Influence of canopy ratio of powered roof support on longwall working stability——A case study

The case study describes longwall coal seam A in a hard coal mine,where longwall coal face stability loss and periodic roof fall occurrences had been registered.The authors have attempted to explain the situation based on in-situ measurements and observations of the longwall working as well as numerical simulation.The calculations included several parameters,such as powered roof support geometry in the form of the canopy ratio,which is a factor that influences load distribution along the canopy.Numerical simulations were realized based on a rock mass model representing realistic mining and geological conditions at a depth of 600 m below surface for coal seam A.Numerical model assumptions are described,while the obtained results were compared with the in-situ measurements.The conclusions drawn from this work can complement engineering knowledge utilized at the stage of powered roof support construction and selection in order to improve both personnel safety and longwall working stability,and to achieve better extraction.

The Impact of Nursing Staff’s Work Attitude on the Fear of Patients Recovering from Benign Tumors:Family Support as a Mediating Variable

The perception of nursing staff’s attitude influences patient fear.Understanding this dynamic is crucial for fostering a supportive environment conducive to patient well-being and effective healthcare practices.The purpose of this research is to investigate how the attitudes and behaviours of nursing staff influence the fear and anxiety levels of patients recovering from benign tumors,aiming to improve patient care and recovery outcomes.Data was collected from a sample of 100 participants,comprising 20 nursing staff and 80 patients recovering from benign tumors.Surveys were administered to gather quantitative data on attitudes and fear levels.Participants were selected randomly from hospital records and outpatient clinics.Our analysis encompassed nursing staff attitude,patient fear levels,the influence of family support,progression of tumor recovery,patient-reported satisfaction,and the quality of healthcare services provided.The quantitative aspect utilized PLS-SEM software to perform regression analysis,evaluating both direct and indirect effects.Statistical analysis assessed the relationships between nursing staff attitudes,patient fear during benign tumor recovery,and the mediating role of family support.The findings of the study demonstrate that better nurse attitudes(Hypothesis 1,β=0.45,p<0.001)and stronger family support(Hypothesis 2,β=0.32;p<0.001) are linked to lower levels of patient fear.Partially mediating the relationship between nurse attitudes and patient fear,according to Hypothesis 3(β=0.28,p<0.002),is family support.Patients’perceptions of family support are highly influenced by nursing behaviour,as demonstrated by Hypothesis 4(β=0.38;p<0.001).Our research showed a strong relationship between the attitudes of nursing personnel and patient fear levels.Family support demonstrated a strong mediating effect on patient fear.Patient-reported satisfaction is positively correlated with family support.However,no significant relationship was found between healthcare service quality and patient fear.

The impact of work support and organizational career growth on nurse turnover intention in China

Objective:The paper aimed to explore the impact of work support and potential for career advancement on the nurse turnover intention in order to find an effective approach to curb high turnover.Methods:A cross-sectional survey of 526 nurses from eight teaching hospitals in Tianjin,China,was conducted to test the hypothesized multilevel model.The organizational career growth scale,work support scale,and the nurse turnover intention scale were used to measure the effect of organizational career growth and work support on nurse turnover intention.Finally,SPSS 17.0 and AMOS 17.0 software were used to analyse the relationship of these variables.Results:The score of the three scales nurse turnover,organizational career growth,and work support were 1.98±0.60,2.40±0.50,and 4.06±0.98,respectively.The total and direct effects of work support on turnover intention were
0.361(p<0.01)and
0.147(p<0.01),respectively.The indirect effect was
0.169 with a 95%bootstrap confidence interval of
0.257 to
0.102.Conclusion:The study showed a lack of work support negatively and directly associated with nurse turnover intention.Additional opportunities for career growth within the organization may strengthen the effect of work support and consequently increase the retention of qualified nursing staff.

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.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

A Method of Shield Attitude Working Condition Classification

Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.

Research and Design of IPv6 Network Management and Operations Support System

IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet and relevant equipment interfaces are not perfect. A Network Management System (NMS) at the network layer helps implement the integrated management of a network with equipment from multiple vendors, including the network resources and topology, end-to-end network performance, network failures and customer Service Level Agreement (SLA) management. Though the NMS will finally realize pure IPv6 network management, it must be accommodated to the management of relevant IPv4 equipment. Therefore, modularized and layered structure is adopted for the NMS in order to implement its smooth transition.

Working-memory training improves developmental dyslexia in Chinese children

Although plasticity in the neural system underlies working memory, and working memory can be improved by training, there is thus far no evidence that children with developmental dyslexia can benefit from working-memory training. In the present study, thirty dyslexic children aged 8-11 years were recruited from an elementary school in Wuhan, China. They received working-memory training including training in visuospatial memory, verbal memory, and central executive tasks. The difficulty of the tasks was adjusted based on the performance of each subject, and the training sessions lasted 40 minutes per day, for 5 weeks. The results showed that working-memory training significantly enhanced performance on the nontrained working memory tasks such as the visuospatial, the verbal domains, and central executive tasks in children with developmental dyslexia. More importantly, the visual rhyming task and reading fluency task were also significantly improved by training. Progress on working memory measures was related to changes in reading skills. These experimental findings indicate that working memory is a pivotal factor in reading development among children with developmental dyslexia, and interventions to improve working memory may help dyslexic children to become more proficient in reading.

基于Cosmos/Works液压支架整架有限元分析

对适用于大倾角的液压支架优化设计技术进行研究,在对液压支架产品建立虚拟样机模型的基础上,按照MT312-2000试验标准,通过Cosmos/Works软件,主要分析支架在顶梁扭转载荷工况下的有限元分析,为支架结构的优化设计分析提供了参考。

Co-effect of Demand-control-support Model and Effort-reward Imbalance Model on Depression Risk Estimation in Humans: Findings from Henan Province of China

Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used respectively. Methods A total of 3 632 males and 1 706 females from 13 factories and companies in Henan province were recruited in this cross-sectional study. Perceived job stress was evaluated with the Job Content Questionnaire and Effort-Reward Imbalance Questionnaire （Chinese version）. Depressive symptoms were assessed by using the Center for Epidemiological Studies Depression Scale （CES-D）. Results DC （demands/job control ratio） and ERI were shown to be independently associated with depressive symptoms. The outcome of low social support and overcommitment were similar. High DC and low social support （SS）, high ERI and high overcommitment, and high DC and high ERI posed greater risks of depressive symptoms than each of them did alone. ERI model and SS model seem to be effective in estimating the risk of depressive symptoms if they are used respectively. Conclusion The DC had better performance when it was used in combination with low SS. The effect on physical demands was better than on psychological demands. The combination of DCS and ERI models could improve the risk estimate of depressive symptoms in humans.

Factors associated with financial support in old age for young and middle-aged rural residents in China

This study examined the factors associated with financial support in old age from three primary mechanisms-personal savings,family support,and social insurance-to explore the implications for the future development of China's rural social welfare system.Cross-sectional surveys of 1392 young and middle-aged rural residents were conducted in July and August2012.The results showed that while social insurance was increasingly acceptable,personal savings and family support still had fundamental value.Combining the three mechanisms,the rural old-age welfare system presented nontraditional features.China's new rural endowment insurance is discussed as a means to address the need for financial support among the rural aging population.

基于SolidWorks的液压支架模型运动仿真与分析

液压支架广泛用于综合机械化采煤中,是综采工作面的重要支护设备之一。利用Solid Works三维造型软件对ZY320型液压支架进行建模,利用COSMOS Motion仿真模块对液压支架升、降、推、移4个运动过程进行运动仿真及分析,以三维动画的形式直观地展示出来。其仿真结果对液压支架的设计具有一定的指导意义。

Work-Family Conflict in Nursing: The Role of Work Schedules, Familial Antecedents and Emotional Regulation

This paper explores the influences of Emotional Regulation (ER) and work schedules on work-family conflict (WFC) among Italian nurses, also accounting for some familial variables. The data used in this study come from a survey conducted on 191 nurses working in two public hospitals of Tuscany (Italy). Stepwise multiple regressions were applied to examine the relationships among these variables, using the WFC as dependent variable. We found that some work related dimensions had direct effects on WFC outcomes;however, these impacts on the criterion variables are modified by the effects exerted by specific ER strategies.

Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving environments.The pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow applications.Since the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered performance.Though a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected drastically.Hence rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workflows.This work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efficient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications.The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows.The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

Analysis and control of hydraulic support stability in fully-mechanized longwall face to the dip with great mining height

The working condition of the hydraulic support in working face can be divided into three kinds of situations in the following： roof fall and col,lapse with cavity, advancing support and supporting. Took single support with four-pole in Iongwall face to the dip as research object, control method was studied to avoid support instability in three situations mentioned above. Based on these researches, the major factors of influencing on support stability and its controlling measures were put forward. According to specific conditions of working face 1215（3）, which is fully-mechanized and Iongwall face to the dip with great mining height in Zhangji Coal Mine, Huainan Mining Group, the effective measures was taken to control supports stability..

A Fast Algorithm for Training Large Scale Support Vector Machines

The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.

工作不安全感如何影响工作-生活平衡?——一个被调节的链式中介模型

基于资源保存理论,考察工作不安全感对工作-生活平衡的影响机制。对346名企业员工进行两阶段纵向追踪调查,结果表明:(1)工作不安全感对工作-生活平衡有直接负向影响;(2)工作认知失败和消极工作反刍在工作不安全感对工作-生活平衡的负面影响间起到中介作用以及链式中介作用;(3)主管支持感强化工作认知失败与消极工作反刍之间的关系从而调节以上链式中介。研究结果有助于为改善由工作不安全感引发的工作和生活失衡提供启发和对策。