Information Network System for WDCDS/NCSDI

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.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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.

Exploring influences and risk of bias of studies on return to sport and work after lateral ankle sprain:A systematic review and metaanalysis

BACKGROUND Lateral ankle sprains are the most common traumatic musculoskeletal injuries of the lower extremity,with an incidence rate of 15%-20%.The high incidence and prevalence highlights the economic impact of this injury.Ankle sprains lead to a high socioeconomic burden due to the combination of the high injury incidence and high medical expenses.Up to 40%of patients who suffer from an ankle sprain develop chronic ankle instability.Chronic instability can lead to prolonged periods of pain,immobility and injury recurrence.Identification of factors that influence return to work(RTW)and return to sports(RTS)after a lateral ankle sprain(LAS)may help seriously reduce healthcare costs.AIM To explore which factors may potentially affect RTW and RTS after sustaining an LAS.METHODS EMBASE and PubMed were systematically searched for relevant studies published until June 2023.Inclusion criteria were as follows:(1)Injury including LAS or chronic ankle instability;(2)Described any form of treatment;(3)Assessment of RTW or RTS;(4)Studies published in English;and(5)Study designs including randomized controlled clinical trials,clinical trials or cohort studies.Exclusion criteria were:(1)Studies involving children(age<16 year);or(2)Patients with concomitant ankle injury besides lateral ankle ligament damage.A quality assessment was performed for each of the included studies using established risk of bias tools.Additionally quality of evidence was assessed using the GRADEpro tool in cases where outcomes were included in the quantitative analysis.A best evidence synthesis was performed in cases of qualitative outcome analysis.For all studied outcomes suitable for quantitative analysis a forest plot was created to calculate the effect on RTW and RTS.RESULTS A total of 8904 patients were included in 21 studies,10 randomized controlled trials,7 retrospective cohort studies and 4 prospective cohort studies.Fifteen studies were eligible for meta-analysis.The overall RTS rate ranged were 80%and 83%in the all treatments pool and surgical treatments pool,respectively.The pooled mean days to RTS ranged from 23-93 d.The overall RTW rate was 89%.The pooled mean time to RTW ranged from 5.8-8.1 d.For patients with chronic ankle instability,higher preoperative motivation was the sole factor significantly and independently(P=0.001)associated with the rate of and time to RTS following ligament repair or reconstruction.Higher body mass index was identified as a significant factor(P=0.04)linked to not resuming sports or returning at a lower level(median 24,range 20-37),compared to those who resumed at the same or higher level(median 23,range 17-38).Patients with a history of psychological illness or brain injury,experienced a delay in their rehabilitation process for sprains with fractures and unspecified sprains.The extent of the delayed rehabilitation was directly proportional to the increased likelihood of experiencing a recurrence of the ankle sprain and the number of ankle-related medical visits.We also observed that 10%of athletes who did return to sport after lateral ankle sprain without fractures described non-ankle-related reasons for not returning.CONCLUSION All treatments yielded comparable results,with each treatment potentially offering unique advantages or benefits.Preoperative motivation may influence rehabilitation after LAS.Grading which factor had a greater impact was not possible due to the lack of comparability among the included patients.

High performance work systems and corporate performance： the influence of entrepreneurial orientation and organizational learning

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.

High Performance Work Systems and Employee Creativity： The Mediating Effect of Knowledge Sharing

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.

Volunteer High Performance Work Systems and Service Performance： An Empirical Study of Beijing Olympic Volunteers

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.

The state of the science of nurse work environments in the United States: A systematic review

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.

Entransy analyses of heat–work conversion systems with inner irreversible thermodynamic cycles

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.

Torsional vibration analysis of lathe spindle system with unbalanced workpiece

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.

Improved edge lightweight YOLOv4 and its application in on-site power system work

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%.

A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems

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.

Design and Implementation of Visualized Workflow Modeling System Based on B/S Structure

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.

Nonequilibrium work equalities in isolated quantum systems

We briefly introduce the quantum Jarzynski and Bochkov-Kuzovlev equalities .in isolated quantum Hamiltonian sys- tems, including their origin, their derivations using a quantum Feynman-Kac formula, the quantum Crooks equality, the evolution equations governing the characteristic functions of the probability density functions for the quantum work, and recent experimental verifications. Some resultsare given here for the first time. We particularly emphasize the formally structural consistence between these quantum equalities and their classical counterparts, which are useful for understanding the existing equalities and pursuing new fluctuation relations in other complex quantum systems.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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.

Analysis on optimal working fluid flowrate and unstable power generation for miniaturized ORC systems

For efficient utilization of a limited geothermal resource in practical projects,the cycle parameters were comprehensively analyzed by combining with the heat transfer performance of the plate heat exchanger,with a variation of flowrate of R245 fa.The influence of working fluid flowrate on a 500 W ORC system was investigated.Adjusting the working fluid flowrate to an optimal value results in the most efficient heat transfer and hence the optimal heat transfer parameters of the plate heat exchanger can be determined.Therefore,for the ORC systems,optimal working fluid flowrate should be controlled.Using different temperature hot water as the heat source,it is found that the optimal flowrate increases by 6-10 L/h with 5 ℃ increment of hot water inlet temperature.During experiment,lower degree of superheat of the working fluid at the outlet the plate heat exchanger may lead to unstable power generation.It is considered that the plate heat exchanger has a compact construction which makes its bulk so small that liquid mixture causes the unstable power generation.To avoid this phenomenon,the flow area of plate heat exchanger should be larger than the designed one.Alternatively,installing a small shell and tube heat exchanger between the outlet of plate heat exchanger and the inlet of expander can be another solution.

Flow characteristics and hot workability of a typical low-alloy high-strength steel during multi-pass deformation

Heavy components of low-alloy high-strength(LAHS) steels are generally formed by multi-pass forging. It is necessary to explore the flow characteristics and hot workability of LAHS steels during the multi-pass forging process, which is beneficial to the formulation of actual processing parameters. In the study, the multi-pass hot compression experiments of a typical LAHS steel are carried out at a wide range of deformation temperatures and strain rates. It is found that the work hardening rate of the experimental material depends on deformation parameters and deformation passes, which is ascribed to the impacts of static and dynamic softening behaviors. A new model is established to describe the flow characteristics at various deformation passes. Compared to the classical Arrhenius model and modified Zerilli and Armstrong model, the newly proposed model shows higher prediction accuracy with a confidence level of 0.98565. Furthermore, the connection between power dissipation efficiency(PDE) and deformation parameters is revealed by analyzing the microstructures. The PDE cannot be utilized to reflect the efficiency of energy dissipation for microstructure evolution during the entire deformation process, but only to assess the efficiency of energy dissipation for microstructure evolution in a specific deformation parameter state.As a result, an integrated processing map is proposed to better study the hot workability of the LAHS steel, which considers the effects of instability factor(IF), PDE, and distribution and size of grains. The optimized processing parameters for the multi-pass deformation process are the deformation parameters of 1223–1318 K and 0.01–0.08 s^(-1). Complete dynamic recrystallization occurs within the optimized processing parameters with an average grain size of 18.36–42.3 μm. This study will guide the optimization of the forging process of heavy components.

Research and Application of Airflow Recirculation System in Working Faces with Leaking Airflow

In order to develop the technology of the controlled recircuIation of airflow in the world, some formulas about the airflow recirculation system in the working face with leaking airflow are deduced,which reduces the error between calculating and real values. on the base of the application of the formulas mentioned above, the problem about lack of airflow in the working face 2712 was solved successfully in Xiandewang Coal Mine.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Constructing Index System for Evaluating Work Level in Rural Endowment Insurance

This paper used fuzzy math principles, analyzed different aspects of the work of the current rural endowment insurance and formed the indices to assess the work level of the rural endowment insurance. After selecting the indices of the work level scientificly, constructed the index system to evaluate the work level of the rural endowment insurance and the index system was expressed in the form of math equation. In this way, an objective standard used to evaluate the work level of rural endowment insurance was establised.

Use of cognitive-behavioral career coaching to reduce work anxiety and depression in public employees

BACKGROUND Public employees worldwide are increasingly concerned about work anxiety and depression.Cognitive-behavioral career coaching has emerged as a promising strategy for addressing these mental health disorders,which can negatively impact on a person's overall well-being and performance.AIM To examine whether cognitive-behavioral career coaching reduces work anxiety and depression among Nigerian public employees.METHODS A total of 120 public employees(n=60)suffering from severe anxiety and depression were randomly assigned to the treatment or control groups in this study.Cognitive behavioral coaching was provided twice a week to those in the treatment group,whereas no treatment was given to those in the control group.As part of the study,the Hamilton Anxiety Rating Scales and Beck Depression Inventory were used to collect data.RESULTS Analysis of covariance of the data from participants indicates a significant effect of cognitive-behavioral career coaching on work anxiety and depression.CONCLUSION Insights into the underlying mechanisms by which cognitive behavior career coaching exerts its effects have been gained from this study.Also,the study has gathered valuable data that can inform future practice and guide the development of strategies for supporting mental health at work.