LIZHI WANG¹, HUILIN SUN², GUIZHI YU¹, ZEJING QU¹, YING JI¹, YANPING CUI^1,*

Oncology Research, Vol.33, No.4, pp. 885-894, 2025, DOI:10.32604/or.2024.051586 - 19 March 2025

Abstract Background: Small cell lung cancer (SCLC) is characterized by its aggressive nature and high propensity for brain metastases. This study investigates the clinical efficacy and safety profile of Anlotinib in combination with Stereotactic Radiotherapy (SRT) for treating brain metastases in patients with small cell lung cancer (SCLC). Methods: This research included 98 SCLC brain metastasis patients treated at Chengde Central Hospital from October 2020 to January 2024. The patients were categorized into a combined treatment group (CTG) (n = 45) and a Simple SRT group (SSG)(n = 53). The CTG (58 lesions) received Anlotinib with… More >

Longgang Wang^2,3,#, Xiangxun Zhuansun^2,3,#, Yao Li^2,3, Qili Yao^2,3, Qi Liu^2,3,*, Huijing Lin^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 441-454, 2025, DOI:10.32604/phyton.2025.061720 - 06 March 2025

Abstract Fructus Ligustri Lucidi (FLL) refers to the dried mature fruit of Ligustrum lucidum Ait., a species from the Oleaceae family, widely distributed across East Asia and India. This study aimed to optimize the extraction process for Fructus Ligustri Lucidi essential oil (FLLO) to develop an efficient and practical extraction method. Additionally, the chemical composition of FLLO was analyzed, and its antioxidant, antimicrobial, and cytotoxic activities were evaluated. FLLO was extracted using supercritical CO₂ extraction, and response surface methodology was applied to optimize the extraction parameters: pressure of 16 MPa, temperature of 40°C, and extraction time of 40… More > Graphic Abstract

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

Hariharan Ramachandran^1,*, Richard Smith², Kenny Awuson David^1,*, Tawfik Al-Hadhrami³, Parag Acharya¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3619-3641, 2025, DOI:10.32604/cmc.2024.054802 - 17 February 2025

Abstract This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios… More >

Afaq Khattak^1,*, Pak-wai Chan², Feng Chen³, Abdulrazak H. Almaliki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1565-1584, 2025, DOI:10.32604/cmes.2025.059914 - 27 January 2025

Abstract Intense wind shear (I-WS) near airport runways presents a critical challenge to aviation safety, necessitating accurate and timely classification to mitigate risks during takeoff and landing. This study proposes the application of advanced Residual Network (ResNet) architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear (NI-WS) events using Doppler Light Detection and Ranging (LiDAR) data from Hong Kong International Airport (HKIA). Unlike conventional models such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), ResNet provides a distinct advantage in addressing key challenges such as capturing intricate… More >

Yi Xuan Chew, Siti Fatimah Abdul Razak^*, Sumendra Yogarayan, Sharifah Noor Masidayu Sayed Ismail

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4397-4417, 2024, DOI:10.32604/cmc.2024.056367 - 19 December 2024

Abstract In the modern world, the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems. This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness, which combines heart rate monitoring and face recognition technologies. The research objectives include developing a non-contact method for detecting driver drowsiness, training and assessing the proposed system using pre-trained machine learning models, and implementing a real-time alert feature to trigger warnings when drowsiness is detected. Deep learning models based on convolutional neural networks (CNNs),… More >

Yuefan Liu, Ducheng Zhang, Chen Guo^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3281-3299, 2024, DOI:10.32604/cmc.2024.057294 - 18 November 2024

Abstract Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents; nevertheless, the prevalence of helmet usage among these riders remains alarmingly low. Consequently, the accurate identification of riders who are wearing safety helmets is of paramount importance. Current detection algorithms exhibit several limitations, including inadequate accuracy, substantial model size, and suboptimal performance in complex environments with small targets. To address these challenges, we propose a novel lightweight detection algorithm, termed GL-YOLOv5, which is an enhancement of the You Only Look Once version 5 (YOLOv5) framework. This model incorporates a Global DualPooling… More >

Liangwei Jiang^1,2, Wei Zhang², Hongyin Yang^1,2,3,*, Xiucheng Zhang¹, Jinghan Wu², Zhangjun Liu²

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 835-851, 2024, DOI:10.32604/sdhm.2024.051125 - 20 September 2024

Abstract Aiming at the problem that it is difficult to obtain the explicit expression of the structural matrix in the traditional train-bridge coupling vibration analysis, a combined simulation system of train-bridge coupling system (TBCS) under earthquake (MAETB) is developed based on the cooperative work of MATLAB and ANSYS. The simulation system is used to analyze the dynamic parameters of the TBCS of a prestressed concrete continuous rigid frame bridge benchmark model of a heavy-haul railway. The influence of different driving speeds, seismic wave intensities, and traveling wave effects on the dynamic response of the TBCS under More >

Shih-Lin Lin^*, Bo-Chen Lin

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456 - 12 September 2024

Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >

Licheng Sun¹, Heping Li^2,3, Liang Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >