The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,t...This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,this work is the first to implement lightweight convolutional neural network architectures leveraging down-scaled versions of inception blocks,residual connections,and dense residual connections applied without pre-training to the PlantVillage dataset.The novel contributions of this work include the proposal of a smart monitor-ing framework outline;responsible for detection and classification of ailments via the devised lightweight net-works as well as interfacing with LED grow-light fixtures to optimize environmental parameters and lighting control for the growth of plants in a greenhouse system.Lightweight adaptation of dense residual connections achieved the best balance of minimizing model parameters and maximizing performance metrics with accuracy,precision,recall,and F1-scores of 96.75%,97.62%,97.59%,and 97.58%respectively,while consisting of only 228,479 model parameters.These results are further compared against various full-scale state-of-the-art model architectures trained on the PlantVillage dataset,of which the proposed down-scaled lightweight models were capable of performing equally to,if not better than many large-scale counterparts with drastically less com-putational requirements.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of ...Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.展开更多
Farming has been the most prominent and fundamental activity for generations.As the population has been mul-tiplying exponentially,the demand for agricultural yield is growing relentlessly.Such high demand in producti...Farming has been the most prominent and fundamental activity for generations.As the population has been mul-tiplying exponentially,the demand for agricultural yield is growing relentlessly.Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour.In the era of digitization,smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management,optimize the process of farming and enhance the yield of crops,hence,ensuring sustainable growth of the increasing production.By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters.It also gives accurate and precise measurements when compared to traditional measurement techniques.This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature,moisture and humidity of their field.Real-time data on temperature,moisture,and humidity enables farmers to make informed decisions about irrigation and crop protection.Furthermore,the use of smart monitoring ensures accurate and precise measurements,surpassing the limitations of traditional techniques.展开更多
With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effec...With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effective and in situ battery monitoring system that does not interfere with the normal operation of the battery is imminent.Traditional monitoring techniques are constrained by size,reliability,and scalability.Optical fiber sensors offer a distinctive advantage in enabling highly sensitive,multiparameter in situ measurements in the harsh electrochemical environment of batteries.By decoding these characteristic parameters,it helps to establish the evolution mechanism of the battery’s safety state.Additionally,the integration of advanced lab-on-fiber technology with battery monitoring systems has attracted considerable attention.This review summarizes the recent advances in optical fiber sensing technology in the fields of battery temperature and mechanical stress/strain and provides an outlook on the future challenges and development of smart batteries.展开更多
Smartphone,as a smart device with multiple built-in sensors,can be used for collecting information(e.g.,vibration and location).In this paper,we propose an approach for using the smartphone as a sensing platform to ob...Smartphone,as a smart device with multiple built-in sensors,can be used for collecting information(e.g.,vibration and location).In this paper,we propose an approach for using the smartphone as a sensing platform to obtain real-time data on vehicle acceleration,velocity,and location through the development of the corresponding application software and thereby achieve the green concept based monitoring of the track condition during subway rail transit.Field tests are conducted to verify the accuracy of smartphones in terms of the obtained data’s standard deviation(SD),Sperling index(SI),and International Organization for Standardization(ISO)-2631 weighted acceleration index(WAI).A vehicle-positioning method,together with the coordinate alignment algorithm for a Global Positioning System(GPS)free tunnel environment,is proposed.Using the time-domain integration method,the relationship between the longitudinal acceleration of a vehicle and the subway location is established,and the distance between adjacent stations of the subway is calculated and compared with the actual values.The effectiveness of the method is verified,and it is confirmed that this approach can be used in the GPS-free subway tunnel environment.It is also found that using the proposed vehicle-positioning method,the integral error of displacement of a single subway section can be controlled to within 5%.This study can make full use of smartphones and offer a smart and eco-friendly approach for human life in the field of intelligent transportation systems.展开更多
This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environment...This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.展开更多
This paper concludes the case study work on the optical sensor, which is a new method for voltage and current measurement. Fiber Bragg gratings (FBG) have been developed and used for decades in the telecommunication i...This paper concludes the case study work on the optical sensor, which is a new method for voltage and current measurement. Fiber Bragg gratings (FBG) have been developed and used for decades in the telecommunication industry. In recent years, FBG sensors have found wide applications in monitoring strain, temperature, voltage and current across all industries. As the process of constructing a robust smart grid, thousands of miles of optical-fibers have been deployed along the power transmission lines for the purpose of power production communication. This paper focuses on using the power optical fiber as voltage/current sensors instead of those copper wired traditional current transformers. By using piezoelectric layers, the optical sensor is able to transform voltage/current magnitude into optical signal, as well as transmit the signal through the optical fiber. The application of using optical fiber will significantly reduce the cost of deploying traditional current transformers all around the power grid. Moreover, the optical sensor is more stable, more accurate and faster, with such characteristics, the smart grid monitoring system could be much better. The application of combining the optical composite low-voltage cable (OPLC) and the optical current sensor in the distribution network for smart distribution monitoring has been analyzed.展开更多
The solutions to our sleep struggles are far more complicated than simply counting sheep.Today,people go to great lengths in pursuit of a good night's rest,with the help of gadgets such as white noise apps,noise-c...The solutions to our sleep struggles are far more complicated than simply counting sheep.Today,people go to great lengths in pursuit of a good night's rest,with the help of gadgets such as white noise apps,noise-canceling earplugs,soothing eye patches,hi-tech pillows and mattresses,and smart sleep monitors.展开更多
Background The Study for Monitoring Antimicrobial Resistance Trends program monitors the activity of antibiotics against aerobic and facultative Gram-negative bacilli （GNBs） from intra-abdominal infections （IAIs） ...Background The Study for Monitoring Antimicrobial Resistance Trends program monitors the activity of antibiotics against aerobic and facultative Gram-negative bacilli （GNBs） from intra-abdominal infections （IAIs） in patients worldwide.Methods In 2011,1 929 aerobic and facultative GNBs from 21 hospitals in 16 cities in China were collected.All isolates were tested using a panel of 12 antimicrobial agents,and susceptibility was determined following the Clinical Laboratory Standards Institute guidelines.Results Among the Gram-negative pathogens causing IAIs,Escherichia coli （47.3％） was the most commonly isolated,followed by Klebsiella pneumoniae （17.2％）,Pseudomonas aeruginosa （10.1％）,and Acinetobacter baumannii （8.3％）.Enterobacteriaceae comprised 78.8％ （1521/1929） of the total isolates.Among the antimicrobial agents tested,ertapenem and imipenem were the most active agents against Enterobacteriaceae,with susceptibility rates of 95.1％ and 94.4％,followed by amikacin （93.9％） and piperacillin/tazobactam （87.7％）.Susceptibility rates of ceftriaxone,cefotaxime,ceftazidime,and cefepime against Enterobacteriaceae were 38.3％,38.3％,61.1％,and 50.8％,respectively.The leastactive agent against Enterobacteriaceae was ampicillin/sulbactam （25.9％）.The extended-spectrum β-lactamase （ESBL） rates among E.coli,K.pneumoniae,Klebsiella oxytoca,and Proteus mirabilis were 68.8％,38.1％,41.2％,and 57.7％,respectively.Conclusions Enterobacteriaceae were the major pathogens causing IAIs,and the most active agents against the study isolates （including those producing ESBLs） were ertapenem,imipenem,and amikacin.Including the carbapenems,most agents exhibited reduced susceptibility against ESBL-positive and multidrug-resistant isolates.展开更多
Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,...Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,deep learning and big data have strongly supported improving mining efficiency and safety in underground hard rock mines.Against this backdrop,this paper focuses on the production processes and vital auxiliary aspects of underground mining in hard rock mines.It delves into six aspects:driling,blasting,transportation,hoisting,ventilation,and support and flling.The paper elaborates on the latest advancements in intelligent technology research for each aspect and provides a summary and outlook on the key technologies relevant to these processes.Research results show that the current intelligent technology used in underground mining not only improves production efficiency but also further improves the safety production level of mining enterprises.To achieve intelligent unmanned mining,bottleneck problems in each primary process must be further addressed.展开更多
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
文摘This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,this work is the first to implement lightweight convolutional neural network architectures leveraging down-scaled versions of inception blocks,residual connections,and dense residual connections applied without pre-training to the PlantVillage dataset.The novel contributions of this work include the proposal of a smart monitor-ing framework outline;responsible for detection and classification of ailments via the devised lightweight net-works as well as interfacing with LED grow-light fixtures to optimize environmental parameters and lighting control for the growth of plants in a greenhouse system.Lightweight adaptation of dense residual connections achieved the best balance of minimizing model parameters and maximizing performance metrics with accuracy,precision,recall,and F1-scores of 96.75%,97.62%,97.59%,and 97.58%respectively,while consisting of only 228,479 model parameters.These results are further compared against various full-scale state-of-the-art model architectures trained on the PlantVillage dataset,of which the proposed down-scaled lightweight models were capable of performing equally to,if not better than many large-scale counterparts with drastically less com-putational requirements.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
文摘Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.
文摘Farming has been the most prominent and fundamental activity for generations.As the population has been mul-tiplying exponentially,the demand for agricultural yield is growing relentlessly.Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour.In the era of digitization,smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management,optimize the process of farming and enhance the yield of crops,hence,ensuring sustainable growth of the increasing production.By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters.It also gives accurate and precise measurements when compared to traditional measurement techniques.This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature,moisture and humidity of their field.Real-time data on temperature,moisture,and humidity enables farmers to make informed decisions about irrigation and crop protection.Furthermore,the use of smart monitoring ensures accurate and precise measurements,surpassing the limitations of traditional techniques.
基金support by the National Key Research and Development Program(2023YFB2503700)the Tsinghua University-China Petrochemical Corporation Joint Institute for Green Chemical Engineering(224247)+1 种基金the Beijing Municipal Science&Technology Commission(Z2311-00006123003)the National Science Foundation of China(22071133).
文摘With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effective and in situ battery monitoring system that does not interfere with the normal operation of the battery is imminent.Traditional monitoring techniques are constrained by size,reliability,and scalability.Optical fiber sensors offer a distinctive advantage in enabling highly sensitive,multiparameter in situ measurements in the harsh electrochemical environment of batteries.By decoding these characteristic parameters,it helps to establish the evolution mechanism of the battery’s safety state.Additionally,the integration of advanced lab-on-fiber technology with battery monitoring systems has attracted considerable attention.This review summarizes the recent advances in optical fiber sensing technology in the fields of battery temperature and mechanical stress/strain and provides an outlook on the future challenges and development of smart batteries.
基金the National Natural Science Foundation of China(Nos.51425804 and U1734207)the National Key R&D Program of China(No.2016YFC0802203-4)。
文摘Smartphone,as a smart device with multiple built-in sensors,can be used for collecting information(e.g.,vibration and location).In this paper,we propose an approach for using the smartphone as a sensing platform to obtain real-time data on vehicle acceleration,velocity,and location through the development of the corresponding application software and thereby achieve the green concept based monitoring of the track condition during subway rail transit.Field tests are conducted to verify the accuracy of smartphones in terms of the obtained data’s standard deviation(SD),Sperling index(SI),and International Organization for Standardization(ISO)-2631 weighted acceleration index(WAI).A vehicle-positioning method,together with the coordinate alignment algorithm for a Global Positioning System(GPS)free tunnel environment,is proposed.Using the time-domain integration method,the relationship between the longitudinal acceleration of a vehicle and the subway location is established,and the distance between adjacent stations of the subway is calculated and compared with the actual values.The effectiveness of the method is verified,and it is confirmed that this approach can be used in the GPS-free subway tunnel environment.It is also found that using the proposed vehicle-positioning method,the integral error of displacement of a single subway section can be controlled to within 5%.This study can make full use of smartphones and offer a smart and eco-friendly approach for human life in the field of intelligent transportation systems.
文摘This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.
文摘This paper concludes the case study work on the optical sensor, which is a new method for voltage and current measurement. Fiber Bragg gratings (FBG) have been developed and used for decades in the telecommunication industry. In recent years, FBG sensors have found wide applications in monitoring strain, temperature, voltage and current across all industries. As the process of constructing a robust smart grid, thousands of miles of optical-fibers have been deployed along the power transmission lines for the purpose of power production communication. This paper focuses on using the power optical fiber as voltage/current sensors instead of those copper wired traditional current transformers. By using piezoelectric layers, the optical sensor is able to transform voltage/current magnitude into optical signal, as well as transmit the signal through the optical fiber. The application of using optical fiber will significantly reduce the cost of deploying traditional current transformers all around the power grid. Moreover, the optical sensor is more stable, more accurate and faster, with such characteristics, the smart grid monitoring system could be much better. The application of combining the optical composite low-voltage cable (OPLC) and the optical current sensor in the distribution network for smart distribution monitoring has been analyzed.
文摘The solutions to our sleep struggles are far more complicated than simply counting sheep.Today,people go to great lengths in pursuit of a good night's rest,with the help of gadgets such as white noise apps,noise-canceling earplugs,soothing eye patches,hi-tech pillows and mattresses,and smart sleep monitors.
文摘Background The Study for Monitoring Antimicrobial Resistance Trends program monitors the activity of antibiotics against aerobic and facultative Gram-negative bacilli （GNBs） from intra-abdominal infections （IAIs） in patients worldwide.Methods In 2011,1 929 aerobic and facultative GNBs from 21 hospitals in 16 cities in China were collected.All isolates were tested using a panel of 12 antimicrobial agents,and susceptibility was determined following the Clinical Laboratory Standards Institute guidelines.Results Among the Gram-negative pathogens causing IAIs,Escherichia coli （47.3％） was the most commonly isolated,followed by Klebsiella pneumoniae （17.2％）,Pseudomonas aeruginosa （10.1％）,and Acinetobacter baumannii （8.3％）.Enterobacteriaceae comprised 78.8％ （1521/1929） of the total isolates.Among the antimicrobial agents tested,ertapenem and imipenem were the most active agents against Enterobacteriaceae,with susceptibility rates of 95.1％ and 94.4％,followed by amikacin （93.9％） and piperacillin/tazobactam （87.7％）.Susceptibility rates of ceftriaxone,cefotaxime,ceftazidime,and cefepime against Enterobacteriaceae were 38.3％,38.3％,61.1％,and 50.8％,respectively.The leastactive agent against Enterobacteriaceae was ampicillin/sulbactam （25.9％）.The extended-spectrum β-lactamase （ESBL） rates among E.coli,K.pneumoniae,Klebsiella oxytoca,and Proteus mirabilis were 68.8％,38.1％,41.2％,and 57.7％,respectively.Conclusions Enterobacteriaceae were the major pathogens causing IAIs,and the most active agents against the study isolates （including those producing ESBLs） were ertapenem,imipenem,and amikacin.Including the carbapenems,most agents exhibited reduced susceptibility against ESBL-positive and multidrug-resistant isolates.
基金supported by the National Natural Science Foundation of China (No.U21A20106)the Chinese Academy of Engineering Project (Nos.2022-33-29 and 2023-XY-44).
文摘Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,deep learning and big data have strongly supported improving mining efficiency and safety in underground hard rock mines.Against this backdrop,this paper focuses on the production processes and vital auxiliary aspects of underground mining in hard rock mines.It delves into six aspects:driling,blasting,transportation,hoisting,ventilation,and support and flling.The paper elaborates on the latest advancements in intelligent technology research for each aspect and provides a summary and outlook on the key technologies relevant to these processes.Research results show that the current intelligent technology used in underground mining not only improves production efficiency but also further improves the safety production level of mining enterprises.To achieve intelligent unmanned mining,bottleneck problems in each primary process must be further addressed.
