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.
Incorporating A Hybrid Fuzzy Logic Model in Diet Style Selection
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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Improving Stock Price Volatility Prediction Models Using Machine Learning and Statistical Techniques
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.
The Impact of Capital Reduction on Share Prices: An Event Study of Companies Listed on the Saudi Stock Exchange (Tadawul)
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 activities may have in increasing the confidence of investors and assuring the organization's capacity to continue operating financially in the future.
Analysis of Ricci Inheritance Collineations for Cylindri cally Symmetric Spacetimes with Degenerate Ricci Tensor
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.
Variational Iteration Method with an Auxiliary Parameter for Some Kinds of Partial Differential Equations
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.
Synthesis, Photophysical Properties and Metal Sensing Potential of Carbon Dots from Fried Onions
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.
Generalization of Hermite-Hadamard Integral Inequality via Caputo Fabrizio Fractional Integral Operator
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.
Hermite-Hadamard Type Inequality Via Caputo-Fabrizio Fractional Integral Operators: A Review Note
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.
Solving Obstacle Problems using Optimal Homotopy Asymptotic Method
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.
FEM Approach for Efficient Solution of Two-Dimensional Navier-Stokes Equations
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.
Controlling the Behavior of Cancer Cells Using Electro magnetic Fields in the Orchestrated Objective Reduction Model
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.
AI-Enabled IoT System for Smart Healthcare and Emergency Response in Developing Countries
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).
Predictive analytics federated learning Smart
Ulam-Hyers Stability with Explicit Bounds for Switched Integro-Differential Systems under Dwell-Time Switching
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.
Double Mohand Integral Transform for Solving Wave Equations
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.
Solving Heat Equations via Double Mohand Transform
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.
A Hybrid Cnn-Lstm-Based Rnn Model for Classification of Schizophrenia
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.
Hyers–Ulam Stability for Fractional Hybrid Switched Systems in Hölder Spaces
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.
Fractional hybrid switched differential equations
The Logistics Factors that Affect the Academic Achievement of Elementary School Students in the Kingdom of Saudi Arabia (Applied Statistical Study)
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.
Discriminant analysis Logistical factors
Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision Making Method-PROMETHEE
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.
Goodness-of-Fit Tests for Inverse Ishita Distribution and Its Application on Renewable Energy Data in the Kingdom of Saudi Arabia
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.
On Double Delay Terms Robbin Boundary Value Problem with Fractional Derivative
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.
In Vitro And Theoretical DNA Interaction Study of Zinc Complexes
In this work, we undertake a tentative analysis into the multi-layered realm of zinc complex DNA interactions, integrating empirical in vitro findings with cutting-edge theoretical modelling. The focus of our study involves the synthesis and thorough characterization of the Zn(DIP)2(DMP)2·2H2O complex, with the aim of establishing a fundamental comprehension of its structural attributes. The inquiry is built upon the structural foundation provided by analytical techniques such as UV-visible and IR spectroscopy, as well as 1H NMR data. In the molecular framework presented, we systematically investigate the principles of binding, thermodynamics, and kinetics that govern the dynamic interactions between zinc complexes and DNA. The progression of our exploration is characterized by a comprehensive examination of several spectral indicators and theoretical simulations, which serve to elucidate alterations in absorption spectra, disparities in binding constants, and structural adaptations. Our study focuses on the theoretical realm, utilizing computational methods and quantum chemistry calculations to explore the electronic structure, energy profiles, and binding energies. This approach allows us to gain a more profound theoretical comprehension of these interactions. This review serves to enhance our comprehension of the complex interactions between zinc and DNA, shedding light on potential applications in drug design, targeted delivery, and DNA-based nanotechnology. It underscores the importance of interdisciplinary collaboration in fully harnessing the diverse capabilities of zinc complexes across multiple domains.
Adaptation of the Optimal Auxiliary Function Method for Solving Highly Non Linear System of Fractional Order Partial Differential Equations
This research work represents the analysis of time-fractional system of highly non linear partial differential equation by applying the Optimal Auxiliary Function method (OAFM) while taking fractional derivative in Caputo sense. The OAFM does not require any polynomials like ADM method and small parameters like Perturbation method. The OAFM contains auxiliary constant which control the convergence of approximate solution in excellent way. Mathematica 13 is utilized to illustrate 2-dimensional curves and three-dimensional graphs and find numerical values that are displayed in a table. Furthermore, the OAFM and the exact solutions are compared. The numerical analysis reveals that the method is both valid and efficient as well as the suitability of the error bound. The results further demonstrate that the OAFM method is an effective approach for solving nonlinear physical models.
Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework
The convergence of digital twin technology and machine learning has ushered in a transformative era in patient monitoring and diagnosis within the healthcare sector. This review article explores the comprehensive integration of digital twin-driven machine learning frameworks, aiming to elucidate the core objectives, pivotal findings, and overarching implications. Our primary objectives encompass the exploration of digital twin technology's adaptation to healthcare, the augmentation of medical assessments through machine learning algorithms, the enabling of real time monitoring with early anomaly detection capabilities, and the personalization of treatment plans rooted in patient profiles generated by digital twins. The key findings underscore the successful adaptation of digital twin technology for healthcare applications, emphasizing its potential to capture dynamic patient data and history. The synergy between machine learning and digital twins enhances the precision of diagnostics and predictive analytics, thus improving healthcare outcomes. Real-time monitoring, made possible through digital twins, ensures proactive patient care with timely interventions. Moreover, personalizing treatment plans, tailored to individual patient profiles, offers a promising avenue for more effective and less invasive interventions. The implications of this review extend to the transformative potential of digital twin driven machine learning in healthcare, with the ability to revolutionize patient care, diagnostics, and monitoring. The review highlights data security and ethical challenges, stressing the need for standardized protocols to protect patient information. Ongoing research and innovation are crucial for maximizing these frameworks' potential, improving patient outcomes, and enhancing healthcare quality.
Do Environmental Policy Stringency and Financial Development Promote a Sustainable Energy Future in the United States? Evidence from Quantile-on Quantile Regression
Over the past few years, environmental challenges and the impacts of climate change have gained significant attention among policymakers and energy experts. Conforming to the objectives outlined in Sustainable Development Goals 7 and 13, which underscore the importance of clean energy provision and climate change mitigation, this study aligns with the sustainability targets set forth in Vision 2030. The research explores the intricate relationships among financial development, the stringency of environmental policies, advancements in information and communication technology (ICT), human capital, and economic growth. It aims to understand their combined effects on sustainable energy in the United States from the initial quarter of 1990 to the concluding quarter of 2020. Employing quantile-based methods including multivariable quantile-on-quantile regression (MQQR) and non-parametric quantile causality, our research reveals a positive influence across all quantiles for financial development, economic growth, environmental policy stringency, ICT innovations, and human capital on sustainable energy. Additionally, our quantile causality analysis confirms these factors significantly predict sustainable energy in the United States. These findings form the basis for policy recommendations.
The Application of PROMETHEE and K-means Clustering Techniques for Enhancing Robotic Assistance in Bariatric Surgery
Robotic bariatric surgery (RBS) uses robotics in weight loss operations. The robotic system has been tremendous in the field of bariatric surgery (BS) owing to its overwhelming advantages. As a result, surgeons perform RBS by controlling robotic arms equipped with surgical equipment from a console. In this study, our focus is to evaluate the BS dataset and techniques such as Roux-en-Y gastric bypass (RYGB), laparoscopic vertical sleeve gastrectomy (LGSV), adjustable gastric band (AGB), mini gastric bypass (MGB), single anastomosis duodenal bypass (SADI-S), open RYGB, biliopancreatic diversion with duodenal switch (BPD-DS), and mini gastric bypass one anastomosis gastric bypass (MGB - OAGBP). We used the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and K-mean clustering analysis to look at how well RBS could help with surgery outcomes. Our result showed that the K-mean of 11 neurons input layer has 100 Self-Organizing Maps (SOM). Also, the PROMETHEE result showed that MGB-OAGBP ranked highest with 0.0056 as the net flow value. The integration of PROMETHEE net flow values, positive and negative outranking values, and BS predicted values demonstrated that MGB-OAGDP ranked highest. In conclusion, our study shows that PROMETHEE and K-means clustering can improve the efficiency of RBS, which can lead to better outcomes for patients.
Novel Homomorphic Characteristics of PU-Algebra in Relation to Discrete Dynamical Systems
PU-algebra theory plays a pivotal role in various applied domains, including information sciences, cybernetics, computer science, and artificial intelligence. This work advances the theoretical understanding of PU-algebras, with a particular emphasis on their homomorphic properties. We explore foundational properties of PU-algebras, focusing on characterizing ideals and subalgebras by introducing discrete dynamical system concepts for instance periodic points, fixed points, invariant sets, and strongly invariant sets within the PU-algebraic framework. Here, (��, ��) denotes a discrete dynamical system where �� is a PU-algebra and �� a homomorphism on ��. The findings not only deepen the theoretical foundation of PU-algebras but also pave the way for future applications in modeling complex dynamical systems and automata theory. This research opens several avenues for further study, including the development of algorithms for computational representation of PU-algebraic structures and the exploration of their utility in enhancing machine learning models and artificial intelligence systems. Additionally, gaps in understanding the interaction between PU-algebra properties and advanced dynamical system concepts suggest a promising direction for future investigation.
Statistical Analysis of Leadership Styles and Their Impact on Hierarchical Effectiveness in Organizations
This research investigates the optimistic role of leadership styles in enhancing employees' hierarchical effectiveness within organizations, analyzed through statistical methodologies. Leadership is a critical factor influencing both the achievement and frustration of employees while managing organizational dynamics. A survey-based approach was employed, collecting data from 350 employees (210 males and 140 females) via questionnaires. Statistical tools, including regression analysis and correlation studies, were used to examine the relationships between leadership styles and hierarchical effectiveness. The findings indicate that charismatic, bureaucratic, and transactional leadership styles are negatively associated with hierarchical effectiveness, while transformational, autocratic, and democratic approaches show a positive correlation. Leaders who adopt optimistic and supportive styles not only improve hierarchical dynamics but also foster an innovative, satisfying, and sustainable organizational environment. This study underscores the importance of applying statistical techniques to evaluate and optimize leadership strategies for enhanced organizational performance.
Some Hermite-Hadamard Type Integral Inequalities and Their Applications via the Modified Riemann-Liouville Fractional Integral Operator
The aim of this manuscript is to introduce a novel form of H-H inequality via ψ-RLFIO for preinvex functions. By employing this approach, we construct a new lemma. In addition, based on this newly derived fractional identity, some new estimation of fractional H-H inequality involving m-preinvex via ψ-RLFIO is investigated. Further, we add some mean-type applications.
Numerical solution of elliptic type of inverse problems by Pascal polynomial method
The Pascal polynomial approach is presented in this paper as an effective method for solving inverse partial differential equations (PDEs) of second order. Two different approaches are suggested. In the first method, Pascal polynomials are used to approximate the source term and the unknown dependent variable. A system of algebraic equations that can be solved is then produced by substituting these approximations and their derivatives into the governing equations and boundary conditions. By imposing a requirement that the source term satisfy Laplace’s equation, the second approach reformulate the inverse problem as a direct one, which is subsequently solved via the Pascal polynomial method. We examine a number of benchmark problems to assess the suggested strategy, showing that it produces high accuracy when given accurate input data. Even though input data noise lowers accuracy, the numerical results are still reliable and acceptable. Despite its sensitivity to noise, these results demonstrate the potential of the Pascal polynomial approach for solving direct and inverse PDEs, especially when the input data is reliable.