Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069820 - 27 December 2025

Abstract The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement… More >

Lino Gonzalez-Garcia^*, Miguel-Angel Sicilia, Elena García-Barriocanal

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070813 - 09 December 2025

Abstract Classifying job offers into occupational categories is a fundamental task in human resource information systems, as it improves and streamlines indexing, search, and matching between openings and job seekers. Comprehensive occupational databases such as NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories, thereby facilitating standardization, cross-system interoperability, and access to metadata for each occupation (e.g., tasks, knowledge, skills, and abilities). In this work, we explore the effectiveness of fine-tuning existing language models (LMs) to classify job offers with occupational descriptors… More >

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 >

Igor Musikhin^*

Revue Internationale de Géomatique, Vol.34, pp. 939-957, 2025, DOI:10.32604/rig.2025.071168 - 09 December 2025

Abstract Urban environmental quality research is crucial, as cities become competitive centers concentrating human talent, industrial activity, and financial resources, contributing significantly to national economies. Municipal and government priorities include retaining residents, preventing skilled worker outflow, and meeting the evolving needs of urban populations. The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk. Using advanced geoinformatics, GIS techniques, and an expert knowledge base, the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize… 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 >

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 >

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 >

Min-kyeong Kim¹, Yeong Geol Lee², Won-Hee Park^2,*, Su-hwan Yun², Tae-Soon Kwon², Duckhee Lee²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1277-1293, 2025, DOI:10.32604/cmc.2024.058386 - 03 January 2025

Abstract Urban railways are vital means of public transportation in Korea. More than 30% of metropolitan residents use the railways, and this proportion is expected to increase. To enhance safety, the government has mandated the installation of closed-circuit televisions in all carriages by 2024. However, cameras still monitored humans. To address this limitation, we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof. We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway More >