Global Journal of Sciences
type: channel
description: Global Journal of Sciences is a peer-reviewed journal committed to advancing high-quality research across all scientific disciplines, including mathematics, physics, biology, chemistry, economics, computer science, and environmental science. Its mission is to address pressing global challenges through rigorous research that fosters interdisciplinary dialogue and collaboration. The journal publishes peer-reviewed research articles and reviews that investigate scientific insights and their societal and environmental implications. By encouraging diverse contributions, it seeks to integrate various scientific perspectives, enhancing understanding of complex issues such as energy systems, environmental modeling, and public health challenges. Through its inclusive approach, the Global Journal of Sciences promotes the exchange of ideas and solutions, making scientific knowledge accessible to a broad audience and supporting informed decision-making for a better future.
publisher: ACS Publisher
language: en
publication name: Global Journal of Sciences
issn: 3049-0456
items: A Mathematical Model of Cancer Transmission with Immune Response Dynamics and Optimal Drug Dosing
items: Using the Fractional Characteristic Method in Practice to Determine the Precise Solution of Fractional Partial Differential Equations
items: Incorporating A Hybrid Fuzzy Logic Model in Diet Style Selection
items: Comparative analysis of banana tree waste, cow dung manure, lignite coal co combustion for reducing pollutants
items: Spatial Analysis and Predictive Modeling of Cancer Incidence in Lake County Illinois
items: Oscillation Criteria of Fourth-Order Nonlinear Differential Equations of the Delay Type
items: Systematic Literature Review of Arabic NLP Datasets: A Meta-Study
items: K-means Clustering in Identifying Epidemiological Patterns of Measles in United States of America Between 2009-2024
items: A Review of Green Technologies for Cellulose Extraction from Lignocellulosic Biomass: Pretreatment Methods and Applications
items: Improving Stock Price Volatility Prediction Models Using Machine Learning and Statistical Techniques
items: The Impact of Capital Reduction on Share Prices: An Event Study of Companies Listed on the Saudi Stock Exchange (Tadawul)
items: Analysis of Ricci Inheritance Collineations for Cylindri cally Symmetric Spacetimes with Degenerate Ricci Tensor
items: Renormalization via deformation of star products
items: Variational Iteration Method with an Auxiliary Parameter for Some Kinds of Partial Differential Equations
items: Synthesis, Photophysical Properties and Metal Sensing Potential of Carbon Dots from Fried Onions
items: Generalization of Hermite-Hadamard Integral Inequality via Caputo Fabrizio Fractional Integral Operator
items: Hermite-Hadamard Type Inequality Via Caputo-Fabrizio Fractional Integral Operators: A Review Note
items: Solving Obstacle Problems using Optimal Homotopy Asymptotic Method
items: FEM Approach for Efficient Solution of Two-Dimensional Navier-Stokes Equations
items: Controlling the Behavior of Cancer Cells Using Electro magnetic Fields in the Orchestrated Objective Reduction Model
items: AI-Enabled IoT System for Smart Healthcare and Emergency Response in Developing Countries
items: Ulam-Hyers Stability with Explicit Bounds for Switched Integro-Differential Systems under Dwell-Time Switching
items: Double Mohand Integral Transform for Solving Wave Equations
items: Solving Heat Equations via Double Mohand Transform
items: A Hybrid Cnn-Lstm-Based Rnn Model for Classification of Schizophrenia
items: Hyers–Ulam Stability for Fractional Hybrid Switched Systems in Hölder Spaces
items: The Logistics Factors that Affect the Academic Achievement of Elementary School Students in the Kingdom of Saudi Arabia (Applied Statistical Study)
items: Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision Making Method-PROMETHEE
items: Goodness-of-Fit Tests for Inverse Ishita Distribution and Its Application on Renewable Energy Data in the Kingdom of Saudi Arabia
items: On Double Delay Terms Robbin Boundary Value Problem with Fractional Derivative

A Mathematical Model of Cancer Transmission with Immune Response Dynamics and Optimal Drug Dosing
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description: Cancer arises from intricate interactions among tumor cell proliferation, immune system dynamics, and therapeutic interventions. In this study, we develop a mathematical model that incorporates tumor growth, immune stimulation and suppression, as well as drug dynamics. The disease-free and endemic equilibria, and the basic reproduction number (R0) are computed to study the dynamics of the disease. Stability analysis is conducted to determine the conditions under which malignant cells either persist or are eradicated. Furthermore, the model is extended by introducing an optimal control parameter for drug infusion, with Pontryagin’s Maximum Principle applied to characterize the optimal therapeutic strategy. This study shows the theoretical insights into the delicate balance between tumor suppression and immune exhaustion, thereby providing guidance for the design of an effective treatment strategy.
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creator: Kamlesh Kumar
creator: Amar Nath Chatterjee
creator: Fahad Al Basir
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-15
publication date: 2026-05-15
starting page: 26
ending page: 35
doi: 10.48165/gjs.2026.3103

Using the Fractional Characteristic Method in Practice to Determine the Precise Solution of Fractional Partial Differential Equations
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description: An effective technique for analytically resolving fractional partial differential equations (FPDEs) is the analytic Fractional Characteristic Method (FCM). In this work, we examine the applicability of the FCM to several FPDEs and demonstrate their efficacy in solving them accurately. We go over the key components of the FCM and its advantages over other numerical and analytical techniques. According to our research, the FCM is a dependable and efficient technique for solving FPDEs, and it has the potential to be used in a variety of technological and scientific fields. Researchers and practitioners interested in using the FCM to find precise solutions to FPDEs can refer to the work’s outcomes, accurate soliton solutions for many applications, which are essential to engineering, optical communications, and nonlinear optics.
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creator: Muhammad Bilal
creator: Javed Iqbal
creator: M Ali Akbar
creator: Ikram Ullah
creator: Mah Noor
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-16
publication date: 2026-05-16
starting page: 82
ending page: 90
doi: 10.48165/gjs.2026.3108

Incorporating A Hybrid Fuzzy Logic Model in Diet Style Selection
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description: The decisions and emotions of people are highly influenced by food pattern which plays a vital role in their daily activities. Dieting patterns differ in terms of the goals set by a certain individual. People are advised to know how to regulate their diets and select eating habits that will promote health by preventing food-related diseases. Six diet styles were evaluated and ranked by considering multiple important factors utilizing a multi-criteria decision-making algorithm; the fuzzy Preference Ranking Organization Method for Enrichment Evaluations (fuzzy PROMETHEE). Of all considered diet styles, the low-carb diet ranked first diet style with a net flow of 0.2066, and the intermittent diet ranked sixth with a net flow of -0.3232; however, depending on the chosen criteria and assigned weights, the outcomes would change dramatically when weights are modified according to the specific person’s profile. This study will help individuals, dieticians, and nutritionists make appropriate diet plans by choosing which pattern to follow with awareness of the probable risk and consequences associated with their choice.
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creator: Natacha Usanase
creator: Berna Uzun
creator: Dilber Uzun Ozsahin
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 37
ending page: 50
doi: 10.48165/gjs.2025.2204

Comparative analysis of banana tree waste, cow dung manure, lignite coal co combustion for reducing pollutants
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description: Combining coal with biomass is the best way to reduce pollution in the environment and contribute to renewable energy sources. The TGA investigation of lignite coal, tree leaves, and their mixes was investigated in this paper. The main objective is to ascertain the thermal properties of coal and tree leaves using the thermogravimetric test. The recommended TGA method makes use of an ambient temperature of 90°C. Temperatures ranging from 105- 110 °C were used to dewater the samples. The greatest amount of volatile matter is attained as you increase the amount of cow dung manure and banana tree waste, and the highest amount of carbon that is fixed (char burning) is LC90+CDM10.There are numerous advantageous conditions for dewatering, devolatilization, and char burning in the lignite coal-to-biomass mixing ratio. The co combustion of these materials can also minimize waste disposal problems by utilizing agricultural residues effectively. Moreover, it supports the circular economy approach by converting waste into valuable energy. These findings suggest that coal–biomass blends can serve as a transitional energy solution toward fully renewable systems in the future.
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creator: Mohammad Siddique
creator: Saadatullah khan
creator: Malik Muhammad Hafeezullah
creator: Arshad Ali
creator: Muhammad Anwar
creator: Behram Ali
creator: Saeed Afzal
creator: Muhammad Hamza Arif
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-15
publication date: 2026-05-15
starting page: 43
ending page: 52
doi: 10.48165/nj8m9a78

Spatial Analysis and Predictive Modeling of Cancer Incidence in Lake County Illinois
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description: Certain regions may have higher cancer rates just by accident. Generally speaking, only when the pattern is statistically significant does it merit further investigation. This research was aimed at determining the relationships between cancer types, hotspots, and spatial patterns while considering environmental and demographic factors. An overview of the distribution of cancer and the relationships between different cancer types was provided by the data on cancer types gathered across ZIP codes from the Lake County Geographic Information System (GIS) in Illinois, as regards the cancer incidence updated (November 2024). Descriptive statistics such as minimum, maximum, and mean were used to summarize the data; skewness and outliers were visualized using a histogram. Scatterplots, bar plots, and spatial mapping were employed to describe the spatial variations and identified hotspots based on relative risks (RR). The relationships between cancer types were explored using correlation, and a linear regression model was employed to assess the factors that affect other types of cancer. Findings showed that cancers have significant maximum incidences such as breast, prostate, and lung cancers, while colorectal and urinary system cancers have lower minimum incidences. From the scatter and bar plot distribution of cancer incidence rates across the study, results revealed areas in Illinois with ZIP codes such as 60069, 60035, 60045 and 60044 are classified as hotspots, with higher chances of cancer incidence. Breast cancer and Urinary cancer System were significantly linked to the other types of cancers, through spatial mapping. These results highlight how critical it is to address demographic and environmental issues that might be causing these trends.
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creator: Abdulrahman Abdullateef
creator: Bello Sirajo
creator: Mashood Olumuyiwa Lawal
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-15
publication date: 2026-05-15
starting page: 36
ending page: 42
doi: 10.48165/gjs.2026.3104

Oscillation Criteria of Fourth-Order Nonlinear Differential Equations of the Delay Type
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description: In this work, we establish new oscillation criteria for a class of fourth-order delay differential equations with noncanonical case. The paper presents certain oscillation criteria using a distinct comparison method approach using lower-order equations and generalized Riccati transformations. The capacity of the suggested approach to simplify the fourth-order problem by transforming it into first-order forms and enabling the use of proven oscillation theories is one of its main advantages. This method provides more effective and widely applicable outcomes in addition to extending current criteria to higher-order FDEs. The method’s efficacy and wider applicability are confirmed by thorough comparisons with prior studies, which also show the viability and importance of our findings as an extension of earlier findings.
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creator: Naseema Al Zawqari
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-13
publication date: 2026-05-13
starting page: 1
ending page: 12
doi: 10.48165/gjs.2026.3101

Systematic Literature Review of Arabic NLP Datasets: A Meta-Study
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description: This meta-analysis is a systematic review and synthesis of the Arabic Natural Language Processing (NLP) dataset landscape, in accordance with PRISMA guidelines. The review locates and categorizes publicly accessible datasets in NLP tasks that span a spectrum of sentiment analysis, text classification, question answering, summarization, dialect detection, and paraphrasing. Key datasets catalogues like Masader and Masader Plus are underscored as driven by enhancing discoverability and metadata standardisation, whereas task and domain-specific creation of resources are represented by ArabSis, SNAD, A-MASA, AGS and WiHArD. As can be seen, although the amount and variety of the datasets have grown in recent years, there is still a considerable number of gaps in the coverage of dialects, domain specificity and quality of annotations. The review ends with suggestions on how to extend underexploited areas, annotation behavior, and open-access to accelerate the Arabic NLP research and application.
subject: Research Articles
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creator: Amani Jamal
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-13
publication date: 2026-05-13
starting page: 13
ending page: 25
doi: 10.48165/gjs.2026.3102

K-means Clustering in Identifying Epidemiological Patterns of Measles in United States of America Between 2009-2024
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description: Tracking measles vaccination coverage is an essential factor in preventing subsequent outbreaks and building herd immunity. Thus, recognizing trends in immunization and surveillance assist in tailoring public health efforts in different locations. The study employs K-means clustering to detect distinctive epidemiological trends among U.S.A. states using measles, mumps, and rubella (MMR) vaccine coverage data obtained across states from kindergarteners from 2009 to 2024 using the CDC dataset. States sourced dataset on population size, percentage surveyed, and coverage of vaccination groups were preprocessed and standardized to exclude outliers. Using the elbow approach and silhouette analysis, we calculated the ideal number of clusters. K-means clustering was then utilized to categorize states based on similarities in surveillance features. In our results, three major clusters appeared: Cluster 1 featured states with substantial populations and wide variation in survey coverage. Cluster 2 included smaller states with regular high survey rates and MMR coverage. Cluster 3 showed modest population sizes and coverage rates. Visualizations indicated major differences in survey approach and immunization criteria across clusters. K means clustering successfully identified hidden patterns in MMR vaccine monitoring trends across USA. These results provide useful insights for modifying public health efforts and enhancing vaccination reporting systems across the country.
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creator: Basil B Duwa
creator: Murat Sayan
creator: James Gambu
creator: Huzaifa Umar
creator: Celestine Iwendi
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-16
publication date: 2026-05-16
starting page: 91
ending page: 106
doi: 10.48165/gjs.2026.3109

A Review of Green Technologies for Cellulose Extraction from Lignocellulosic Biomass: Pretreatment Methods and Applications
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description: The majority of the material called lignocellulose biomass (LCB), a type of plant biomass, is made up of these three compounds. The most common polymer to break down on Earth is cellulose, which is very useful and has many uses. Because cellulose is securely linked to the compound’s hemicellulose and lignin in lignocellulosic materials, it is highly challenging to separate the material from LCB components; therefore, effective pretreatment techniques are required to make the material separation easier. Green pretreatment approaches have been proposed as a gentler and more environmentally responsible way to address these problems. When assessing these green processes, a variety of factors are taken into account, such as the effectiveness of delignification with a lower solvent demand, the impact on the environment, the economic feasibility, and the suitability for new sustainable processing solutions. We also assessed how using biomass affected land use, production of greenhouse gases, and group effects, among other socioeconomic and environmental factors. This paper examines the challenges, ongoing research, and recent developments in effective biomass-based energy systems to increase energy output while lowering environmental concerns. Additionally, it provides a summary of how cellulose is separated from various lignocellulosic biomass using sustainable and green methods.
subject: Review Article
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creator: Muhammad Asif
creator: Arshad Ali
creator: Abdul Ghaffar Bugti
creator: Namatullah
creator: Mohammad Siddique
creator: Saadatullah Khan Suri
creator: Muladad
creator: Anwar khan
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-15
publication date: 2026-05-15
starting page: 67
ending page: 81
doi: 10.48165/gjs.2026.3107

Improving Stock Price Volatility Prediction Models Using Machine Learning and Statistical Techniques
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description: This research aims to improve forecasting models for the volatility of stock prices in Saudi banks by using machine learning algorithms and parameter optimization techniques such as Grid Search. The goal of this study is to improve forecasting models. Grid Search is going to be used in order to attain this goal. We examined the price information that Al Rajhi Bank has accumulated over a considerable length of time and the results were analyzed. The starting and closing prices, the highest and lowest values, the trading volume, and the daily percentage change were all included in this. Additionally, the trade volume was an important factor. In order to throw light on the general patterns that were present, we used descriptive data analysis. Given that the Saudi financial market is driven by both internal and external variables, the data indicated that there was a substantial degree of variation in both the trading volume and the price. This is typical for the Saudi market since it is influenced by both types of factors. Next, a machine learning model that was produced based on Random Forest was developed, along with a linear regression model and a ridge model. After that these models were constructed. Taking into account the outcomes of the research, the Random Forest model, which had been enhanced via the use of Grid Search, was the most accurate in terms of predicting future volatility. The coefficient of determination (R2) value of about 0.86 makes it superior than linear models in terms of its capacity to make assumptions and predictions. Moreover, the results indicate the need of using nonlinear models in combination with optimization methodologies in order to focus on the complicated relationships that exist between financial issues and price fluctuations. This is because the outcomes highlight the need to use nonlinear models.
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creator: Razaz Houssien Felimban
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-11-15
publication date: 2025-11-15
starting page: 112
ending page: 131
doi: 10.48165/gjs.2025.2209

The Impact of Capital Reduction on Share Prices: An Event Study of Companies Listed on the Saudi Stock Exchange (Tadawul)
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description: This research evaluates the impact that capital reduction announcements have on the stock prices of firms that are listed on the Saudi Stock Exchange (Tadawul) between 2020 and 2024. Specifically, the study will focus on the period between 2020 and 2024. This research makes use of the event study methodology in order to assess the effectiveness of the market as well as the actions of investors in response to business events. In order to achieve this task, it is necessary to evaluate the course of market responses and the timing of those reactions. To be more specific, the objective of this study is to analyze whether or not the reduction in capital serves as an early warning sign of financial difficulties, or whether or not it is a deliberate process of financial restructuring with the idea of boosting the organization's worth over the long run. According to the findings of empirical research, announcements of a decrease in capital at the time of the event do not evoke a market reaction that is statistically significant in a negative direction. After an initial period of investor concern, the data suggests that there was a relatively favorable cumulative impact. This is in contrast to the previous statement. The fact that this is the case shows that market participants may see these declarations as components rather than indicators of a crisis or of aggressive financial management. The purpose of this study is to contribute to the knowledge of financial restructuring, company signaling, and market efficiency in emerging countries. The dissemination of data extracted from the expanding Saudi capital market is how this objective is fulfilled. These results shed light on the need of conducting an evaluation of activities that are targeted at decreasing capital within the context of a strategic governance framework that is in accordance with the goals of Saudi Arabia's Vision 2030. As a result of this, they bring attention to the possible importance that these activitie…
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creator: Najla Fath Al-Rahman Al-Qadi
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-11-20
publication date: 2025-11-20
starting page: 143
ending page: 165
doi: 10.48165/gjs.2025.2211

Analysis of Ricci Inheritance Collineations for Cylindri cally Symmetric Spacetimes with Degenerate Ricci Tensor
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description: This research explores the Ricci Inheritance Collineations (RICs) within cylindrically symmetric Marder type spacetimes. We categorize these spacetimes based on their RICs, examining both degenerate and non-degenerate Ricci tensor configurations. Through solving a system of coupled partial differential equations across thirteen distinct scenarios, we provide a thorough examination of inheritance symmetries. Our findings demonstrate the existence of infinite dimensional RICs in multiple cases, offering valuable insights into the geometric and physical characteristics of these spacetimes.
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creator: Imran Khan
creator: Amjad Ali
creator: Suhail Khan
creator: Anum Azad
creator: Bakht Zada
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-11-20
publication date: 2025-11-20
starting page: 132
ending page: 142
doi: 10.48165/gjs.2025.2210

Renormalization via deformation of star products
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description: Search for non-perturbative extensions of deformation quantization is one of the most important fundamental challenges in mathematical physics and quantum theories. Applying infinite combinatorics for the construction of mathematical foundations of non-perturbative quantum field theory has already been considered and developed as an alternative practical methodology for the study of strongly coupled gauge field theories. The present article reports the impact of this new practical methodology to deformation quantization. It is shown that the topological enrichment of the Hopf algebra of renormalization leads us to generate a new family of star products which (i) encode the renormalization of combinatorial Dyson–Schwinger equations, and (ii) provide a new non-perturbative quantization program for a certain family of noncommutative algebras. Results of this study addresses a new interconnection between deformation quantization and mathematical foundations of gauge field theories in the context of the topological Hopf algebra of renormalization
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creator: Ali Shojaei Fard
rights: Copyright (c) 2026 Global Journal of Sciences
date: 2026-05-15
publication date: 2026-05-15
starting page: 53
ending page: 66
doi: 10.48165/gjs.2026.3106

Variational Iteration Method with an Auxiliary Parameter for Some Kinds of Partial Differential Equations
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description: In this paper, we present the Modified Variational Iteration Algorithm-I (MVIA-I) for obtaining numerical solutions of the modified equal width (MEW) wave equation and the regularized long wave (RLW) equation. This recently developed technique introduces an auxiliary parameter that significantly accelerates the convergence of the series solutions. The method provides both approximate and exact solutions with easily computable terms for linear and nonlinear PDEs, without requiring Adomian polynomials, small perturbations, discretization, or linearization. To evaluate its accuracy, reliability, and efficiency, the results obtained by the proposed algorithm are compared with those of the standard Variational Iteration Method (VIM) and the Reduced Differential Transform Method (RDTM). The comparison clearly shows that MVIA-I is computationally efficient, highly accurate, and superior to existing methods. Two numerical test problems are included to assess the performance of the modified algorithm, and the accuracy is measured by calculating the absolute errors for different parameter values. The results demonstrate that MVIA-I converges rapidly and provides solutions of excellent precision, confirming its effectiveness as a robust tool for solving nonlinear PDEs.
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creator: Waqar Amin
creator: Tufail Ahmad Khan
creator: Jutarat Kongson
creator: Amir Muhammad
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 86
ending page: 93
doi: 10.48165/gjs.2025.2207

Synthesis, Photophysical Properties and Metal Sensing Potential of Carbon Dots from Fried Onions
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description: Using onion as a precursor and a simple hydrothermal procedure, we present a novel, straight forward and environmentally friendly method for synthesizing highly luminous carbon dots (CDs). The optical and physiochemical properties of the synthesized CDs were investigated using scanning electron microscopy (SEM), X ray diffraction (XRD), Fourier transform infrared (FTIR), UV-visible, fluorescence spectroscopy, and elemental analysis. These CDs exhibited excellent aqueous dispersibility, excitation dependent fluorescence emission, and remarkable stability under various conditions such as pH, high ionic strength, and continuous irradiation. Notably, the presence of Fe2+ and Cr6+ ions result in significant fluorescence quenching. Therefore, the CDs were employed as a fluorescent probe for the selective detection of Fe2+ and Cr6+ ions, achieving a good linear correlation (R2 = 0.996) in the concentration range of 0-20 μM with a detection limit of 0.31 μM. The detection capability of these CDs for Fe2+ and Cr6+ was enhanced by their high selectivity, photo stability, and excellent biocompatibility.
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creator: Amir Saddiq
creator: Izhar Ul Haq Ali Shah
creator: Shah Zeb Ullah
creator: Qismat Ullah
creator: Safdar Alam
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 23
ending page: 36
doi: 10.48165/gjs.2025.2203

Generalization of Hermite-Hadamard Integral Inequality via Caputo Fabrizio Fractional Integral Operator
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description: Certainly, fractional calculus has grabbed the keen interest of numerous investigators in the last couple of decades. Numerous real-world problems and demonstrations have involved the development of the Caputo-Fabrizio fractional operator. Our article’s primary goal is to enhance Hermite-Hadamard (H-H) integral inequality and its generalizations by applying the Caputo-Fabrizio fractional integral operator (CFFIO) and the preinvexity concept. We additionally offer some applications of our key findings to specific methods.
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creator: Ajab Ali
creator: Muhammad Tariq
creator: Asif Ali Shaikh
creator: Hijaz Ahmad
creator: Clemente Cesarano
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 15
ending page: 22
doi: 10.48165/gjs.2025.2202

Hermite-Hadamard Type Inequality Via Caputo-Fabrizio Fractional Integral Operators: A Review Note
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description: The idea of convexity is mainly employed in the realm of fractional calculus to address a number of issues in pure and practical research. The purpose of this review note is to demonstrate Hermite-Hadamard (H-H) inequalities via Caputo Fabrizio fractional integral operators associated with various topic of convexity.
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creator: Ajab Ali
creator: Muhammad Tariq
creator: Asif Ali Shaikh
creator: Dawood Khan
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 51
ending page: 60
doi: 10.48165/gjs.2025.2205

Solving Obstacle Problems using Optimal Homotopy Asymptotic Method
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description: Differential equations play a vital role in explaining real-world phenomena across science and engineering, from fluid motion and population growth to the mechanics of bridges. In particular, the cantilever bridge problem can be framed as a homogeneous obstacle problem, which illustrates the practical importance of such mathematical models. Although obstacle problems have been studied extensively, many existing approaches leave gaps that limit their effectiveness. In this work, we apply the Optimal Homotopy Asymptotic Method (OHAM) to derive exact solutions for several obstacle problems. Our findings not only demonstrate the accuracy and efficiency of this approach but also highlight interesting structural symmetries, as revealed through graphical results that provide deeper insight into the nature of these problems.
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creator: Muhammad Amjad
creator: Haider Ali
creator: Muntazim Abbas Hashmi
creator: Umber Rana
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 94
ending page: 111
doi: 10.48165/gjs.2025.2208

FEM Approach for Efficient Solution of Two-Dimensional Navier-Stokes Equations
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description: In this paper, the Galerkin finite element method was used to solve the Navier-Stokes equations for two dimensional steady flow of Newtonian and incompressible fluid with no body forces usingMATLAB. The method was applied to the lid-driven cavity problem. The eight-noded rectangular element was used for the formulation of element equations. The velocity and pressure components were located at all of 8 nodes of the element. From location of velocity components and pressure, it is obvious that this element consists of 20 unknowns for velocities and pressure. The quadratic interpolation functions represent velocity components while bilinear interpolation function represents pressure. Finite element codes were developed for implementation.
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creator: Haider Ali
creator: Muhammad Amjad
creator: Tehreem
creator: Shah Jahan
creator: Muhammad Zaman
creator: Aamir Hussain Khan
creator: Muhammad Ramzan
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 61
ending page: 85
doi: 10.48165/gjs.2025.2206

Controlling the Behavior of Cancer Cells Using Electro magnetic Fields in the Orchestrated Objective Reduction Model
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description: In this paper, we propose a novel theoretical framework within the orchestrated objective reduction (Orch OR) model to investigate the role of electromagnetic fields in controlling the behavior of cancer cells. According to our approach, the brain captures, stores, and analyzes cellular information through electromagnetic signaling, subsequently sending regulatory commands to maintain normal cellular function. When cells undergo malignant transformation, the stored information and the corresponding electromagnetic signals change, providing an opportunity for early detection of tumor formation. We further suggest that by storing the correct information and transmitting appropriate electromagnetic signals, the brain may create the conditions necessary to restore cancerous cells to their normal state. This work introduces a new perspective on the early diagnosis and potential control of cancer based on quantum neurobiological principles.
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creator: Jamal Al Karaki
creator: Muhammad Al Zafar Khan
creator: Raul Valverde
creator: Chet Swanson
creator: Alireza Sepehri
creator: Massimo Fioranelli
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-10-08
publication date: 2025-10-08
starting page: 1
ending page: 14
doi: 10.48165/gjs.2025.2201

AI-Enabled IoT System for Smart Healthcare and Emergency Response in Developing Countries
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description: This paper presents a novel AI-enabled Internet of Things (IoT) framework to transform healthcare delivery and emergency response in developing countries. The proposed system combines wearable sensing based on TinyML, deep learning inference on-device, and triage powered by reinforcement learning in a hierarchical edge–cloud architecture optimized for data privacy and low latency. To enable adaptive edge–cloud orchestration for real-time analytics and predictive diagnostics without jeopardizing patient data, federated learning is used for decentralized, secure model training. By assessing vital signs, geolocation, geographical location, and resource availability, a reinforcement learning agent dynamically prioritizes emergencies, maximizing response times and triage effectiveness. In a simulated rural Sub-Saharan African environment, field validation produced diagnostic accuracy of over 93%, outperforming conventional cloud-only systems in speed and efficiency. The user-friendly interface facilitates multilingual, cross-modal communication through voice confirmation, app alerts, and SMS for populations with low literacy and internet access. The system's 2.3-second real-time warning latency makes it particularly useful for tracking infectious diseases, chronic conditions, and maternal care. This scalable, morally sound paradigm offers a guide for AI-driven health innovation in underprivileged environments, and it is in line with the UN Sustainable Development Goals (SDGs 3 and 9).
subject: Research Articles
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creator: Bulus Bali
creator: Huzaifa Umar
creator: Basil Duwa
creator: Yoshebel F Zirra
creator: Kefas Ibrahim Hyelda
creator: Berna Uzun
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 43
ending page: 52
doi: 10.48165/gjs.2025.2104

Ulam-Hyers Stability with Explicit Bounds for Switched Integro-Differential Systems under Dwell-Time Switching
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description: This paper establishes Ulam-Hyers stability for first-order switched Volterra integro-differential equations under average dwell time switching constraints. We analyze systems of the form:y′(t)=pσ(t)(t)y(t)+qσ(t)(t)+∫0tKσ(t)(t,s)y(s) ds,t∈[0,T]y′(t) = p_{\sigma(t)}(t)y(t) + q_{\sigma(t)}(t) + \int_0^t K_{\sigma(t)}(t, s)y(s) \, ds,\quad t \in [0, T]y′(t)=pσ(t)​(t)y(t)+qσ(t)​(t)+∫0t​Kσ(t)​(t,s)y(s)ds,t∈[0,T]where σ(t):[0,T]→{1,…,N}\sigma(t) : [0, T] → \{1, \dots, N\}σ(t):[0,T]→{1,…,N} is a switching signal with average dwell time τa>0\tau_a > 0τa​>0. Using successive approximation methods, we prove existence and uniqueness of solutions while deriving explicit error bounds for approximate solutions. The stability constantC=T(N0+T/τa)e(M1+M2T)TC = T(N_0 + T/\tau_a)e^{(M_1 + M_2T)T}C=T(N0​+T/τa​)e(M1​+M2​T)Tquantifies robustness by incorporating switching parameters, kernel bounds, and the time horizon. Our results extend classical integro-differential equation theory to switched systems with memory effects, providing a foundation for robust control design in applications such as power electronics, biological processes, and thermal management systems.
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creator: Bakht Zada
creator: Naveed Hussain
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 53
ending page: 62
doi: 10.48165/gjs.2025.2105

Double Mohand Integral Transform for Solving Wave Equations
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description: Many fields of the physical sciences like physics, chemistry and mathematics use partial differential equations to model a physical system. As a result, there are diverse techniques in the literature for the solution of partial differential equations which tries to attain symmetry. This paper proposes a fresh double transform method recognized as the double Mohand Transform Method. Besides the explanation and major remarks of the proposed double transform method, new results on partial derivatives are also given. Moreover, to validate the importance of the double Mohand Transform Method in solving systems of partial differential equations, a variety of symmetric examples on the wave equation with various properties are solved using this approach.
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creator: Muhammad Abbas
creator: Shahab Ali
creator: Shakir Ullah
creator: Wahid Rehman
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 99
ending page: 111
doi: 10.48165/gjs.2025.2109

Solving Heat Equations via Double Mohand Transform
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description: Partial Differential Equations are vital in applied mathematics, chemistry, and physics since they model a variety of phenomena including the dynamics of fluids, the conduction of heat, and the propagation of waves. Due to their wide-ranging applications, PDEs have received a lot of attention for the development of efficient and accurate solution methodologies. Most of these attempts stress on accomplishing symmetry that simplifies the equations and facilitates easier solutions, whether analytical or numerical, to be found. To offer a different and efficient method to solving PDEs, we introduce in this article the Double Mohand Transform Method, an innovative integral transform method. This innovative double transform technique builds upon former integral transforms by incorporating vital elements that enhance solution efficiency and applicability. In this work, we explain comprehensively the double Mohand transform with special focus on its defining features, advantages, and unique qualities. In a more theoretical context, we present new results regarding its foundations and the partial derivatives of the transform. To explore the importance as well as effectiveness of the Double Mohand Transform Technique, we apply it on some cases of the heat equation with different boundary and initial conditions. By exploiting the symmetry features of the heat equation, our method correctly deals with complex PDEs by providing elegant and efficient solutions. The outcomes illustrate how this novel transformation could further progress the mathematical methods for differential equations applicable in various branches of science and engineering.
subject: Research Articles
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creator: Hasnain Haider
creator: Shahab Ali
creator: Muhammad Abbas
creator: Shakir Ullah
creator: Wahid Rehman
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 63
ending page: 74
doi: 10.48165/gjs.2025.2106

A Hybrid Cnn-Lstm-Based Rnn Model for Classification of Schizophrenia
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description: Schizophrenia is a mental health condition that affects the behavior and thought process of a person. With proper treatment, such as medication and therapy, many people with this medical condition are able to manage their symptoms and live meaningful lives. Thus, there is a need for early diagnosis of schizophrenia. However, due to the wide range of symptoms displayed by different schizophrenic patients, accurately identifying schizophrenia can proof challenging. Different conventional methods to try to classify schizophrenia are applied in many instances. Nevertheless, all aspects of the condition could not be sufficiently addressed by any of the conventional classification techniques. Researchers are currently testing the application of deep learning approaches for improved schizophrenia categorization. In this study, we present a novel hybrid RNN model that uses both CNN and LSTM technology for use in the classification of schizophrenia. The twelve layered model was developed using a total of 28 samples as input data. The dataset consisted of EEG signal recordings from 14 healthy controls and 14 schizophrenic patients. After training and testing, an accuracy of 79.35% was recorded by the model. This model has the potential to enable early detection of schizophrenia thereby leading to more optimized treatment and management methods. The study describes a new twelve layered CNN-LSTM hybrid framework for identifying schizophrenia employing EEG signals. Trained on data from 14 patients and 14 healthy controls, the framework obtained 79.35% accuracy, highlighting deep learning's promise for early success in schizophrenia detection.
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creator: Teyei Ruth Mangai
creator: Basil B Duwa
creator: Huzaifa Umar
creator: Dilber Uzun Ozsahin
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 88
ending page: 98
doi: 10.48165/gjs.2025.2108

Hyers–Ulam Stability for Fractional Hybrid Switched Systems in Hölder Spaces
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description: This paper establishes a comprehensive theory for fractional hybrid switched differential equations (FHSDEs) in Hölder spaces. We prove the existence of solutions using Schauder’s fixed-point theorem, uniqueness via Banach’s contraction principle, and Hyers-Ulam stability through a direct fixed-point approach. The switched system framework incorporates abrupt changes in fractional dynamics at switching points, while Hölder spaces provide the natural setting for solution regularity. Detailed proofs feature explicit calculations of key constants and inequalities. An illustrative example demonstrates the applicability of our results, including explicit stability constant computations. This work extends hybrid fractional theory to switched systems with Hölder-continuous solutions.
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creator: Bakht Zada
creator: Imran Khan
creator: Naveed Hussain
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 112
ending page: 121
doi: 10.48165/gjs.2025.2110

The Logistics Factors that Affect the Academic Achievement of Elementary School Students in the Kingdom of Saudi Arabia (Applied Statistical Study)
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description: This study aimed to examine the impact of logistical services on the academic achievement of primary school students using Exploratory Factor Analysis (EFA) and Stepwise Discriminant Analysis (DA). The EFA revealed five factors that explained 64.1% of the total variance: School Environment, Distance Between School and Home, Logistics Factors 1, Level of Education According to School Location, and Logistics Factors 2. These factors were extracted based on eigenvalues greater than 1 and were rotated using the Varimax method. Subsequently, four of these components were sequentially entered into the discriminant model through stepwise analysis: Level of Education According to School Location, Logistics Factors 1, Logistics Factors 2, and School Environment. Results indicated that Level of Education by Location was the most significant discriminator (Wilks’ Lambda = .338, F = 3420.828, p < .001), followed by logistical and environmental factors. The final model achieved a Wilks’ Lambda of .203. The standardized canonical discriminant function coefficients showed that the Level of Education According to School Location had the most decisive influence on the first discriminant function (1.055). As results indicate: • School location significantly impacts academic achievement, whether a school is located in an urban or rural area. • Improving logistical services, such as school transportation and delivering the textbook early, as the delay of these factors affects student achievement negatively. • Attention to the school environment, including lighting, ventilation, and recreational facilities, positively affects students' academic achievement. • The study recommends conducting future studies to investigate the remaining variables using alternative statistical methods.
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creator: S A Alsawadi
creator: Salwa L AlKhayyat
creator: R A Bakoban
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-08-23
publication date: 2025-08-23
starting page: 75
ending page: 87
doi: 10.48165/gjs.2025.2107

Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision Making Method-PROMETHEE
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description: The combination of multi-criteria decision-making (MCDM) methods and fuzzy logic technique offers novel approaches to decision-making in the treatment planning of liver cancer; hepatocellular carcinoma (HCC), particularly the decision on how to propose the right therapeutic approach depending on multiple criteria. Since none of the treatment methods can provide fully satisfactory results for liver cancer when considering different patients, it is crucial to identify the optimal therapeutic method tailored for each individual, based on certain pertinent criteria. This study provides insight into the various factors that are likely to influence HCC pharmacological treatments. All the chosen drugs were assessed based on their effectiveness in meeting each of the criteria considered. To achieve this, we applied an MCDM-fuzzy hybrid model that combines both fuzzy logic and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) technique. We evaluated eight FDA-approved medications for HCC treatment alternatives. These drugs include, Cabozantinib, sorafenib, Lenvatinib, Atezolizumab, Tivantinib, Nivolumab and Pembrozumab. Similarly, based on the multiple criteria approach, five alternatives were adopted such as efficacy, cost, safety, drug development stage and side effects. The ranking result revealed that Sorafenib ranked the highest and Tivantinib ranked the least. The study offers a structured and data-driven approach in the classification of drugs which provides valuable insights for health practitioners and policy makers and HCC treatment optimization.
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creator: Basil B. Duwa
creator: Natacha Usanase
creator: Berna Uzun
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 24
ending page: 36
doi: 10.48165/gjs.2025.2102

Goodness-of-Fit Tests for Inverse Ishita Distribution and Its Application on Renewable Energy Data in the Kingdom of Saudi Arabia
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description: In this article, we presented the goodness-of-fit tests for the inverse Ishita distribution with one parameter based on complete sample. Moreover, the maximum likelihood estimation method was used to estimate the unknown parameter of the distribution. In addition, a Monte Carlo simulation was also performed to verify the behavior of the maximum likelihood estimator. Ultimately, we applied the proposed distribution on renewable energy data in Saudi Arabia and compared it with Weibull and Shanker distributions.
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creator: Souha K Badr
creator: Dalal A Aljdani
creator: Hanaa Abu Zinadah
rights: Copyright (c) 2025 Global Journal of Sciences
date: 2025-03-31
publication date: 2025-03-31
starting page: 37
ending page: 42
doi: 10.48165/gjs.2025.2103

On Double Delay Terms Robbin Boundary Value Problem with Fractional Derivative
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description: In this manuscript, the existence theory of a mixed delay Robbin boundary value problem (RBVP) together with impulsive conditions is investigated. For the required results, we use topological degree of non-compactness. The concerned tools have the ability to relax the strong compact criteria to some weaker one. As compared to fixed point theory, the aforesaid proposed tools are more powerful and applicable in dealing various nonlinear problems of fractional order differential equations (FODEs) with different kinds of boundary conditions. Proper example is given to illustrate the results.
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creator: Sana Ullah
creator: Atta Ullah
creator: Mostafa M. A. Khater
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 88
ending page: 106
doi: 10.48165/gjs.2024.1210