Archives of Computational Methods in Engineering

In structural optimization, the implicit nature of the cost function with respect to the optimization parameters, i.e. through the solution of the structural problem calculated with fixed values of these parameters, leads to prohibitive computations whatever the adopted formulation. Consequently, it yields limitations in both the number of parameters and the size of the structural problem. Moreover, some know-how is required to define a relevant structural problem and a well-behaved cost function. Here, we profit from the ability of the Proper Generalized Decomposition (PGD) method to handle large-dimensionality problems to transform the optimization parameters into variables of an augmented-structural problem which is solved prior to optimization. As a consequence, the cost function becomes explicit with respect to the parameters. As the augmented-structural problem is solved a priori, it becomes independent from the a posteriori optimization. Obviously, such approach promises numerous advantages, e.g. the solution of the structural problem can be easily analyzed to provide a guide to define the cost function and advanced optimization schemes become numerically tractable because of the easy evaluation of the cost function and its gradients.

Linear parabolic diffusion theories based on Fourier’s or Fick’s laws predict that disturbances can propagate at infinite speed. Although in some applications, the infinite speed paradox may be ignored, there are many other applications in which a theory that predicts propagation at finite speed is mandatory. As a consequence, several alternatives to the linear parabolic diffusion theory, that aim at avoiding the infinite speed paradox, have been proposed over the years. This paper is devoted to the mathematical, physical and numerical analysis of a hyperbolic convection-diffusion theory.

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

The prediction and early diagnosis of eye diseases are critical for effective treatment and prevention of vision loss. The identification of eye diseases has recently been the subject of much advanced research. Vision problems can significantly affect a person’s quality of life, limiting their ability to perform daily activities, impacting their independence, and leading to emotional and psychological distress. Lack of timely and accurate identification of the cause of vision problems can lead to significant challenges and consequences. Delayed diagnosis prolongs the period of impaired vision and its associated negative impact on an individual’s well-being. Deep learning techniques have emerged as powerful tools for analyzing medical images, including retinal images and predicting various eye diseases. This review provides an analysis of deep learning techniques commonly used for eye disease prediction. The techniques discussed include Convolutional Neural Networks (CNNs), Transfer Learning, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Explainable Deep Learning. The application of these techniques in eye disease prediction is explored, highlighting their strengths and potential contributions. The review emphasizes the importance of collaborative efforts between deep learning researchers and healthcare professionals to ensure the safe and effective integration of these techniques. The analysis highlights the promise of deep learning in advancing the field of eye disease prediction and its potential to improve patient outcomes.

The subloading surface model is endowed with the intrinsic far-reaching ability to describe the wide classes of irreversible mechanical behavior, e.g. the monotonic and the cyclic loading behavior of elastoplastic and viscoplastic materials, the friction behavior and the crystal plastic behavior as has been examined in the former paper (Hashiguchi in Arch Comput Methods Eng 20:361–417, 2013). However, the past formulations of the subloading surface model have contained several inexact equations, which have been modified repeatedly since the concept of the subloading surface was proposed in 1977 (Hashiguchi and Ueno 1977). The exact formulation is presented first in this article for the hypoelastic-based plasticity, which enjoys the distinguished superiority in the both aspects of the description of material behavior in high accuracy and of the numerical calculation in high efficiency. It is further provided for all the four basic frameworks, i.e. the infinitesimal hypoelastic-based plasticity, the infinitesimal hyperelastic-based plasticity, the hypoelastic-based plasticity and the multiplicative hyperelastic-based plasticity for finite strain. Further, the subloading-crystal plasticity model is formulated modifying the former one (Hashiguchi 2013) by incorporating the decomposition of the crystalline shear strain rate into the elastic and the plastic parts. This would be the guidebook to the subloading surface model and also the memorial monograph for the historical development of the subloading surface model.

Helmholtz resonator (HR) is an elementary resonating structure predominantly used for acoustic wave manipulation. The sound absorption capabilities of HR are well examined and widely accepted, and it has extensive applications in engineering acoustics. Perhaps, low-frequency sound mitigation is a major technological challenge wherein, HR based absorbers play a pivotal role. In this review, the recent advancements in various HR based sound absorbers are considered in general and low-frequency absorbers in particular for a detailed comparison and critical evaluation. Since the majority of the reported investigations have numerical predictions to corroborate the experimental findings, a detailed review of analytical and computational methods is necessary. Initially, finite element computations of a conventional HR are performed to assess the efficacy of trusted simulation techniques such as thermo-viscous, narrow-region and poro-acoustics models. Then, the structural aspects and noise absorption characteristics of various alterations of conventional HR configurations are critically examined using an analytical approach. Thereafter, a detailed appraisal of the low frequency sound attenuation properties of different HR combinations such as arrays of resonators, hybrid models, and acoustic metamaterials is performed. Moreover, a non-dimensional performance parameter is introduced for uniform comparison among available absorbers and to identify suitable candidates for efficient low-frequency acoustic attenuation. Finally, different optimization approaches including forward and inverse design strategies for selecting appropriate sub-wavelength HR designs for targeted low-frequency noise mitigation are also provided. The development of effective strategies for the creation of HR structures amenable to the real-life industrial environment that provide low-frequency acoustic attenuation is discussed as a future direction.

Heart disease is a widespread global concern, underscoring the critical importance of early detection to minimize mortality. Although coronary angiography is the most precise diagnostic method, its discomfort and cost often deter patients, particularly in the disease's initial stages. Hence, there is a pressing need for a non-invasive and dependable diagnostic approach. In the contemporary era, machine learning has pervaded various aspects of human life, playing a significant role in revolutionizing the healthcare industry. Decision support systems based on machine learning, leveraging a patient's clinical parameters, offer a promising avenue for diagnosing heart disease. Early detection remains pivotal in mitigating the severity of heart disease. The healthcare sector generates vast amounts of patient and disease-related data daily. Unfortunately, practitioners frequently underutilize this valuable resource. To tap into the potential of this data for more precise heart disease diagnoses, a range of machine learning algorithms is available. Given the extensive research on automated heart disease detection systems, there is a need to synthesize this knowledge. This paper aims to provide a comprehensive overview of recent research on heart disease diagnosis by reviewing articles published by reputable sources between 2014 and 2022. It identifies challenges faced by researchers and proposes potential solutions. Additionally, the paper suggests directions for expanding upon existing research in this critical field.

This study explores the intricate relationship between climate change, marine dynamic processes, and feedback mechanisms, emphasizing the marine environment’s crucial role in global climate regulation and biodiversity support. Ocean currents, such as the Gulf Stream, play a pivotal role in heat dispersion and climate stability. Diverse ecosystems, including coral reefs and mangroves, contribute essential services. Positive feedback loops, like methane release and glacier melt, amplify climate change effects, while negative loops, such as ocean heat exchange and adaptive responses, mitigate impacts. Diverse datasets, including satellite observations and climate models, offer a holistic understanding. The study addresses modeling limitations and advocates for a two-pronged approach: global regulations and coordinated efforts for adaptation and mitigation. Successful conservation projects, like marine protected areas and sustainable fisheries management, highlight the need for collaborative global action. The findings stress the human capacity to mitigate climate change consequences through understanding, legislation, and effective conservation. Future research should focus on improving modeling techniques, refining biogeochemical understanding, and exploring diverse emission scenarios to bridge existing knowledge gaps and ensure sustainability.