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SSrN 2023年

作者： Wen, Junren Sui, Yong Shi, Jianbin Cai, Sha Xiong, Tian Cai, Fang Li, Shuyi Zhou, Lei Mei, Xin KeyLaboratoryofAgro-ProductsColdChainLogistics MinistryofAgricultureandRuralAffairs Institute of Agro-Products Processing and Nuclear-Agricultural Technology Agro-product ProcessingResearchSub-centerofHubeiInnovationCenterofAgricultureScienceandTechnology Hubei Academy of Agricultural Science Wuhan430064 China College of Food Science and Technology Huazhong Agricultural University Wuhan430070 China SchoolofModern Industry for Selenium Science andEngineering Wuhan Polytechnic University National R&D Center for Se-Rich Agricultural Products Processing Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products Wuhan430023 China

The chemical composition and changes in phenolic profile during in vitro digestion of five sweet potato leaves were investigated. Firstly, sweet potato leaves mainly consist of caffeoylquinic acid and its derivatives. Secondly, our data revealed that simulated digestion increased the antioxidant activity of the samples. Besides, the data revealed that 3-O-caffeoylquinic acid had relatively better bioaccessibility in gastric and intestinal phases of Ecaishu No.10, which were 60.01 and 188.18 μg chlorogenic acid equivalent/g dW, respectively. Approximately 40-80% of phenolics were dialysable, with the highest phenolic bioaccessibility level being 4-O-caffeoylquinic acid in Ecaishu No.10 was 68.86%. In summary, superior dietary nutrition, phenolic acid release characteristics, and bioactivity of Ecaishu No.10 were observed in this study, indicating it has the potential as a functional food ingredient, which is conducive to the exploitation of the sweet potato advance processing industry from a long-term perspective. © 2023, The Authors. All rights reserved.

关键词： Bioactivity

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research on Weight-on-bit Self-adjusting dual-diameter PdC Bit design and Effect Evaluation Utilizing Stress-releasing Effect of rock at Bottomhole

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Arabian Journal for Science and engineering 2021年第7期46卷 6925-6937页

作者： Hu, Huaigang Guan, Zhichuan Wang, Bin Lu, Baoping Chen, Weiqing Xu, Yuqiang Liu, Yongwang CNPC Engineering Technology R&D Company Limited Beijing China China University of Petroleum (East China) Qingdao China Shandong Ultra-Deep Drilling Process Control Tech R&D Center Qingdao China Qingdao Refining and Chemical Co. LTD Qingdao China Sinopec Research Institute of Petroleum Engineering Beijing China King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

rock-breaking efficiency is not so satisfied using the conventional PdC bit, especially in the hard, soft–hard interbedded, and strong abrasive formations in ultra-deep wells. A method is proposed to make full use of stress-releasing effect at bottomhole, thereby improving the rock-breaking efficiency, and a supporting weight-on-bit (WOB) self-adjusting dual-diameter PdC bit (WSdB) has been invented. The WSdB can dynamically adjust the depth-of-cut control characteristics, which could improve the rate of penetration (rOP) while mitigating the stick–slip vibration caused by the rapid change of depth of cut. The stress field of the rock at the bottomhole is calculated with a numerical method, and the working mechanism of the bit is analyzed. The application effect of the WSdB is evaluated through laboratory tests preliminarily. The results show that the stress concentration area on the shoulder of the reaming bit is decreased obviously and a larger stress unloading area emerges in the intersection between reaming and pilot bit. Thus, mechanical specific energy (MSE) can be greatly reduced when the reaming bit drills into this section. Indoor tests show that the maximum rOP of WSdB can be reached up to 70% higher compared to the conventional PdC bit, and higher rOP could be obtained with a relatively lower bit torque. With drilling parameters greater than the critical revolution and WOB, the reduction in MSE can be reached up to 36% during the rock-breaking process. Making further efforts into the optimization of bit structure to reduce the vibration intensity is expected to achieve more improved bit performance.

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SrS in Clinical System using FL Based System Implementation

SRS in Clinical System using FL Based System Implementation

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： G. Padmapriya Gnanajeyaraman rajaram T Saju raj Geetha Ponnaian Hind Majid Abdul redha Hussein Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chenni Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Collage of dentist national university of science and technology dhi qar iraq Abdul redha hussein sabr computer engineer department al-kunooze university college basra iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Using internally integrated sensors, this work tackles the major difficulty of establishing robust and accurate proprioception in soft robots working under loose packing limitations. Instead of assuming constant curvature, we use a modified off-the-shelf resistive sensor to detect local deformations at several places. Across the soft body, we improve the resolution of local deformations by forming several electrical connections. With an average relative error of around 4%, our method incorporates these sensor measurements into a finite element method (FEM) model to estimate the soft body form and magnitude of external force. Especially, our technique uses fuzzy logic which allows for internal fluidic actuation. Promising for soft manipulation platforms, the suggested integrated sensing and modelling approach shows a 13& relative inaccuracy in external force computation within the 0 to 5 N range.

关键词： Fuzzy logic Force measurement deformation Computational modeling Force Measurement uncertainty Soft robotics

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Synergistic Effect of Microfluidization and Transglutaminase Cross-Linking on the Structural and Oil-Water Interface Functional Properties of Whey Protein Concentrate for Improving the Thermal Stability of Nanoemulsions

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SSrN 2022年

作者： Chen, Yufeng Sun, Yi Meng, Yueli Liu, Shulai ding, Yicheng Zhou, Xuxia Wu, Huawei ding, Yuting College of Food Science and Technology Zhejiang University of Technology Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province National R&D Branch Center for Pelagic Aquatic Products Processing Hangzhou310014 China Collaborative Innovation Center of Seafood Deep Processing Dalian Polytechnic University Dalian116034 China Ningbo Today Food Co Ltd Ningbo315502 China

Generally, whey protein concentrate (WPC) undergoes high-temperature denaturation and aggregation, which reduces its emulsifying properties and is not conducive to as an emulsifier to maintain the thermal stability of emulsions. In this study, dynamic high-pressure microfluidization technology (dHPM) combined with TGase (TG) cross-linking was applied to prepare dHPM-TG-WPC, and the thermal stabilization mechanism of nanoemulsions prepared with dHPM-TG-WPC was explored. results showed dHPM treatment could promote the formation of TG-crosslinked WPC polymers. Compared to WPC, the free sulfhydryl and free amino group content of dHPM-TG-WPC was significantly decreased (P © 2022, The Authors. All rights reserved.

关键词： Thermodynamic stability

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Enhancing Predictive Maintenance in Industrial IoT through Machine Learning Models

Enhancing Predictive Maintenance in Industrial IoT through M...

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Innovation in technology (ASIANCON), Asian Conference on

作者： A. Jaya Mabel rani V. Prithivirajan S Arumai Shiney Ch.Babaiah Hemanth Swamy Sivagamidevi. G Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S A Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions India Department of AI & DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, Lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions.

关键词： Training Technological innovation Machine learning Production Predictive models real-time systems Maintenance Tuning Predictive maintenance Textiles

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deep Learning-Based Approaches for Enhanced Image Compression in Computer Vision Applications

Deep Learning-Based Approaches for Enhanced Image Compressio...

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distributed Systems, Computer Networks and Cybersecurity (ICdSCNC), International Conference on

作者： Avinash Avinash Christian rafael Quevedo Lezama ramya Murugesan K Anitha C.r. Bharathi Abid Y Department of MBA St. Josephs College of Engineering Chennai India Department of Electrical and Electronics Engineer National University of San Marcos Lima Peru Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of ECE Saveetha Engineering College Thandalam Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of VLSI Microelectronics Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Image compression is required for many computer vision applications, but traditional methods like JPEG and PNG have trouble preserving image quality at high compression levels. The study suggests a deep learning-based method for efficiently compressing images while maintaining visual information, utilizing convolutional neural networks (CNNs). Preparing the dataset, choosing CNN architectures (such as autoencoders and GANs), training with loss functions like MSE, and assessing using PSNr and SSIM metrics are all part of the process. According to the results, the deep learning approach outperforms JPEG (18:1) and PNG (6:1) in terms of compression ratios for complicated textures, reaching up to 25:1. The deep learning approach's PSNr values are 35.2 dB, while its SSIM scores are 0.92 in contrast to JPEG's (0.87) and PNG's (0.84) values. The process preserves visual quality while offering increased storage and transmission efficiency.

关键词： deep learning Training Measurement Computer vision Visualization Image coding Computational modeling Transform coding Streaming media Convolutional neural networks

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Effect of grain refinement on sensitization of high manganese austenitic stainless steel

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Materials Today: Proceedings 2021年 44卷 2802-2807页

作者： Sourabh Shukla Awanikumar P. Patil Ashlesha P. Kawale rakesh Kumar Haldkar Ashish dahiwale Ankur Bansod Mechanical Engineering Department G H Raisoni College of Engineering Nagpur 440016 India Materials and Metallurgical Department. Visvesvaraya National Institute of Technology Nagpur 440010 India Mechanical Department Vivekanand Polytechnic Sitasaongi Tumsar 441907 India Department of Mechanical Engineer Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

Effect of grain size on degree of sensitization (dOS) was been evaluated in Nickel free steel. Manganese and nitrogen contained alloy is a Ni free austenitic stainless steel (ASS) having type 202 grade. In the present investigation, the deformation of 202 grade analyzed using Xrd and microstructural testing. Optical Microstructure of Ni free austenitic stainless steel (ASS) has been done for cold worked samples with thermally aged at 900 °C_6 Hrs. Ni free austenitic stainless steel (ASS) appears to be deformed more rapidly due to its higher stacking fault energy which gave results in rapid transformation from strain induced martensite to austenite in form of recrystallized grains, i.e., it concluded that as cold work percentage increases more rapidly recrystallization occurs. Xrd results also indicate that more fraction of martensite formed as percentage of CW increases but as thermal ageing reverted those all martensite to austenite. double Loop Electro-potentio reactivation Method (dLEPr) test used for findings of degree of sensitization. So investigation gives the conclusion which suggest that with high deformation at higher temperature and duration gives very less degree of sensitization (dOS).

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Experiment on the Influence of drill String damping and Pressurization device on Bottom Hole Wob(Weight on Bit) Fluctuation

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SSrN 2022年

作者： Yongwang, Liu Zhang, Hongning Niu, Yixiang Lin, Guojun Liu, Wentao Li, Yiwu School of Petroleum Engineering in China University of Petroleum Qingdao266580 China Ministry of Education Qingdao266580 China Shandong Ultro-deep Drilling Process Control Tech R&D Center Qingdao266580 China SINOPEC Research Institute of Petroleum Engineering Beijing100101 China Zhengzhou Shiqi Measurement and Control Technology CO. LTD Zhengzhou45001 China Beijing Liuhe Greatness Technology Co. Ltd. Beijing100160 China

Large amplitude and high frequency WOB fluctuation will lead to problems such as low rock breaking efficiency, short service life and slow drilling speed. This problem is particularly prominent in hard and difficult to drill formation and multi interlayer formation. The development of drilling string damping and pressurization device provides an idea for the utilization of vibration energy. Theoretically, the installation of drill string damping and pressurization device can ensure the smooth operation of the bit. However, up to now, the influence of the characteristic parameters and application methods of drill string damping and pressurization device on the WOB fluctuation at the bit is not clear. Based on the drill string dynamics experimental device, the influence of drill string damping and pressurization device on WOB fluctuation at the bit is tested and analyzed. The results show that whether the drill string vibration damping and pressurization device can stabilize the bit WOB is related to its elastic stiffness, installation position, bit speed and WOB;When the WOB remains unchanged, the elastic stiffness of the elastic element of the drill string damping and pressurization device increases, and the fluctuation range of the WOB at the bit increases;Increasing the rotating speed and increasing the WOB is conducive to controlling the WOB fluctuation of the bit. Under the condition of high rotating speed, shortening the distance between the drill string damping and pressurization device and the bit is conducive to restraining the WOB fluctuation. The research results provide a reference for the design, development and application of damping elements of drilling string damping and pressurization device. © 2022, The Authors. All rights reserved.

关键词： damping

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Light spectrum optimization based Adaboost SVM for the secured communication in vehicular Ad Hoc network

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AIP Conference Proceedings 2025年第1期3204卷

作者： M. M. Vijay S. Gopi Kumar r. Siva Subramanian M. Karthikeyan G. Krishnaraj N. Manikanda Prabu Signal integrity Engineer Caliber interconnect solutions Tirunelveli Tamilnadu India School of Mechanical and Construction Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Mechanical Engineering Sri Krishna college of Engineering and Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Mahaguru Institute of Technology Alappuzha Kerala India

Inter-vehicle subjected to communication nowadays is easily viable in all area for study, standardization, and development as more cars are outfitted with wireless communication and computer technology. Numerous applications are made possible by VANETs, including blind crossing prevention, real-time traffic condition monitoring, safety, dynamic route planning, and collision prevention. However, there is a possibility that these data were attacked by the malicious node. To prevent this, we propose an innovative machine approach known as the Adaboost Support Vector Machine (Ada-SVM)-based Light Spectrum Optimizer (LSO). This approach classifies the information as intrusion and non-intrusion data. The LSO is utilized to select the cluster head (CH) of vehicles after forming the clusters using the spectral clustering approach. The experiment is demonstrated in the NS2 simulator and analyzed for various parameters. Our approach is compared with state-of-the-art works and estimated as surpassing all the other approaches and being used for secured VANET communication.

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Optimal dietary Management via data Analytics, ANN Based Health Insights, and Ecosystem for Continuous Nutrient Analysis

Optimal Dietary Management via Data Analytics, ANN Based Hea...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy ranganathan Arshadh Ariff Mohamed Abuthahir riazulhameed G. Swathi Pavithra M.r S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： data analysis Ecosystems Transforms Propulsion Nutrients real-time systems Internet of Things

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