World Data Center-D for Seismology(WDCDS)is a member of the World Data Center System under ICSU and is one of the nine World Data Centers located in China(WDC-D).During the period from 1993 to 1996,an information netw...World Data Center-D for Seismology(WDCDS)is a member of the World Data Center System under ICSU and is one of the nine World Data Centers located in China(WDC-D).During the period from 1993 to 1996,an information network system,called CSDInet,was developed in National Center for Seismic Data and Information(NCSDI)and has become the basic technical supporting system for WDC-D for Seismology.CSDInet consists of four basic parts:computer network center,LAN(Local Area Network),link to Internet,and nationwide PSTN(with dialing-up telephone line)user network.In this paper a "multi-layer multi-mode" management of data flow for this system will be explained and the data and information services will be described.展开更多
The working group on design and evaluation of intelligent system ergonomics,under SAC/TC 7,Ergonomics,was officially established in Qingdao city,Shandong province on June 18.The launching ceremony was jointly organize...The working group on design and evaluation of intelligent system ergonomics,under SAC/TC 7,Ergonomics,was officially established in Qingdao city,Shandong province on June 18.The launching ceremony was jointly organized by the Sub-institute of Fundamental Standardization of CNIS and China Standard Certification Co.,Ltd.It was attended by more than 60 experts and industry representatives in fields such as smart home technology,intelligent driving,wearable devices,medical health,and nuclear engineering.展开更多
BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 ...BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.展开更多
The optimization of working fluids in single-well coaxial geothermal systems presents a critical pathway for advancing the use of enhanced geothermal systems(EGS)in renewable energy applications.This study evaluates t...The optimization of working fluids in single-well coaxial geothermal systems presents a critical pathway for advancing the use of enhanced geothermal systems(EGS)in renewable energy applications.This study evaluates the thermo-hydraulic performance of three working fluids(H_(2)O,CO_(2),and H_(2))in a single-well coaxial geothermal system,focusing on the effects of their injection temperatures.Using a 3D finite element model in COMSOL Multiphysics,simulations were conducted at three injection temperatures(17℃,27℃,40℃)under constant mass flow rates.The results reveal that hydrogen significantly outperforms water and carbon dioxide,achieving a 297.77% and 5453.76% higher thermal output,respectively.Notably,the heat transfer efficiency is significantly improved when the injected working fluids are at 40℃,compared to 27℃;this demonstrates a positive correlation between injection temperature and thermal recovery.Though water systems exhibit better geological compatibility,the superior thermal properties of hydrogen position it as a promising alternative-despite potential subsurface challenges.This study provides critical insights for advancing the application of high-efficiency geothermal systems as well as the development of non-aqueous working fluids,thus contributing to the sustainable utilization of geothermal energy.展开更多
This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(...This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.展开更多
BACKGROUND There has been an increasing focus in recent years on health-care disparities.Studies investigating return to work(RTW)or sports are often performed in large,urban areas.Relatively few studies have investig...BACKGROUND There has been an increasing focus in recent years on health-care disparities.Studies investigating return to work(RTW)or sports are often performed in large,urban areas.Relatively few studies have investigated rates of return to farming or other heavy labor that is of interest to patients in rural areas.AIM To evaluate the literature regarding RTW in farming or heavy labor after orthopedic hip,knee,or shoulder surgery.METHODS A search was performed in the PubMed and EMBASE databases using Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.Studies were included if they reported patients employed in farming or heavy labor,RTW rates after orthopedic surgery of the hip,knee,or shoulder,and had a minimum 6-month follow-up.A meta-analysis of proportions using a random-effects model was performed on three single-arm observational studies to estimate the pooled RTW rate following arthroscopic shoulder surgery.RESULTS Ten studies were included,and 101 farmers were identified among 440 total patients.One study involved hip surgery,two studies involved knee surgery,and seven studies involved shoulder surgery.RTW rates across studies varied by type of surgery and follow-up interval,ranging from 24%to 100%.The RTW rate was only 53.6%at 1 year following total hip arthroplasty.No studies investigated RTW in farmers following total knee arthroplasty.Among non-comparative studies,meta-analysis revealed a pooled RTW rate of 89%following arthroscopic shoulder surgery,with low heterogeneity(I^(2)=30.1%).Among comparative studies,one study reported significantly higher RTW odds for patients undergoing anatomic total shoulder arthroplasty compared to reverse shoulder arthroplasty(odds ratio=5.45).Overall,surgical intervention for shoulder pathology was associated with a high likelihood of RTW across multiple techniques,with particularly favorable outcomes for anatomic total shoulder arthroplasty.CONCLUSION This systematic review highlights the high rates of RTW in farmers and heavy laborers after shoulder surgery.However,our findings also underscore the need for more rural-specific research to guide patient counseling,rehabilitation expectations,and shared decision-making in this underserved population,particularly for orthopedic surgery of the hip and knee.展开更多
This study investigates the functioning mechanisms of how high performance work systems （HPWS） affect organizational performance. We propose that （HPWS） can positively affect organizational performance through the...This study investigates the functioning mechanisms of how high performance work systems （HPWS） affect organizational performance. We propose that （HPWS） can positively affect organizational performance through the mediating role of entrepreneurial orientation. An organization with high performance work systems can perform better if it enjoys high level of organizational learning. We design and administer a survey questionnaire to high-level executives or founders of companies from manufacturing and service industries and receive 176 valid responses. The results of the empirical data indicate that the relationship between high performance work systems and corporate performance is more positive when organizational learning is stronger. Entrepreneurial orientation partially mediates the relationship between high performance work systems and organizational performance. This study opens new research avenues by extending and incorporating explanations and predictions of HPWS and entrepreneurial orientation, two areas that largely have been considered independently of each other. Implications for practice and directions for future research are provided.展开更多
Employee creativity is both the core element of a firm＇s innovation capabilities and the sources for its growth. To improve an organization＇s ability to innovate, it is necessary to improve the creativity of its emp...Employee creativity is both the core element of a firm＇s innovation capabilities and the sources for its growth. To improve an organization＇s ability to innovate, it is necessary to improve the creativity of its employees. Based on theories from strategic human resource management, creativity and organizational learning, this paper investigates the relationship between high performance work systems and employee creativity and explores the role knowledge sharing plays in their relationship. A questionnaire is designed and administered to a group of part-time executive students in the winter of 2012. Two hundred students are invited to answer the survey questions with 117 valid responses. Data are collected and processed by using statistical regressions. The empirical findings reveal that high performance work systems positively affect knowledge sharing and employee creativity. Knowledge sharing plays a mediating role in the relationship between high performance work systems and employee creativity. Implications for practice and future research are discussed.展开更多
This study examines the key human resources factors that affect volunteers＇ service performance from the perspectives of volunteers and managers in the Beijing Summer Olympic Games of 2008. Survey data were collected...This study examines the key human resources factors that affect volunteers＇ service performance from the perspectives of volunteers and managers in the Beijing Summer Olympic Games of 2008. Survey data were collected from 1,727 volunteers and 243 managers at the Beijing Olympics test events held at 10 venues between November 2007 and April 2008. Regression analyses and a moderation test were combined to test the hypotheses. A set of high performance work systems （HPWS） for volunteers in the Beijing Summer Olympic Games were developed which include performance management, training, recognition, teamwork and volunteer participation. Volunteer HPWS were positively related to psychological empowerment, which was in turn positively related to service recovery performance. Moreover, transformational leadership moderates the relationship between volunteer HPWS and psychological empowerment in such a way that the relationship is stronger when transformational leadership is at a higher level than when it is at a lower level.Implications and limitations were also discussed.展开更多
A healthy nurse work environment is a workplace that is safe,empowering,and satisfying.Many research studies were conducted on nurse work environments in the last decade;however,it lacks an overview of these research ...A healthy nurse work environment is a workplace that is safe,empowering,and satisfying.Many research studies were conducted on nurse work environments in the last decade;however,it lacks an overview of these research studies.The purpose of this review is to identify,evaluate,and summarize the major foci of studies about nurse work environments in the United States published between January 2005 and December 2017 and provide strategies to improve nurse work environments.Databases searched included MEDLINE via PubMed,CINAHL,PsycINFO,Nursing and Allied Health,and the Cochrane Library.The literature search followed the PRISMA guideline.Fifty-four articles were reviewed.Five major themes emerged:1)Impacts of healthy work environments on nurses'outcomes such as psychological health,emotional strains,job satisfaction,and retention;2)Associations between healthy work environments and nurse interpersonal relationships at workplaces,job performance,and productivity;3)Effects of healthy work environments on patient care quality;4)Influences of healthy work environments on hospital accidental safety;and 5)Relationships between nurse leadership and healthy work environments.This review shows that nurses,as frontline patient care providers,are the foundation for patient safety and care quality.Promoting nurse empowerment,engagement,and interpersonal relationships at work is rudimental to achieve a healthy work environment and quality patient care.Healthier work environments lead to more satisfied nurses who will result in better job performance and higher quality of patient care,which will subsequently improve healthcare organizations'financial viability.Fostering a healthy work environment is a continuous effort.展开更多
In this paper, we try to use the entransy theory to analyze the heat–work conversion systems with inner irreversible thermodynamic cycles. First, the inner irreversible thermodynamic cycles are analyzed. The influenc...In this paper, we try to use the entransy theory to analyze the heat–work conversion systems with inner irreversible thermodynamic cycles. First, the inner irreversible thermodynamic cycles are analyzed. The influences of different inner irreversible factors on entransy loss are discussed. We find that the concept of entransy loss can be used to analyze the inner irreversible thermodynamic cycles. Then, we analyze the common heat–work conversion systems with inner irreversible thermodynamic cycles. As an example, the heat–work conversion system in which the working fluid of the thermodynamic cycles is heated and cooled by streams is analyzed. Our analyses show that larger entransy loss leads to larger output work when the total heat flow from the high temperature heat source and the corresponding equivalent temperature are fixed.Some numerical cases are presented, and the results verify the theoretical analyses. On the other hand, it is also found that larger entransy loss does not always lead to larger output work when the preconditions are not satisfied.展开更多
For the purpose of analyzing the torsional vibration caused by the gravitational unbalance torque arisen in a spindle system when it is machining heavy work piece,a 10-DOF lumped parameter model was made for the machi...For the purpose of analyzing the torsional vibration caused by the gravitational unbalance torque arisen in a spindle system when it is machining heavy work piece,a 10-DOF lumped parameter model was made for the machine tool spindle system with geared transmission.By using the elementary method and Runge-Kutta method in Matlab,the eigenvalue problem was solved and the pure torsional vibration responses were obtained and examined.The results show that the spindle system cannot operate in the desired constant rotating speed as far as the gravitational unbalance torque is engaged,so it may cause bad effect on machining accuracy.And the torsional vibration increases infinitely near the resonant frequencies,so the spindle system cannot operate normally during these spindle speed ranges.展开更多
The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this p...The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.展开更多
A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithm...A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice...Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.展开更多
For leader-following networked systems with the topology switching and the aperiodic silence,limited-energy output formation tracking problems are investigated.Firstly,a new output formation tracking control protocol ...For leader-following networked systems with the topology switching and the aperiodic silence,limited-energy output formation tracking problems are investigated.Firstly,a new output formation tracking control protocol is proposed,which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents,respectively,and the aperiodic silence,the topology switching and the energy constraint index is introduced properly.Then,a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader,and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented,which are extended into networked systems without the aperiodic silence.Especially,a partition checking algorithm is presented to check limitedenergy output formation tracking design criteria.Finally,a numerical example is illustrated to demonstrate the validness of theoretical results.展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
According to the necessity of flexible workflow management system, the solution to set up the visualized workflow modelling system based on B/S structure is put forward, which conforms to the relevant specifications o...According to the necessity of flexible workflow management system, the solution to set up the visualized workflow modelling system based on B/S structure is put forward, which conforms to the relevant specifications of WfMC and the workflow process definition meta-model. The design for system structure is presented in detail, and the key technologies for system implementation are also introduced. Additionally, an example is illustrated to demonstrate the validity of system.展开更多
文摘World Data Center-D for Seismology(WDCDS)is a member of the World Data Center System under ICSU and is one of the nine World Data Centers located in China(WDC-D).During the period from 1993 to 1996,an information network system,called CSDInet,was developed in National Center for Seismic Data and Information(NCSDI)and has become the basic technical supporting system for WDC-D for Seismology.CSDInet consists of four basic parts:computer network center,LAN(Local Area Network),link to Internet,and nationwide PSTN(with dialing-up telephone line)user network.In this paper a "multi-layer multi-mode" management of data flow for this system will be explained and the data and information services will be described.
文摘The working group on design and evaluation of intelligent system ergonomics,under SAC/TC 7,Ergonomics,was officially established in Qingdao city,Shandong province on June 18.The launching ceremony was jointly organized by the Sub-institute of Fundamental Standardization of CNIS and China Standard Certification Co.,Ltd.It was attended by more than 60 experts and industry representatives in fields such as smart home technology,intelligent driving,wearable devices,medical health,and nuclear engineering.
基金Supported by Shanghai Municipal Health Commission’s Special Clinical Research Project for the Hygiene Industry,No.20244Y0041Youth Initiation Fund of Naval Medical University,No.2023QN028 and No.2023QN030。
文摘BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.
基金funded by the China National Administration of Coal Geology Science and Technology Innovation Project"Research on Clean Energy Exploration and Development Technology"(ZMKJ-2021-ZX04)the China National Administration of Coal Geology Special Task Project"Research on Geothermal Resource Exploration and Development Technology"(ZMKJ-2023-JBGS06)。
文摘The optimization of working fluids in single-well coaxial geothermal systems presents a critical pathway for advancing the use of enhanced geothermal systems(EGS)in renewable energy applications.This study evaluates the thermo-hydraulic performance of three working fluids(H_(2)O,CO_(2),and H_(2))in a single-well coaxial geothermal system,focusing on the effects of their injection temperatures.Using a 3D finite element model in COMSOL Multiphysics,simulations were conducted at three injection temperatures(17℃,27℃,40℃)under constant mass flow rates.The results reveal that hydrogen significantly outperforms water and carbon dioxide,achieving a 297.77% and 5453.76% higher thermal output,respectively.Notably,the heat transfer efficiency is significantly improved when the injected working fluids are at 40℃,compared to 27℃;this demonstrates a positive correlation between injection temperature and thermal recovery.Though water systems exhibit better geological compatibility,the superior thermal properties of hydrogen position it as a promising alternative-despite potential subsurface challenges.This study provides critical insights for advancing the application of high-efficiency geothermal systems as well as the development of non-aqueous working fluids,thus contributing to the sustainable utilization of geothermal energy.
基金Supported by the National Natural Science Foundation of China(No.62203390).
文摘This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.
文摘BACKGROUND There has been an increasing focus in recent years on health-care disparities.Studies investigating return to work(RTW)or sports are often performed in large,urban areas.Relatively few studies have investigated rates of return to farming or other heavy labor that is of interest to patients in rural areas.AIM To evaluate the literature regarding RTW in farming or heavy labor after orthopedic hip,knee,or shoulder surgery.METHODS A search was performed in the PubMed and EMBASE databases using Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.Studies were included if they reported patients employed in farming or heavy labor,RTW rates after orthopedic surgery of the hip,knee,or shoulder,and had a minimum 6-month follow-up.A meta-analysis of proportions using a random-effects model was performed on three single-arm observational studies to estimate the pooled RTW rate following arthroscopic shoulder surgery.RESULTS Ten studies were included,and 101 farmers were identified among 440 total patients.One study involved hip surgery,two studies involved knee surgery,and seven studies involved shoulder surgery.RTW rates across studies varied by type of surgery and follow-up interval,ranging from 24%to 100%.The RTW rate was only 53.6%at 1 year following total hip arthroplasty.No studies investigated RTW in farmers following total knee arthroplasty.Among non-comparative studies,meta-analysis revealed a pooled RTW rate of 89%following arthroscopic shoulder surgery,with low heterogeneity(I^(2)=30.1%).Among comparative studies,one study reported significantly higher RTW odds for patients undergoing anatomic total shoulder arthroplasty compared to reverse shoulder arthroplasty(odds ratio=5.45).Overall,surgical intervention for shoulder pathology was associated with a high likelihood of RTW across multiple techniques,with particularly favorable outcomes for anatomic total shoulder arthroplasty.CONCLUSION This systematic review highlights the high rates of RTW in farmers and heavy laborers after shoulder surgery.However,our findings also underscore the need for more rural-specific research to guide patient counseling,rehabilitation expectations,and shared decision-making in this underserved population,particularly for orthopedic surgery of the hip and knee.
文摘This study investigates the functioning mechanisms of how high performance work systems （HPWS） affect organizational performance. We propose that （HPWS） can positively affect organizational performance through the mediating role of entrepreneurial orientation. An organization with high performance work systems can perform better if it enjoys high level of organizational learning. We design and administer a survey questionnaire to high-level executives or founders of companies from manufacturing and service industries and receive 176 valid responses. The results of the empirical data indicate that the relationship between high performance work systems and corporate performance is more positive when organizational learning is stronger. Entrepreneurial orientation partially mediates the relationship between high performance work systems and organizational performance. This study opens new research avenues by extending and incorporating explanations and predictions of HPWS and entrepreneurial orientation, two areas that largely have been considered independently of each other. Implications for practice and directions for future research are provided.
文摘Employee creativity is both the core element of a firm＇s innovation capabilities and the sources for its growth. To improve an organization＇s ability to innovate, it is necessary to improve the creativity of its employees. Based on theories from strategic human resource management, creativity and organizational learning, this paper investigates the relationship between high performance work systems and employee creativity and explores the role knowledge sharing plays in their relationship. A questionnaire is designed and administered to a group of part-time executive students in the winter of 2012. Two hundred students are invited to answer the survey questions with 117 valid responses. Data are collected and processed by using statistical regressions. The empirical findings reveal that high performance work systems positively affect knowledge sharing and employee creativity. Knowledge sharing plays a mediating role in the relationship between high performance work systems and employee creativity. Implications for practice and future research are discussed.
文摘This study examines the key human resources factors that affect volunteers＇ service performance from the perspectives of volunteers and managers in the Beijing Summer Olympic Games of 2008. Survey data were collected from 1,727 volunteers and 243 managers at the Beijing Olympics test events held at 10 venues between November 2007 and April 2008. Regression analyses and a moderation test were combined to test the hypotheses. A set of high performance work systems （HPWS） for volunteers in the Beijing Summer Olympic Games were developed which include performance management, training, recognition, teamwork and volunteer participation. Volunteer HPWS were positively related to psychological empowerment, which was in turn positively related to service recovery performance. Moreover, transformational leadership moderates the relationship between volunteer HPWS and psychological empowerment in such a way that the relationship is stronger when transformational leadership is at a higher level than when it is at a lower level.Implications and limitations were also discussed.
文摘A healthy nurse work environment is a workplace that is safe,empowering,and satisfying.Many research studies were conducted on nurse work environments in the last decade;however,it lacks an overview of these research studies.The purpose of this review is to identify,evaluate,and summarize the major foci of studies about nurse work environments in the United States published between January 2005 and December 2017 and provide strategies to improve nurse work environments.Databases searched included MEDLINE via PubMed,CINAHL,PsycINFO,Nursing and Allied Health,and the Cochrane Library.The literature search followed the PRISMA guideline.Fifty-four articles were reviewed.Five major themes emerged:1)Impacts of healthy work environments on nurses'outcomes such as psychological health,emotional strains,job satisfaction,and retention;2)Associations between healthy work environments and nurse interpersonal relationships at workplaces,job performance,and productivity;3)Effects of healthy work environments on patient care quality;4)Influences of healthy work environments on hospital accidental safety;and 5)Relationships between nurse leadership and healthy work environments.This review shows that nurses,as frontline patient care providers,are the foundation for patient safety and care quality.Promoting nurse empowerment,engagement,and interpersonal relationships at work is rudimental to achieve a healthy work environment and quality patient care.Healthier work environments lead to more satisfied nurses who will result in better job performance and higher quality of patient care,which will subsequently improve healthcare organizations'financial viability.Fostering a healthy work environment is a continuous effort.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51376101 and 51356001)
文摘In this paper, we try to use the entransy theory to analyze the heat–work conversion systems with inner irreversible thermodynamic cycles. First, the inner irreversible thermodynamic cycles are analyzed. The influences of different inner irreversible factors on entransy loss are discussed. We find that the concept of entransy loss can be used to analyze the inner irreversible thermodynamic cycles. Then, we analyze the common heat–work conversion systems with inner irreversible thermodynamic cycles. As an example, the heat–work conversion system in which the working fluid of the thermodynamic cycles is heated and cooled by streams is analyzed. Our analyses show that larger entransy loss leads to larger output work when the total heat flow from the high temperature heat source and the corresponding equivalent temperature are fixed.Some numerical cases are presented, and the results verify the theoretical analyses. On the other hand, it is also found that larger entransy loss does not always lead to larger output work when the preconditions are not satisfied.
基金Project(10033135-2009-11) supported by the Korean Ministry of Knowledge Economy (MKE) through HNK. Co,Ltd.
文摘For the purpose of analyzing the torsional vibration caused by the gravitational unbalance torque arisen in a spindle system when it is machining heavy work piece,a 10-DOF lumped parameter model was made for the machine tool spindle system with geared transmission.By using the elementary method and Runge-Kutta method in Matlab,the eigenvalue problem was solved and the pure torsional vibration responses were obtained and examined.The results show that the spindle system cannot operate in the desired constant rotating speed as far as the gravitational unbalance torque is engaged,so it may cause bad effect on machining accuracy.And the torsional vibration increases infinitely near the resonant frequencies,so the spindle system cannot operate normally during these spindle speed ranges.
文摘The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.
基金supported by the Science and technology project of State Grid Information&Telecommunication Group Co.,Ltd (SGTYHT/19-JS-218)
文摘A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金supported by the Instrument Developing Project of the Chinese Academy of Sciences(YZ201540)the National Science Foundation for Distinguished Young Scholars of China(31525010)+4 种基金the General Program of the National Science Foundation of China(31471049)the Key Research Project of Frontier Science of the Chinese Academy of Sciences(QYZDB-SSW-SMC009)China–Netherlands CAS-NWO Programme:Joint Research Projects,The Future of Brain and Cognition(153D31KYSB20160106)the Key Project of Shanghai Science and Technology Commission(15JC1400102,16JC1400101)the State Key Laboratory of Neuroscience,China
文摘Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.
基金supported by the National Natural Science Foundation of China(Nos.62176263,62103434,62003363,61867005,61703411)the Science and Technology Nova Plan of Beijing,China(No.Z201100006820122)+4 种基金the Shaanxi Natural Science Foundation for Distinguished Young Scholars,China(No.2021JC-35)the Shaanxi Natural Science Foundation for Youths,China(No.2021JQ-375)China Postdoctoral Science Special Foundation(No.2021T140790)China Postdoctoral Research Foundation of China(No.271004)the Gansu Provincial First-Class Discipline Program of Northwest Minzu University,China(No.11080305)。
文摘For leader-following networked systems with the topology switching and the aperiodic silence,limited-energy output formation tracking problems are investigated.Firstly,a new output formation tracking control protocol is proposed,which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents,respectively,and the aperiodic silence,the topology switching and the energy constraint index is introduced properly.Then,a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader,and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented,which are extended into networked systems without the aperiodic silence.Especially,a partition checking algorithm is presented to check limitedenergy output formation tracking design criteria.Finally,a numerical example is illustrated to demonstrate the validness of theoretical results.
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
基金Shanghai Municipal Science Committee key project(061612058,06JC14066,06DZ12001,061111006)Nationalscience and technology supporting project(2006BAF01A46)
文摘According to the necessity of flexible workflow management system, the solution to set up the visualized workflow modelling system based on B/S structure is put forward, which conforms to the relevant specifications of WfMC and the workflow process definition meta-model. The design for system structure is presented in detail, and the key technologies for system implementation are also introduced. Additionally, an example is illustrated to demonstrate the validity of system.
