In this study, we explore the applicability of computational intelligence approaches i. e., genetic algorithms (GA) to enhance material performance. Central to these methodologies is processes optimization and data-driven models development. In particular, our work gives a detailed overview of the state-of-the-art of materials development through innovative usage of computational intelligence (CI) methods to help them in materials innovation and exploration. In particular, the approach of the study provides a data-driven guide toward desirable properties for AZ61 magnesium alloy. A metaheuristics optimization tool, GA, is used with linear regression to estimate the optimal rolling process parameters.

The use of enterosorbents—materials which can be administered orally and eliminate toxic substances from the gastrointestinal tract (GIT) by sorption—offers an attractive complementary protection of humans against acute and chronic poisoning. In this study, we report the results of developing a microgranulated binary biomedical preparation for oral use. It was designed with a core-shell structure based on pectin with low degree of esterification as the core, and nanoporous activated carbon produced from rice husk, AC-RH, as the shell, designated as AC-RH@pectin. The adsorption properties of the synthesized materials were studied in aqueous solutions for the removal of lead (II) nitrate as a representative of toxic polyvalent metals and sodium diclofenac as an example of a medicinal drug. The composite enterosorbent demonstrated high adsorption capacity for both adsorbates studied. Adsorption kinetics of lead and diclofenac adsorption by AC-RH, pectin, and AC-RH@pectin, fitted well a pseudo-second-order model. According to the Langmuir adsorption isotherm model, the best fitted isotherm model, the maximum adsorption capacity, qmax, of AC-RH@pectin for diclofenac and for lead (II) was 130.9 mg/g and 227.8 mg/g, respectively. Although qmax of AC-RH for diclofenac, 537.6 mg/g, and qmax of pectin for lead (II), 245.7 mg/g, were higher, the maximum adsorption capacity of AC-RH for lead (II), 52.7 mg/g, was much lower than that of the composite AC-RH@pectin and the adsorption capacity of pectin for diclofenac was negligible. Therefore, the composite material AC-RH@pectin demonstrated substantial efficiency of removing both species which potentially defines it as a more universal enterosorbent suitable for treating poisoning caused by substances of different chemical nature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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.