Zain Anwar Ali^1,2,*, Dingnan Deng¹, Muhammad Kashif Shaikh³, Raza Hasan⁴, Muhammad Aamir Khan²

Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866 - 26 January 2024

Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in… More >

Ali Akbar Firoozi^1,*, Ali Asghar Firoozi²

Structural Durability & Health Monitoring, Vol.18, No.1, pp. 1-18, 2024, DOI:10.32604/sdhm.2023.044573 - 11 January 2024

Abstract The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures. This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment. After a detailed exploration of damage tolerance concepts and their historical progression, the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures. The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures, marking a pivotal stride in damage tolerance by establishing an autonomous More >

Uma Ramasamy^*, Sundar Santhoshkumar

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 1-14, 2024, DOI:10.32604/jimh.2023.046995 - 10 January 2024

Abstract More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive… More >

Yue Xue¹, Dunqiu Fan², Shen Su^1,3,*, Jialu Fu¹, Ning Hu¹, Wenmao Liu², Zhihong Tian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 69-101, 2024, DOI:10.32604/cmes.2023.031488 - 30 December 2023

Abstract Decentralized finance (DeFi) is a general term for a series of financial products and services. It is based on blockchain technology and has attracted people’s attention because of its open, transparent, and intermediary free. Among them, the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention. However, the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years. Herein, we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues. To that end, we investigate the More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388 - 30 December 2023

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an… More > Graphic Abstract

Ayat N. Mahmood¹, Amer A. Abdulrahman¹, Laith S. Sabri^1,*, Abbas J. Sultan¹, Hasan Shakir Majdi², Muthanna H. Al-Dahhan³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.2, pp. 239-256, 2024, DOI:10.32604/fdmp.2023.028015 - 14 December 2023

Abstract Hydrodynamics characterization in terms of flow regime behavior is a crucial task to enhance the design of bubble column reactors and scaling up related methodologies. This review presents recent studies on the typical flow regimes established in bubble columns. Some effort is also provided to introduce relevant definitions pertaining to this field, namely, that of “void fraction” and related (local, chordal, cross-sectional and volumetric) variants. Experimental studies involving different parameters that affect design and operating conditions are also discussed in detail. In the second part of the review, the attention is shifted to cases with More >

Peter Watson¹, Sebastien Vincent Bonnieu², Marcello Lappa^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.1, pp. 1-29, 2024, DOI:10.32604/fdmp.2023.029280 - 08 November 2023

Abstract We present a short retrospective review of the existing literature about the dynamics of (dry) granular matter under the effect of vibrations. The main objective is the development of an integrated resource where vital information about past findings and recent discoveries is provided in a single treatment. Special attention is paid to those works where successful synthetic routes to as-yet unknown phenomena were identified. Such landmark results are analyzed, while smoothly blending them with a history of the field and introducing possible categorizations of the prevalent dynamics. Although no classification is perfect, and it is… More >

Baydaa Abdul Kareem^1,2, Salah L. Zubaidi^2,3, Nadhir Al-Ansari^4,*, Yousif Raad Muhsen^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954 - 22 September 2023

Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML… More > Graphic Abstract

David-Dan Nguyen^1,*, Massine Fellouah^2,*, Anna-Lisa V. Nguyen³, David-Christian Kazu⁴, Isabel Baltzan⁵, Muhieddine Labban⁶, Shubha De⁷, Kevin C. Zorn⁸, Bilal Chughtai⁹, Dean S. Elterman¹, Quoc-Dien Trinh⁶, Naeem Bhojani⁸

Canadian Journal of Urology, Vol.30, No.3, pp. 11546-11550, 2023

Abstract Introduction: 5α-reductase inhibitors (5ARI) are commonly prescribed medications. There is ongoing controversy about the adverse events of these medications. The aim of this study is to characterize lawsuits in Canada involving medical complications of 5ARIs use.
Materials and methods: Legal cases were queried from CanLII. Cases were included if they involved a party taking a 5ARI who alleged an adverse event. Relevant full cases were retained, and pertinent characteristics were extracted with the help of a legal expert.
Results: Our deduplicated search yielded 67 unique legal documents from December 2013 to February 2019. Twelve of these documents met… More >

NEETU TRIPATHI, DIBYENDU S. BAG^*, MAYANK DWIVEDI

Journal of Polymer Materials, Vol.40, No.3-4, pp. 227-269, 2023, DOI:10.32381/JPM.2023.40.3-4.8

Abstract Over the last three decades, there has been considerable interest in the captivating mechanical properties displayed by auxetic materials, highlighting the advantages stemming from their distinct negative Poisson's ratio. The negative Poisson's ratio observed in auxetic polymeric materials is a result of the distinctive geometries of their unit cells. These unit cells, encompassing structures such as chiral, re-entrant, and rotating rigid configurations, are carefully engineered to collectively generate the desired auxetic behaviour. This comprehensive review article explores the field of auxetic polymeric materials, offering a detailed exploration of their geometries, fabrication methods, mechanical properties, and More >