Traian-Radu Ploscă^1,*, Alexandru-Mihai Pescaru², Bianca-Valeria Rus¹, Daniel-Ioan Curiac^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069707 - 10 November 2025

Abstract Objective expertise evaluation of individuals, as a prerequisite stage for team formation, has been a long-term desideratum in large software development companies. With the rapid advancements in machine learning methods, based on reliable existing data stored in project management tools’ datasets, automating this evaluation process becomes a natural step forward. In this context, our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems. For this, we mathematically formalize two categories of expertise: technology-specific expertise, which denotes the skills required for a particular technology, and general expertise, which encapsulates overall knowledge More >

Fengnan Guo^1,*, Yiming Li¹, Hua Zhang¹, Jianwei Cui²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-3, 2025, DOI:10.32604/icces.2025.011409

Abstract The stress intensity factor (SIF) is one of the most crucial parameters in fracture mechanics, as it can effectively characterize the state of the crack and determine its propagation behavior. The methods for evaluating the stress intensity factor mainly include the J-integral method, interaction integral method, and displacement extrapolation method [1,2]. However, the conventional J-integral and interaction integral methods involve derivative terms of material parameters, which cause a great difficulty in applying these methods to deal with non-homogeneous materials containing material interfaces. In order to overcome this difficulty, an improved interaction integral method has been… More >

Lin Huang^1,2, Ying Zhou², Jingjing Yang^1,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4587-4606, 2025, DOI:10.32604/cmc.2025.068370 - 23 October 2025

Abstract The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application. However, for rare and precious metal materials, existing data resources are often decentralized. This results in persistent issues such as data silos and fragmentation, which significantly hinder efficient data utilization and collaboration. In response to these challenges, this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials. The research begins by analyzing current trends in material data platforms, both domestically and internationally. These insights help inform the architectural… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >

Ali Abdulshaheed^1,*, Faizal Mustapha¹, Mohd Anuar²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.9, pp. 2289-2304, 2025, DOI:10.32604/fdmp.2025.066983 - 30 September 2025

Abstract A pump operating as a turbine (PAT) is a type of hydraulic machine capable of functioning both as a pump and as a turbine by reversing the flow direction. The pump-as-turbine (PAT) approach presents an effective method of hydropower generation, particularly suitable for addressing the increasing global energy demands in rural and remote areas. In addition to its adaptability, PAT-based micro-hydropower systems typically incur lower operating costs than conventional hydrodynamic turbines, despite requiring higher initial investment. Recent research has focused on integrating PATs into pipe distribution systems to harness untapped hydraulic energy. This study presents… More >

Anshul Pandey, Sachin Kumar^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3251-3286, 2025, DOI:10.32604/cmes.2025.067858 - 30 September 2025

Abstract The phase field model can coherently address the relatively complex fracture phenomenon, such as crack nucleation, branching, deflection, etc. The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies. However, accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively. A broad region must be discretized without prior knowledge of the crack path, further augmenting the computational expenses. In this proposed work, we present an automated framework utilizing a posteriori error-indicator (MISESERI) to demarcate and sufficiently refine the mesh along the… More >

Uzair Abbas¹, Zahid Maqbool¹, Muhammad Adnan Shahid^1,2,*, Muhammad Safdar^1,2, Saif Ullah Khan^1,3

Revue Internationale de Géomatique, Vol.34, pp. 655-668, 2025, DOI:10.32604/rig.2025.064279 - 11 August 2025

Abstract This study was conducted to devise an integrated methodology for retrieval of surface soil moisture (SSM) using Landsat 8 TIRS data. For this purpose, Landsat 8 images of 15 May 2021 (pre-monsoon) and 20 November 2021 (post-monsoon) were processed for retrieval of soil moisture index (SMI) based on land surface temperature (LST). Moreover, field-based SM in the laboratory was also determined and correlated with satellite-based SMI. A moderate correlation between field-based SM and satellite-based SMI with R² = 0.60 was obtained. Based on this relationship, SSM maps of Tehsil Faisalabad Saddar for the pre-and post-monsoon seasons… More >

Naif Alsharabi^1,*, Akashdeep Bhardwaj^2,*

Journal of Cyber Security, Vol.7, pp. 135-163, 2025, DOI:10.32604/jcs.2025.064019 - 18 June 2025

Abstract In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive More >

Alaa Mahmood, İsa Avcı^*

Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

Yaping Qi^1,*, Weijie Yang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1555-1559, 2025, DOI:10.32604/cmc.2025.064061 - 16 April 2025

Abstract AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of Solid-State Electrolyte (DDSE) demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development. These databases facilitate data standardization, high-throughput screening, and cross-disciplinary collaboration, addressing key challenges in materials informatics. As AI techniques advance, materials databases are expected to play an increasingly vital role in accelerating research and innovation. More >