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Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

Flow measurements in pipes and open channels are critical for successful water resource management as the economic value of water has increased. Orifice meters are typically used as flow-measuring devices in pipes because they are cheap and simple compared to other devices. Also, orifices are used as energy dissipation methods in water hammer protection devices and hydroelectric power tunnels. Although traditional circular orifice meters have been extensively studied, many points need to be studied. So, experimental and numerical research is carried out to study the effect of orifice geometry on energy loss and the discharge coefficient. The experimental tests are carried out using two different types of orifice plates: circular and triangular, for each one the cross-sectional area is changed four times. The orifice is installed on a 10 cm diameter transparent pipe. The flow rate is changed ten times for each orifice ranging from 13.8 to 49.2 m3/hr. A general correlation equation for the coefficient of discharge is deduced. It was found that the triangular orifice shapes are better than the circular orifice shapes in terms of performance, with reduced head loss and a larger discharge coefficient. By using computational fluid dynamics techniques, the flow behavior through the orifice is analyzed by ANSYS Fluent software. The numerical results confirmed the experimental ones where the pressure head loss for the triangular orifice is lower than the circular orifice and vena contracta is located at a distance equal to half the pipe diameter downstream of the orifice plate.

This study delves into analyzing how Fourth Industrial Revolution concepts impact architectural and urban spaces within university campuses, aiming to foster smartness and sustainability. It focuses on scrutinizing the experiences of global universities in this realm. Employing a robust research methodology, the study utilizes a documentary descriptive approach to identify Fourth Industrial Revolution concepts, an analytical framework to examine instances of global universities embracing these concepts, and an inductive method to elucidate the roles of these concepts and mechanisms in promoting smartness and sustainability within university campus environments. Highlighting the significance of integrating Fourth Industrial Revolution concepts into architectural and urban spaces on university campuses, the study emphasizes their pivotal role in advancing smartness and sustainability. It identifies mechanisms that can be implemented within universities to effectively apply these concepts, thereby enhancing smartness and sustainability within architectural and urban contexts on campus. Drawing from its findings, the study advocates for the implementation of these identified mechanisms and technologies in universities across the Arab and African regions. It stresses the importance of considering local circumstances, institutional capacities, and university-specific resources in this endeavor. Furthermore, it suggests tailoring the adoption of appropriate mechanisms to the unique needs of each university campus, facilitating the transition towards smart and sustainable campus environments rooted in Fourth Industrial Revolution principles. تمثل هذه الدراسة تحليلاً لتأثير مفاهيم الثورة الصناعية الرابعة على الفراغات المعمارية والحضرية في الحرم الجامعي لتحقيق الذكاء والاستدامة، مع التركيز على تحليل تجارب الجامعات العالمية في هذه المجال. وقد تم إستخدام منهجية بحثية تتضمن المنهج الوصفي الوثائقي لتحديد مفاهيم الثورة الصناعية الرابعة، والمنهج التحليلي في تحليل أمثلة الجامعات العالمية التي تكيفت مع مفاهيم الثورة الصناعية الرابعة وتحليل التقنيات والآليات المستخدمة، والمنهج الاستقرائي في تحديد دور مفاهيم وآليات الثورة الصناعية الرابعة في تحقيق الذكاء والاستدامة بالفراغات المعمارية والحضرية بالحرم الجامعي. تم الوصل إلى أهمية تبني مفاهيم الثورة الصناعية الرابعة في الفراغات المعمارية والحضرية بالحرم الجامعي لتحقيق الذكاء والاستدامة. تم استخلاص آليات يمكن تبنيها في الجامعات لتطبيق مفاهيم الثورة الصناعية الرابعة لتساهم في تحقيق الذكاء والاستدامة بالفراغات المعمارية والحضرية في الحرم الجامعي. بناءً على النتائج، توصي الدراسة تطبيق هذه الآليات والتقنيات في الجامعات بالوطن العربي والأفريقي، مع مراعاة الظروف المحلية والإمكانيات والموارد الخاصة بالجامعة، واختيار الآليات المناسبة بناءً على احتياجات كل حرم جامعي لتحقيق التحول إلى حرم جامعي ذكي ومستدام يحقق مفاهيم الثورة الصناعية الرابعة.

The study aims to evaluate the extent of application of architectural design standards for autism centers, and to know the concepts and principles of special architectural design standards for individuals with autism, within their buildings, facilities, and environments. The problem of the research lies in the inadequacy of architectural design in providing the needs and specifications within the building for the individuals of the same unit. The methodology relied on previous studies to identify the special concepts in the field of care for people with special needs, define and understand the behaviours inside buildings that affect individuals with autism, study and analyze a case study of a building specialized in caring for individuals with autism, and distribute a questionnaire to users to obtain their opinions about the integrity of the building’s specifications and the extent to which they are provided for individuals with autism.The results of the study indicate that 53% strongly agree that the center’s facilities provide appropriate needs for autism, the design meets security and safety standards, the optimal use of lighting is met, and that the colors used in the center are appropriate. 41% agree that the center is consistent with the applications existing, that the furniture used, that the sanitary, mechanical installations and windows used are appropriate for individuals with autism. تهدف الدراسة إلى تقييم مدى تطبيق معايير التصميم المعماري لمراكز التوحد، ومعرفة المفاهيم والمبادئ لمعايير التصميم المعمارية الخاصة لأفراد التوحد، داخل المباني والمرافق والبيئات الخاصة لهم. تكمن مشكلة البحث في قصور التصميم المعماري في توفير الاحتياجات والمواصفات داخل المبنى لأفراد التواحد. اعتمدت المنهجية على الدراسات السابقة للتعرف على المفاهيم الخاصة لمجال رعاية ذوي الاحتياجات الخاصة، وتعريف وفهم السلوكيات داخل المباني التي تؤثر على أفراد التوحد، ودراسة وتحليل حالة دراسية لمبنى متخصص لرعاية أفراد التوحد، وتوزيع استبانة على المستخدمين لاستجلاء أراءهم حول تكامل مواصفات المبنى ومدى توفيرها لأفراد التوحد. تشير نتائج الدراسة بأن 53% يتفقون بشدة على أن مرافق المركز توفر الاحتياجات المناسبة للتوحد، كما أن التصميم مستوفي لمعايير الأمن والسلامة والاستخدام الأمثل للإضاءة بنسبة 44% متفقين بشدة، أما الألوان المستخدمة بنسبة 41% متفقين، ويتضح بأن المركز متناسق مع التطبيقات الموجودة، والاثاث المستخدم والتركيبات الصحية والميكانيكية والنوافذ المستخدمة مناسبة لأفراد التوحد.

World Heritage sites face many challenges that affect the quality of life (QOL), it is thus empirical that more research studies shed light upon this important topic. This research examines the most important problems of Egyptian World Heritage Sites, with a focus on the Memphis site due to its outstanding universal value (OUV) and the posed threats that may affect its global value. The necessity of maintaining world heritage sites in accordance with UNESCO's guidelines has recently come to the state's attention so a supreme committee was formed, according to a presidential decree. Thus, this paper aims to present an applied strategy for managing Memphis site in an integrated manner to contribute to raising the QOL indicators while maintaining the OUV of the site through a multiphase research approach; first the theoretical approach, where a literature review and the basic concepts; second, an empirical desk-based literature review of similar sites. This in turn yielded quantitative and qualitative results and allowed for the measurement of the link between QOL and heritage sites management. Then a third phase which consisted of devising an applicable strategy management plan for Memphis site. The strategy has been proposed in light of the country’s vision, strategy for sustainable development: Egypt vision 2030, and the strategic plan for Greater Cairo Region 2050.

Both mathematics and science exhibit a reciprocally advantageous and close relationship. The undeniable significance of mathematics in diverse scientific fields, including electrical engineering, physics, biology, and medicine, is beyond dispute. Due to the multitude of applications of mathematical biology in the present era, scholars have demonstrated a marked intrigue in this specific realm. In the past few years, there has been a discernible rise in the application of different fractional operators as a method of expressing the given problem. The utilization of this approach has gained significant acceptance as a customary technique for examining the propagation of epidemics. The current investigation explores innovative fractional operators in the framework of a model for an infectious disease. The mechanical characteristics of the model with fractional orders are established by employing diverse numerical methods and adjusting parameters related to time fractional components. To substantiate the theoretical discoveries, computational simulations are implemented for the suggested approach over a variety of fractional orders, displaying the outcomes of both fractional and fractal orders. We employed a highly efficient numerical methodology to obtain solutions for both the model and simulations. Additionally, we provide the criteria for the existence of a solution to the proposed epidemic model, and we determine the reproductive number under specific state conditions of the analyzed dynamic system. A fractional-order model of infectious disease is proposed to be analyzed through simulations, aiming to ascertain the possible efficacy of disease transmission within the community. These aspects are tackled through the application of the Banach contraction principle. Moreover, we have proposed future research directions incorporating the utilization of a novel hybrid fractional operator. Fractional calculus emerges as a significant area of investigation within applied mathematics, offering valuable tools for problem-solving across diverse fields such as medicine, science, and engineering. Recent scholars have emphasized the importance of mathematical techniques, especially those related to fractional and integer calculus modeling, as extremely advantageous when examining the dynamics of different disease models. The utilization of the fractional operator allows for the investigation of the dynamic impact of diseases on society, providing valuable insights for analysis, decision-making, and disease control.

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

The study aims to address the need for accurate and real-time monitoring of glucose levels in college athletes during physical activities. This work reports on the development of an electrochemical sensor that uses glucose oxidase (GOx) immobilized on PdO nanoparticles to reduce graphene oxide (rGO) nanocomposite printed on a cellulose substrate (GOx/PdO-rGO/C-PE). The successful reduction of GO to rGO, the production of the PdO-rGO nanocomposite, and the electropolymerization of GOx on the PdO-rGO nanocomposite were all validated by the material characterization. The biosensor's electrochemical response investigation showed that its detection limit was 0.046 μM and its sensitivity was 0.03239 μA/μM. Excellent stability, reproducibility, and glucose selectivity were shown by the GOx/PdO-rGO/C-PE, which makes it a viable option for consistent and dependable glucose sensing in real-world applications. The real sample analysis assessed how well the combination of GOx/PdO-rGO/C-PE could identify glucose in human serum. Furthermore, under a variety of real-world conditions, such as during various physical activities and at different times of the day, the sensor demonstrated outstanding performance in real-time glucose monitoring. These findings imply that the GOx/PdO-rGO/C-PE offers accurate and dependable readings in the field of non-invasive glucose monitoring, which will be especially helpful for college and professional athletes.

The Duffing equation and other nonlinear equations of motion of nonlinear vibrations are widely applied in the fields of engineering and science, especially mechanical and electrical engineering. The modified Mickens extended iteration method has been utilized in this article to investigate for further accurate solutions to the Duffing equation and an equation of motion of nonlinear vibrations. The third approximate frequency shows good agreement with the exact result for both oscillators. For simplicity in algebraic calculations, the increasing harmonic terms have been taken to the fifth order. In order to establish the reliability and effectiveness of the proposed approach, the results obtained through this method are compared with those available in the literature. The comparison of the obtained solutions with numerical solutions shows a remarkable accuracy. Also, it becomes evident that the modified Mickens extended iteration method is significantly simpler, more accurate, efficient, and straightforward than other existing methods. The highest percentage error in the third approximate frequency of an equation of motion of nonlinear vibrations is less than 0.0488, and the maximal percentage error in the third approximate frequency of the Duffing oscillator is less than 0.0065. The proposed technique is principally exhibited in nonlinear models where the nonlinear terms are strong, but it can also be broadly applicable to other problems arising in engineering. Mathematics Subject Codes: 34A34, 34C25, 37N15 (AMS Mathematics Subject Classification 2010)

In today's world, enhancing the performance of recycled aggregate concrete is an essential need, as it ensures the effective utilization of demolished construction waste. The advantages of fly ash and polypropylene fiber in conventional strength concrete are well known. However Further research is required to evaluate the impact of using fly ash, silica fume and polypropylene fiber in case of RA and its effect on the properties of recycled aggregate concrete (RAC). This study evaluates the effects of using 50%, 75%, and 100% recycled aggregate (RA) and 1%, 2%, and 3% polypropylene fiber with a fixed amount of fly ash and silica fume 87 kg/m3 and 21.75 kg/m3. The objective is to determine how these combinations affect concrete strength and durability properties (compression, tensile strength, acid resistance and water absorption). This study also investigates the reliability of non-destructive tests, ultrasonic pulse velocity and rebound number, in the presence of polypropylene (PP) fibers and fly ash with recycled aggregate. The experimental investigation showed that the combination of RA based concrete, fly ash (FA), and polypropylene fiber (PPF) improved concrete durability and strength. The concrete compressive strength made with 50% RA was 13% lesser than that of control concrete, but with the incorporation 3% PP fiber the same mix achieved 5% higher strength than control mix. On the other hand, the concrete compressive strength made with 75% RA and 100% RA was experimentally found 19% and 22% lower, respectively than control mix. The combinations containing constant fly ash and PPF, in addition to 50% recycled concrete aggregate (RCA), demonstrate greater resistance when exposed to sulfuric acid (H2SO4) environments, in comparison to the control mixture. However, the worst performance is shown by mix (100% RA).

Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they're impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes.

Traditional methods for detecting harmful gases in air are often limited in their widespread deployment, accuracy, and real-time monitoring capabilities due to their complexity and cost. To address this challenge, optimization algorithms such as the Particle Swarm Optimization (PSO) algorithm have shown promise. The PSO algorithm, is applied to calculate the concentrations of harmful gases in air, maximizing detection accuracy. Detecting indoor gas pollution is a crucial concern due to the abundance of odors and vapors, particularly those emanating from activities such as cooking. The presence of these substances in the air poses a challenge in identifying traces of other harmful gases. This research endeavors to pioneer a novel approach characterized by heightened sensitivity, even in the presence of unidentified elements in the air. In this work, PSO algorithm is used in conjunction with Chirped Spectral Modulation (CSM) technique to increase system sensitivity to detect small traces of harmful gases inside buildings and protect the environment through early detection of pollution. The use of PSO and CSM altogether allowed for detecting carbon dioxide CO2, carbon monoxide CO, and nitrogen dioxide NO2 down to 10−6 % in volume, and sulfur dioxide SO2 down to 5×10−4 % in volume, while keeping the error below 0.1%

This study observes and records land cover changes in Al-Baha to track urban growth. The study develops a schematic vision of urban mass's current and future shape and distribution and its growth to achieve sustainable urban development, reduce service and social differences between city areas, and integrate urban and rural areas. The study uses inductive methods to monitor urban developments, population growth, and land use through Al-Baha's structural schemes by extracting digital data from satellite imagery to survey historical land cover changes. The mapping software ArcGIS v10.8 processed these digital data and the final map compositions. Through this urban mass study, the research predicted city growth and development. Capabilities and functions must shape urban and rural-urban communities. The development must also consider the National Urban Development Strategy, the relative importance of rural-urban communities, and their expected functional role in influencing the development of small rural communities adjacent to them to reduce migration from rural areas near the city. The study highlighted changes in city land coverage. Al-Baha expanded from 1982 to 1995. Al-Baha's transformation from village to city accelerated, increasing its area from 31 km2 to 44 km2. Many villages beyond 40 were included in the decisions. From 2006–2021, the city's area grew 221%. It increased from 60 km2 to 133 km2 in an urban area, indicating false urban growth. The study stressed the importance of geographic information systems (GIS) and remote sensing (RS). The study recommends urban development in suitable areas because agricultural development is not feasible, especially in the northeast. Land use changes should be prevented by planning regulations because these agricultural areas are essential. Helping urban development move away from agricultural areas, especially on the eastern axis, is crucial. Create new urban centers along Al-Baha's western cliff to attract residents and relieve pressure. Limited linear expansion, especially in the northeast, and more expansive urbanization are also significant. Maps should also show when the Al-Baha region's expansionary scope changed and the urban boundaries that had to be reached by a specific time while development plans were efficiently monitored.

In today's modern world, marketing is a fruitful source to grow a business by reaching a larger audience. This paper considers a popular social media source called LinkedIn.com as a way of marketing or reaching a larger audience. We consider a regression approach to explore the statistically significant role of the LinkedIn medium as a tool for reaching a larger audience. In addition to statistically exploring the role of the LinkedIn medium in the financial sector, we also introduce a new Weibull-based probability distribution, namely, the exponential TX inverse Weibull (for short'ETXI-Weibull') distribution. The ETXI-Weibull distribution is specially designed for analyzing LinkedIn marketing data. The beauty of the ETXI-Weibull distribution is that it satisfies the heavy-tailed characteristics, which are explored visually and mathematically. For the ETXI-Weibull distribution, the estimators are derived whose performances are evaluated visually and numerically through a simulation study. The ETXI-Weibull distribution is applied practically by considering the LinkedIn marketing data set as an example. Using certain established statistical tests, we observe that the ETXI-Weibull distribution provides an optimal fit to the LinkedIn marketing data set. Furthermore, a bivariate extension of the exponential TX inverse Weibull model is also introduced. Finally, the bivariate extension of the exponential TX inverse Weibull model is illustrated by modeling the LinkedIn marketing and sales data.

Wearable antennas are important in many areas of our lives, including the Internet of Things (IoT), health care, sports, the automobile industry, security, and entertainment. Wearable antennas should be designed with a compact size in order to be readily integrated and conform to the body shape, as well as a high performance to withstand mechanical and environmental changes. 5 G and 6 G technologies offer sufficient solutions for wearable antennas utilized for wireless body area networks (WBANs) and IoT applications in terms of high reliability, high gain, compactness, low cost, and high performance. In this work, we present for the first time a comprehnsive review of 5 G and 6 G wearable antennas discussing their significance, types, applications, and design issues in detail. A state-of-the-art for the recent 5 G wearable antenna reviews is also outlined. The main contribution of this work is explaining the compactness and performance enhancement methods at both bands in ascending order starting from low frequency, sub 6 GHz, 5 G mmWave, up to 6 G (high mmWavw and THz wave), so the reader will differentiate between the antennas’ design requirements and challenges for different bands easily. The impacts of flexibility, bending, and on-body/off-body on antenna performance as well as specific absorption rate (SAR) calculation are also taken into account. This review can be considered a valuable tool for designers and researchers in designing many types of wearable antennas at 5 G and 6 G frequency bands for various applications by understanding the antenna's design problems, topologies, and types of substrate and conductive materials.

This paper presents a type of A-stable spectral deferred correction (SDC) method. The scheme is initiated by the first-order backward Euler method. We adopt the linear stabilization approach for the Allen-Cahn model to get the linear semi-implicit SDC scheme. This is done by the addition and subtraction of the linear stabilization operators that have been provided for the Allen-Cahn problem. The ranges of the stabilization factors are then given. The aim is to achieve A-stability and error estimates for the implicit part of the scheme. Finally, the semi-implicit SDC scheme coupled with the Fourier spectral method is used to simulate the Allen-Cahn problem, which is also suitable for the highly nonlinear problem. Numerical experiments are given to numerically demonstrate the high-order accuracy and the energy decay property of the scheme with A-stability parameters.

This study investigates fluid flow and natural convection heat transfer in a parallelogram-shaped cavity with a porous medium under non-uniform temperature condition. The bottom wall is heated with a non-uniform temperature, the side walls are cooled, and the upper wall is adiabatic. The governing equations, along with corresponding boundary conditions, are formulated in dimensionless stream function and vorticity using the extended Darcy model for the porous medium. The equations are solved by employing the finite difference method. The study presents outcomes for isotherms, streamlines, and Nusselt numbers across range of parameters, including the Rayleigh number (104≤Ra≤108), Darcy number (10−1≤Da≤10−6), Prandtl numbers (Pr=0.71,1,7.1), fluid porosity (ϵ=0.8), and inclination angles (30∘≤ϕ≤90∘), about flow and heat transfer characteristics. The results indicate that an increase in Ra, Pr, and ϕ, along with a decrease in Da, leads to an enhancement in the strength of vortexes. It is observed that for constant Ra⁎=RaDa, Rate of heat transfer increases with an increase in Ra and a decrease in Da. The maximum value observed is 10.15. Also rate of Heat transfer increases with the increase in angle and maximum rate of heat transfer is observed at ϕ=90∘.

In light of fractional theory, this paper presents several new effective solitonic formulations for the Langmuir and ion sound wave equations. Prior to this study, no previous research has presented the comparision and obtained the generalized fractional soliton solutions of this kind with power law kernel and Mittag-Leffler kernel. The ion sound and Langmuir wave equations are essential in plasma physics, offering insights into the collective behavior of charged particles in plasmas and enabling diagnostics and control of these complex, ionized gas systems. The two distinct fractional order differential operators are substituted for the traditional order derivative to reshape the examined model. The Atangana-Baleanu non-singular and non-local operator and conformable fractional operator are the fractional-order operators that are used to create the fractional complex system equations for Langmuir waves and ion sound. A constructive approach new auxiliary equation method utilizes to obtain the exact analytical soliton solutions for ion sound and Langmuir wave equation. A wide range of soliton solutions is obtained, including mixed complex solitary shock solutions, singular solutions, mixed shock singular solutions, mixed trigonometric solutions, mixed singular solutions, exact solutions, mixed periodic solutions, and mixed hyperbolic solutions, dark soliton, bright soliton, trigonometric solutions, periodic results, and hyperbolic results. The solitons solution of the ion sound and Langmuir wave equations lies in their ability to maintain wave stability, their role in modeling wave propagation and nonlinear effects, their potential use as diagnostic tools, and their relevance in wave-particle interactions in plasma physics. The solitons provide a valuable framework for understanding the behavior of waves in plasmas and offer insights into the complex dynamics of these charged particle systems. A graphical comparison analysis of a few solutions is also shown here, taking into account appropriate parametric values through the use of the software package. Moreover, the results of this study have important implications for Hamilton's equations and generalized momentum, where solitons are employed in long-range interactions.

This study examines the molecules motion influences and thermal attributes in power-law non-Newtonian dissipative fluids' magnetohydrodynamic behavior, Joule heating, electromigration, and Cattaneo-Christov effects. Effects of Hall on both flow velocity and temperature, as well as the heat transport are considered. The investigation looks at heat transference that occurs when there is a slippage consequence, how the temperature influences the viscosity of the fluid, and how the potential parameters influence the exponential non-Newtonian flow of fluid via an extensible sheet. Runge-Kutta-Fehlberg's approach, which is based on the shooting procedure, was used to develop computational solutions for nonlinear ordinary differential equations. When exposed to a deformation that is generated by expansion, pressure, or agitation, a dilatant fluid is characterized by a phenomenon in which its viscosity rises and may harden. This phenomenon is referred to as the dilatation phenomenon. High shearing rates provide the impression that it is stable. When expanded, subjected to pressure, or agitated, dilatant fluids become more viscous and transform into solids. Both the non-equilibrium that occurs within the fluid and the reduction of the irreversibility processes that occur within it are caused by the acquired charges that are a result of Hall effect of fluid molecules.

Design of efficient test plans to assessing product reliability rapidly, is a critical step to obtain product reliability accurately. In this paper, we are adopted the problem of statistical inference of two generalized inverted exponential populations in a competing duration. To save the balance between the optimal test time and the number of failures needing for statistical inference, joint adaptive type-II hybrid progressive censoring scheme is applied. The model parameters are estimated with classical methods (maximum likelihood and bootstrap) for point estimate and the corresponding confidence intervals. Also, Bayes estimators of model parameters are computed under importance sample technique with the corresponding Bayes credible intervals. A real data sets are analyzed for illustrating purposes. The results are compared and assessed through Monte Carlo simulation study.

This study investigates the complex dynamics governing free convection flow in nanofluids under Magnetohydrodynamics (MHD) within a porous medium along an infinite vertical surface. We specifically focus on scenarios involving constant and uniform heat flux as well as heat sources. The governing equations, incorporating the Boussinesq approximation, continuity, and momentum equations, are formulated and solved using Caputo-Fabrizio derivatives and Laplace transforms. This comprehensive approach integrates the Boussinesq approximation and Caputo-Fabrizio fractional derivatives, while the use of Laplace transforms enables precise analytical solutions. Additionally, our investigation highlights the significant impact of nanometer-sized copper particles on fluid properties, transforming the behavior from Newtonian (water-like) to non-Newtonian. This transformation profoundly affects thermal conductivity and velocity behavior within the system. Overall, this research establishes a robust foundation for future studies and provides a framework for exploring non-Newtonian nanofluid systems in the context of MHD-driven free convection flow.

This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called ‘novel’. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.

In light of the extensive contamination of water sources by microplastics, their substantial specific surface area makes them favorable candidates as adsorbents for the simultaneous removal of coexisting contaminants in wastewater. In this regard, polyethylene microplastics were utilized to eliminate methylene blue dye from water. MB adsorption onto microplastics reached equilibrium in just 30 min at pH 7. The better fit of fractional power and Redlich-Peterson models on kinetic and equilibrium adsorption data, respectively, revealed that the MB removal process is a chemisorption in multilayer adsorption on the heterogeneous surface of the microplastics particles. The reusability of the microplastics adsorbent was confirmed based on the promising outcomes observed after five cycles. The results of the random forest regression exhibited an R2 of 97.55% for the correlation between the model-computed and measured amounts of MB reduction. The sensitivity analysis illustrated that the MB sorption process on microplastics is highly influenced by the initial MB concentration and adsorbent mass. These results show that although microplastics may pose potential risks to water environments, their adsorption potential can be utilized to simultaneously omit other pollutants from the aqueous solutions.

Financial engineering is crucial for effectively combining finance with quantitative approaches. This study aims to forecast the performance of the Nasdaq stock market by considering numerous factors like wind, hydro, thermal, gas, and nuclear variables. To accomplish this, we utilize sophisticated predictive models, namely adaptive lasso (ALasso), elastic net (Enet), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM). By using these advanced methods, our goal is to offer perceptive and precise predictions, which will enhance comprehension of the complex dynamics within the financial markets. The evidence suggests that the LSTM model has demonstrated superior accuracy in predicting changes in the Nasdaq stock market when compared to ALasso, Enet, ANN, and CNN. While ALasso, Enet, ANN, and CNN exhibit comparable RMSE and MAE values, their performance is slightly less competitive than that of the LSTM model. The marginal differences in RMSE (ALasso: 0.319, Enet: 0.317, ANN: 0.3, CNN: 0.32) and MAE (ALasso: 0.277, Enet: 0.276, ANN: 0.252, CNN: 0.278) emphasize the comparable effectiveness of various methods, but they somewhat drop below the LSTM model in terms of precision. The findings showed the significance of well-known and advanced ML techniques, particularly LSTM, for enhanced accuracy in financial market predictions.

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings.© 2017 Elsevier Inc. All rights reserved.

This work reports on the creation of an electrochemical sensor for the real-time monitoring of paclitaxel (PCX) concentrations in biological fluids. The sensor is based on a poly-melamine-reduced graphene oxide nanocomposite-modified glassy carbon electrode (p-Melamine/rGO/GCE). The electrochemical sensor revealed a linear relationship between the peak current of the DPVs and PCX concentrations between 1 and 1180 μM, with a detection limit of 12 nM. The Ip value of PCX dropped to 98.15% of the starting value after 25 days, indicating excellent stability and repeatability of the p-Melamine/rGO/GCE sensors, which were assessed for long-term stability. The sensor's peak currents of PCX did not exhibit any significant variation when 50 μM organic compounds and inorganic ions were present, suggesting that it could find application in clinical samples. Relative standard deviation values for recovery tests on prepared urine samples ranged from 3.36% to 4.72%. In the area of chemotherapeutic agent monitoring, this work presents a novel method that shows remarkable sensitivity and selectivity in the real-time monitoring of PCX concentrations in biological fluids. The suggested sensor offers a practical and affordable means of tracking PCX concentrations in biological fluids in real time, with potential uses in therapeutic medication monitoring and clinical diagnostics.

This study explores a new family of distributions to enhance the analysis capabilities of COVID-19 events, providing valuable insights for informed decision-making and effective public health management. Accurate understanding and prediction of COVID-19 transmission patterns and their impacts are crucial in reducing the mortality rate, especially in proactive control and mitigation efforts. The proposed approach focuses on a best-fit probabilistic model that incorporates a comprehensive assessment of various statistical measures. Additionally, seven well-recognized classical point estimation methods are employed to identify the most suitable approach for assisting epidemiologists in their analysis. The study analyzes COVID-19 data from multiple countries, including the Netherlands, Mexico, the United Kingdom, China, Canada, Saudi Arabia, and Italy, considering different aspects such as mortality rates and the number of deaths. By evaluating the performance of the new alpha generated family of distributions in modeling COVID-19 events. This research contributes to the advancement of our understanding of the disease's probabilistic nature. The findings have practical implications, guiding the development of public health policies, resource allocation strategies, and intervention plans, ultimately facilitating more effective control and mitigation of COVID-19 outbreaks.

This study presents a methodological framework for optimal placement of pressure sensors in Water Distribution Networks (WDNs) for leakage monitoring under uncertainty. Monte Carlo simulation is utilized to simulate leakages of different magnitudes at various nodes in the WDN taking into consideration background noise and minimum resolution of pressure sensors. A novel sensor preselection algorithm based on community detection and maximum entropy computation to reduce the search space of the pressure Sensor Placement Problem (SPP) is presented. The pressure SPP is formulated as a multi-objective optimization problem that seeks to maximize Joint Entropy, Coverage, and minimize Total Correlation. NSGA-II is used to solve the SPP and the solutions in the optimal Pareto front are ranked using a hybrid Entropy TOPSIS to eliminate potential bias and subjective human judgement in optimal sensor configuration implementation. The sensor preselection algorithm achieved a 67% reduction in the search space (possible sensor positions) of the case study, C-TOWN WDN, with only 2.78% reduction in coverage. The result of the pressure SPP indicates only 21 pressure sensors are needed to cover 95.45% of the WDN under study. Finally, the overall performance of the proposed methodological framework is presented and compared with other related works.

Microorganism motility holds significant importance in the realm of biomedical research. It contributes to the investigation of bacterial infections, microbial pathogenesis, and the formulation of strategies for combating antimicrobial resistance. Motile microorganisms embody a multifaceted and dynamic facet of microbial existence, exerting influence on ecosystems, human health, and advancements in technology. The significance of the current investigations is to develop a steady 3D computational model for the magnetic field hydrodynamics micropolar nanofluid along with the Buongiorno model which incorporates Brownian motion and thermophoretic diffusion effects to describe the heat transfer enhancement of micropolar nanofluid. The influence of thermal radiation on convective boundary conditions is also considered here. Similarity transformations are employed to convert an equation of motion into an ordinary differential equation. The final shape of ODEs is solved numerically with the aid of MATLAB software. Graphical results against convergence parameters like the influence of magnetic parameter, velocity ratio parameter, Prandtl number, micropolar parameter, porosity parameter thermophoretic number, Brownian motion, bio-convective Schmidt number, Peclet number thermal radiation parameter is demonstrated on velocity, temperature, concentration, motile and angular profiles. The default values of physical parameter used our analysis are: 0.2≤M≤0.5, 0.2≤λ≤0.6, 0.2≤K≤0.5, 0.0≤K1≤2.0, 1.0≤Sb≤1.4, 0.1≤σ≤0.9 0.1≤Nr≤0.9, 0.3≤Nt≤1.8, 1.5≤A≤3.5, 0.0≤B≤2.0; 0.0≤Nb≤2.0, 0.5≤γ≤2.5, 0.0≤S≤0.8, 1.0≤Sc≤1.8, 0.0≤D≤2.0, 0.1≤Pe≤0.5.The novelty of the present model is validated with the previously published data and found an excellent agreement. Our obtained results signifies that a rise in the mass and temperature convective parameter causes the velocity profile to grow, but a rise in the porosity and magnetic parameter causes it to decrease. The temperature profile increases with a rise in, thermophoretic parameter, and magnetic parameter, whereas it decreases with a rise in temperature exponent and Prandtl number. This research brings esteemed insights the micropolar nanofluids make them attractive options for a variety of uses in a range of sectors, such as manufacturing, energy, electronics, and healthcare.

This paper investigates the behavior of magnetohydrodynamic (MHD) peristaltic flow with electroosmosis. The Jeffrey fluid in microchannel under the influence of homogeneous-heterogeneous chemical reaction has been taken. The heat absorption and nonlinear radiation are also scrutinized here. This study contributes to the fundamental understanding of complex fluid dynamics in microscale systems and offer valuable insights for designing and optimizing microfluidic devices. In the framework of mathematical simulation, the relevant dimensional nonlinear equations are reduced into dimensionless equations by using linear transformations. Thus Debye-Hückel linearization has been employed. The streamline approach with mathematical modelling has been performed within the limits of δ≪1 and Re→0. The appropriate equations for temperature and concentration with boundary conditions are solved by using perturbation approach whereas, the exact solution is found for velocity equation. A graphical depiction of crucial physical characteristics on velocity, temperature, concentration and streamlines are reported in the last section. It is observed that the velocity distribution decreases for the higher value of M (0.5≤M≤2) however, it increases for the escalating values of Uhs (−0.5≤Uhs≤1.5). When Rn(0.1≤Rn≤0.6) gets stronger then temperature profile decreases. It is noticed that the concentration profile decays owing to enhancement in Sc(−0.5≤Sc≤1.5). As the M (0.5≤M≤2) increases, the size of trapped bolus decreases. Escalating values of temperature ratio parameterθw(1.0≤θw≤1.6)enhances the Nusselt number. The novelty of this study lies in the comprehensive analysis of multiple complex phenomena in a single framework. The interplay between MHD electroosmotic flow, homogeneous heterogeneous chemical reactions, peristalsis, nonlinear thermal radiation, heat absorption and no-slip condition has not been explored in previous research.

Diabetes Mellitus (DM) is an enduring metabolic illness that disturbs many individuals globally. This study addresses the global impact of Diabetes Mellitus (DM) and emphasizes the critical role of accurate DM detection in early diagnosis, effective treatment, and prevention of complications. The research introduces an optimized DM detection model, the GBM-DRU (Gradient Boosting Machine - Data Reduction Unit) network, which integrates feature engineering and ensemble learning techniques to enhance prediction accuracy and support clinical decision-making. The GBM-DRU network combines the powerful gradient boosting machine algorithm with a data reduction unit (DRU) for efficient feature selection, reducing dimensionality and improving computational efficiency. Feature engineering enhances discriminatory power, while ensemble learning methods, including bagging and boosting, improve overall model performance. Rigorous experiments on a comprehensive dataset of DM patients demonstrate that the proposed approach outperforms existing models in terms of accuracy, sensitivity, specificity, and AUC-ROC. The optimized model provides valuable insights into feature importance, aiding clinical decision-making and deepening the understanding of DM risk factors. Therefore, the GBM-DRU network, utilizing feature engineering and ensemble learning, presents a viable approach to precise diagnosis of diabetes mellitus, with favorable implications for patient outcomes, disease control, and public health campaigns. The improved prediction accuracy, feature interpretability, and clinical decision support capabilities of the model may have a beneficial effect on public health campaigns, disease management, and patient outcomes.

Graphene has appeared as one of the most promising nanomaterials with unique thermal, electronic and mechanical properties which make it suitable for many applications in materials science, energy storage, electronics, water filtration and medicine. In this paper, we have studied the (2+1)-dimensional graphene sheets (GS) equation with variable heat transmission thermophoretic motion by using both Bäcklund transformation and similarity reduction techniques. As a result, from Bäcklund transformation one soliton solution was obtained and from similarity reduction technique the GS equation is reduced to a nonlinear ordinary differential equation which can be solved using the trial equation method; where many novel wave solutions were obtained. As an additional remark, the Bäcklund transformation obtained can be found by the traditional Painléve test. Finally, some graphs are given to illustrate the effect of the variable coefficients on the behavior of the thermal wave transmission.

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes security and privacy considerations paramount in virtual assistant platforms. The gaps and weaknesses in existing systems can be identified by researching the context of current practice concerning their features, usability, limits in security procedures, and privacy restrictions. Therefore, we present a framework that combines blockchain-based security with federated learning (FL) to address the current shortcomings of virtual assistant technology. The examination focuses on two primary facets of cutting-edge virtual assistants. Firstly, it evaluates existing IoT-based virtual personal assistant systems designed for persons with disabilities, examining their features, usability, and limitations. The aim is to identify the specific needs and requirements of individuals with disabilities, considering their unique challenges and preferences in utilizing virtual assistant technologies. Second, considering the sensitivity of the information sent between users and virtual assistants, it explores the issues of security and privacy that arise while using such systems. The investigation covers authentication, data encryption, access control, and data privacy rules to provide a snapshot of the prevailing state protecting virtual assistants. Besides this, the framework strengthens the privacy and security of virtual assistants using blockchain technology. Through several empirical trials, it is found that the framework maintains better performance and usability, along with the provision of robust security mechanisms to safeguard user data and guarantee privacy.

The growth of the world population has led to an increase in the demand for fresh water. Water pollution is often caused by hasty urbanization without proper management of hazardous waste. Therefore, it is vital to implement new methods to remove pollutants from water. In the current study, an antimicrobial nanocomposite adsorbent composed of Arabic gum-grafted-poly(para phenylene diamine) (AG-g-PpPA) decorated with metal-organic framework and copper nanoparticles (AG-g-PpPA@MOF(Fe)/Cu) was synthesized via in-situ copolymerization. The nanocomposite was utilized for eliminating the antibiotic cloxacillin from water solutions. The effects of different factors were analyzed, including pH, the quantity of adsorbent, contact time, and preliminary cloxacillin concentrations. Ideal parameters were pH 7, 5 mg adsorbent dosage, 10 min contact time, and an initial 500 mg/L cloxacillin concentration. The adsorption process followed the Freundlich isotherm equation, with a maximum adsorption ability of 400 mg/g. The kinetics information appropriately fits a pseudo-second-order model. Thermodynamic calculation showed the adsorption occurred spontaneously. The nanocomposite also displayed antibacterial action in contradiction to Escherichia coli and Staphylococcus aureus. Additionally, the adsorbent could be recovered and reused effectively. The excellent adsorption ability highlights the potential of AG-g-PpPA@MOF(Fe)/Cu adsorbent for eliminating pharmaceutical pollutants from wastewater.

The aim of our work is to study two-dimensional fractured flow simulations for groundwater by using non-identical aperture of fractures via Analytic Element Method, and make a comparison between processing time of both techniques i.e. iterative and matrix method. Numerical solutions for the considered problems are totally based on series expansions, whereas the effects of every fracture are expressed as a series which obey Laplace's equation. In all examined cases, it is evident that the iterative method is not able to converge, while by introducing the matrix method, the expected results are obtained with minimum computational cost (processing time).

This study investigates the intricate interplay among various factors, including melting heat, Brownian motion, activation energy, and thermophoresis in the Maxwell fluid along a stretching sheet. To model these complex phenomena, we have formulated mathematical equations that are subsequently transformed into a set of partial differential equations. Through a similarity renovation, these equations are transformed into ordinary differential equations, and we solve the resulting boundary value problem using numerical techniques, specifically the BVP4C method, known for its fourth-order accuracy. Utilizing the numerical capabilities of BVP4C, we delve into the influence of different parameters, including thermophoresis, melting effect, Brownian motion, and activation energy, on critical flow characteristics. These characteristics encompass the concentration profile and the Sherwood number profile. Our work has been validated against previously published research, showing good matching. The primary objective of this work is to provide plots depicting Nusselt number, skin friction, and Sherwood number profiles for different parameters. This examination aims to shed light on the interconnected effects of these complex phenomena, which have direct relevance across numerous industrial and engineering applications, spanning areas such as metallurgy, nanotechnology, and chemical processing. The primary finding from this research underscores that both the melting heat effect and activation energy contribute to an increase in the concentration profile.

The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms.

In this study, donepezil hydrochloride (DZ HCl), a crucial medication for the treatment of Alzheimer's disease, is detected using a new sensor built on CuO-TiO2 hybrid nanostructures. The sensor showed improved performance characteristics with a computed limit of quantitation of 0.211 and a limit of detection of 0.063. These qualities included improved stability, selectivity, and sensitivity. The fabrication process demonstrated repeatability, with a 4.27% relative standard deviation. After 10 successive readings, the sensor maintained 95% of its initial response, demonstrating its long-term stability. By quantifying DZ HCl in urine samples, the practical applicability of the sensor was confirmed. Remarkably, the sensor achieved a remarkable recovery rate of 97.00–99.41% and a suitable relative standard deviation range of 3.29–4.71%. The results of this work contribute to the development of trustworthy electrochemical sensors for medicinal chemicals and have prospective uses in pharmacological, biological, and therapeutic fields.

In this manuscript, we present an innovative approach to address nonlinear Bratu's problem by applying the Discontinuous Galerkin method. Our method accurately computes two-branched numerical solutions of the problem. Depending on the initial guess function, the resulting numerical solution converges either to the upper or to the lower solution of Bratu's problem. We show that our method achieves an optimal convergence rate of order p+1 when p-degree polynomials are used. To confirm the accuracy and efficiency of our proposed method, we present various numerical results. These findings demonstrate the high precision achieved by Discontinuous Galerkin method, enabling the computation of both branches of the solution.

The current work investigates the effects of drilling parameters on hole quality, including delamination and circularity, produced in hybrid composite materials consisting of neat glass fibre (NG) and hybrid glass fibre/Al-wire mesh in two distinct sequences, the first one being with Al-mesh in the outermost layers (AG) and the other with Al-mesh in the central layer (GA). The operation's process parameters are feed rate (F = 20, 40, and 60 mm/min), spindle speed (N = 1000, 2000, and 3000 rpm), and drill point angle (Ɵ = 90°, 120°, and 135°). Taguchi's L9 orthogonal array is used for the study. The optimal levels and effects of the process parameters were determined. Gray relational analysis (GRA) was employed to examine the complicated interactions influencing the results and ANOVA was used to evaluate the influence of process parameters. The results indicated that for all specimens, push-out delamination was more severe than peel-up. Circularity is minimized by using N = 3000 rpm, F = 20 mm/min, and Ɵ = 90°. At speed (N) of 3000 rpm circularity reduces by 83.5% and 76.5% compared to 1000 or 2000 rpm, respectively. Compared to other specimens, AG-specimen produced minimal delamination and circularity by an average of 6% and17.38%. The results of the confirmation test and regression model provide good agreement between the experimental and predicted results.

This study mainly concerns the controllability of semilinear noninstantaneous impulsive neutral stochastic differential equations via the Atangana-Baleanu (AB) Caputo fractional derivative (FD). The essential findings are created using methods and concepts from semigroup theory, stochastic theory, fractional calculus, K-set contraction, and measure of noncompactness. Finally, an example is provided to demonstrate the applications of the key findings.

In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model’s prediction distribution and enhance the model’s robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results.

This study aims to investigate the Joule effect, thermal radiation, and Coriolis effects in unsteady magnetohydrodynamics (MHD) micropolar flow over a 3-D unstable sheet. A magnetic field is typically applied to the surface; additionally, it is assumed that the fluid is conducting electricity. Other micro-rotations are also considered. The physical issue is resolved with the help of the fundamental equations, and the issue's complexity is reduced with the use of similarity variables. In the present approach, the homotopy analysis method (HAM) is employed. The heat, skin's friction factor, temperature, micro movements, and velocity associated with the emerging parameters and transfer rates are considered. The boundary layer thickness is increased as the parameter of vorticity increases. When the magnitude of the magnetic field increases, the skin friction coefficient decreases. The state variables are approximated up to six decimal places numerically. The numerical values of −f″(0) and −θ′(0) are computed to higher decimal places and compared with the available literature to validate the results. The outcomes obtained are compared with the available literature; the results here are corroborated, and the performance of the HAM is demonstrated.

The utilization of solar radiation by converting them into thermal energy is discussed in this paper. Nanoparticles improve the ability of heat transfer therefore, it is beneficial in the use of solar thermal systems and energy storage devices. The novel mixture of nanoparticles Graphene and Polythiophene in base fluid, which has high thermodynamic properties for the improvement of thermal effect with electromagnetic effect by using Maxwell fluid model is discussed. Polyvinyl alcohol water is taken as base fluid flowing through a moveable flat plat. The governing partial differential equations are transformed into ordinary differential equations. The semi-analytical technique, homotopy analysis method is used to obtain the solution of the ordinary differential equations. The velocity is enhanced with magnetic and electric field strength. The increase of the Prandtl number, Eckert number and chemical reaction parameter, exceeds the thermal effect which produces more entropy generation and heat enhancement. The results show that the hybrid nanofluid with this Novel mixture is highly thermodynamic with higher entropy and rapid thermal augmentation which can be used in energy production and energy storage devices. A novel intelligent numerical computing technique multi-layer perceptron with feed-forward back-propagation, an artificial neural networking method with the Levenberg-Marquard algorithm is used in this model. The data is gathered for the neural networking method training, validation, and testing. The efficiency of the model is obtained and mean square error is obtained by artificial neural networking.

The article proposes a concept of organizing access to the guaranteed resource of the 5 G cluster information environment, structured in the form of a hierarchy of logical network segments. This framework presents an interrupt access control scheme that exhibits survivability characteristics important to mission-critical applications. Analytically, the author's concept is represented by an irreversible Markov process, for which a system of equilibrium equations and stationary probabilities are defined. Based on the latter, a qualitative metric was formulated, which included such indicators as the average number of requests accepted for service in the 5 G cluster, the probability of losing an incoming request, and the average volume of network resource units used in the 5 G cluster. For the single input data that characterizes a real NS 5 G cluster, a comparison of the author's and known competing access control schemes in terms of formulated qualitative metrics is implemented. The obtained results showed that the author's concept prevails over the competitor in the range of low loads. Note that in this range, the advantage of the author's scheme over the competitor significantly increases with the increase in the number of network segments into which the information environment of the 5 G cluster is divided.

Intrusion Detection Systems, specifically Network Anomaly Detection Systems (NADSs) are vital tools in network security. The NADSs are affected by data imbalance issues in classifying minority classes. Also, designing an efficient detection framework is sought after to achieve a higher detection rate for minority classes, especially when utilizing ensemble learning methods. To solve the issue of imbalanced data, a hybrid method of sampling techniques is proposed. This imbalance processing tool integrates the Synthetic Minority Oversampling Technique (SMOTE) and the K-means clustering algorithm (SKM). SMOTE over-samples the minority class, and K-means is used to perform a cluster-based under-sampling. We use Denoising Autoencoder (DAE) to select the top 15 features to reduce data dimensionality based on their higher weights. For anomaly detection, the XGBoost algorithm is deployed and the SHapley Additive exPlanation (SHAP) approach is deployed to provide explanations of the proposed techniques. The performance of the SKM-XGB model is assessed using the NSL-KDD and UNSW-NB15 datasets. A comparative analysis and series of experiments were carried out using several ensemble models with multiple base classifiers. The experimental findings indicate that the model's detection rate for binary classification and multiclass classification using the UNSW-NB15 dataset is 99.01% and 97.49%, respectively. The model achieves a 99.37% detection rate for binary classification and a 99.22% detection rate for multiclass classification on the NSL-KDD dataset. We conducted a comparative analysis of various ensemble models with multiple base classifiers. The results indicate that SKM-XGB outperforms the other investigated models and outperforms the performance of state-of-the-art models.

In this research, we focus on a novel class of mean inequalities involving Riemann-Liouville fractional integrals. We employ these integrals to investigate various fundamental identities that help us to explore mean inequalities. By utilizing a generalized concept of convexity, we establish a unique set of these problems. To ensure the accuracy of our findings, we generate 2D and 3D graphs accompanied by corresponding numerical data using specific functions, effectively illustrating the inequalities. Furthermore, it is easy to observe that some known results from previous studies manifest as special cases of our primary outcomes. This approach enables us to substantiate the validity of our findings and strengthen our conclusions. The connection of the main finding with the context of statistics and mathematics is provided, playing a significant role in addressing real-life problems.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T-X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies.