Yi Feng^1,#, Haoxin Yang², Guicai Liang¹, Jun Chen³, Tao Li¹, Yingjuan Wang⁴, Jilin Chang¹, Yan Li³, Meng Yang¹, Xilong Zhou¹, Zhiqiang Wang^5,*, Chunlei Ge^1,*

Oncology Research, Vol.33, No.12, pp. 3801-3836, 2025, DOI:10.32604/or.2025.067824 - 27 November 2025

Abstract Immune checkpoint inhibitor (ICI) has limited efficacy in the treatment of immune “cold” tumors. Due to insufficient T cell infiltration and heterogeneous programmed death ligand 1 (PD-L1) expression, the ORR is only 5%–8% compared with 30%–40% of “hot” tumors. This article reviews the synergistic mechanism, clinical efficacy and optimization strategy of oncolytic virus (OVs) combined with ICIs in the treatment of refractory malignant tumors. Systematic analysis of mechanistic interactions across tumor types and clinical trial data demonstrates that OVs transform the immunosuppressive microenvironment by inducing immunogenic cell death and activating innate immunity. Concurrently, ICIs enhance… More >

Lucila M. Curi^*, Maria C. Area, Maria E. Vallejos

Journal of Renewable Materials, Vol.13, No.11, pp. 2109-2137, 2025, DOI:10.32604/jrm.2025.02025-0069 - 24 November 2025

Abstract The global demand for renewable and sustainable non-petroleum-based resources is rapidly increasing. Lignocellulosic biomass is a valuable resource with broad potential for nanocellulose (NC) production. However, limited studies are available regarding the potential toxicological impact of NC. We provide an overview of the nanosafety implications associated mainly with nanofibrillated cellulose (CNF) and identify knowledge gaps. For this purpose, we present an analysis of the studies published from 2014 to 2025 in which the authors mention aspects related to toxicity in the context of packaging. We also analyze the main methods used for toxicity evaluations and… More > Graphic Abstract

Ayaz Hussain¹, Asad Hayat², Muhammad Hasnain^1,*

Journal on Artificial Intelligence, Vol.7, pp. 485-498, 2025, DOI:10.32604/jai.2025.071933 - 17 November 2025

Abstract Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the… More >

Zheda Zhao, Tao Xu, Tong Yang, Yunpeng Wu^*, Fengxiang Guo^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1457-1472, 2025, DOI:10.32604/sdhm.2025.069611 - 17 November 2025

Abstract Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA… More >

Nurlan Zhangabay^1,*, Marco Bonopera^2,*, Konstantin Avramov³, Maryna Chernobryvko³, Svetlana Buganova⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 241-261, 2025, DOI:10.32604/cmes.2025.069544 - 30 October 2025

Abstract This research extends ongoing efforts to develop methods for reinforcing damaged main gas pipelines to prevent catastrophic failure. This study establishes the use of scaled-down experimental models for assessing the dynamic strength of damaged pipeline sections reinforced with wire wrapping or composite sleeves. A generalized dynamic model is introduced for numerical simulation to evaluate the effectiveness of reinforcement techniques. The model incorporates the elastoplastic behavior of pipe and wire materials, the influence of temperature on mechanical properties, the contact interaction between the pipe and the reinforcement components (including pretensioning), and local material failure under transient… More >

Tao Lin^1,*, Chengdai Chen¹, Liyao Chen¹, Fengqin Han¹, Guanghui He²

Frontiers in Heat and Mass Transfer, Vol.23, No.5, pp. 1661-1680, 2025, DOI:10.32604/fhmt.2025.070787 - 31 October 2025

Abstract Targeting spontaneous coal combustion during stacking, we developed an efficient heat dissipation & self-supplied wireless temperature measurement system (SPWTM) with gravity heat pipe-thermoelectric integration for dual safety. The heat transfer characteristics and temperature measurement optimization of the system are experimentally investigated and verified in practical applications. The results show that, firstly, the effects of coal pile heat production power and burial depth, along with heat pipe startup and heat transfer characteristics. At 60 cm burial depth, the condensation section dissipates 98% coal pile heat via natural convection. Secondly, for the temperature measurement error caused by… More >

Kunal Thakur¹, Ashu Taneja^1,*, Ali Alqahtani², Nayef Alqahtani³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025

Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

Atta Rahman^*, Fahad Abdullah Alatallah, Abdullah Jafar Almubarak, Haider Ali Alkhazal, Hasan Ali Alzayer, Younis Zaki Shaaban, Nasro Min-Allah, Aghiad Bakry, Khalid Aloup

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3507-3525, 2025, DOI:10.32604/cmc.2025.067547 - 23 September 2025

Abstract This study presents an automated system for monitoring Personal Protective Equipment (PPE) compliance using advanced computer vision techniques in industrial settings. Despite strict safety regulations, manual monitoring of PPE compliance remains inefficient and prone to human error, particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province. The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes, including safety vests, hard hats, safety shoes, gloves, and their absence (no_hardhat, no_safety_vest, no_safety_shoes, no_gloves) along with person detection. The system is designed to perform real-time detection of safety gear while… More >

Asmat Ullah¹, Yar Muhammad^1,*, Bakht Zada¹, Korhan Cengiz², Nikola Ivković^3,*, Mario Konecki³, Abid Yahya⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3767-3786, 2025, DOI:10.32604/cmc.2025.067145 - 23 September 2025

Abstract Low visibility conditions, particularly those caused by fog, significantly affect road safety and reduce drivers’ ability to see ahead clearly. The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks, which face challenges in adaptability and demonstrate lower performance under varying environmental conditions. To overcome these challenges, we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions. The proposed method begins by identifying specific regions of interest (ROI) in the CCTV images and focuses on extracting specific… More >

Mohamed Gamal^1,#, Hamdy Ashour^1,#, Omar Huzayyin², Maran Marimuthu³, Ghulam E Mustafa Abro^4,*, Lina Mohamed¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.8, pp. 2027-2046, 2025, DOI:10.32604/fdmp.2025.067678 - 12 September 2025

Abstract Atrium spaces, common in modern construction, provide significant fire safety challenges due to their large vertical openings, which facilitate rapid smoke spread and reduce sprinkler effectiveness. Traditional smoke management systems primarily rely on make-up air to replace the air expelled through vents. Inadequate calibration, particularly with air velocity, can worsen fire conditions by enhancing oxygen supply, increasing soot production, and reducing visibility, so endangering safe evacuation. This study investigates the impact of make-up air velocity on smoke behaviour in atrium environments through 24 simulations performed using the Fire Dynamics Simulator (FDS). Scenarios include various fire… More >