检索结果-内蒙古大学图书馆

Optimizing thermal performance of evacuated tube solar collectors with cascaded phase change materials

Case Studies in Thermal engineering 2025年 70卷

作者： Said, Sana Mellouli, Sofiene Alqahtani, Talal Algarni, Salem Ajjel, Ridha Alshammari, Badr M. Kolsi, Lioua Ibn El Jazzar Faculty of Medicine Street Mohamed Karoui Sousse4002 Tunisia College of Engineering and Computer Science Mechanical Engineering Department Jazan University Jazan Saudi Arabia The National School of Engineering of Monastir University of Monastir Tunisia College of Engineering Mechanical Engineering Department King Khalid University Abha Saudi Arabia Center for Engineering and Technology Innovation King Khalid University Abha61421 Saudi Arabia Department of Electrical Engineering College of Engineering University of ha'Il Ha'il City81451 Saudi Arabia Department of Mechanical Engineering College of Engineering University of ha'Il Ha'il City81451 Saudi Arabia

This study assesses the performance of an Evacuated Tube Solar Collector (ETSC) featuring cascaded Phase Change Materials (PCMs). Numerical simulations are employed to conduct a comparative analysis of two new design configurations of the ETSC that utilize cascaded PCMs. Configuration 1 is an ETSC with stacked PCM rings, while Configuration 2 is an ETSC with surrounding PCM layers. Five different PCMs are evaluated to identify the best combination of cascaded PCMs for the ETSC. The impact of the cascaded PCMs' frequency and arrangement on the system's thermal efficiency is examined. A comparative analysis of cascaded configurations and single PCM cases in ETSCs is conducted. The findings suggest that PCM 3 (Rubitherm RT50) is the most suitable option for a standard ETSC utilizing a single PCM, achieving a maximum efficiency of 34.74 %. The optimal arrangement for Configuration 1, featuring three stacked PCM rings, achieves the highest overall efficiency of 36.73 % and an Energy Enhancement Ratio (EER) of 1.74 % compared to a standard ETSC. In contrast, the optimal arrangement for Configuration 2, which incorporates two surrounding PCM layers, reaches a maximum efficiency of 36.64 %. These results underscore the importance of PCM selection and arrangement in optimizing the performance of an ETSC that utilizes cascaded PCMs for improved energy storage and efficiency. © 2025 The Author(s).

关键词： Evacuated tube solar collector Single PCM Cascaded PCMs Stacked PCM rings Surrounding PCM layers Thermal energy storage

来源：评论

学校读者我要写书评

暂无评论

Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

来源：评论

学校读者我要写书评

暂无评论

Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

引用

arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

来源：评论

学校读者我要写书评

暂无评论

Shortening surgical time in high myopia treatment: a randomized controlled trial comparing non-OVD and OVD techniques in ICL implantation

引用

BMC Ophthalmology 2025年第1期25卷 1-15页

作者： Khan, Muhammad Ahmad Khan, Saif Ur Rehman Lin, Ding Group Co Aier Eye Hospital Ltd ChangshaHunan China School of Computer Science and Engineering Central South University Yuelu District ChangshaHunan China German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Computer Science Rhineland-Palatinate Technical University of Kaiserslautern-Landau Kaiserslautern Germany Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan

High myopia correction is essential for improving visual acuity and quality of life. Traditional intraocular collamer lens (ICL) implantation using an ophthalmic viscosurgical device (OVD) is effective but often results in prolonged surgical times, impacting patient comfort and recovery. This study investigates a novel non-OVD ICL implantation technique aimed at reducing surgical duration and improving patient outcomes. This prospective, randomized controlled trial was conducted at Changsha Aier Eye Hospital from May to August 2024, enrolling 162 right eyes from high myopic patients aged 21 to 45 years. Participants were randomly assigned to either the non-OVD group, which utilized a disposable sterile irrigator for anterior chamber maintenance, or the OVD group, which underwent traditional ICL implantation. Primary outcomes included surgical time, endothelial cell density (ECD), intraocular pressure (IOP), and patient-reported experiences. The non-OVD group demonstrated a significant reduction in surgical time (113 ± 103 s) compared to the OVD group (436 ± 118 s, p < 0.001). ECD and IOP remained stable postoperatively, indicating no adverse effects on ocular health. Patient experiences were assessed through pain levels and self-perception reports; the non-OVD group reported comparable pain levels with enhanced self-perception compared to the OVD group. The novel non-OVD ICL implantation technique significantly reduces surgical duration while maintaining ocular health and improving patient self-perception. Additional to it, we also explored patients' self-perception, which refers to their subjective evaluation of recovery, well-being, and visual outcomes post-surgery. Unlike satisfaction, which primarily measures how pleased patients are with the results, self-perception encompasses how patients feel about their overall recovery process, including psychological and emotional factors. This approach may enhance operational efficiency and patient comf

关键词： Non-OVD ICL implantation Patient comfort and satisfaction Surgical complexity reduction

来源：评论

学校读者我要写书评

暂无评论

The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

引用

PeerJ computer Science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Escape or D13: Understanding Youth Perspectives of AI through Educational Game Co-design 25

Escape or D13: Understanding Youth Perspectives of AI throug...

引用

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Jared Ordoña Lim Grace Barkhuff Jane Awuah Sophie Clyde Riya Sogani Christina Gardner-McCune David Touretzky Judith Odili Uchidiuno School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA Department of Computer and Information Science and Engineering University of Florida Gainesville Florida USA Computer Science Department Carnegie Mellon University Pittsburgh Pennsylvania USA

来源：评论

学校读者我要写书评

暂无评论

Error-Corrected Quantum CNN for Gender-Based Micro-Expression Analysis in Affective Computing

Error-Corrected Quantum CNN for Gender-Based Micro-Expressio...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Hafiz Khizer Bin Talib Kaiwei Xu Yanlong Cao Muhammad Zaman Muhammad Hunzala Toqeer Adnan Akhunzada School of Mechanical Engineering Zhejiang University Hangzhou China Department of Computer Science & IT Superior University Lahore Pakistan Department of Computer Science University of Lahore Lahore Pakistan Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Micro-expression recognition has emerged as a critical area of study in affective computing, with significant applications in psychological analysis, security, and human-computer interaction. However, traditional methods often struggle to accurately capture and interpret subtle, rapid, and gender-specific facial micro-expressions due to challenges such as noise, limited datasets, and variations in expression intensity. This paper introduces an innovative approach, the Error-Corrected Quantum Convolutional Neural Network (EQCNN), to address these challenges and enhance the recognition of gender-based micro-expressions. The proposed EQCNN leverages the principles of quantum computing, incorporating error detection and correction mechanisms within its architecture. By employing quantum unitary gates, Hadamard gates, and CNOT gates, the model ensures data integrity and mitigates the effects of quantum decoherence. Additionally, the framework utilizes a 3π/2 phase shift and logical qubits to correct errors dynamically, optimizing the representation of micro-expression features. To validate the effectiveness of EQCNN, the model was evaluated on a gender-annotated micro-expression dataset. The results demonstrate outstanding performance, achieving an accuracy of 99.3%, an F1-score of 98.9%, a precision of 96.7%, and a recall of 98.4%. Comparative analyses show that EQCNN significantly outperforms classical and quantum-enhanced models, particularly in capturing subtle gender-specific nuances in facial expressions. The findings of this study not only advance the field of micro-expression recognition but also underline the potential of quantum computing in real-world applications requiring high precision and robustness. The proposed EQCNN framework is generalizable and can be extended to other domains involving complex image classification tasks, offering a powerful tool for tackling challenges in quantum-enhanced machine learning.

关键词： Affective computing Computational modeling Qubit Psychology Logic gates Quantum decoherence Robustness Convolutional neural networks Security Robots

来源：评论

学校读者我要写书评

暂无评论

Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

引用

Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

来源：评论

学校读者我要写书评

暂无评论

Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare

Cloud-Enhanced Transfer Learning Framework for Medical Image...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： S. Prabu N. Subhash Chandra M Bhavani V K Chaitanya Varma Mukesh Soni Department of Electronics and Communication Engineering Mahendra Institute of Technology Namakkal India Department of Computer Science and Engineering CVR College of Engineering Mangalpalle India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of Mechanical Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals.

关键词： Cloud computing Accuracy Image analysis Sensitivity Computational modeling Transfer learning computer architecture Medical diagnostic imaging Tuning Standards

来源：评论

学校读者我要写书评

暂无评论

PneuX-CNN: A Novel Architecture for Pneumonia Classification and Grad-Cam Explainability

PneuX-CNN: A Novel Architecture for Pneumonia Classification...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Habibur Rahman Rifat Arun Roy Md. Shahadat Bin Shahid Kashfi Shormita Kushal Md. Shamim Bin Shahid Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Graduate School Life Sciences and Health Université Paris-Saclay Paris France Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh UFR Sciences et Techniques Université Bourgogne Europe Dijon France

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Pneumonia is an infection that results in thickening of the air spaces in either of the lungs. It is extremely hazardous for elderly adults and infants. Therefore, it is imperative that we diagnose it at the earliest opportunity. In the conventional method of diagnosis, a patient undergoes a variety of tests, including a chest X-ray, blood test, and sputum analysis, which are subsequently evaluated by medical professionals. The evaluation of these tests and their results typically necessitates a significant amount of time. In certain instances, the disease can be fatal, particularly for small children, if the physicians or diagnosis centers are unable to detect it at an early stage. Therefore, it is essential to design a system that can easily and quickly identify pneumonia at a low cost. To identify pneumonia from chest X-ray images, numerous researchers implemented a deep learning (DL) approach. In this study, an automated enhanced model is proposed which can predict the presence of pneumonia in a person by automatically extracting features from images. The proposed model obtained an exactness of 98.20% by utilizing the image data augmentation technique and image normalization, which is more accurate than many other models. Lastly, the Grad-CAM interpretation gives us a better understanding of the proposed model’s output.

关键词： Deep learning Pediatrics Pneumonia Computational modeling Medical services Predictive models Feature extraction Data models Older adults X-ray imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：