Junwei Zhang¹, Pei Liu¹, Huihang Li¹, Guokang Huang¹, Bozheng Yuan¹, Wenjing Wei¹, Xiaoshun Zhang^2,*

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.072462 - 27 April 2026

Abstract Addressing global climate challenges necessitates urgent low carbon transitions in high energy consuming enterprises (HECEs). This study proposes a comprehensive framework to assess their carbon reduction potential (CRP) by integrating electricity usage behavior analysis and dynamic carbon emission factor (DCEF) prediction. HECEs are classified into “electricity reduction” and “electricity transfer” categories based on load characteristics, enabling tailored optimization strategies. The framework employs machine learning to predict DCEFs, capturing real time variations in grid carbon intensity. A low carbon optimization model is then formulated to minimize emissions while adhering to production requirements and grid constraints, solved… More > Graphic Abstract

Farzana Yasmeen^1,#, Abdul Manan^1,#,*, Wook Kim¹, Sangdun Choi^1,2,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.076072 - 22 April 2026

Abstract Objectives: Vascular endothelial growth factor receptor 2 (VEGFR2) is a critical therapeutic target in hepatocellular carcinoma (HCC) due to its role in angiogenesis and tumor progression. While several inhibitors are currently used, clinical utility is often limited by resistance and adverse effects, necessitating the discovery of novel therapeutic agents. The aim of this study was to identify and characterize novel, highly effective VEGFR2 inhibitors using an integrated computational pipeline to advance the development of new HCC treatments. Methods: A comprehensive dataset from the ChEMBL database was curated and standardized for Quantitative Structure-Activity Relationship (QSAR) modeling.… More > Graphic Abstract

Simone Tammaro¹, Francesco Di Fiore², Felice Crocetto³, Celeste Manfredi^1,*, Claudia Collà Ruvolo³, Gianluigi Califano³, Biagio Barone⁴, Davide Arcaniolo¹, Lorenzo Spirito¹, Francesco Paolo Calace², Pasquale Reccia², Ferdinando Fusco¹, Marco De Sio¹, Raffaele Balsamo²

Canadian Journal of Urology, Vol.33, No.2, pp. 249-259, 2026, DOI:10.32604/cju.2025.072617 - 20 April 2026

Abstract Background: Thulium laser vaporization of the prostate (ThuVAP) is an established treatment for benign prostatic obstruction, but its impact on urodynamic parameters remains poorly defined. This study aimed to quantify the de-obstructive efficacy of ThuVAP through pre- and postoperative urodynamic comparisons and to assess the relationship between urodynamic improvement and symptom relief. Methods: In a prospective single-center cohort (June 2022–June 2024), men with urodynamically confirmed obstruction underwent standardized ThuVAP with a 200-W thulium:YAG system. Baseline and 6-month invasive urodynamics and 12-month clinical follow-up were performed. The primary endpoint was the change in the bladder outlet… More >

Yasemin O. Ciftci¹, İlknur K. Durukan¹, Upasana Rani², Peeyush Kumar Kamlesh^3,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078722 - 09 April 2026

Abstract This study examines the phase-dependent structural, electronic, optical, and thermodynamic characteristics of the cubic, tetragonal, and orthorhombic phases of BaTiO₃ using DFT simulations. Lattice parameters and bulk moduli computed through structural optimizations within the GGA-PBE framework are in good agreement with existing experimental and theoretical studies. All phases exhibit negative formation energies, indicating thermodynamic stability, with the orthorhombic phase being the most stable. Electronic structure calculations reveal indirect band gaps of 2.86, 2.96, and 3.43 eV for the cubic, tetragonal, and orthorhombic phases, respectively. The density of states analysis indicates that O-p states dominate the valence… More >

Xue Wang^1,2, Cen Yang¹, Yonghang Wang¹, Mingming Wang^1,3, Ying Chen⁴, Ping Li^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077868 - 09 April 2026

Abstract Tungsten-copper laminated composites are promising materials for high heat-flux applications, but their performance is often limited by interfacial instability caused by the thermal-mechanical mismatch between tungsten and copper. In this study, W/W-30Cu/CuCrZr three-layer composites are fabricated by high-pressure torsion (HPT) processing. Experimental characterization and molecular dynamics (MD) simulations are used to systematically investigate the influence of HPT process parameters and intermediate-layer composition on the evolution of microstructure and mechanical properties. HPT processing significantly refines the grains of the W-xCu composites and enhances their homogeneity. After applying 15 revolutions of HPT on W-30Cu composites, the crystallite… More >

Jiajia Liu^1,2, Shuchen Pang³, Peng Xie³, Haitao Zhou³, Chenxi Du³, Haoran Hu³, Bo Tang³, Jianhua Liu³, Fei Jia¹, Huibing Zhang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077824 - 09 April 2026

Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >

Xu Chen, Li Yang^*, Guohao Qiu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077513 - 09 April 2026

Abstract Traffic prediction plays a crucial role in the efficient operation of satellite networks. However, due to resource consumption arising from redundant training of multiple individual prediction models, the dynamic and coupled spatial-temporal relationship of traffic, and maintenance of accurate traffic proportions, this problem is non-trivial to solve. Therefore, we consider this problem and makes the following contributions. First, a multi-granularity traffic prediction framework based on a shared feature extraction is designed to jointly predict total network traffic and service-specific traffic of satellite networks. This design ensures that both global and per service predictions benefit from… More >

Bin Gao¹, Xinyu Jiang¹, Lusheng Wang^1,*, Jun Ding¹, Yanhong Peng¹, Xin Yang², Hongzhou Yan³, Shaojie Gu^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077091 - 09 April 2026

Abstract This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials, a critical area due to the widespread application of metals and their susceptibility to complex failure in engineering practice. The paper first outlines the mechanisms of damage evolution and crack propagation across different spatial and temporal scales. It then introduces commonly used simulation approaches spanning micro- to macro-scales for studying damage and fracture in metals, analyzing the evolution of mechanical properties from defect initiation to ultimate failure, and elucidating the underlying damage mechanisms at More >

Yu Wang^1,2, Yabin Wang¹, Liang Wen¹, Bingyu Li¹, Mengze Qin¹, Fang Li¹, Zhonghua Cheng^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077026 - 09 April 2026

Abstract Remaining useful life (RUL) prediction for complex equipment is a critical technology for ensuring the safe and reliable operation of industrial systems. However, existing data-driven models commonly suffer from limitations such as weak cross-operational condition generalization, insufficient physical interpretability, and unstable training on non-stationary time-series data. To address these challenges, this paper proposes a temporal degradation prediction model that integrates context adaptation and physics-consistent constraints, named the Context-Adaptive Physics-informed Time-aware meta-Network (CAPTAIN). The model incorporates four core components: a Context-Aware Meta-Learning (CAML) module that enables lightweight parameter adaptation to diverse scenarios; Physics-Informed Neural Network (PINN)… More >

Zhu Fang^1,2,*, Zhengquan Xu^1,2, Weizhen He³, Bohao Xu³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076043 - 09 April 2026

Abstract In view of the problem that the IP address jump law is easy to predict in the current mobile target defense, this paper proposes a network address jump active defense method based on a dynamic random graph, designed to improve the unpredictability of IP address translation. Firstly, in order to make IP address transformation unpredictable in space and time, a random graph model is designed to generate a pseudo-random sequence of IP address randomization; these pseudo-random can meet the unpredictability of IP address translation in both space and time. Then, based on these pseudo-random sequences… More >