Archives of Computational Methods in Engineering最新文献

The large intestine is an important part of the human digestive system and the target of many diseases. The biomechanical properties of the large intestine are closely related to the proper functioning of its mechanical behavior. Therefore, the study of its biomechanical properties can help to better understand the key factors of lesions and provide a theoretical basis for the research and application of disease treatment, artificial anal sphincter, and other related medical devices. Physiologic structure of the large intestine is the basis for the study of its biomechanical properties. Constitutive models are commonly used to describe the biomechanical properties of soft tissues and to provide a mathematical characterization of the effects of biological components on the behavior of materials. The studies and results of biomechanical experiments provide a basis for determining the material parameters and the causes of functional damage to the material. In this paper, the biomechanical properties of the large intestine are investigated from two aspects: active properties and passive properties, and three perspectives: physiological structure, constitutive models, and biomechanical experiments. The paper concludes that their effective combination is an important comprehensive method to study the biomechanical properties of the large intestine. It provides a new perspective for the study of biomechanical properties of the large intestine, and provides a theoretical basis for future related biomechanical studies and the development of related medical devices.

Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms inspired by the behavior of an ant colony to find the shortest path for food. The multi-objective ACO (MOACO) is a modified variant of ACO introduced to deal with multi-objective optimization problems (MOPs). The MOACO is seeking to find a set of solutions that achieve trade-offs between the different objectives, which help the decision-makers select the most appreciated solution according to their preferences. Recently, a large number of MOACO research works have been published in the literature, reaching 384 research papers according to the SCOPUS database. In this review paper, 189 different research works of MOACOs published in only scientific journals are considered. Through this research, researchers will gain insights into the expansion of MOACO, the theoretical foundations of MOPs and the MOACO algorithm, various MOACO variants documented in existing literature will be reviewed, and the specific application domains where MOACO has been implemented will be summarized. The critical discussion of the MOACO advantages and limitations is analyzed to provide better insight into the main research gaps in this domain. Finally, the conclusion and some possible future research directions of MOACO are also given in this work.

Computational intelligence has previously demonstrated its existence beyond the limitations of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and Grover (1996) provide massive parallelism and searching techniques, vastly expanding the computational capacity of soft computing. This paper aims to analyze articles that consider both computational intelligence and quantum computing, referred to here as the quantum computational intelligence (QCI) category, to solve non-deterministic problems efficiently. The category includes 3067 research papers published from 2014 to 2023 that are indexed in high-quality databases like SCI and SCOPUS. This study examines QCI publishing patterns utilizing scientometric analysis employing co-occurrence, co-citation, and bibliographic coupling methodologies. Additionally, it provides insights into the citation patterns of publications, affiliations, and authors. China, USA, and India published more than half (53%) of the articles. The primary emphasis of application fields throughout this decade includes ‘Ground State Preparation’ and ‘Financial Forecasting’ among others. The pertinent keywords that have lately been studied are quantum particle swarm optimization (2022), optimization (2021), quantum circuits (2020), and deep learning (2019). Five quantum-based computation techniques were identified using a mix of critical review and cluster analysis: quantum machine learning, quantum neural networks, quantum particle swarm optimization, quantum variational Monte Carlo, and quantum-inspired evolutionary algorithms. The primary objective of this study is to address key queries that could contribute to future research in this field.

Restoration of damaged artwork is an important task to preserve the culture and history of humankind. Restoration of damaged artwork is a delicate, complex, and irreversible process that requires preserving the artist’s style and semantics while removing the damages from the artwork. Digital restoration of artworks can guide artists in physically restoring artworks. This paper groups the virtual artwork restoration methods into various categories: image processing, machine learning, encoder-decoder neural networks, and generative adversarial network-based methods. This paper discusses and analyses different restoration methods’ underlying merits and demerits. The category-wise review of various artwork restoration methods reveals that the generative adversarial network-based methods have attracted the attention of researchers in recent years for restoring damaged artworks. This paper describes datasets used for training and testing of artwork restoration methods and discusses various metrics used for performance evaluation of the artwork restoration methods. This paper compares the restoration results of various methods quantitatively using performance evaluation metrics and qualitatively using visual inspection of the results. Further, the paper also identifies research gaps, challenges, and future directions for research in this field. The proposed review aims to provide researchers with an important reference for working in the artwork restoration field.

Piled raft foundation is a sustainable foundation approach in the current era due to the development of immense infrastructure. Initially, this foundation was primarily used for the construction of high-rise buildings in weaker soil bases, however, the application of piled raft systems is now extending to offshore and marine structures. This paper mainly concentrates on the review of available literature in the domain of the piled raft foundation and presents a critical review of the evolution of the piled raft from the past to till date. This state-of-the-art review starts from the early-age research and analytical works on the piled raft foundation. Various approaches suggested by several researchers are described here. The review also contains the experimental research conducted on piled rafts including the numerical analysis carried out on the existing case studies. The applications of different numerical software for the modelling of piled rafts are also described elaborately and the research related to the parametric analysis is accumulated together. The load sharing nature, interaction behaviour, and the influence of the loading system are reviewed separately depending upon the contribution of the research papers to the particular domain. There exist several design processes and selection criteria for the piled raft foundation and this is mainly due to the inadequate perception regarding the nature of the piled raft foundation during its overall applications. This literature review would be helpful for other researchers to acquire a clear idea of the behaviour of piled rafts and also would be useful to identify the future prospects of the piled raft foundation.

Ultrasound imaging is widely used for the clinical assessment and study of musculoskeletal tissues because of its capacity for real-time imaging, low cost, high availability and portability. Objectively identifying and segmenting these tissues in ultrasound images can enhance disease diagnosis and biomechanical research. Manual segmentation is tedious, time-consuming and examiner-dependent. At the same time, ultrasound images suffer from poor image quality and low contrast between different regions in the image, making visual interpretation difficult. Hence, there is a need for reliable algorithms for computerised segmentation. This paper reviews the techniques developed for automated and semi-automated segmentation of vital musculoskeletal tissues (i.e. tendon, ligament, bone, muscle, plantar fascia and cartilage) from ultrasound images. This paper comprehensively explains each methodology and discusses distinguishing features, advantages and limitations to help the reader decide the most appropriate method on an application-specific basis.

Scour is one of the most difficult challenges faced by hydraulic engineers, which refers to the erosion of sediments that surrounds hydraulic structures. In the past, scour prediction has generally relied on physical models as well as empirical formulae. However, these methods may not satisfactorily account for the complex nature of scour processes. Hence, this paper aims to provide a concise overview of the latest advancements in the field of scour prediction, particularly focusing on the use of machine learning (ML) techniques. The review begins by examining the basic ideas and methodologies of various machine learning algorithms which are commonly employed, it then looks into the key factors that affect scour processes, including flow velocity, sediment characteristics, and bed morphology. The paper provides an in-depth assessment of the advantages and drawbacks of current machine learning models used for estimating scour, taking into account various issues such as the availability of data, models understandability, and their capacity to adapt in changing environmental conditions. This study will be a helpful resource for researchers, practitioners, and decision-makers in the field of hydraulic engineering. It provides insights into the evolving field of ML applications for predicting scour and sets the stage for the advancement of more precise and versatile scour prediction models.