Gang Niu^1,2, Zhaoyang Jin², Wei Zhang^3,*, Yiqun Huang³

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 161-179, 2024, DOI:10.32604/sdhm.2023.044580

Abstract Amid urbanization and the continuous expansion of transportation networks, the necessity for tunnel construction and maintenance has become paramount. Addressing this need requires the investigation of efficient, economical, and robust tunnel reinforcement techniques. This paper explores fiber reinforced polymer (FRP) and steel fiber reinforced concrete (SFRC) technologies, which have emerged as viable solutions for enhancing tunnel structures. FRP is celebrated for its lightweight and high-strength attributes, effectively augmenting load-bearing capacity and seismic resistance, while SFRC’s notable crack resistance and longevity potentially enhance the performance of tunnel segments. Nonetheless, current research predominantly focuses on experimental analysis, lacking comprehensive theoretical models. To… More > Graphic Abstract

Fayi Yan^*, Xuejian Pei, He Lu, Shuzhen Zong

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 287-304, 2024, DOI:10.32604/fhmt.2023.045038

Abstract As compact and efficient heat exchange equipment, helically coiled tube-in-tube heat exchangers (HCTT heat exchangers) are widely used in many industrial processes. However, the thermal-hydraulic research of liquefied natural gas (LNG) as the working fluid in HCTT heat exchangers is rarely reported. In this paper, the characteristics of HCTT heat exchangers, in which LNG flows in the inner tube and ethylene glycol-water solution flows in the outer tube, are studied by numerical simulations. The influences of heat transfer characteristics and pressure drops of the HCTT heat transfers are studied by changing the initial flow velocity, the helical middle diameter, and… More >

Peter Kasten^a, Severin Zimmermann^a, Manish K. Tiwari^a, Bruno Michel^b, Dimos Poulikakos^a,*

Frontiers in Heat and Mass Transfer, Vol.1, No.2, pp. 1-10, 2010, DOI:10.5098/hmt.v1.2.3006

Abstract Electronic data center cooling using hot water is proposed for high system exergetic utility. The proof-of-principle is provided by numerically modeling a manifold micro-channel heat sink for cooling microprocessors of a data center. An easily achievable 0.5l/min per chip water flow, with 60°C inlet water temperature, is found sufficient to address the typical data center thermal loads. A maximum temperature difference of ~8°C was found between the solid and liquid, confirming small exergetic destruction due to heat transport across a temperature differential. The high water outlet temperature from the heat sink opens the possibility of waste heat recovery applications. More >

R. Sangeetha, Usha Devi Gandhi^*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 395-411, 2024, DOI:10.32604/csse.2023.041217

Abstract Wireless Body Area Network (WBAN) is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body. WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature, blood pressure, pulse rate, oxygen level, body motion, and so on. They sense the data and communicate it to the Body Area Network (BAN) Coordinator. The main challenge for the WBAN is energy consumption. These issues can be addressed by implementing an effective Medium Access Control (MAC) protocol that reduces energy consumption and increases network lifetime. The purpose of… More >

Ahmad Najjaran¹, Ali R. Tahavvor, Abdolkarim Najjaran, Mohammad A. Ahmadfard

Frontiers in Heat and Mass Transfer, Vol.3, No.3, pp. 1-6, 2012, DOI:10.5098/hmt.v3.3.3007

Abstract In this paper, external radiation heat transfer coefficient and flux of a standing human body model are calculated for different emissivity coefficients and various temperature differences. To do this, a standing human body sample is designed in such a way that hands and feet are totally open and stretched. Soles are in contact with the ground and so do not have heat transfer. The results are extracted by weighted average method. Despite the existence of the air around the body in reality, the convection heat transfer of human body with the surrounding air is neglected in order to prevent errors… More >

Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as “OFF” or “ON” based… More >

Bin Zhou, Bo Li^*, Wenfei Lan, Congwen Tian, Wei Yao

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 633-652, 2024, DOI:10.32604/cmc.2023.046667

Abstract Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the… More >

Aysha Naseer¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Abdulwahab Alazeb⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1381-1398, 2024, DOI:10.32604/cmc.2023.042963

Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects… More >

Xiaoyi Wang¹, Xinyue Chang², Wenxuan Wang^1,*, Zijie Qiao³, Feng Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1775-1796, 2024, DOI:10.32604/cmes.2023.043913

Abstract The objective of reliability-based design optimization (RBDO) is to minimize the optimization objective while satisfying the corresponding reliability requirements. However, the nested loop characteristic reduces the efficiency of RBDO algorithm, which hinders their application to high-dimensional engineering problems. To address these issues, this paper proposes an efficient decoupled RBDO method combining high dimensional model representation (HDMR) and the weight-point estimation method (WPEM). First, we decouple the RBDO model using HDMR and WPEM. Second, Lagrange interpolation is used to approximate a univariate function. Finally, based on the results of the first two steps, the original nested loop reliability optimization model is… More >

Ghawar Said¹, Ata Ullah², Anwar Ghani^1,*, Muhammad Azeem¹, Khalid Yahya³, Muhammad Bilal⁴, Sayed Chhattan Shah^5,*

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 83-99, 2023, DOI:10.32604/iasc.2023.036079

Abstract The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with key-value pair for each… More >