Archives of Computational Methods in Engineering最新文献

Novel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

This comprehensive review and analysis delve into the intricate facets of bias within the realm of deep learning. As artificial intelligence and machine learning technologies become increasingly integrated into our lives, understanding and mitigating bias in these systems is of paramount importance. This paper scrutinizes the multifaceted nature of bias, encompassing data bias, algorithmic bias, and societal bias, and explores the interconnectedness among these dimensions. Through an exploration of existing literature and recent advancements in the field, this paper offers a critical assessment of various bias mitigation techniques. It examines the challenges faced in addressing bias and emphasizes the need for an intersectional and inclusive approach to effectively rectify disparities. Furthermore, this review underscores the importance of ethical considerations in the development and deployment of deep learning models. It highlights the necessity of diverse representation in data, fairness-aware algorithms, and interpretability as key elements in creating bias-free AI systems. By synthesizing existing research and providing a holistic overview of bias in deep learning, this paper aims to contribute to the ongoing discourse on mitigating bias and fostering equity in artificial intelligence systems. The insights presented herein can serve as a foundation for future research and as a guide for practitioners, policymakers, and stakeholders to navigate the complex landscape of bias in deep learning.

Quantum cryptography (QC), rooted in the principles of quantum mechanics, stands as a beacon of security, offering an unparalleled level of protection against quantum attacks. This exceptional attribute has spurred researchers from diverse scientific disciplines to actively collaborate in advancing the field toward practical implementation. Physicists, computer scientists, engineers, and mathematicians are collectively channeling their efforts, leading to a substantial body of research outcomes. Hence, through this study, we delve into comprehending the multidisciplinary research landscape of QC through scientometrics. Here, we analyze the research outcomes in QC to discern its pattern in terms of publications and citations. Additionally, we identify the most influential countries, authors, and communication sources contributing to various facets of QC. Furthermore, this study also provides a research trajectory that outlines the prevalent research themes and current areas of research in QC. This information serves as a guiding light for newcomers, offering them direction and insight into the dynamic field of QC.

In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

Seismic pounding has taken place in several earthquake events since adjacent structures that lack adequate separation distance usually suffer from repetitive, severe collisions. These collisions result in considerable impact forces in addition to acceleration spikes, thus dealing damage to both structural and non-structural elements. So, a meaningful effort has been widely directed towards the investigation of that phenomenon, leading to a considerable number of publications that are related to that field of study. A review of these publications has thus become a matter of interest. Accordingly, this paper mainly aims to present a detailed state-of-the-art review concerned with seismic pounding between adjacent buildings. Firstly, general definitions, types, and causes of seismic pounding are addressed. Later, facts and statistics of historical earthquake incidents that reflect the scale of the threat caused by seismic pounding are clarified. Moreover, the effect of seismic pounding on fixed-base and base-isolated buildings is discussed. Furthermore, the effect of soil-structure interaction is also presented. Additionally, alternative mitigation methods for seismic pounding are presented. Their classification, types, efficiency, and applicability are also discussed. Eventually, different impact analytical models that can be used to simulate seismic pounding in theoretical studies are discussed. By the end of this paper, deficiencies in previous studies are clarified in order to be taken into account throughout future studies.

The objective of this study is to investigate the use of photoelectric conversion technology in the process of creating enhanced photoelectric signal sampling systems using photoelectric conversion technology. The purpose of this research is to strengthen the sensitivity and dependability of signal capture by focusing on three primary aspects: the improvement of absorption, the downsizing of the device, and the efficiency of the conversion. It is possible to increase the amount of light that is absorbed by using localized surface Plasmon resonance for the goal of absorption augmentation. This, in turn, allows for an expansion of the spectrum of signals that may be detected. When compared to other components, the miniaturization component provides a greater emphasis on the design of small devices. This facilitates integration into small-scale sensors and systems, which ultimately results in improved mobility and flexibility. The author of the book “Conversion Efficiency” explores the benefits of plasmonic effects pertaining to the enhancement of photoelectric conversion, which finally leads to an improvement in the performance of the system. The use of photoelectric conversion technology has significant repercussions for a broad variety of applications, some of which include sensing, communication, and instrumentation, amongst others. The promise that this method will bring about a revolution in the processes of data collection is that it will make it possible to provide signal sampling devices that are exceedingly sensitive, compact, and efficient. It is clear that the findings of this research provide a significant contribution to the advancement of technology in domains where precision and reliability are of the highest significance. In addition, this discovery constitutes a significant advancement in the development of methods for sampling photoelectric signals.

This work presents a comparative review and classification between some well-known thermodynamically consistent models of hydrogel behavior in a large deformation setting, specifically focusing on solvent absorption/desorption and its impact on mechanical deformation and network swelling. The proposed discussion addresses formulation aspects, general mathematical classification of the governing equations, and numerical implementation issues based on the finite element method. The theories are presented in a unified framework demonstrating that, despite not being evident in some cases, all of them follow equivalent thermodynamic arguments. A detailed computational analysis is carried out where Taylor–Hood elements are employed in the spatial discretization to satisfy the inf-sup condition and to prevent spurious numerical oscillations. The resulting discrete problems are solved using the FEniCS platform through consistent variational formulations, employing both monolithic and staggered approaches. We conduct benchmark tests on various hydrogel structures, demonstrating that major differences arise from the chosen volumetric response of the hydrogel. The significance of this choice is frequently underestimated in the state-of-the-art literature but has been shown to have substantial implications on the resulting hydrogel behavior.

This policy framework, when implemented over time, will provide a strategic roadmap for smart cities to achieve net-zero emissions. This promotes sustainable urban expansion and the battle against climate change. It takes a broad approach, considering the requirements of the society, technological advancements, and collaborating with individuals from other nations. It requires the involvement of stakeholders, the implementation of policies, and the tracking of progress. Among the most crucial aspects are collaborating with local governments, being active in the community (including business), and collaborating with foreign organizations. A phased implementation plan, resource allocation, and constant monitoring demonstrate how crucial it is to have the flexibility and adaptability to change. At the conclusion, people are reminded of the connections between issues pertaining to the environment, society, and economics. Cooperation is also required of governments and all other significant stakeholders. A customizable global road map is one component of the architecture. As a result, cities have a stronger and more equitable future. This is the route map provided by the framework.

Alzheimer’s disease (AD) is a degenerative neurological ailment that progressively affects a large number of individuals globally. Timely and precise diagnosis of this ailment is crucial for effective therapy and control. In recent years, DL algorithms have demonstrated encouraging outcomes in assisting AD diagnosis by utilizing medical imaging datasets. Nevertheless, current DL models for AD classification encounter specific obstacles, including restricted interpretability and elevated computational cost. This article introduces a novel hybrid DL approach to address these difficulties. This model integrates conventional ML and DL techniques to perform better classification. The hybrid model is trained and tested using a substantial dataset comprising AD patients and individuals without AD. This study aims to comprehensively analyze the performance of the suggested hybrid model by comparing it to other advanced DL models already in use. The findings demonstrate that the proposed hybrid model surpasses current DL models in accuracy, sensitivity, and specificity. It possesses enhanced interpretability, facilitating doctors in effectively communicating the diagnosis to patients. The hybrid model exhibits reduced computing complexity, rendering it more efficient and feasible for real-time diagnosis. This study enhances the advancement of a novel hybrid DL model for AD classification by integrating the advantages of conventional ML algorithms and DL approaches. The results indicate the model's capacity for precise and efficient diagnosis. Subsequent studies can investigate the model's use with different medical imaging datasets to diagnose various neurodegenerative illnesses.

The interaction across the contact interfaces has a wide range of applications in many engineering and industrial processes. The development of computational methods for simulating these processes is one of the major challenges of computational contact mechanics. To this end, the isogeometric analysis technique has gained significant popularity over the conventional finite element method due to its several advantageous features. This review mainly concentrates on the various isogeometric contact algorithms and their applications that serve to evaluate contact interfaces. First, a brief overview of the isogeometric analysis framework is provided, followed by its mathematical implementation in contact mechanics. Then, the available isogeometric contact algorithms are examined. This is followed by different practical applications of isogeometric contact analysis. Finally, the paper is concluded, setting the possibilities for future research needs in the current state of the art.