Erdinç Uslan^1,*, Emin Ulugergerli²

Revue Internationale de Géomatique, Vol.35, pp. 31-51, 2026, DOI:10.32604/rig.2026.075081 - 05 February 2026

Abstract Climate classification systems are essential tools for analyzing regional climatic behavior, assessing long-term aridity patterns, and evaluating the impacts of climate change on water resources and ecosystem resilience. This study introduces a new Climate Classification Method based on uniform and unitless variables, referred to as the U2 Climate Classification (U2CC). The proposed U2 Index was designed to overcome structural limitations of the classical De Martonne (1942) and Erinç (1949) indices, which rely on raw precipitation–temperature ratios and are sensitive to extreme values, particularly subzero temperatures. The U2 methodology consisted of two key steps: (i) normalization… More >

Sana Shahab¹, Vladimir Simic^2,*, Ashit Kumar Dutta^3,4, Mohd Anjum^5,*, Dragan Pamucar^6,7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073945 - 29 January 2026

Abstract Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation… More >

Ping Wang^1,2,#, Yan-Han Chen^1,2,#, Ze-Tao Zhan^1,2, Jun-Xiang Zeng^1,2, Yu Chen^3,4, Yuan Lin^1,2, Tao Chen^1,5,*, Wei-Jie Zhou^1,2,5,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.071902 - 19 January 2026

Abstract Background: Thimerosal is a mercury-containing preservative widely used in vaccines. This study aimed to investigate its potential antitumor effects and mechanisms in solid malignancies, particularly colorectal cancer (CRC) and melanoma. Methods: A combination of in vitro and in vivo approaches was employed. Cell proliferation, apoptosis, migration, and invasion were assessed using Cell Counting Kit-8 (CCK-8), colony formation, ATP viability, Western blotting, flow cytometry, wound-healing and Transwell assays. Subcutaneous, lung metastases, and Azoxymethane/Dextran Sulfate Sodium Salt (AOM/DSS)-induced colitis-associated CRC models were established to examine antitumor efficacy and safety. The functional role of mercury ions was validated using structural… More >

Jeongeun Ryu¹, Riyeong Kim², Soomin Lee¹, Sumin Kim¹, Hyunwoo Choi^1,2, Seongmin Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074871 - 12 January 2026

Abstract As containerized environments become increasingly prevalent in cloud-native infrastructures, the need for effective monitoring and detection of malicious behaviors has become critical. Malicious containers pose significant risks by exploiting shared host resources, enabling privilege escalation, or launching large-scale attacks such as cryptomining and botnet activities. Therefore, developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems. To this end, we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter (eBPF) to monitor container activities directly within the Linux kernel. The framework simultaneously collects flow-based network… More >

Rayeesa Mehmood^*, Sergei Koltcov, Anton Surkov, Vera Ignatenko

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072735 - 12 January 2026

Abstract Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that More >

Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072343 - 12 January 2026

Abstract Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data… More >

Kai Gui^1,#, Tianyi Yang^1,#, Chengying Xiong¹, Yue Wang¹, Zhiqiang He¹, Wuxian Li^2,3,*, Min Tang^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070143 - 30 December 2025

Abstract Objectives: The mechanism by which specific tumor subsets in colorectal cancer (CRC) use alternative metabolic pathways, particularly those modulated by hypoxia and fructose, to alter the tumor microenvironment (TME) remains unclear. This study aimed to identify these malignant subpopulations and characterize their intercellular signaling networks and spatial organization through an integrative multi-omics approach. Methods: Leveraging bulk datasets, single-cell RNA sequencing, and integrative spatial transcriptomics, we developed a prognostic model based on hypoxia-and fructose metabolism-related genes (HFGs) to delineate tumor cell subpopulations and their intercellular signaling networks. Results: We identified a specific subset of stanniocalcin-2 positive (STC2+)… More > Graphic Abstract

Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… 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 >

Vinh Truong Hoang^1,*, Nghia Dinh¹, Viet-Tuan Le¹, Kiet Tran-Trung¹, Bay Nguyen Van¹, Kittikhun Meethongjan^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-40, 2026, DOI:10.32604/cmc.2025.068733 - 10 November 2025

Abstract The Financial Technology (FinTech) sector has witnessed rapid growth, resulting in increasingly complex and high-volume digital transactions. Although this expansion improves efficiency and accessibility, it also introduces significant vulnerabilities, including fraud, money laundering, and market manipulation. Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data. Graph Neural Networks (GNNs), capable of modeling intricate interdependencies among entities, have emerged as a powerful framework for detecting subtle and sophisticated anomalies. However, the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability, performance, and More >