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Numerical and physical simulations of array laterolog in deviated anisotropic formation

Petroleum Science 2022年第5期19卷 2107-2119页

作者： Yi-Zhi Wu Zhen-Guan Wu Yi-Ren Fan Tao Xing Chao-Liu Li Chao Yuan School of Geosciences China University of PetroleumQingdao266580ShandongChina Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao266071ShandongChina CNPC Key Laboratory for Well Logging China University of PetroleumQingdao266580ShandongChina Research Institute of Petroleum Exploration and Development PetroChinaBeijing100083China

Due to the tremendous amount of high-resolution measurement information,array laterolog is widely used in evaluations of deviated anisotropic ***,the precision of a complementary numerical simulation should be improved as high as the core of fine-scale reservoir ***,the 3D finite element method(3D-FEM)is presented to simulate the array laterolog ***,a downscaled physical simulation system is introduced to validate and calibrate the precision of the ***,the size of the downscaled system is determined by ***,the surrounding and investigated beds are represented by a sodium chloride solution and planks soaked in solution,***,a half-space measurement scheme is presented to improve the experimental ***,the corresponding sensitivity function and separation factor are established to analyze the effects of the formation anisotro py and dipping angle on the array laterolog *** numerical and experimental results indicate that the half-space method is practical,and the mean relative error between the numerical and experimental results is less than 5%,which indicates that the numerical simulation is *** the proposed approach,the reversal angle of array laterolog response curves in anisotropic formations can be observed,and this range is determined to be 50°-62°.

关键词： Anisotropic formation Array laterolog Downscaled physical simulation Sensitivity function Reversal angle

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Study on Grid Control Methods Based on Three-Phase Current Source Type 5-Level Converters

Study on Grid Control Methods Based on Three-Phase Current S...

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Annual Conference of Industrial Electronics Society

作者： Jianyu Bao Cheng Yan Nannan Gu Liu Yang School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research and Development Department Ningbo Deye Inverter Technology Co. Ltd Ningbo China Research Institute of Interdisciplinary Intelligent Science Ningbo University of Technology Ningbo China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Against the backdrop of global energy transition and climate change, enhancing the grid integration efficiency and quality of renewable energy sources is pivotal for achieving sustainable development goals. This paper introduces a three-phase current source type 5-level grid-connected inverter and its control method, designed to offer a system with adjustable power factor for high-quality stable grid operation. This system is particularly suitable for grid integration of renewable energy generation, effectively suppressing the resonance of the CL filter and reducing harmonic distortion in grid current. Detailed simulations of the system demonstrate that the design maintains exceptional performance under various conditions, particularly showing outstanding adaptability and stability in response to grid voltage fluctuations and load changes.

关键词： Reactive power Renewable energy sources Power system stability Inverters Stability analysis Resonance Harmonic distortion Voltage control Phase locked loops Power harmonic filters

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Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models

Hard Cases Detection in Motion Prediction by Vision-Language...

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IEEE Symposium on Intelligent Vehicle

作者： Yi Yang Qingwen Zhang Kei Ikemura Nazre Batool John Folkesson Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Stockholm Sweden Research and Development Scania CV AB Södertälje Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure safety, it is crucial to detect and manage these scenarios effectively for autonomous driving systems. However, the rarity and high-risk nature of these cases demand extensive, diverse datasets for training robust models. Vision-Language Foundation Models (VLMs) have shown remarkable zero-shot capabilities as being trained on extensive datasets. This work explores the potential of VLMs in detecting hard cases in autonomous driving. We demonstrate the capability of VLMs such as GPT-4v in detecting hard cases in traffic participant motion prediction on both agent and scenario levels. We introduce a feasible pipeline where VLMs, fed with sequential image frames with designed prompts, effectively identify challenging agents or scenarios, which are verified by existing prediction models. Moreover, by taking advantage of this detection of hard cases by VLMs, we further improve the training efficiency of the existing motion prediction pipeline by performing data selection for the training samples suggested by GPT. We show the effectiveness and feasibility of our pipeline incorporating VLMs with state-of-the-art methods on NuScenes datasets. The code is accessible at https://***/KTH-RPL/Detect_VLM.

关键词： Training Codes Roads Pipelines Predictive models Trajectory Safety

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Hyperthermic Response Optimization for Mn0.5zn0.5fe2o4 Magnetic Fluid for its Application in Magnetic Fluid Hyperthermia

SSRN

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SSRN 2024年

作者： Parekh, Kinnari Patel, Hima Dr. K. C. Patel Research and Development Centre Charotar University of science and technology CHARUSAT Campus Gujarat Changa388421 India

Ferrite based magnetic nanoparticles are very much suitable for biomedical application due to its well-known tunability of magnetic properties, toxicity and biocompatibility. The Mn0.5Zn0.5Fe2O4 ferrite nanoparticles composition is having low Curie temperature and high pyromagnetic co-efficient. Due to which it is considered to be a potential composition for magnetic fluid hyperthermia (MFH) in which controlling the temperature of 42-45 °C is an important crucial parameter. To maintain this window temperature, the material Curie temperature helps and hence Mn0.5Zn0.5Fe2O4 composition is selected for the work. The synthesis of Mn0.5Zn0.5Fe2O4 are done using well known co-precipitation, microwave, autoclave and hydrothermal techniques. The XRD results confirms the pure and single-phase spinel structure for the samples synthesized using all the techniques. All the samples are coated with polyethylene glycol as a surfactant to make the fluid biocompatible. The coating is confirmed by FTIR and TGA, whereas the stability of the magnetic fluids is measured by the Zeta potential analyzer. The magnetic properties measured by VSM shows that with the increase in the crystallinity and size of the samples the domain magnetization of the particles increases. In the MFH, it is seen that the hyperthermia temperature window is successfully achieve when synthesized using a hydrothermal method. © 2024, The Authors. All rights reserved.

关键词： Hydrothermal synthesis

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Machine Learning for Prognostication: Harnessing Chronic Wound Registry Data for Predicting Wound Healing Outcomes

Machine Learning for Prognostication: Harnessing Chronic Wou...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Richmond J. X. Sin Ruchir Srivastava Ee Ping Ong David Y. Y. Tan Jingxian Zhang Kyaw Kyar Toe Priya Bishnoi Yi Zhen Ng Rosa Q. Y. So Institute for Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore National University of Singapore Singapore Skin Research Institute of Singapore (SRIS) Agency for Science Technology and Research (A*STAR) Singapore Diagnostics Development Hub (DxD Hub) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

Chronic wounds have been a pressing concern worldwide, and the most significant issue is unnecessary amputations. It is made worse by delays in receiving suitable treatment which can be avoided. To address this, this paper introduces a pivotal algorithm that utilizes Gradient Boosting classifier to forecast the likelihood of wound healing within specific time frames. The wound healing predictions are made at three distinct intervals: 1 month, 3 months, and 6 months following the initial visit to the healthcare facility by the patient. The algorithm relies exclusively on the data gathered during the patient's initial visit and a systematically assembled chronic wound registry. The algorithm yields an area under the receiver operating characteristic curve (AUC) of 0.80, 0.74, and 0.70 for the prediction at the 3 time-points using the baseline features. Additionally, this paper also yields an additional result of 0.76 and 0.73 for the prediction at the three-months and six-months using the baseline plus one-month data. The proposed prediction model uses mainly tabular data which are readily available to clinicians after the initial visit. This helps the clinicians to have a more accurate diagnosis of the wound healing time, therefore allowing the clinicians to intervene early to prevent unnecessary amputations that are very costly to the patients and to provide the appropriate treatment to prevent the loss of lives through chronic wound injuries.

关键词： Accuracy Prevention and mitigation Amputation Receivers Wounds Predictive models Prediction algorithms Boosting Classification algorithms Robots

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Enhanced Drug-Drug Interaction Prediction with Graph Neural Networks and SVM

Enhanced Drug-Drug Interaction Prediction with Graph Neural ...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： A G Gnanabaskaran K Tamil Bharathi S P Nandakumar B Sanjay R Praveen Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Research & Development ICU Medical India LLP Chennai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Predicting drug-drug interactions (DDIs) is crucial for medication safety and efficacy. Traditional methods often face challenges in capturing complex interactions within large-scale drug networks. In this study, we propose an enhanced approach leveraging Graph Neural Networks (GNNs) and Support Vector Machines (SVM) to improve DDI prediction accuracy. Our method integrates graph representation learning with SVM-based classification, effectively capturing intricate relationships between drugs and their interactions. Through extensive experimentation on benchmark datasets, we demonstrate superior performance compared to existing methods, achieving higher prediction accuracy of ${9 7 \%}$ and f1 measure of ${9 8 \%}$. Moreover, our approach offers interpretability, enabling insights into underlying interaction mechanisms. Overall, our study highlights the potential of combining GNNs and SVM for advancing drug interaction prediction, with implications for enhancing medication safety and clinical decision-making.

关键词： Support vector machines Drugs Representation learning Knowledge engineering Accuracy Decision making Predictive models

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Proteomic and phosphoproteomic analysis reveal threonine deficiency increases hepatic lipid deposition in Pekin ducks via reducing STAT phosphorylation

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Animal Nutrition 2023年第2期13卷 249-260页

作者： Yong Jiang Zhong Zhuang Wenqia Jia Zhiguo Wen Ming Xie Hao Bai Yulin Bi Zhixiu Wang Guobin Chang Shuisheng Hou Guohong Chen College of Animal Science and Technology Yangzhou UniversityYangzhou 225009China State Key Laboratory of Animal Nutrition Key Laboratory of Animal(Poultry)Genetics Breeding and ReproductionMinistry of Agriculture and Rural AffairsInstitute of Animal SciencesChinese Academy of Agricultural SciencesBeijing 100193China Joint International Research Laboratory of Agriculture and Agri-Product Safety the Ministry of Education of ChinaInstitutes of Agricultural Science and Technology DevelopmentYangzhou UniversityYangzhou 225009China Key Laboratory of Feed Biotechnology of Ministry of Agriculture and Rural Affairs Institute of Feed ResearchChinese Academy of Agricultural SciencesBeijing 100081China

Dietary threonine(Thr)deficiency enhances triglyceride(TG)deposition in the liver of Pekin ducks,which injures hepatic function and impairs growth ***,the underlying molecular mechanisms remain *** the present study,we investigated the effects of dietary Thr deficiency on the expressions of proteins and phosphoproteins in liver of Pekin ducks,to identify the underlying molecular changes.A total of 300 one-day-old ducklings were divided into 3 groups with 10 replicates of 10 *** ducks were fed corn-wheat-peanut meal diets containing 0.46%,0.71%,and 0.96%Thr,respectively,from 1 to 21 days of *** performance,serum parameters,hepatic TG content,and expression of genes involved in lipid metabolism of Pekin ducks were determined.A Thr deficiency group(Thr-D,0.46%Thr)and a Thr sufficiency group(Thr-S,0.71%Thr)were selected for subsequent proteomic and phosphoproteomic *** results showed that Thr-D reduced the growth performance(P<0.001),and increased the plasma concentrations of cholesterol,high-density lipoprotein cholesterol,low-density lipoprotein cholesterol,and hepatic TG(P<0.05).Thr-D increased gene expression related to fatty acid and TG synthesis(P<0.05).A total of 176 proteins and 259 phosphosites(containing 198 phosphoproteins)were observed to be differentially expressed as a result of *** upregulated proteins were enriched in the pathway related to amino acid metabolism,*** down-regulated proteins were enriched in linolenic and arachidonic acid metabolism,and the Janus kinase-signal transducer and activator of transcription(JAK-STAT)signaling *** upregulated phos-phoproteins were enriched in the pathways related to fatty acid biosynthesis,fructose and mannose metabolism,and glycolysis/***-D reduced the phosphorylation of STAT1 at S729 and STAT3 at S728,and expression of *** contrast,Thr-D increased non-receptor tyrosine-protein kinase(TYK2)expression and STAT1 phosphoryla

关键词： Threonine deficiency Triglyceride accumulation Proteomic Phosphoproteomic Pekin duck

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Tweets to Predict: LSTM Model for Crime Analysis Using Twitter Time Series Data

Tweets to Predict: LSTM Model for Crime Analysis Using Twitt...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： P Kaladevi M Gopalraj K Harish A Guna R Praveen Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Research & Development ICU Medical India LLP Chennai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In recent years, social media platforms have emerged as rich sources of real-time data that can be leveraged for various predictive analytics tasks. This study explores the utility of Twitter data for crime analysis through the application of Long Short-Term Memory (LSTM) models. Specifically, we propose a novel LSTM-based approach for time series forecasting using Twitter data to predict crime occurrences. The methodology involves the collection of large-scale Twitter data streams relevant to crime incidents in urban areas. We preprocess the data to extract pertinent features and transform it into a suitable format for LSTM modeling. Through a series of experiments, we evaluate the performance of the LSTM model in forecasting crime trends based on historical Twitter time series data. Our results demonstrate the efficacy of the proposed LSTM-based approach in predicting crime occurrences with high accuracy and temporal precision. By analyzing Twitter signals, our model effectively captures subtle patterns and correlations underlying crime dynamics, enabling proactive measures for crime prevention and law enforcement.

关键词： Analytical models Social networking (online) Blogs Time series analysis Urban areas Predictive models Feature extraction

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Use of a Long Short-Term Memory Network in Radio Tomography to Track People Indoors 20

Use of a Long Short-Term Memory Network in Radio Tomography ...

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20th ACM Conference on Embedded Networked Sensor Systems, SenSys 2022

作者： Rymarczyk, Tomasz Klosowski, Grzegorz Adamkiewicz, Przemyslaw Styla, Michal Kiczek, Bartlomiej University of Economics and Innovation in Lublin Poland Lublin University of Technology Lublin Poland Information Technology Research and Development Center Rzeszów Poland Netrix S.A. Lublin Poland Maria Curie-Sklodowska University Lublin Poland

ISBN: (纸本)9781450398862

The aim of the research is to develop a system enabling effective and efficient tracking of people inside buildings using radio waves. The presented concept uses radio tomography imaging (RTI) as a passive analysis of radio wave interference as well as active connections with transmitting and receiving devices - -mainly smartphones. A long short-term memory (LSTM) neural network was used to solve the inverse tomographic problem of converting measurements into images. The presented concept uses a proprietary design of transducers, which are transmitting and receiving devices that can exchange information with each other and establish connections with other devices. The novelty is the hybrid nature of the people location system, using both device-free and device-based methods. Another new approach is using the LSTM network to solve the inverse problem in RTI. Both solutions make the location system much more flexible, which makes imaging much more accurate and reliable. © 2022 Owner/Author.

关键词： Wireless networks

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Continuous and Non-Contact Extraction of Respiratory Signal and Rate from Thermal Video

Continuous and Non-Contact Extraction of Respiratory Signal ...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Maryam Saidi Mohammad Rezaei Maryam Beigzadeh Amin Mohammadian Abdolaziz Azizi Lari Bioelectric Department Electrical Engineering Faculty Shahrood University of Technology Shahrood Iran Biosignals processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Achieving a simple, non-contact, non-invasive, and non-disruptive method for measuring and monitoring vital signs, including respiratory signals, is crucial in this field of research. This paper aims to implement algorithms for extracting respiratory signals and breathing rates from thermal facial images. Unlike many previous studies, where the algorithm was validated under resting conditions, this paper designs a protocol to capture a diverse range of respiratory activity datasets in varying conditions. Data were collected from six healthy individuals, with thoracic and abdominal contact-based respiratory signals used as reference signals. Processing steps, including robust facial tracking in thermal images and precise selection of target areas around the nose is implemented to extract respiratory signal and rates from the thermal video. The effectiveness of the proposed computational method is assessed through both quantitative and qualitative metrics, considering a variety of breathing patterns beyond just resting conditions

关键词： Training Protocols Correlation Target tracking Shape Neural networks Nose Infrared imaging Feature extraction Recording

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