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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ramya, J. Poongodi, A. Department of Software Applications Agurchand Manmull Jain College Chennai India Department of Computer Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in diagnostic imaging has garnered a lot of attention in recent years, particularly for the detection of lung cancer. On the other hand, manual CT scan analysis takes a lot of time and is inaccurate or prone to mistakes. Given these limitations, the accurate categorization of CT images as either malignant or non-cancerous is accelerated by the application of computer approaches, including machine learning and deep learning algorithms. In this work, we provide a new and improved CNN model architecture with FCN and Deeplab V3+ for segmentation and classification using hybrid efficient net B0 to investigate lung cancer. The suggested model achieves superior results in both segmentation and classification. © 2025 IEEE.

关键词： Lung cancer

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A 3-D whole-body human thermoregulatory model to simulate cold-induced vasodilation in the hands and feet

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Computers in Biology and Medicine 2024年 180卷 108935页

作者： Singh, Manpreet Reifman, Jaques Rubio, Jose E. Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center United States Army Medical Research and Development Command Fort Detrick MD United States The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc. Bethesda MD United States

The cold-induced vasodilation (CIVD) response of the human body to Arctic-like environments helps delay or prevent cold injuries to peripheral regions, such as the hands and feet. To more comprehensively predict the thermal responses of these body regions to cold stress, here we extended our previously developed and validated anatomically accurate three-dimensional whole-body thermoregulatory human model by incorporating a new phenomenological formulation of the CIVD mechanism. In this formulation, we modulated the cyclic vasodilation and vasoconstriction flow of warm blood from the body core to the peripheral regions solely by determining the heat-transfer exchange between the skin and the surrounding environment, and deactivated it when the core body temperature decreased to 36.5 °C. In total, we calibrated and validated the model using eight distinct studies involving 153 unique male subjects exposed to 10 diverse experimental conditions, including cold-air exposure of the whole body as well as air exposure and cold-water immersion of the hand or the foot. With CIVD incorporated, the model predictions generally yielded root mean square errors (RMSEs) of © 2024

关键词： Heat transfer

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A mathematical model for efficient extraction of key locations from point-cloud data in track area

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Industrial Artificial Intelligence 2023年第1期1卷 1-14页

作者： Chen, Shuyue Wu, Jiaolv Lu, Jian Wang, Xizhao College of Mathematics and Statistics Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institue of Information Technology Shenzhen China Shenzhen No. 3 Vocational School of Technology Shenzhen China College of Engineering Huaqiao University Quanzhou China Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Lab. of Intelligent Information Process Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

During the construction of a metro system, it is inevitable that deviations will occur between the excavated tunnel and the original designed scheme. As such, it is necessary to adjust the designed scheme to accommodate these discrepancies. Specifically, the adjustment of the designed scheme involves a rigorous process of repeatedly selecting and verifying the feasibility of the proposed modifications using point-cloud data obtained from the tunnel. However, this process can be considerably time-consuming due to the large-scale and potentially redundant nature of the point-cloud data. This paper proposes a mathematical model for point-cloud data acquired in measuring a mined tunnel, which may deviate from the originally designed one. The modeling, which mainly includes determining its normal plane, and building the equation of tunnel point-cloud data, is to quickly extract several key locations in the tunnel surface for modifying the original design in order to achieve a minimum error between the modified design and the mined tunnel. In comparison with the conventional processing of extracting several key locations directly from point-cloud data, our model shows a significant promotion of extraction efficiency under an acceptable error bound. The model is tested in a real tunnel point-cloud data and the testing results confirm the increase of fitting accuracy and the decrease of computational load.

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Feasibility Analysis of the Sanchez-Pomraning Method to Treat the Particle Size Distribution in Particle-Dispersed Fuel

SSRN

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SSRN 2024年

作者： Qin, Shuai Zhang, Qian Feng, Xing Liang, Yuechao Laboratory for Advanced Nuclear Energy Theory and Applications Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China Huaneng Shandong Shidao Bay Nuclear Power Co. LTD. Shandong Rongcheng264300 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China

The non-uniform size of particles appears in the particle-dispersed fuel generally, but this phenomenon is usually neglected. This study aims to evaluate the feasibility of the Sanchez-Pomraning method to solve the particle size distribution problem in particle-dispersed fuel. The Pu-rich agglomerate phenomenon in the MOX fuel and manufacturing tolerance in the Fully Ceramic Microencapsulated (FCM) fuel are calculated by ALPHA-DH code, which implements the Sanchez-Pomraning method based on the MOC transport calculation and improved subgroup resonance calculation. Numerical results show that the Sanchez-Pomraning method has the capability to treat the particle size distribution effect accurately, where the differences bwtween the Sanchez-Pomraning method and the Monte Carlo method are generally lower than 160 pcm. For the Pu-rich agglomerate phenomenon, the results show that ignoring the Pu-rich agglomerate effect introduces the reactivity difference of around 600 pcm, and ignoring the size distribution of the Pu agglomerate will cause the reactivity of up to 200 pcm in the extra-high or extra-low Pu enrichment. The more discrete size distribution and the smaller Pu-spots, the more significant influence will occur in the MOX fuel. For the FCM fuel, the influence brought by the TRISO particle size distribution can be ignored when only TRISO particles are filled. However, ignoring the size distribution when TRISO particles and poison particles are both added into the FCM fuel introduces the differences reaching 600-1000 pcm. These results demonstrate the importance of considering the particle size distribution for analyzing the MOX fuel and FCM fuel in practice. And the Sanchez-Pomraning method is reliable to analyze the effect of particle size distribution. © 2024, The Authors. All rights reserved.

关键词： Particle size analysis

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0305 Can We Rely on Wearable Sleep-Tracker Devices for Fatigue Management?

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Sleep 2024年第SUPPLEMENT_1期47卷 A131–A132页

作者： Reifman, Jaques Priezjev, Nikolei Vital-Lopez, Francisco Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Development Command

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Research on Application of Heterogeneous Resonance Integral for Double Heterogeneous System

SSRN

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SSRN 2024年

作者： Qin, Shuai Zhang, Qian Wang, Kai Huang, Dong Li, Song Liang, Yuechao Laboratory for Advanced Nuclear Energy Theory and Applications Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China School of Nuclear Science and Technology Xi’an Jiaotong University 28 West Xianning Road Shaanxi Xi’an710049 China College of Nuclear Science and Technology Naval University of Engineering Wuhan China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China

The Double Heterogeneous (DH) system, where fuel particles are randomly dispersed in the non-fissile matrix, is challenging for the reactor physics calculation. The Sanchez-Pomraning method accurately handles the DH system, but integrating it into existing reactor physics code requires code development. This study adopts the Sanchez-Pomraning coupled Ultra-Fine-Group (SP-UFG) slowing-down calculation to generate the heterogeneous Resonance Integral (RI) for DH system treatment with simple volume homogenization. Fully Ceramic Micro-encapsulated (FCM) fuel pin-cells and plates with varying configurations are calculated for verification. Effective cross-sections (XSs) and keff calculated by the heterogeneous RI are compared with SP-UFG results. Results show that the maximum bias of XSs and keff caused by the XS biases are less than 5% and 200 pcm, respectively. The maximum bias of keff when compared with Monte Carlo calculated results is -213 pcm, demonstrating that only considering the DH effect in the resonance energy region is acceptable. © 2024, The Authors. All rights reserved.

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Deficient Tin Oxide Nanofibers with Regulated Valence for Efficient Nitrate Reduction to Ammonia

SSRN

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SSRN 2023年

作者： Wang, Kaihe Pan, Jiaqi Hu, Jiachong Fu, Xingyue Dou, Yao Xian, Haohong Guo, Haoran Chen, Jun Song Li, Tingshuai School of Materials and Energy University of Electronic Science and Technology of China Sichuan Chengdu611731 China Software Department Chengdu Polytechnic Chengdu610095 China Department of Chemistry Key Laboratory of Environmentally Friendly Chemistry and Applications Ministry of Education Key Laboratory for Green Organic Synthesis and Application of Hunan Province Xiangtan University Hunan Xiangtan411105 China Institute for Advanced Study Chengdu University Sichuan Chengdu610106 China

Electrocatalytic nitrate reduction is a promising process for removal of nitrate and simultaneous production of value-added ammonia, but robust electrocatalysts with high selectivity are required due to the large dissociation energy of NºO in nitrate. Herein, we propose deficient Tin oxide nanofibers with regulated bivalence (SnO) and tetravalency (SnO2-d) as efficient catalysts for nitrate reduction reaction (NitRR). Such quadrivalent catalyst achieves a large NH3 yield of 17.27 mg h−1 mgcat.−1 and an extremely high Faradaic Efficiency (FE) of 95.67% in 0.1 M PBS with 0.1 M NO3−, which also exhibits excellent electrochemical stability. Material calculations reveal that SnO2-d exhibits a direct band gap of 0.79 eV, smaller than 1.17 eV for SnO with an indirect band gap. The potential determining step (*NO2 → *NO) hits a free energy change of only 0.1 eV. © 2023, The Authors. All rights reserved.

关键词： Ammonia

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A Geometric Understanding of Deep Learning

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Engineering 2020年第3期6卷 361-374页

作者： Na Lei Dongsheng An Yang Guo Kehua Su Shixia Liu Zhongxuan Luo Shing-Tung Yau Xianfeng Gu DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian University of TechnologyDalian 116620China Department of Computer Science Stony Brook UniversityStony BrookNY 11794-2424USA School of Computer Science Wuhan UniversityWuhan 430072China School of Software Tsinghua UniversityBeijing 100084China Center of Mathematical Sciences and Applications Harvard UniversityCambridgeMA 02138USA

This work introduces an optimal transportation(OT)view of generative adversarial networks(GANs).Natural datasets have intrinsic patterns,which can be summarized as the manifold distribution principle:the distribution of a class of data is close to a low-dimensional *** mainly accomplish two tasks:manifold learning and probability distribution *** latter can be carried out using the classical OT *** the OT perspective,the generator computes the OT map,while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution;both can be reduced to a convex geometric optimization ***,OT theory discovers the intrinsic collaborative-instead of competitive-relation between the generator and the discriminator,and the fundamental reason for mode *** also propose a novel generative model,which uses an autoencoder(AE)for manifold learning and OT map for probability distribution *** AE–OT model improves the theoretical rigor and transparency,as well as the computational stability and efficiency;in particular,it eliminates the mode *** experimental results validate our hypothesis,and demonstrate the advantages of our proposed model.

关键词： Generative Adversarial Deep learning Optimal transportation Mode collapse

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0171 EEG Markers from Routine Sleep Discriminate Individuals Who Are Vulnerable or Resilient to Sleep Loss

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Sleep 2024年第SUPPLEMENT_1期47卷 A74–A74页

作者： Subramaniyan, Mannivannan Vital-Lopez, Francisco Hughes, John Doty, Tracy Reifman, Jaques Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Development Command Behavioral Biology Branch Walter Reed Army Institute of Research

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0151 Personalized Caffeine Recommendations to Achieve Optimal Alertness Levels

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Sleep 2024年第SUPPLEMENT_1期47卷 A66–A66页

作者： Vital-Lopez, Francisco Doty, Tracy Anlap, Ian Killgore, William Reifman, Jaques Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Development Command Behavioral Biology Branch Walter Reed Army Institute of Research University of Arizona College of Medicine

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