Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at ...Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.展开更多
Background:Dihydrogen(H_(2))is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates.Over the past few years,numer-ous studies have demonstrated the significant therapeutic ...Background:Dihydrogen(H_(2))is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates.Over the past few years,numer-ous studies have demonstrated the significant therapeutic potential of H_(2)in various pathophysiological contexts,making the characterization of its production in labora-tory species of major preclinical importance.Methods:This study proposes an innovative solution to accurately monitor H_(2)pro-duction in free-moving rodents while respecting animal welfare standards.The devel-oped device consisted of a wire rodent cage placed inside an airtight chamber in which the air quality was maintained,and the H_(2)concentration was continuously analyzed.After the airtightness and efficiency of the systems used to control and maintain air quality in the chamber were checked,tests were carried out on rats and mice with different metabolic phenotypes,over 12 min to 1-h experiments and repeatedly.H_(2)production rates(HPR)were obtained using an easy calculation algorithm based on a first-order moving average.Results:HPR in hyperphagic Zucker rats was found to be twice as high as in control Wistar rats,respectively,2.64 and 1.27 nmol.s^(−1)per animal.In addition,the ingestion of inulin,a dietary fiber,stimulated H_(2)production in mice.HPRs were 0.46 nmol.s^(−1)for animals under control diet and 1.99 nmol.s^(−1)for animals under inulin diet.Conclusions:The proposed device coupled with our algorithm enables fine analysis of the metabolic phenotype of laboratory rats or mice with regard to their endogenous H_(2)production.展开更多
The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions.Traditional methods,often costly,bulky,and prone to subjectivity,fall ...The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions.Traditional methods,often costly,bulky,and prone to subjectivity,fall short of meeting the need for daily monitoring.Digital and portable wearable monitoring devices have emerged as a promising research frontier.This study introduces a wearable system that integrates electrocardiogram(ECG)and phonocardiogram(PCG)detection.By ingeniously pairing a contact-type PZT heart sound sensing structure with ECG electrodes,the system achieves the acquisition of high-quality ECG and PCG signals.Notably,the signal-to-noise ratios(SNR)for ECG and PCG signals were measured at 44.13 dB and 30.04 dB,respectively,demonstrating the system’s remarkable stability across varying conditions.These collected signals were subsequently utilized to derive crucial feature values,including electromechanical delay(EMD),left ventricular ejection time(LVET),and pre-ejection period(PEP).Furthermore,we collected a dataset comprising 40 cases of ECG and PCG signals,enabling a comparative analysis of these three feature parameters between healthy individuals and coronary heart disease patients.This research endeavor presents a significant step forward in the realm of early,non-invasive,and intelligent monitoring of cardiovascular diseases,offering hope for earlier detection and more effective management of these life-threatening conditions.展开更多
1 An edible(可食用的)robot made by EPFL scientists uses a combination of biodegradable(可生物降解的)fuel and surface tension to move quickly around the water's surface,creating a safe and nutritious alternative to...1 An edible(可食用的)robot made by EPFL scientists uses a combination of biodegradable(可生物降解的)fuel and surface tension to move quickly around the water's surface,creating a safe and nutritious alternative to environmental monitoring devices made from artificial plastics and electronics.展开更多
Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the ...Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.展开更多
This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the tec...This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the technical principles,features,and working modes of such devices and makes a comparison with traditional ECG monitoring methods.Through a well-designed experimental approach involving data collection and analysis using specific evaluation metrics and standards,the accuracy of arrhythmia detection is evaluated.The relationship between arrhythmia and myocardial ischemia is explored,along with its impact on diagnosis,prognosis,and treatment strategy development.The application of these devices in daily activities,including feasibility,compliance,and analysis during different activity states and long-term trends,is also examined.Despite the potential benefits,technical limitations and barriers to clinical acceptance are identified,and future research directions are proposed.The findings contribute to a better understanding of the role and value of intelligent wearable ECG monitoring devices in the management of asymptomatic myocardial ischemia patients.展开更多
IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices...IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.展开更多
Within a LIFE＋ project IPNOA （improved flux prototype for n2o emission from agriculture）, LIFE11 ENV/IT/302 is a mobile prototype was developed to evaluate at field scale N20 emissions using a fast chamber techniqu...Within a LIFE＋ project IPNOA （improved flux prototype for n2o emission from agriculture）, LIFE11 ENV/IT/302 is a mobile prototype was developed to evaluate at field scale N20 emissions using a fast chamber technique. Main challenge was to develop a mobile system capable of moving on various field surfaces, equipped with very reliable N20 gas analyser （Los Gatos Research Inc.）, electrically autonomous （with batteries） and enough robust to face up to field conditions. In this paper, we report the major features of this prototype studied during two field campaigns. The N20 flux IPNOA prototype was compared with other methodological implementations： first, during an INGOS （integrated non-CO2 greenhouse gas observing systems） campaign on a grazed grassland at Easter Bush （Scotland） by Eddy correlation method, and then after on an arable crop at Grignon （France） using automatic and manual chambers fitted with QC-TILDAS （Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer, Aerodyne Research Inc.）, with the 46C model of thermo-instrument analyser or with a GC （gas chromatography） analysis.展开更多
The long-term monitoring of respiratory status is crucial for the prevention and diagnosis of respiratory diseases.However,existing continuous respiratory monitoring devices are typically bulky and require either ches...The long-term monitoring of respiratory status is crucial for the prevention and diagnosis of respiratory diseases.However,existing continuous respiratory monitoring devices are typically bulky and require either chest strapping or proximity to the nasal area,which compromises user comfort and may disrupt the monitoring process.To overcome these challenges,we have developed a flexible,attachable,lightweight,and miniaturized system designed for extended wear on the wrist.This system incorporates signal acquisition circuitry,a mobile client,and a deep neural network,facilitating long-term respiratory monitoring.Specifically,we fabricated a highly sensitive(11,847.24 kPa^(−1))flexible pressure sensor using a screen printing process,which is capable of functioning beyond 70,000 cycles.Additionally,we engineered a bidirectional long short-term memory(BiLSTM)neural network,enhanced with a residual module,to classify various respiratory states including slow,normal,fast,and simulated breathing.The system achieved a dataset classification accuracy exceeding 99.5%.We have successfully demonstrated a stable,cost-effective,and durable respiratory sensor system that can quantitatively collect and store respiratory data for individuals and groups.This system holds potential for everyday monitoring of physiological signals and healthcare applications.展开更多
During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recov...During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.展开更多
基金This work has been supported by.Central University Research Fund(No.2016MS116,No.2016MS117,No.2018MS074)the National Natural Science Foundation(51677072).
文摘Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.
基金UniversitéGrenoble Alpes through the Cross Disciplinary Program(CDTools)“My Health Companions”.
文摘Background:Dihydrogen(H_(2))is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates.Over the past few years,numer-ous studies have demonstrated the significant therapeutic potential of H_(2)in various pathophysiological contexts,making the characterization of its production in labora-tory species of major preclinical importance.Methods:This study proposes an innovative solution to accurately monitor H_(2)pro-duction in free-moving rodents while respecting animal welfare standards.The devel-oped device consisted of a wire rodent cage placed inside an airtight chamber in which the air quality was maintained,and the H_(2)concentration was continuously analyzed.After the airtightness and efficiency of the systems used to control and maintain air quality in the chamber were checked,tests were carried out on rats and mice with different metabolic phenotypes,over 12 min to 1-h experiments and repeatedly.H_(2)production rates(HPR)were obtained using an easy calculation algorithm based on a first-order moving average.Results:HPR in hyperphagic Zucker rats was found to be twice as high as in control Wistar rats,respectively,2.64 and 1.27 nmol.s^(−1)per animal.In addition,the ingestion of inulin,a dietary fiber,stimulated H_(2)production in mice.HPRs were 0.46 nmol.s^(−1)for animals under control diet and 1.99 nmol.s^(−1)for animals under inulin diet.Conclusions:The proposed device coupled with our algorithm enables fine analysis of the metabolic phenotype of laboratory rats or mice with regard to their endogenous H_(2)production.
基金support provided by the Shanxi Province Outstanding Young Academic Leader Program of Colleges and Universities(2024Q043)Basic Research General Program of Shanxi Province(202303021221186)+2 种基金Shanxi College of Technology Scientific Research Startup Fund Project(009018)National Natural Science Foundation of China(62001430)19th Graduate Science and Technology Project of North University of China(20231938).
文摘The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions.Traditional methods,often costly,bulky,and prone to subjectivity,fall short of meeting the need for daily monitoring.Digital and portable wearable monitoring devices have emerged as a promising research frontier.This study introduces a wearable system that integrates electrocardiogram(ECG)and phonocardiogram(PCG)detection.By ingeniously pairing a contact-type PZT heart sound sensing structure with ECG electrodes,the system achieves the acquisition of high-quality ECG and PCG signals.Notably,the signal-to-noise ratios(SNR)for ECG and PCG signals were measured at 44.13 dB and 30.04 dB,respectively,demonstrating the system’s remarkable stability across varying conditions.These collected signals were subsequently utilized to derive crucial feature values,including electromechanical delay(EMD),left ventricular ejection time(LVET),and pre-ejection period(PEP).Furthermore,we collected a dataset comprising 40 cases of ECG and PCG signals,enabling a comparative analysis of these three feature parameters between healthy individuals and coronary heart disease patients.This research endeavor presents a significant step forward in the realm of early,non-invasive,and intelligent monitoring of cardiovascular diseases,offering hope for earlier detection and more effective management of these life-threatening conditions.
文摘1 An edible(可食用的)robot made by EPFL scientists uses a combination of biodegradable(可生物降解的)fuel and surface tension to move quickly around the water's surface,creating a safe and nutritious alternative to environmental monitoring devices made from artificial plastics and electronics.
文摘Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.
文摘This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the technical principles,features,and working modes of such devices and makes a comparison with traditional ECG monitoring methods.Through a well-designed experimental approach involving data collection and analysis using specific evaluation metrics and standards,the accuracy of arrhythmia detection is evaluated.The relationship between arrhythmia and myocardial ischemia is explored,along with its impact on diagnosis,prognosis,and treatment strategy development.The application of these devices in daily activities,including feasibility,compliance,and analysis during different activity states and long-term trends,is also examined.Despite the potential benefits,technical limitations and barriers to clinical acceptance are identified,and future research directions are proposed.The findings contribute to a better understanding of the role and value of intelligent wearable ECG monitoring devices in the management of asymptomatic myocardial ischemia patients.
文摘IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.
文摘Within a LIFE＋ project IPNOA （improved flux prototype for n2o emission from agriculture）, LIFE11 ENV/IT/302 is a mobile prototype was developed to evaluate at field scale N20 emissions using a fast chamber technique. Main challenge was to develop a mobile system capable of moving on various field surfaces, equipped with very reliable N20 gas analyser （Los Gatos Research Inc.）, electrically autonomous （with batteries） and enough robust to face up to field conditions. In this paper, we report the major features of this prototype studied during two field campaigns. The N20 flux IPNOA prototype was compared with other methodological implementations： first, during an INGOS （integrated non-CO2 greenhouse gas observing systems） campaign on a grazed grassland at Easter Bush （Scotland） by Eddy correlation method, and then after on an arable crop at Grignon （France） using automatic and manual chambers fitted with QC-TILDAS （Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer, Aerodyne Research Inc.）, with the 46C model of thermo-instrument analyser or with a GC （gas chromatography） analysis.
基金supported by the National Key Research and Development Program of China(2023YFB3208600)the National Natural Science Foundation of China(No.62274140)+3 种基金Key Program of the National Natural Science Foundation of China(62433017)the Science and Technology on Vacuum Technology and Physics Laboratory Fund(HTKJ2023KL510008)the Fundamental Research Funds for the Central Universities(20720230030)the Xiaomi Young Talents Program/Xiaomi Foundation,Shenzhen Science and Technology Program(JCYJ20230807091401003).
文摘The long-term monitoring of respiratory status is crucial for the prevention and diagnosis of respiratory diseases.However,existing continuous respiratory monitoring devices are typically bulky and require either chest strapping or proximity to the nasal area,which compromises user comfort and may disrupt the monitoring process.To overcome these challenges,we have developed a flexible,attachable,lightweight,and miniaturized system designed for extended wear on the wrist.This system incorporates signal acquisition circuitry,a mobile client,and a deep neural network,facilitating long-term respiratory monitoring.Specifically,we fabricated a highly sensitive(11,847.24 kPa^(−1))flexible pressure sensor using a screen printing process,which is capable of functioning beyond 70,000 cycles.Additionally,we engineered a bidirectional long short-term memory(BiLSTM)neural network,enhanced with a residual module,to classify various respiratory states including slow,normal,fast,and simulated breathing.The system achieved a dataset classification accuracy exceeding 99.5%.We have successfully demonstrated a stable,cost-effective,and durable respiratory sensor system that can quantitatively collect and store respiratory data for individuals and groups.This system holds potential for everyday monitoring of physiological signals and healthcare applications.
基金supported in part by the Science and Technology Project of China Southern Power Grid (No. 090000KK52190169/SZKJXM2019669)in part by the Open Fund of State Key Laboratory of Power System and Generation Equipment,Tsinghua University (No. SKLD21KM04)。
文摘During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.
