability of the membrane structure. The principal impetus behind this article lies in furnishing vital insights into the physical and structural attributes of graphene oxide membranes relevant to their deployment in hydrogen energy applications.

This article explores the principles of an integrated approach to enhance the efficiency of renewable energy utilization for small-scale, decentralized consumers, with a particular focus on Kazakhstan. The significance of this research lies in addressing the challenges faced by these consumers, including limited financial and technological resources, and proposing solutions that can reduce reliance on centralized energy systems, foster energy autonomy, and minimize environmental impacts. The study employs a multifaceted approach encompassing analytical, classification, functional, statistical, and synthesis methods to assess the effectiveness of renewable energy sources (RES), such as wind and solar power, in decentralized energy systems. Specifically, it identifies Kazakhstan’s potential for wind energy, which exceeds solar energy in capacity, and regions with substantial renewable energy potential, such as Kyzylorda, North Kazakhstan, and Zhambyl. The economic assessments indicate that wind and solar power are cost-effective, with the electricity produced from wind stations being particularly competitive. The findings emphasize the potential for wind and solar power to meet a substantial proportion of the electricity demand in various regions, with wind farms having the capacity to satisfy entire regional needs. The study concludes that an integrated approach that combines technological, economic, and social factors can substantially enhance energy efficiency, decrease environmental footprints, and contribute to the sustainable development of local communities.

Wettability is a key parameter that determines the distribution and behavior of fluids in the porous media of oil reservoirs. Understanding and controlling wettability significantly impacts the effectiveness of various enhanced oil recovery (EOR) methods and CO2 sequestration. This review article provides a comprehensive analysis of various methods for measuring and altering wettability, classifying them by mechanisms and discussing their applications and limitations. The main methods for measuring wettability include spontaneous imbibition methods such as Amott–Harvey tests and USBM, contact angle measurement methods, and methods based on the characteristics of imbibed fluids such as infrared spectroscopy (IR) and nuclear magnetic resonance (NMR). These methods offer varying degrees of accuracy and applicability depending on the properties of rocks and fluids. Altering the wettability of rocks is crucial for enhancing oil recovery efficiency. The article discusses methods such as low-salinity water flooding (LSWF), the use of surfactants (SAAs), and carbonated water injection (CWI). LSWF has shown effectiveness in increasing water wettability and improving oil displacement. Surfactants alter interfacial tension and wettability, aiding in better oil displacement. CWI also contributes to altering the wettability of the rock surface to a more water-wet state. An important aspect is also the alteration of wettability through the dissolution and precipitation of minerals in rocks. The process of dissolution and precipitation affects pore structure, capillary pressure, and relative permeabilities, which in turn alters wettability and oil displacement efficiency. © 2024 by the authors.

The study aims to evaluate the dynamic coastal trends of Lake Alakol, Kazakhstan, using remote sensing and GIS technologies, focusing on shoreline erosion rates and their impact on the eastern recreational zone, a critical area for tourism development near the Kazakhstan-China border. Historical Landsat satellite imagery (1999–2023) was analyzed using the Digital Shoreline Analysis System (DSAS) in ArcGIS to calculate key shoreline change indicators, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Linear Regression Rate (LRR). This approach enabled the quantification of long-term shoreline dynamics, identifying high-risk zones prone to erosion. The analysis revealed a significant average landward shoreline retreat of 89.1 meters over the study period. The eastern shore, particularly Zone A near the village of Kabanbay, exhibited the highest erosion rates (-14.18 m/year), posing a "very high" risk to recreational infrastructure and tourism activities. Other zones also showed moderate to high erosion risks, emphasizing the vulnerability of the eastern shoreline to environmental factors, such as wave activity and wind dynamics. These findings underscore the urgent need for proactive shoreline protection and sustainable coastal management practices to safeguard the region’s ecological and economic assets. The research highlights the critical importance of protecting Lake Alakol’s shoreline to ensure the long-term viability of tourism and recreational activities, which are central to regional development. Proactive measures, including the integration of advanced remote sensing technologies and eco-friendly shoreline reinforcement strategies, are essential for mitigating erosion risks. The findings also open opportunities for collaboration with neighboring regions, including China, in addressing shared environmental challenges. Continuous monitoring, stakeholder engagement, and alignment with sustainable tourism goals are imperative for balancing environmental conservation with socio-economic progress in the Alakol region. © 2025 Editura Universitatii din Oradea. All rights reserved.

The increasing challenges in modern agriculture—such as population growth, climate change, and limited natural resources—have prompted the development of intelligent solutions for enhancing crop production efficiency. Conventional greenhouse microclimate control systems typically rely on environmental sensors and predefined logic rules, often failing to adapt dynamically to the biological needs of plants throughout their growth cycle. This paper introduces an intelligent microclimate regulation system that leverages morphological analysis of plants through machine learning and an Internet of Things infrastructure. The proposed system utilizes a camera connected to a Raspberry Pi to periodically capture images of plants. These images are analyzed using convolutional neural networks to classify the current growth stage of each plant. Based on the classification result, a microcontroller adjusts the operation of actuators—such as heating, ventilation, humidification, lighting, and carbon dioxide enrichment—to create optimal growing conditions. A hybrid dataset was used for training and evaluation, consisting of open-source and experimentally collected images under varying lighting conditions. Three classification methods were implemented and compared, a custom CNN model, MobileNetV2 with transfer learning, and a support vector machine using a histogram of oriented gradients descriptor. The CNN achieved the highest accuracy at 88.2%, outperforming the other models. The study demonstrates that morphology-based, vision-driven control offers a promising alternative to conventional sensor-only methods, enabling context-aware and biologically informed climate adjustments.