Adwaa Mohammed Abdulmajeed¹, Duaa Abdul Rida Musa², Ola Abdul Hussain², Emad Kadum Njim³, Royal Madan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076622 - 09 April 2026

Abstract A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The… More >

Aditya Mairal^*, Todd Rossi, Michael Muller

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.075576 - 27 March 2026

Abstract An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting. EPA categorizes facility carbon footprints in three scopes. Scope-2 emissions include electricity, heat or steam purchased from a utility provider. This paper evaluates the existing calculation methods for scope-2 CO₂ emissions for purchased electricity. The electricity grid in US is complex and is divided spatially into states, eGRID regions, balancing authorities (BAs), and utilities. Up to hourly temporal granularity can be obtained from available datasets. A matrix is developed that categorizes different datasets based on the complexity to calculate the carbon… More >

Francisco Raúl Borzone^1,2,*, María Belén Giorello¹, Agustina Freire³, Leandro Marcelo Martinez⁴, Leonardo Feldman⁵, Federico Dimase⁶, Pablo Evelson³, Irene Larripa⁷, Emilio Batagelj⁸, Marcela Beatriz González Cid⁹, Norma Alejandra Chasseing^1,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074321 - 23 March 2026

Abstract Backgrounds: Breast cancer metastasis remains the leading cause of mortality and frequently targets the bone. Breast cancer cells release soluble factors and extracellular vesicles that disrupt bone marrow (BM)/bone homeostasis, promoting osteoclastogenesis and the accumulation of senescent cells. In line with updated cancer hallmarks, senescent mesenchymal stem/ stromal cells (MSCs), osteoblasts, and osteocytes contribute to remodeling of the BM microenvironment, thereby favoring pre-metastatic niche (PMN) formation and subsequent bone metastasis. We previously demonstrated that untreated stage III-B breast cancer patients (BCPs) exhibit increased oxidative stress and elevated reactive oxygen species (ROS) levels, accompanied by senescent… More > Graphic Abstract

Shuwen Sun^1,2,3,4,#, Jingcheng Zhang^1,2,3,#, Zongtai Zheng^5,#, Yajuan Hao^1,3, Tianyuan Xu^1,2,3, Ji Liu^1,3, Liang Sun², Aimin Wang², Yadong Guo^1,3, Shiyu Mao^1,3, Xu Zhang⁶, Yunfei Xu^1,3,*, Yifan Chen^1,2,3,*, Yang Yan^1,2,3,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2025.072373 - 23 March 2026

Abstract Objectives: Bladder cancer (BCa) progression is closely linked to the immune microenvironment. However, the key molecules that regulate this microenvironment and their specific mechanisms remain poorly understood. This study aims to identify a key molecule and elucidate its mechanisms, providing a theoretical basis for identifying novel therapeutic targets. Methods: Immune microenvironment-related genes in BCa were identified using The Cancer Genome Atlas and Shanghai Tenth People’s Hospital datasets. Proteasome 26S subunit non-ATPase 2 (PSMD2) expression was validated via quantitative polymerase chain reaction (qPCR), Western blot (WB) analysis, and immunofluorescence (IF). In vitro and in vivo experiments confirmed the… More > Graphic Abstract

Yingying Zhao^1,2, Jiakang Ma^1,3, Rujin Huang^1,2, Shuxian Pan^1,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.070208 - 23 March 2026

Abstract Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression; however, their contribution to the functional classification and personalized treatment of gastric cancer remains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatment strategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated to characterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction, trajectory inference, and cell–cell communication analyses, respectively. Key metastasis-associated fibroblast modules were identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct a prognostic model, which was further… More > Graphic Abstract

Qian Zhang¹, Lei Fu^1,2, Renzhou Chen³, Xu Zhan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074939 - 12 March 2026

Abstract Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated,… More >

Anfeng Yang, Fei Kang, Wenjuan Bu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073979 - 12 March 2026

Abstract Large language models (LLMs) have demonstrated significant capabilities in semantic understanding and code generation. However, cybersecurity tasks often require prompting the adaptation of open-source models to this domain. Despite their effectiveness, large-parameter LLMs incur substantial memory usage and runtime costs during task inference and downstream fine-tuning for cybersecurity applications. In this study, we fine-tuned six LLMs with parameters under 4 billion using LoRA (Low-Rank Adaptation) on specific cybersecurity instruction datasets, employing evaluation metrics similar to Hackmentor. Results indicate that post-fine-tuning, smaller models achieved victory or parity rates up to 85% against larger models like Qwen-1.5-14B… More >

Luca Giammichele^*, Valerio D’Alessandro, Matteo Falone, Renato Ricci

Frontiers in Heat and Mass Transfer, Vol.24, No.1, 2026, DOI:10.32604/fhmt.2026.072535 - 28 February 2026

Abstract The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections. During the COVID-19 pandemic, the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2. The face mask is able to significantly reduce the saliva droplet emission in front of the person. However, the use of masks also produces a particle leakage towards the back of the person, which could increase the infection risk of people behind the subject. Most of the experimental investigations applied invasive and/or complex… More >

Wanqing Lin^1,2,*, Mohd Azrin Mohd Nasir¹, Suzila Binti Ismail¹

Journal of Psychology in Africa, Vol.36, No.1, pp. 143-152, 2026, DOI:10.32604/jpa.2026.073231 - 26 February 2026

Abstract This systematic review synthesizes empirical research on external risk factors for adolescent smartphone addiction. Scopus and Web of Science were searched for English peer-reviewed empirical articles from 2008 onward; 28 met inclusion criteria (excluding non-adolescents, generic internet addiction, non-empirical work, or non-English). Thematic synthesis organized findings into three external risk domains—family, school, and peers—considering cultural/contextual mechanisms. Family dynamics (parental phubbing, harsh parenting, dysfunction), school stressors, and adverse peer relationships were identified as accumulating, direct and indirect contributors to smartphone addiction. These operate within a techno-ecological framework, where digital technologies amplify vulnerabilities and create new pathways More >

Muhamad Hakimi Sahbudin¹, Noraain Mohamed Saraf^1,*, Saiful Aman Sulaiman¹, Abdul Rauf Abdul Rasam², Nafisah Khalid¹, Lau Chong Luh¹

Revue Internationale de Géomatique, Vol.35, pp. 101-120, 2026, DOI:10.32604/rig.2026.072359 - 24 February 2026

Abstract The integration of Light Detection and Ranging (LiDAR) technology into consumer electronics like smartphones has created new opportunities for the use of three-dimensional (3D) modelling, especially in the domains of infrastructure inspection and civil engineering. This paper presents the accuracy of a 3D bridge model generated using a smartphone LiDAR application in comparison with conventional surveying methods. In this study, LiDAR data were captured using an iPhone 13 Pro and processed to generate 3D models. The accuracy of the generated model was then validated against reference data obtained from a tacheometry survey, which served as… More >