Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diab...Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.展开更多
On August 1, 2002, a day that featured a clear sky and a gentle breeze, Prof. Ding Linghui and I paid a visit to Prof. Lobsang Toindain, Director of the Gesar Research Institute of Tibet University, at his home. He re...On August 1, 2002, a day that featured a clear sky and a gentle breeze, Prof. Ding Linghui and I paid a visit to Prof. Lobsang Toindain, Director of the Gesar Research Institute of Tibet University, at his home. He received us in his spacious sitting room, and served us buttered tea. Our interview lasted from 10:00 to 11:30 in the morning.展开更多
This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and s...This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.展开更多
The field of digital audio forensics aims to detect threats and fraud in audio signals.Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech,recognize speak...The field of digital audio forensics aims to detect threats and fraud in audio signals.Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech,recognize speakers,and recognize recording devices.User-generated audio recordings from mobile phones are very helpful in a number of forensic applications.This article proposed a novel method for recognizing recording devices based on recorded audio signals.First,a database of the features of various recording devices was constructed using 32 recording devices(20 mobile phones of different brands and 12 kinds of recording pens)in various environments.Second,the audio features of each recording device,such as the Mel-frequency cepstral coefficients(MFCC),were extracted from the audio signals and used as model inputs.Finally,support vector machines(SVM)with fractional Gaussian kernel were used to recognize the recording devices from their audio features.Experiments demonstrated that the proposed method had a 93.4%accuracy in recognizing recording devices.展开更多
Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium i...Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’experience.This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients.The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care(MIMIC)-IV.The time point of serum sodium detection was selected from the ICU clinical records,and the ICU records of 25risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis.A prediction model of serum sodium value within 48 h was established using a feedforward neural network,and compared with previous methods.Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h,and has better prediction effect than the serum sodium formula and other machine learning models.展开更多
为探讨机器学习方法在电子病历领域应用的研究现状、研究热点与前沿,以2000~2022年中国知网数据库和Web of Science核心合集数据库中关于机器学习在电子病历中应用的相关文献为数据来源,运用CiteSpace软件绘制国家/地区、作者、机构、...为探讨机器学习方法在电子病历领域应用的研究现状、研究热点与前沿,以2000~2022年中国知网数据库和Web of Science核心合集数据库中关于机器学习在电子病历中应用的相关文献为数据来源,运用CiteSpace软件绘制国家/地区、作者、机构、关键词共现以及关键词突现5个方面科学知识图谱进行可视化对比分析,以便了解国内外研究的差异,为该领域的研究和发展提供参考。展开更多
作物病虫害研究是人工智能技术与智慧农业交叉领域的热点问题。现有的研究受到数据获取困难、技术实施成本高以及作物病虫害发生态势复杂等因素的限制。北京市“植物诊所”形成的植物电子病历(plant electronic medical records,PEMRs)...作物病虫害研究是人工智能技术与智慧农业交叉领域的热点问题。现有的研究受到数据获取困难、技术实施成本高以及作物病虫害发生态势复杂等因素的限制。北京市“植物诊所”形成的植物电子病历(plant electronic medical records,PEMRs)为作物病虫害的诊断与防治提供了新的研究方向。PEMRs以多模态数据的形式存储,包含了丰富的植物信息、病虫害信息和环境信息,如何挖掘PEMRs信息并利用其辅助后续研究是亟待解决的问题。鉴于知识图谱的信息表示能力、机器学习的挖掘能力和深度学习的特征抽取能力,根据电子病历特点,利用结构化数据构建作物病虫害知识图谱,利用非结构化数据和领域知识进行知识增强,进一步利用Neo4j图数据库和图数据科学(graph data science,GDS)结合机器学习算法从“热”点发现、联系链路发现、相似病虫害发现3个维度进行关联挖掘。在此基础上,将基于Transformer的双向编码器(bidirectional encoder representation from transformers,BERT)与卷积神经网络(convolutional neural network,CNN)结合,利用非结构化文本数据实现文本特征抽取和病虫害诊断,模拟植物医生实现智能化服务,在20种常见病虫害上的综合准确率可达到93.13%。本研究可为作物病虫害的及时诊断、对症防治、科学用药和辅助决策提供理论支持,创新了农业科技社会化服务新模式、新业态。展开更多
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.
文摘On August 1, 2002, a day that featured a clear sky and a gentle breeze, Prof. Ding Linghui and I paid a visit to Prof. Lobsang Toindain, Director of the Gesar Research Institute of Tibet University, at his home. He received us in his spacious sitting room, and served us buttered tea. Our interview lasted from 10:00 to 11:30 in the morning.
文摘This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.
基金supported by the Jiangsu University Student Training Program[SJCX19_0529]the research fund of Nanjing Institute of Engineering[CXY201931]the National Natural Science Foundation of China(61871213).
文摘The field of digital audio forensics aims to detect threats and fraud in audio signals.Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech,recognize speakers,and recognize recording devices.User-generated audio recordings from mobile phones are very helpful in a number of forensic applications.This article proposed a novel method for recognizing recording devices based on recorded audio signals.First,a database of the features of various recording devices was constructed using 32 recording devices(20 mobile phones of different brands and 12 kinds of recording pens)in various environments.Second,the audio features of each recording device,such as the Mel-frequency cepstral coefficients(MFCC),were extracted from the audio signals and used as model inputs.Finally,support vector machines(SVM)with fractional Gaussian kernel were used to recognize the recording devices from their audio features.Experiments demonstrated that the proposed method had a 93.4%accuracy in recognizing recording devices.
基金supported by the National Natural Science Foundation of China(No.12345678)。
文摘Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’experience.This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients.The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care(MIMIC)-IV.The time point of serum sodium detection was selected from the ICU clinical records,and the ICU records of 25risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis.A prediction model of serum sodium value within 48 h was established using a feedforward neural network,and compared with previous methods.Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h,and has better prediction effect than the serum sodium formula and other machine learning models.
文摘中文电子病历实体包含大量的医学领域词汇并具有明显的嵌套特征。嵌套实体识别时往往存在目标实体定位不完整、不准确的问题。针对这一问题,提出了一种基于机器阅读理解的中文电子病历嵌套命名实体识别模型MRC-PBM(machine reading comprehension-position information biaffine and MLP)。该模型将命名实体识别(named entity recognition,NER)转化为机器阅读理解任务,将中文电子病历文本和预定义的查询语句串联作为输入,使用基于医学的预训练模型MC_BERT获取词向量,然后通过双向长短期记忆网络模型(BiLSTM)和多粒度扩张卷积模型分别获取双向的特征信息以及单词之间的信息,得到相应的特征向量,最后使用Hybrid-PBM预测器进行实体预测。在嵌套和平面NER数据集上进行实验。实验表明,该模型在糖尿病语料和公开医学数据集上优于其他主流神经网络模型,F1值比基线模型提高了1.21%~5.80%。
文摘为探讨机器学习方法在电子病历领域应用的研究现状、研究热点与前沿,以2000~2022年中国知网数据库和Web of Science核心合集数据库中关于机器学习在电子病历中应用的相关文献为数据来源,运用CiteSpace软件绘制国家/地区、作者、机构、关键词共现以及关键词突现5个方面科学知识图谱进行可视化对比分析,以便了解国内外研究的差异,为该领域的研究和发展提供参考。
文摘作物病虫害研究是人工智能技术与智慧农业交叉领域的热点问题。现有的研究受到数据获取困难、技术实施成本高以及作物病虫害发生态势复杂等因素的限制。北京市“植物诊所”形成的植物电子病历(plant electronic medical records,PEMRs)为作物病虫害的诊断与防治提供了新的研究方向。PEMRs以多模态数据的形式存储,包含了丰富的植物信息、病虫害信息和环境信息,如何挖掘PEMRs信息并利用其辅助后续研究是亟待解决的问题。鉴于知识图谱的信息表示能力、机器学习的挖掘能力和深度学习的特征抽取能力,根据电子病历特点,利用结构化数据构建作物病虫害知识图谱,利用非结构化数据和领域知识进行知识增强,进一步利用Neo4j图数据库和图数据科学(graph data science,GDS)结合机器学习算法从“热”点发现、联系链路发现、相似病虫害发现3个维度进行关联挖掘。在此基础上,将基于Transformer的双向编码器(bidirectional encoder representation from transformers,BERT)与卷积神经网络(convolutional neural network,CNN)结合,利用非结构化文本数据实现文本特征抽取和病虫害诊断,模拟植物医生实现智能化服务,在20种常见病虫害上的综合准确率可达到93.13%。本研究可为作物病虫害的及时诊断、对症防治、科学用药和辅助决策提供理论支持,创新了农业科技社会化服务新模式、新业态。
