Scopus Research — Riyadh Hamid Al-Obaidi

This study focuses on oscillatory rotating convection in the presence of a chemical reaction, magnetic field, and helical force. Both linear and weakly nonlinear analyses are conducted. The nondimensional governing equations for linear stability analysis are solved using the normal mode approach, resulting in an eigenvalue problem. Weakly nonlinear behavior is analyzed using multiple scale analysis, and an analytical expression for the Rayleigh number is derived using the one-term Galerkin method. Neutral stability curves for both oscillatory and stationary instabilities are plotted for various physical parameters. They have no appreciable effect on stationary convection in this Prandtl value ((Formula presented.)). However, the Lewis number (Formula presented.), Solutal Rayleigh number (Formula presented.), Taylor number (Formula presented.), Solutal diffusivity (Formula presented.), helical force parameter (Formula presented.), Thermal Rayleigh number (Formula presented.), and Hartmann number (Formula presented.) all work together to stabilize and destabilize the flow. On the other hand, it is found that the flow in oscillatory convection is stabilized and destabilized by the Solutal Rayleigh number, Hartmann number, Thermal Rayleigh number, Prandtl number, Taylor number, Lewis number, and helical force parameter. The weakly nonlinear analysis yielded the amplitude equation. © 2025 Wiley Periodicals LLC.

Hypersoft sets (HSSs) were initiated as an extension of soft sets (SSs) to address real-life scenarios involving multiple disjoint sets with different traits. One such extension is the interval-valued fuzzy hypersoft set (IVFHSS), which has proven effective in decision-making (DM). However, the IVFHSS model lacks a mechanism to incorporate the degree of acceptance of DM opinions, which is crucial for accurate decision-making. To overcome this limitation, our work aims to develop a novel hyperstructure called a possibility interval-valued fuzzy hypersoft set (PIVFHS-set). We begin by introducing essential operations and their properties, such as PIVFHS-subset, PIVFHS-null set, PIVFHS-absolute set, and complement of a PIVFHS-set. These concepts are illustrated through numerical examples to demonstrate their practical applications. Next, we delve into set-theoretic operations of PIVFHS sets, including union, intersection, AND, OR, and relevant laws. These operations are further elucidated through numerical examples, matrix representations, and graphical illustrations. Additionally, we present two algorithms based on AND and OR operations, providing step-by-step explanations and showcasing their effectiveness through illustrative examples. Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. Alongside an analytical study of the advantages and disadvantages of this model, we provide suggestions for future research based on the identified limitations. © 2025, University of New Mexico. All rights reserved.

In this careful study, through the concept possibility interval valued neutrosophic hyper soft set (abbreviated as piv-NHSS) which is combined from the hypersoft set (HSS) and Interval-valued neutrosophic set under the posobolity degree and each iv-NHSS is assigned a possibility degree in the interval [0, 1]. Based on this concept, we present a more flexible, expanded method for a previous concept named possibility interval valued neutrosophic hyper soft matrix (piv-NHSM) as a new generalization of piv-NHSS. In this work, we also present several algebraic operations and also all the mathematical properties associated with this model. In addition to the above, we have presented a clear algorithm based on the matrix properties of this model, which has been used to solve one of the multi-property decision-making problems. Finally, the correlation coefficient for this concept was defined and explained in detail according to an approved mechanism, with a numerical example provided to illustrate the mechanism of use. Moreover, we develop a new algorithm for solving the decision-making issue based on the proposed correlation coefficient for piv-NHSS. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.

This study presents an advanced artificial intelligence-driven framework designed to enhance the speed and accuracy of bone fracture detection, addressing key limitations in traditional diagnostic approaches that rely on manual image analysis. The proposed framework integrates the YOLOv8 object detection model with a ResNet backbone to combine robust feature extraction and precise fracture classification. This combination effectively identifies and categorizes bone fractures within X-ray images, supporting reliable diagnostic outcomes. Evaluated on an extensive data set, the model demonstrated a mean average precision of 0.9 and overall classification accuracy of 90.5%, indicating substantial improvements over conventional methods. These results underscore a potential framework to provide healthcare professionals with a powerful, automated tool for orthopedic diagnostics, enhancing diagnostic efficiency and accuracy in routine and emergency care settings. The study contributes to the field by offering an effective solution for automated fracture detection that aims to improve patient outcomes through timely and accurate intervention. © 2024 American Chemical Society.

The Geometric Bonferroni Mean (GBM), is an extension of The Bonferroni mean (BM), that combines both BM and the geometric mean, allowing for the representation of correlations among the combined factors while acknowledging the inherent uncertainty within the decision-making process. Within the framework of Pythagorean neutrosophic set (PNS) that encompasses truth, indeterminacy, and falsity-membership degrees, each criterion can be integrated into a unified PNS value, portraying the overall evaluation of that criterion by employing the Geometric Bonferroni mean. This study aims to enhance decision-making in Pythagorean neutrosophic framework by introducing an aggregation operator to PNS using the Geometric Bonferroni Mean. Additionally, it proposes a normalized approach to resolve decision-making quandaries within the realm of PNS, striving for improved solutions. The novel Pythagorean Neutrosophic Normalized Weighted Geometric Bonferroni Mean (PNNWGBM) aggregating operator has been tested in a case of multi-criteria decision-making (MCDM) problem involving the selection of Halal products suppliers with several criteria. The result shows that this aggregating operator is offering dependable and pragmatic method for intricate decision-making challenges and able to effectively tackle uncertainty and ambiguity in MCDM problem. © 2025, University of New Mexico. All rights reserved.

This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced. © 2023 Hydrogen Energy Publications LLC

Owing to their great electronic reactivity and affinity toward cisplatin drug molecules, nano-materials have been considered as effective drug sensors. Within this study, an aluminum carbide (C3Al) monolayer was employed and its interaction with cisplatin was thoroughly scrutinized via density functional theory (DFT) computations. According to our calculations, the C3Al monolayer exhibited great reactivity and affinity toward cisplatin. Cisplatin interacted with the C3Al monolayer with the adsorption energy (Eads) of − 25.53 kcal/mol. There was a considerable charge transport, demonstrating a rise in conductivity. Also, the electron density differences further expounded on the charge density. The process of adsorption had a significant impact upon the LUMO and HOMO levels, thereby reducing Eg of cisplatin/C3Al by roughly 34.73%. Moreover, there was a significant improvement in electrical conductivity because of the reduction in Eg, leading to the generation of an electrical signal. We could conclude the possibility of using C3Al as a promising electronic drug sensor for cisplatin. © The Author(s) under exclusive licence to Associação Brasileira de Engenharia Química 2023.

This study suggests employing a dynamic natural and bio-inspired algorithm (DNBIA) to strengthen the confidentiality, integrity, and availability of digital information exchanges. You may think of the suggested method as a clever approach to Fusion Processing. Fusion Processing is the practice of combining and analyzing information from many databases. The efficiency and reaction time of e-communication systems may be increased by the use of the suggested DNBIA algorithm, which processes and integrates data from different sources. It is also possible to see the multi-objective optimization study presented in this work as a type of Fusion Processing. Cyberattacks and other types of computer security risks are the focus of this study, which seeks to optimize numerous objectives concurrently in order to eliminate them. The study can give a complete solution to improve the security of e-communication systems by combining different goals. The suggested method of enhancing e-communication and information transmission using DNBIA and multi-objective optimization analysis can be seen as a type of Fusion Processing. Efficient e-communication systems may be achieved by collecting data from a variety of sources and analyzing the results. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

High-temperature Solid Oxide Fuel Cell (SOFC) technology has great potential as a clean and effective energy source. However, several issues have prevented their widespread adoption, like high operating costs, difficulty integrating with other components, and being sensitive to fuel contamination. In order to address these challenges, the main novelties of the present work are carbon capture and utilization for clean energy production, using the flue gas condensation process as a cost-effective and energy-efficient strategy, and the combination of Molten Carbonate Fuel Cells (MCFC) for increased energy efficiency and most effective component integration. The proposed novel system is also equipped with a gasifier and vanadium chloride unit for syngas and clean hydrogen production. The system's practicality is evaluated by analyzing the key performance indicators from thermodynamic, exergo-economic, exergo-environmental, and sustainability viewpoints. Also, the Sankey diagram is presented to investigate each component's effectiveness from the exergy destruction facet. According to the findings, at the most optimum design condition, the acceptable energy and exergy efficiencies of 61.06% and 50.66%, respectively, are achieved, revealing the system's effectiveness. The suggested system is also financially attractive since it achieves a reasonable levelized power cost of 17.6 $/MWh at a total cost of 44.1 $/h. The findings show that carbon dioxide recovery is crucial in reducing the pollutants due to the low emission index of 5.01 kg/GWh. Finally, the exergo-environmental index, environmental damage effectiveness, and exergy stability factor have equivalent values of 0.012, 0.59, and 0.54. © 2023 The Institution of Chemical Engineers

To meet users' expectations for speed and reliability, 5th Generation (5G) networks and other forms of mobile communication of the future will need to be highly efficient, flexible, and nimble. Because of the expected density and complexity of 5G networks, sophisticated network control across all layers is essential. In this context, self-organizing network (SON) is among the essential solutions for managing the next generation of mobile communication networks. Self-optimization, self-configuration, and self-healing (SH) are typical SON functions. This research creates a framework for analyzing SH by exploring the impact of recovery measures taken in precarious stages of health. For this reason, our suggested architecture takes into account both detection and compensating operations. The system is broken down into some faulty states and the "fuzzy c-means"(FCM) approach is used to conduct the classifying. In the compensation process, the network is characterized as the Markov decision model (MDM), and the linear programming (LP) technique is implemented to find the most effective strategy for reaching a goal. Numerical findings acquired from a variety of situations with varying objectives show that the suggested method with optimized operations in the compensation stage exceeds the approach with randomly chosen actions. © 2023 R. Mohandas et al.

In this paper, the (3+1)-dimensional variable-coefficient generalized nonlinear wave equation arising in a liquid with gas bubbles is studied. The Hirota bilinear technique and binary bell polynomials are considered. Some new analytic solutions containing one-lump soliton, two-lump soliton, and three-lump soliton, and also 1-breather and 1-lump, the interaction 1-bell-shaped soliton with 1-lump or 1-breather, and other soliton solutions to the mentioned equation are obtained and analyzed. To create the solutions of Maple symbolic package is used. Using suitable mathematical assumptions and Hirota’s bilinear technique, the new types of M-soliton and N-soliton solutions are derived and established in view of the hyperbolic, trigonometric, and rational functions of the governing equation. To achieve this, an illustrative example of the nonlinear wave equation in a liquid with gas bubbles is provided to demonstrate the feasibility and reliability of the procedure used in this study. The trajectory solutions of the N-soliton are shown explicitly and graphically (2D, density, and 3D graphs). The effect of parameters on the behavior of acquired solutions for N= 3 , 4 , 5 orders are also discussed. By comparing the proposed method with the other existing methods, the results show that the execution of this method is concise, simple, and straightforward. The results are useful for obtaining and explaining some new soliton phenomena. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

High turnover in the pharmaceutical industry, the location of placebos in urban spaces, and the high rate of corruption of products in this industry are the distinguishing features of the drug supply chain. Thus, to survive and maintain competitive advantages in the current business environment, managers are active in this background to implement the theoretical foundations of supply chain management. One of the influential areas in this category is integrated inventory management, inventory control, and vehicle routing. Therefore, this study mainly aims to analyze and define a routing problem, inventory in the drug supply chain with perishable products, and travel time dependence on multiple graphs with travel time dependence. The Box-Jenkins forecasting method has been utilized to meet the study's aim and deal with demand uncertainty. This method can identify the best pattern governing the data. Finally, the mathematical model is validated, and managerial perspectives are provided. The study results demonstrated the possibility of achieving cost-saving and reducing product spoilage. Applying the solutions of this model can provide some inherent social and environmental advantages, including reducing traffic load and emissions. © 2022 Madad Ali et al.

Smartphones have recently surged in popularity, and the Internet has transcended time and geography to become a global instructional tool. The design of educational activities for digital learning and the utilization of technology tools are the core concerns of integrated education. Students were examined and questionnaires were completed in order to better understand their perspectives on digital learning. In order to better understand students' perspectives on digital learning, they were examined and asked to fill out questionnaires. A total of 116 students from four higher education institutions were chosen as educational research subjects. The findings showed that digital learning has a bigger influence on learning stimulation than traditional teaching, as well as a greater impact on learning outcomes. In addition, learning motivation has been found to have a large beneficial impact on learning outcomes, as well as a surprisingly good impact on learning acquisition, and it plays a vital part in improving the quality of university education to develop successful teaching practices, it is anticipated to conform with current teaching trends and take advantage of the benefits of digital learning. © 2022, Association Res Militaris. All rights reserved.

The local convergence analysis of the multi-step seventh order method to solve nonlinear equations is presented in this paper. The point of this paper is that our proposed study requires a weak hypothesis where the Fréchet derivative of the nonlinear operator satisfies the (Formula presented.) -continuity condition, which thereby extends the applicability of the method when both Lipschitz and Hölder conditions fail. The convergence in this study is considered under the hypotheses on the first-order derivative without involving derivatives of the higher-order. To find a subset of the original convergence domain, a strategy is devised here. As a result, the new Lipschitz constants are at least as tight as the old ones, allowing for a more precise convergence analysis in the local convergence case. Some concrete numerical examples showing the performance of the method over some existing schemes are presented in this article. © 2022 by the authors.

This paper deals along the solutions numerical and presentence estimates As fractional equation as differential class, while the problem nonlinear part admits some distinct hypotheses. In particular, As precise parameter localization, the non-0 solution presentence is recognized requiring the sub linearity nonlinear part at infinity and origin. Furthermore, theoretical and numerical examples of applications are provided. © Published under licence by IOP Publishing Ltd.