Mohamed Diouf¹, Elisée Toe^1,*, Manel Grichi², Haïfa Nakouri^1,3, Fehmi Jaafar¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077139 - 09 April 2026

Abstract Software security bugs present significant security risks to modern systems, leading to unauthorized access, data breaches, and severe operational and financial consequences. Early prediction of such vulnerabilities is therefore essential for strengthening software reliability and reducing remediation costs. This study investigates the extent to which static software quality metrics can identify vulnerable code and evaluates the effectiveness of machine learning models for large-scale security-bug prediction. We analyze a dataset of 338,442 source files, including 33,294 buggy files, collected from seven major open-source ecosystems. These ecosystems include GitHub Security Advisories (GHSA), Python Package Index (PyPI), OSS-Fuzz… More >

Yibin Guo¹, Lingxiao Ye^1,*, Xiang Wang¹, Di Wu¹, Zirong Wang¹, Hao Wang²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072163 - 27 March 2026

Abstract Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies. However, the strong nonlinearity and multi-scale temporal characteristics of crude oil prices pose significant challenges to traditional forecasting methods. To address these issues, this study proposes a hybrid CEEMDAN–HOA–Transformer–GRU model that integrates decomposition, complexity analysis, adaptive modeling, and intelligent optimization. Specifically, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original series into multi-scale components, after which entropy-based complexity analysis quantitatively evaluates each component. A differentiated modeling strategy… More > Graphic Abstract

Yaxin Zhao¹, Qi Han², Hui Shu², Yan Guang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070511 - 09 December 2025

Abstract Large Language Models (LLMs) are increasingly applied in the field of code translation. However, existing evaluation methodologies suffer from two major limitations: (1) the high overlap between test data and pretraining corpora, which introduces significant bias in performance evaluation; and (2) mainstream metrics focus primarily on surface-level accuracy, failing to uncover the underlying factors that constrain model capabilities. To address these issues, this paper presents TCode (Translation-Oriented Code Evaluation benchmark)—a complexity-controllable, contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework. The dataset is carefully designed to control complexity along multiple dimensions—including syntactic… More >

Qing-Xing Qu^1,*, Le Zhang^2,*, Fu Guo¹, Vincent G. Duffy³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4607-4627, 2025, DOI:10.32604/cmc.2025.069245 - 23 October 2025

Abstract This study aims to validate the Object-Oriented User Interface Customization (OOUIC) framework by employing Use Case Analysis (UCA) to facilitate the development of adaptive User Interfaces (UIs). The OOUIC framework advocates for User-Centered Design (UCD) methodologies, including UCA, to systematically identify intricate user requirements and construct adaptive UIs tailored to diverse user needs. To operationalize this approach, thirty users of Product Lifecycle Management (PLM) systems were interviewed across six distinct use cases. Interview transcripts were subjected to deductive content analysis to classify UI objects systematically. Subsequently, adaptive UIs were developed for each use case, and… More >

Go-Eun Woo, Sang-Bo Park, Gi-Tae Park, Muhammad Junaid, Hyung-Won Kim^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3241-3257, 2025, DOI:10.32604/cmc.2025.063723 - 03 July 2025

Abstract On-device Artificial Intelligence (AI) accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field, where frequent retraining is crucial due to frequent production changes. Batch normalization (BN) is fundamental to training convolutional neural networks (CNNs), but its implementation in compact accelerator chips remains challenging due to computational complexity, particularly in calculating statistical parameters and gradients across mini-batches. Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources, limiting their practical deployment. We present a hardware-optimized BN accelerator… More >

Gabriel C. Kanu^1,*, Noah Adeji¹, Tobias C. Obi², Elom S. Omena³, Raphael U. Anike⁴, Alexander U. Amaechi¹

Journal of Psychology in Africa, Vol.35, No.1, pp. 1-6, 2025, DOI:10.32604/jpa.2025.065768 - 30 April 2025

Abstract This study examined how psychological meaningfulness moderates job complexity and work-family conflict in Nigerian secondary school teachers. This study included 1694 teachers from 17 Nigerian secondary schools (female = 69.54%, mean age = 33.19, SD = 6.44 years). The participants completed the Work-family Conflict Scale, Job Complexity Scale, and Psychological Meaningfulness Scale. Study design was cross-sectional. Hayes PROCESS macro analysis results indicate a higher work-family conflict with job complexity among the secondary school teachers. While psychological meaningfulness was not associated with work-family conflict, it moderated the link between job complexity and work-family conflict in secondary More >

Tatiana Henriksson^1,*, Katharina Mitchell², Reima El Naili³, Ali Hajiran²

Canadian Journal of Urology, Vol.32, No.2, pp. 95-99, 2025, DOI:10.32604/cju.2025.065002 - 30 April 2025

Abstract Renal cell carcinoma is a heterogeneous group of renal tumors characterized by several histological subtypes. Herein, we discuss an unusual case of a 55-year-old male who presented as a consultation to our urology clinic with an incidentally found renal mass. After shared decision making patient proceeded with a Robotic Assisted Laparoscopy (RAL) left sided partial nephrectomy. Final pathology confirmed the presence of high nuclear grade mixed clear cell and papillary renal cell carcinoma (RCC) of the left kidney (pT3aN0M0). This case elucidates a very rare incidence of a patient seen to have a collision tumor, More >

Yining Zhou¹, Yufeng Li², Chen Zhang², Tao Wu¹, Jingru Zhang², Bowen Yun², Rui Tan², Wei Yan^2,*

Energy Engineering, Vol.122, No.5, pp. 1823-1837, 2025, DOI:10.32604/ee.2025.062199 - 25 April 2025

Abstract This study investigated the micro-sliding frictional behavior of shale in fracturing fluids under varying operational conditions using Chang 7 shale oil reservoir core samples. Through systematic micro-sliding friction experiments, the characteristics and governing mechanisms of shale friction were elucidated. Complementary analyses were conducted to characterize the mineral composition, petrophysical properties, and micromorphology of the shale samples, providing insights into the relationship between microscopic structure and frictional response. In this paper, the characteristics and variation law of shale micro-sliding friction under different types of graphite materials as additives in LGF-80 (Low-damage Guar Fluid) oil flooding recoverable… More >

Sarah M. Kamel^1,*, Mai A. Fadel², Lamiaa Elrefaei^1,3, Shimaa I. Hassan^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 373-411, 2025, DOI:10.32604/cmes.2025.062837 - 11 April 2025

Abstract Visual question answering (VQA) is a multimodal task, involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer. In this paper, we propose a VQA system intended to answer yes/no questions about real-world images, in Arabic. To support a robust VQA system, we work in two directions: (1) Using deep neural networks to semantically represent the given image and question in a fine-grained manner, namely ResNet-152 and Gated Recurrent Units (GRU). (2) Studying the role of the utilized multimodal bilinear… More >

Can Hu¹, Shanqing Zhang^2,*, Kewei Tao², Gaoming Yang¹, Li Li²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4913-4930, 2025, DOI:10.32604/cmc.2025.059770 - 06 March 2025

Abstract The surge of large-scale models in recent years has led to breakthroughs in numerous fields, but it has also introduced higher computational costs and more complex network architectures. These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization. To address this issue, various network compression techniques have been developed, such as network pruning. A typical pruning algorithm follows a three-step pipeline involving training, pruning, and retraining. Existing methods often directly set the pruned filters to zero during retraining, significantly reducing the parameter space. However, this… More >