Archives of Computational Methods in Engineering最新文献

This paper presents a comprehensive review of finite element methods (FEMs) applied to a diverse range of reaction-diffusion equations (RDEs). Beginning with a historical overview of FEMs, we then provide a summary of various FEMs, including standard Galerkin (both conforming and non-conforming), mixed Galerkin, discontinuous Galerkin, and weak Galerkin. Additionally, a priori and a posteriori error have been discussed for standard Galerkin. In further discussion related to RDEs, we provide insights into the evolution of these equations and their significance in various fields. We then systematically review these FEMs for solving different types of RDEs, including more recent advances pertaining to RDEs with nonlinear reaction terms, and advection reaction-diffusion equations. Finally, we briefly highlight the applications of machine learning and deep neural networks to FEM.

This paper reviews modern approximate analytical methods for solving symmetric and non-symmetric dynamical problems, including the Perturbation Method using the Green function, the Regular Perturbation Method, the Adomian Decomposition Method, the Undetermined Coefficient Method, the Poincaré-Lindstedt Method, and Multiple-Scale Analysis. The applicability of each method is assessed based on its purpose, constraints, mathematical domain, and accessibility. Example applications demonstrate the solution process and the effectiveness of each method, with analytical solutions verified against numerical results for accuracy and stability. A comparison of the advantages, disadvantages, and suitable applications is presented in tabular form to aid in selecting the appropriate method for specific problems. Finally, this evaluation highlights future trends and potential applications in engineering and applied sciences.

Cancer continues to be a primary cause of death worldwide, highlighting the critical need for early diagnosis methods. Automated, quick, and efficient technologies are critical to this endeavor, yet considerable gaps remain in this field. A comprehensive review was undertaken to examine seven cancer types characterized by elevated prevalence and mortality: lung, prostate, brain, skin, breast, leukemia, and colorectal cancer. The study aimed to reveal gaps in the existing research and compare traditional machine learning (TML) with deep learning (DL) methodologies, since such contrasts have been not much explored. A total of 320 publications were carefully chosen for study, including 150 that focused on TML methods and 170 that address DL techniques for the classification of cancer. Diverse parameters were used to assess these investigations, encompassing publication year, employed databases, data sample, classifier, modalities, and evaluation metrics. Separate evaluations were conducted for each cancer type and methodology, yielding 14 unique review tables. The assessment of each cancer type using ML/DL independently relied on four standard criteria: High performance (> 99%), Limited performance (< 85%), key findings, and key challenges. These studies were accompanied by a brief descriptive outline of the features, classifiers, public databases, and evaluation metrics that were utilized in the review process. The study concluded by offering general conclusions that highlighted the overall findings, overall challenges observed during the investigation. This thorough review seeks to improve clinical applications and guide future research initiatives in the persistent fight against cancer.

With the rapid evolution of technologies like IoT (Internet of Things) and ML (Machine Learning), applications are becoming much smarter, thus giving more opportunity for the exploitation of various sectors. With such connectivity of devices giving rise to abundant amount of data thus boosting the deployment of Machine learning. Though Machine learning has found immense applicability in various sector, transportation is one such sectors that has attracted many researchers for transforming the paradigm of transportation into intelligence and enhancement. During the review, we considered the various layers of Machine learning in Transportation and reviewed them one by one. The application area of ML in transportation is reviewed thoroughly, discussing the recent advancements carried out in areas such as route optimization, logistics, accident detection, and many others. The purpose of the review is to present a self-contained critical review discussing every aspect of the deployment of the approach in Transportation. From the review, it is found that though ML is a strong aspect for the future revolution of transportation systems, there are some challenges, such as privacy concerns, that cannot be ignored and need good research to overcome these challenges.

Implementation of 3D concrete printing technology has transformed the construction sector by providing enhanced design freedom, accelerated building timelines, and decreased material waste. This systematic study offers an in-depth review of current progress in 3D printed concrete, emphasizing materials, technological innovations, and environmental and economic factors. The research investigates several 3D printing methodologies, including extrusion-based and powder-based processes, and analyses the impact of materials such as fibre-reinforced, geopolymers, and high-strength concrete on the mechanical characteristics and workability of printed structures. Critical technological issues, such as layer adhesion, rheological properties, and printability, are examined to ascertain existing constraints and prospective research avenues. The analysis underscores case examples that demonstrate the actual uses of 3DCP, including buildings, bridges, and ornamental features, highlighting the technology’s capacity to decrease building expenses and environmental effects. The future potential of 3D concrete printing in large-scale building projects is discussed along with suggestions for the advancement of materials and printing methodologies to enhance performance and sustainability.

Cancer detection has long been a continuous key performer in oncological research. The revolution of artificial intelligence (AI) and its application in the field of cancer turned out to be more promising in the recent years. This paper provides a detailed review of the various aspects of AI in different cancers and their staging. The role of AI in interpreting and processing the imaging data, its accuracy and sensitivity to detect the tumors is examined. The images obtained through imaging modalities like MRI, CT, ultrasound etc. are considered in this review. Further the review highlights the implementation of AI algorithms in 12 types of cancers like breast cancer, prostate cancer, lung cancer etc. as discussed in the recent oncological studies. The review served to summarize the challenges involved with AI application. It revealed the efficacy of AI in detecting the region, size, and grade of cancer. While CT and ultrasound proved to be the ideal imaging modalities for cancer detection, MRI was helpful for cancer staging. The review bestows a roadmap to fully utilize the potential of AI in early cancer detection and staging to enhance patient survival.

Model Order Reduction (MOR) techniques play a crucial role in reducing the computational complexity of high-dimensional mathematical models, enabling efficient simulations and analysis. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in various domains, including MOR. This survey paper provides an overview of AI-based MOR techniques, exploring how AI methods are being integrated into traditional MOR approaches. Different AI algorithms, such as machine learning, deep learning, and evolutionary computing, and their applications in MOR are discussed in this paper. The advantages, challenges, and future directions of AI-based MOR techniques are also highlighted.

Maximum power point tracking (MPPT) is an essential technique used to extract the maximum power from a photovoltaic (PV) system. Fuzzy logic-based control is one of the popular methods used for MPPT because it provides excellent performance under varying environmental conditions. The world is now facing a challenge in terms of energy. Solar energy is a key to solve that issue, so it must be optimized. Among the MPPT optimization methods used to improve the photovoltaic modules efficiency, fuzzy logic control (FLC) seems to be the one that is really adapted. However, it has a lot of drawbacks like the complexity of implementation and its performance depends not only on the chosen error, but also on the established inference rules. To solve these problems this method has been modified in various ways. The present work makes a diversified review of MPPT algorithms using fuzzy logic control for PV applications. It is subdivided into three main parts. The first part deals with modified FLC algorithms. The second part deals with FLC algorithms associated with other classical algorithms and the third with MPPT algorithms associated with intelligent methods. The different works analyzed have tested their innovative approaches by simulation and have for the most part validated them by an. It can be noted that the third category is the one that offers a better increase in efficiency even if it has a higher complexity. The second category is more suitable for variable weather conditions and the first one is recommended especially for its very low cost. The suggested asymmetrical fuzzy logic-based MPPT technique uses an asymmetric membership function and a rule-based controller to improve the tracking accuracy and speed. The performance of the suggested technique was evaluated and compared with two existing MPPT techniques. The evaluation was conducted through simulations with MATLAB/Simulink. Overall, the results suggest that the proposed asymmetrical fuzzy logic-based MPPT technique is a promising approach for improving the speed and tracking accuracy MPPT in photovoltaic systems.

This study provides an in-depth review and analysis of the nature-inspired Sand Cat Swarm Optimization (SCSO) algorithm. The SCSO algorithm effectively focuses on exploring solution areas inspired by sand cat hearing and finding the most suitable solutions for their hunting behavior. This algorithm is easily adaptable to various problems due to its stability, low-cost, flexibility, simple implementation, simplicity, derivative-free mechanism, and reasonable computation time. For these reasons, although it was published recently, it has begun to attract the attention of researchers. SCSO-based research has been presented in prestigious international journals such as Elsevier, Springer, MDPI, and IEEE since its inception in 2022. The studies cited in this paper are examined in three categories: improved, hybrid, and adapted. Research trends show that 39, 21, and 40% of SCSO-based studies fall into these three categories, respectively. Additionally, research on solving various problems inspired by the SCSO algorithm is discussed from two different perspectives: global optimizations and real-world applications. Analysis of the applications shows that 15 and 85% of the studies belong to these two fields, respectively.

This article presents an extensive analysis of the metaverse, an emerging technology that lets users submerge themselves in a fully realized 3D virtual world and engage with others in diverse virtual experiences. The study offers a thorough comprehensive taxonomy of distinct applications of metaverse across numerous sectors, including business, real estate, media, entertainment, manufacturing, online commerce, tourism, education, and healthcare. Additionally, the research offers a statistical analysis of the Google Trends data for the keyword "metaverse," exhibiting the heightened interest and engagement in this innovative technology worldwide. Furthermore, the article discusses the critical components of the metaverse, including hardware, virtual platforms, and high-speed internet, essential for building a robust and seamless metaverse ecosystem. The study also examines the social, legal, and environmental ramifications of utilizing metaverse technology, emphasizing the demand for deliberate thought and meticulous design. Finally, the study offers an all-encompassing and thorough exposition of the metaverse concept, its vast and varied potential applications, and its prospects in a diverse range of domains. The article concludes that the Metaverse has the potential to revolutionize the way people communicate, learn, work, and interact and that it will likely be a key driver of innovation in various fields in the coming years.