alexandria engineering journal最新文献

We extend the virtual element method to the two-dimensional time-fractional parabolic PDE, characterized by a fractional derivative of order $\alpha \in (0,1)$ in time. To illustrate the working of this fractional virtual element scheme, a numerical investigation of the following time-fractional problem over distorted polygonal meshes is conducted. ${}^c{D}_t^{\alpha }u(z,t)-\Delta u=f(z,t)\text{in}z\in \Omega ,t\in (0,T],$where $\Omega$ is a spatial domain, $\alpha$ is fractional order, and $t$ is time variable. Our methodology is based on the fundamental technical component, fractional version of the Grunwald–Letnikov approximation. We prove the method’s well-posedness, that is the approximate solution’s existence and uniqueness. The fully discrete scheme inherently maintains stability and consistency by leveraging the discrete maximal regularity and the energy projection operator. The convergence in the $L^2$-norm and $H^1$-norm over distorted mesh configuration is validated by numerical results, underlining the practical effectiveness of the proposed method.

Hybrid nanoliquids are not underestimated for their involvement in microelectronics, transportation, coolant processes, ships, biological processes and power generation. Hence motivation for current work is to examine homogeneous-heterogeneous reactions in entropy optimized flow by curved sheet stretched with nonlinear velocity. Trihybrid nanoliquid is synthesized through 2 % of ferrous oxides$\left(F{e}_3{O}_4\right)$, 2 % of silver$\left(Ag\right)$ and 2 % of copper$\left(Cu\right)$. Kerosene oil is employed as the base fluid. These specific kinds of nanoparticles are taken into consideration because of their numerous applications in heat dissipation, air filters, dynamic sealing, biosensors and catalysts process. Kerosene oil may have applications in many different domains such as energy storage, cleaning agent, portable heaters, fuel additive and industrial lubricants. Darcy-Forchheimer model is employed. Analysis in presence of radiation, dissipation, Ohmic heating, entropy generation and heat generation/absorption are organized. Unlike the previous considerations, the thermal expression here consists of impacts through Darcy-Forchheimer relation. Adequate transformations are implemented. Computations have been arranged by applying finite difference technique (FDM) using MATLAB. Quantities for physical interest are addressed. The presented analysis may have relevance for solar systems, chemical reacting processes, cooling towers and polymer data processes. The conclusions are also organized for important key findings. It is noticed that trihybrid nanomaterial has more entropy rate when compared with hybrid and classical nanoliquids when volume fraction is chosen as 2 % for trihybrid fluid. It is proposed that the ability to transfer heat is enhanced when nanoparticles are added to conventional fluids. Decay in concentration is noticed for both homogeneous and heterogeneous parameters. Reduction for Bejan number is noticed through homogeneous diffusion factor. Comparison with previous study is also presented and found great consensus between them.

The primary focus of this study is to introduce some kinds of piecewise fractional derivatives (PFDs). These derivatives are defined using fractional derivatives in both the Atangana–Baleanu and Caputo senses. They are considered to generate a novel collection of fractional variational problems that rely on an indefinite integral. A numerical method established on the piecewise Chebyshev cardinal functions (as a suitable family of basis functions for such situations) is utilized to solve these problems. To this end, some operational matrices for PFDs of the expressed cardinal functions are derived and used to generate the presented method. Using the proposed technique, solving the desired problems is converted into solving associated algebraic systems. The effectiveness of the procedure is checked by solving some illustrative examples.

Uncommon genetic illnesses pose significant challenges for detection due to their low occurrence and intricate genetic makeup. Traditional targeted genetic testing methods are often limited, missing rare or unidentified genetic variations. This project proposes a novel strategy that leverages Whole-Genome Sequencing (WGS) data and Random Forest (RF) analysis to overcome these limitations. WGS provides a comprehensive view of an individual's genetic profile, capturing a wide array of genetic variations that targeted approaches might overlook. By employing the RF method, which excels at handling complex datasets and detecting non-linear interactions, this project aims to uncover intricate links between rare genetic diseases and gene variations. The Swedish Genome Reference dataset will serve as the foundation for this research. RF analysis will be applied to this extensive dataset to identify patterns and connections that might reveal new genetic markers and previously unknown risk factors for these illnesses. This approach allows for exploring vast genetic datasets to detect structures and associations, providing deeper insights into the genetic underpinnings of rare diseases. Combining WGS with RF analysis offers a powerful tool for discovering genetic indicators and risk factors contributing to rare genetic disorders, achieving an accuracy rate of 97 %. This innovative approach can significantly enhance understanding, diagnosis, and treatment of these conditions. By highlighting the value of advanced computational techniques and comprehensive WGS databases, the project aims to pave the way for more personalized and specialized medical treatments, ultimately improving patient outcomes for those affected by rare genetic diseases.

Currently, dual-layer tubes play a pivotal role in sectors like nuclear, oil, gas, and steam generation. Recent research focuses on enhancing energy absorption in these tubes using the spinning forming process. This thin-walled nature benefits their application as efficient energy absorbers in transportation like trains and aircraft. A novel approach integrates coarse and fine-threaded ridges between steel and aluminum layers to create a mechanical interlock using the spinning forming technique. This interlock enhances the bond through flow forming, resulting in robust dual-layer tubes. Interlayer connection strength is rigorously tested through shear tests, pre and post flow forming, indicating a significant post-flow forming enhancement. This process substantially bolsters the overall structural integrity of dual-layer tubes. In crush tests, both single-layer and dual-layer steel and aluminum tubes undergo pressing through flow forming. Comparative analysis of energy absorption parameters—specific energy and crushing percentage—reveals notable performance differences. An annealed version of the tube with an aluminum layer is also included due to prior aluminum layer failures. Among dual-layer samples, the flow formed variant with fine-threaded ribs exhibits superior energy absorption and bonding. Finite element simulation of the pressed dual-layer sample demonstrates an 8.42 % deviation between simulated and experimental energy absorption outcomes. The study highlights that tightly bonded layers result in enhanced bonding characteristics.

To achieve carbon neutrality for China, the transmission mechanism, path, diffusion effect,and allocation of Emission Reduction Targets (ERT) among industries must be investigated. Setting short-term ERT will create a reverse pressure mechanism for the industry's carbon reduction, ensuring the achievement of carbon emission control target. However, few studies have explored how to influence the entire system by controlling key industries from the perspective of network control. Therefore, this study investigates Inter-industry Carbon Emission Transfer (ICET) in China from 1997 to 2017,and found that ICET can achieve fair distribution of ERT. Inspired by the controllability theory of complex networks to integrate the driving factors, the transmission paths, diffusion effects, and allocation of ERT into a unified analytical framework. Then, breaking the traditional top-down hierarchical decomposition mechanism and transforming it into a bottom-up target forcing mechanism, a decomposition algorithm for the total control target was proposed.The results reveal that ICET accounts for the majority of carbon emission. The characteristics of network structure have a significant impact on the ICET.Different industries have varying control capabilities. Compared with industry-wide control, target control on the Minimum Control Industry Set (MCMCS) has a slightly worse emission reduction effect, but the economic loss is smaller, making it an effective policy choice.

The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection.

Asymmetrical probability models are helpful for analyzing skewed data sets since they allow you to describe the form of the distribution and anticipate the chance of extreme events. This article defines a novel approach to continuous moment exponential distribution called the exponentiated generalized moment exponential model. The extension has two additional parameters accounting for the distribution’s shape. We extend this distribution probability density, cumulative distribution, hazard rate, and survival functions and establish different key statistical properties. Parameter estimation is obtained using different procedures, notably maximum likelihood estimation, least square, and Bayesian methods. A Monte Carlo simulation experiment is conducted to assess parameter performance and indicator risk measures. This article examines two distinct actual data sets in order to highlight the significance of the proposed model as well as its application in a variety of settings. The new model is compared to a large number of well-known extensions that were developed by other businesses.

This study explores inventory management strategies specifically designed for non-instantaneous deteriorating goods under inflationary conditions, utilizing a dual-warehouse system—one owned and the other rented with limited storage capacity. We examine the effects of advertising frequency and product selling price on demand rates, taking into account the gradual decline in customer patience, which leads to partial backlogging of shortages. The primary objective is to determine optimal replenishment policies for retailers that effectively minimize total costs per unit time. In real life, managing oil inventory is crucial for industries where factors like gradual deterioration, demand fluctuations due to advertising and pricing, and sensitivity to inflation require sophisticated inventory models to optimize replenishment policies and minimize costs. To validate our proposed inventory model, we provide a numerical. A sensitivity study using MATLAB R2024a software highlights the impact of parameter changes, providing significant information for decision-makers across various industries.

The present examination focuses on the Falkner-Skan flow of micropolar hybridized nanofluid via a wedge surface. The proposed study examines thermal radiative fluxing and heat dissipation in hybridized nanoparticle aqueous solutions. Simulations of uni-directional radiative transport in optically dense fluids use Rosseland's diffusion model. This study created a Cu-TiO₂/water hybrid nanofluid by mixing Cu and TiO₂ nanomolecules with H₂O. Partial differential equations from Naiver-Stokes theory are used to generate the regulating flow phenomena, then convert them into ordinary differentiation equations using an appropriate similarity approach. Additionally, the three-stage Lobatto IIIA method is used to compute the formulae. Calculations are done using MATLAB's built-in bvp5c function. We found that increasing material characteristics slows fluid flow because micropolar nanofluids minimize drag. These alterations alter fluid flow and boost temperature. However, boosting thermal radiation and Eckert number slows heat movement but improves temperature profiles. Much prior research ignored thermal radiative fluxing and heat dissipation in the aqueous solution of hybrid nanomolecules (Cu and TiO₂) in Falkner-Skan micropolar flow via a wedge surface. Tables show wall frictional factor and Nusselt quantity results. Micropolar fluid characteristic decreases velocity but increases micro-rotational velocity. Power-law parameters, volume fractions, radiation, heat source, and Eckert amount affect thermal contours.