Ain Shams Engineering Journal最新文献

We study the comparative exact solutions of the Kairat-II equation describing the physical behaviors of nonlinear systems. The newly introduced Kairat equation has numerous applications in the fields of plasma physics, optical communications, differential geometry engineering, oceanography and physics. Two types of fractional operators known as β and M-truncated derivatives have been applied for creating the complex fractional Kairat-II equation. The two newly integrated methods, known as modified generalized Riccati equation mapping method and the generalized exponential rational function method approach, are under consideration to investigate the governing system. The implemented methodologies are distinguished by their efficacy, straightforwardness, and flexibility, which allow for the integration of diverse types of soliton solutions within a unified framework. In addition, to visualize the solution behaviors with different parameter values, we plot the different graphs with the associated parameter values under the effect of M-truncated and β-fractional derivatives.

The rise in everyday internet users worldwide can be ascribed to advancements made to the internet worldwide. Attacks by malware cause data loss, hardware destruction, and large financial losses. As a result, this research creates a novel mathematical compartmental dynamical model that, under both fixed and dynamic size network assumptions, can be utilized to precisely predict malware outbreaks. The modeling processes divided the network into seven distinct states and considered the diverse ways that users of the internet interacted with potentially hazardous links. The Hilfer-Katugampola fractional operator was employed to obtain the intended varied states of networks. The existence, uniqueness, equilibrium, and stability of the provided models are thoroughly examined. Then the best control plan is implemented for every network topology. The controllers' objectives are to minimize the costs of data loss due to infections, malware tracing, and public awareness improvements. Ultimately, we confirm the theoretical results by observing the spread of malware using numerical simulations. Results demonstrated the effectiveness of the enforced control measures in maintaining the necessary goals of malware infection management.

The current paper examines the Darcian Forchheimer for the magneto-Maxwell fluid comprising of bio-nanofluid in the suction/injection effect due to rotating disk. Thermal radiative, heat source/sink, and chemical reaction are taken into account. Convective and zero heat mass flux have been introduced in the thermal and concentration conditions. The nonlinear partial differential equations that govern the system are converted into ordinary differential equations using a similarity substitution. These changed equations are subsequently solved numerically using the Bvp4c method. A thorough analysis is conducted, comparing the current findings with previously published publications, resulting in a remarkable level of achievement. The implications of gyrotactic microorganisms are incorporated into the model that is used to describe the bioconvection. Given the conditions of convective heat with zero heat and mass flux, it is presumed that the surface of the rotating is prepared for the possibility of nanofluid wall suction or injection. The main finding of the current work is radial and axial velocity is depreciated with enlarging values of Deborah number while rise with stretching variable. This study is anticipated to be beneficial in gaining a better understanding of the mechanisms involved in heat transfer in a variety of physical sectors.

The current study tries to investigate gold extraction in a membrane extractor in unsteady mode. The model was successfully validated using experimental results. The results showed that the diffusive flux on the zone close to the membrane surface is dominant in the first minutes of extraction but later the convective flux is the main flux, particularly at the centre of the shell side. The extraction time of one hour was not ample to extract all gold from the aqueous solution when the volume of solutions was 2000 mL and 3000 mL. Finally, the change in feed flow rate in the range of 30–55 cm3/min did not have a significant influence on the gold extraction time. However, the outlet dimensionless concentration of gold after one hour was increased from 0.24 to 44 with the increase of feed flow rate from 30 cm³/min to 55 cm³/min.

Biophilic design, in the context of modern interior spaces, focuses on incorporating natural elements and materials to counter the growing disconnect between people and nature. It aims to enhance human well-being, particularly within office environments, ultimately influencing performance and corporate success. In the era of globalization, where office spaces play a pivotal role, the interplay between office design, occupant well-being, and performance cannot be underestimated. Several factors are integral to the success of office design, including lighting, color schemes, ergonomic furniture, and construction materials. A well-designed office should not only facilitate work but also lower stress levels, boost motivation, and promote physical health among its users. This academic study delves into the identification of biophilic design elements that contribute positively to the office experience. It commences with a comprehensive literature review on biophilic design principles and their components. These principles are then transposed into the realm of office design, forming the basis for an expert evaluation survey. The survey findings highlight three paramount biophilic design elements for integration into office spaces: access to natural light, the use of evolving natural materials, and the provision of a nature view. Interestingly, participants with expertise in landscape architecture and biophilic design place higher importance on incorporating natural elements into their office designs. This research emphasizes the pivotal role played by biophilic design in shaping corporate environments, rendering them conducive to work, while concurrently promoting the wellbeing and productivity of their inhabitants.

Nonlinear fractional partial differential equations can explain a vast scope of engineering and science, like atomic physics, wireless transmission, nonlinear optics, acoustics, economics, materials science, control theory, plasma physics, quantum plasma in mechanics, biological systems of nonlinear case and so on. In this study, the new generalized $(G\prime /G)$-expansion technique have been studied for examine the nonlinear fractional equations and built exact analytical traveling as well as solitary wave solutions namely space–time one-dimensional fractional wave equation used to simulate wave transmission in a nonlinear medium with blending and space–time fractional regularized long wave equation enables bore expansion and wave generating in nonlinear model of solitary waves, ion acoustic plasma waves, shallow waves in water, and nonlinear dispersive waves with the help of conformable derivatives. The creatable equations are transformed into ordinary differential equations by fractional complex transformation. Some dynamical wave shapes of multiple solitons, single soliton, periodic anti-kink, anti-kink, flat-kink type solitary wave models have been created, and 3D, and contour have been built to represent these solutions. The results are expressed using hyperbolic, rational functions, and trigonometric and Maple or Mathematica utilized to graphically represent the obtained solutions. It is essential to point out that all resultant solutions are directly compared to the original solutions to ensure their exactness. The new generalized $(G\prime /G)$-expansion method is an operative, compatible, and fruitful approach for analyzing nonlinear fractional traveling waves.

Histopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues. This research work presents a new method that merges deep learning with Reinhardstain normalization, aiming to revolutionize histopathology image analysis. The multi-data stream attention-based generative adversarial network is an innovative architecture designed to enhance histopathological image analysis by integrating multiple data streams, attention mechanisms, and generative adversarial networks for improved feature extraction and image quality. Multi-data stream attention-based generative adversarial network capitalizes on attention mechanisms and generative adversarial networks to process multi-modal data efficiently, enhancing feature extraction and ensuring robust performance even in the presence of staining variations. This approach excels in exact disease detection and classification, emerging as an invaluable tool for both clinical diagnoses and research endeavors across diverse datasets. The obtained accuracy of the proposed method for the SCAN dataset is 97.75%, the BACH dataset is 99.50% and the Break His dataset is 99.66%. The proposed method significantly advances histopathology image analysis, offering improved diagnostic accuracy and deeper insights by integrating multi-data streams, attention mechanisms, and generative adversarial networks. This innovative approach enhances feature extraction, image quality, and overall effectiveness in medical image analysis.

This work investigates the Ohmic heating, nonuniform heat generation, and Hall effects with Cattaneo-Christov model (CCM) on flow and heat transfer of power-low non-Newtonian fluids (NNFs) past stretching surface embedded in a porous medium. Runge-Kutta method is used to solve non-linear ODEs numerically using a shooting procedure. We study non-Newtonian fluids for power law values of 0.7 and 1.2, respectively. Innovation of this work lies in studying the effect of Hall currents in presence of buoyancy force and an irregular heat source on slip flow of NNFs moving through Darcy porous medium. Varying velocities, surface frictional forces, and Nusselt numbers are examined. Resulting entropy is also examined using computational flow problem investigation. Model-simulated results suggested that various factors play critical role in constructing thermal systems. It is asserted that Hall parameter, mixed convection parameter, Biot numbers, and thermal relaxation time improve heat transference rates by directly affecting fluid molecules temperature.

In this work, we examine thin film flow of a non-Newtonian third-grade fluid flowing on a vertical moving belt. The modelled differential equation is solved using the Homotopy perturbation method and the Optimal axillary functions method. The solutions obtained by the proposed methods are compared through graphs and tables. Results reveal the efficiency, reliability, and fast convergence of both methods as the obtained solutions are in close agreement. The effect of different flow parameters for velocity profile, flow rate, and shear stress on the belt is also investigated. HPM and OAFM are particularly effective in solving nonlinear differential equations compared to some traditional numerical methods. OAFM is specifically useful in solving nonlinear boundary value problems. Both methods offer a flexible and efficient approximate solution for complex systems described by nonlinear differential equations and have a wide range of applications in heat conduction, fluid dynamics and structural mechanics

According to the world health organization (WHO), stroke stands as a significant contributor to mortality and adult disability, causing motor function impairment, paralysis, and intense back pain. In aiding stroke patients' mobility recovery, physiotherapists employ diverse therapeutic techniques. This research introduces an automated method for identifying distinct therapeutic exercises performed by stroke patients during their rehabilitation process. For detecting rehabilitation exercises, leveraging pre-trained neural networks like GoogLeNet, InceptionV3, ResNet-101, DenseNet-201, Xception, InceptionResNetV2, and DarkNet-53, all potent models trained on extensive datasets. The proposed models were trained on a dataset of 2250 diverse RGB images to classify eight rehabilitation exercises: Elbow Extension, Knee Flexion, Neck Exercise, Planter Flexion of Foot, Trunk Extension, Trunk Flexion, Wrist Extension, and Wrist Flexion. The resulting average validation accuracies are as follows: InceptionV3 (97.4%), Xception (97.3%), ResNet101 (97.3%), DarkNet53 (96.7%), GoogLeNet (95.9%), DenseNet201 (94.3%), and InceptionResNetV2 (93.4%). Fuzzy EDAS is then employed to rank the models. This ranking suggests that the inception-v3 model has high rank for most of the classes and is highly recommended for physiotherapy exercises classification.