A Study on the Work Process-Based Practical Training Model for Basic Nursing Skills in Vocational Colleges

Objective:To explore and analyze the work process-based practical training teaching model for basic nursing skills in vocational colleges and its implementation effects.Methods:A total of 82 nursing students from our school were selected for the study,which was conducted from April 2023 to April 2024.Using a random number table method,the students were divided into an observation group and a control group,each with 41 students.The control group received conventional practical training teaching,while the observation group followed the work process-based practical training model for basic nursing skills.The assessment scores and teaching satisfaction of the two groups were compared.Results:The comparison of assessment scores showed that the observation group performed significantly better than the control group(P<0.05).The comparison of teaching satisfaction also indicated that the observation group had significantly higher satisfaction than the control group(P<0.05).Conclusion:The work process-based practical training teaching model for basic nursing skills in vocational colleges can improve students’assessment scores and enhance teaching satisfaction,demonstrating its value for wider application.

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.

Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

Relationship between physical activity and specific working memory indicators of depressive symptoms in university students

BACKGROUND The detection rate of depression among university students has been increasing in recent years,becoming one of the main psychological diseases that endangers their physical and mental health.According to statistics,self-harm and suicide,for which there is no effective intervention,are the second leading causes of death.AIM To explore the relationship between different elements and levels of physical activity and college students’depression-symptom-specific working memory indicators.METHODS Of 143 college students were analyzed using the Beck Depression Self-Rating Scale,the Physical Activity Rating Scale,and the Working Memory Task.RESULTS There was a significant difference between college students with depressive symptoms and healthy college students in completing verbal and spatial working memory(SWM)tasks correctly(all P<0.01).Physical Activity Scale-3 scores were significantly and positively correlated with the correct rate of the verbal working memory task(r=0.166)and the correct rate of the SWM task(r=0.210)(all P<0.05).There were significant differences in the correct rates of verbal and SWM tasks according to different exercise intensities(all P<0.05)and different exercise durations(all P<0.05),and no significant differences in the correct rates of verbal and SWM tasks by exercise frequency(all P>0.05).CONCLUSION An increase in physical exercise among college students,particularly medium-and high-intensity exercise and exercise of 30 min or more,can improve the correct rate of completing working memory tasks.

A Review on Technologies for the Use of CO2 as a Working Fluid in Refrigeration and Power Cycles

The use of carbon dioxide as a working fluid has been the subject of extensive studies in recent years, particularly in the field of refrigeration where it is at the heart of research to replace CFC and HCFC. Its thermodynamic properties make it a fluid of choice in the efficient use of energy at low and medium temperatures in engine cycles. However, the performance of transcritical CO2 cycles weakens under high temperature and pressure conditions, especially in refrigeration systems;On the other hand, this disadvantage becomes rather interesting in engine cycles where CO2 can be used as an alternative to the organic working fluid in small and medium-sized electrical systems for low quality or waste heat sources. In order to improve the performance of systems operating with CO2 in the field of refrigeration and electricity production, research has made it possible to develop several concepts, of which this article deals with a review of the state of the art, followed by analyzes in-depth and critical of the various developments to the most recent modifications in these fields. Detailed discussions on the performance and technical characteristics of the different evolutions are also highlighted as well as the factors affecting the overall performance of the systems studied. Finally, perspectives on the future development of the use of CO2 in these different cycles are presented.

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.

Positive Effect of Transcranial Direct Current Stimulation on Visual Verbal Working Memory in Patients with Attention-Deficit/Hyperactivity Disorder

Background: Working memory is an executive function that plays an important role in many aspects of daily life, and its impairment in patients with attention-deficit/hyperactivity disorder (ADHD) affects quality of life. The dorsolateral prefrontal cortex (DLPFC) has been a good target site for transcranial direct current stimulation (tDCS) due to its intense involvement in working memory. In our 2018 study, tDCS improved visual-verbal working memory in healthy subjects. Objective: This study examines the effects of tDCS on ADHD patients, particularly on verbal working memory. Methods: We conducted an experiment involving verbal working memory of two modalities, visual and auditory, and a sustained attention task that could affect working memory in 9 ADHD patients. Active or sham tDCS was applied to the left DLPFC in a single-blind crossover design. Results: tDCS significantly improved the accuracy of visual-verbal working memory. In contrast, tDCS did not affect auditory-verbal working memory and sustained attention. Conclusion: tDCS to the left DLPFC improved visual-verbal working memory in ADHD patients, with important implications for potential ADHD treatments.

The Impact of the Digital Economy on Rural Residents’Working Hours:Mechanisms and Empirical Evidence

The rapid development of the digital economy has provided a new impetus for rural residents to extend their working hours.Based on the data collected by the China Labor-force Dynamics Survey(CLDS)in 2014,2016,and 2018,this paper measured the development level of the digital economy in China from the perspectives of internet development and digital financial inclusion,and tested the mechanisms of how the digital economy affected rural residents’working hours.The results showed that the digital economy extended rural residents’working hours by expanding information channels and enhancing human capital,and this mechanism was affected by heterogeneity in rural residents’educational background,age,and social capital.Building on these findings,this paper holds that to increase rural residents’income by extending their working hours and achieving common prosperity for all,it is necessary to expand the opportunities for rural residents to participate in skills training and promote their accumulation of human capital.

Feasibility of Synchronously Straightening Working Line of Adjacent Open-cast Mines

Taking Heidaigou Open-cast Mine and Haerwusu Open-cast Mine in Zhungeer mining area as the research objects,this paper deeply analyzes the current mining status of the two mines.Based on the existing working line length,mining technology,transportation system,equipment configuration,transportation access and many other factors,it systematically discusses the feasibility of straightening the working line of adjacent open-cast mines.Through in-depth theoretical analysis,it establishes the mathematical model of impact indicators and cost calculation,and it is concluded that the work line straightening can be technically achieved.However,from the perspective of economic benefits,straightening the working line does not show obvious advantages.

Text Difficulty,Working Memory Capacity and Mind Wandering During Chinese EFL Learners’Reading

This experimental study investigated how text difficulty and different working memory capacity(WMC)affected Chinese EFL learners’reading comprehension and their tendency to engage in task-unrelated thoughts,that is,mind wandering(MW),in the course of reading.Sixty first-year university non-English majors participated in the study.A two-factor mixed experimental design of 2(text difficulty:difficult and simple)×2(WMC:high/large and low/small)was employed.Results revealed that 1)the main and interaction effects of WMC and text difficulty on voluntary MW were significant,whereas those on involuntary MW were not;2)while reading the easy texts,the involuntary MW of high-WMC individuals was less frequent than that of low-WMC ones,whereas while reading the difficult ones,the direct relationship between WMC and involuntary MW was not found;and that 3)high-WMC individuals had a lower overall rate of MW and better reading performance than low-WMC individuals did,but with increasing text difficulty,their rates of overall MW and voluntary MW were getting higher and higher,and the reading performance was getting lower and lower.These results lend support to WM theory and have pedagogical implications for the instruction of L2 reading.

借助外力 让感觉升华为思维——基于“Working Model”对2023年新课标卷第6题的拓展分析

“Working Model”是一个物理仿真模拟平台,可直观展示动力学中的物理过程和运动图像,形成真实的感觉。该平台可用于辅助教学,帮助学生通过感觉、知觉、记忆、想象、思维等认知过程来理解和掌握物理概念和规律。以2023年新课标卷第6题为切入点,结合近三年全国高考真题为例,探讨借助外力将感觉升华为思维的可能性,通过对“Working Model”的拓展分析提出一种新的思维模式,旨在帮助学生通过直观刺激和辅助工具将感觉体验转化为深度思考和创新思维。并通过理论分析和实证研究,探讨这种思维模式的实施方法、可行性和效果,为教育教学和认知科学领域的研究提供新的思路和启示。

利用Working Model模拟探究普适性的杠杆平衡条件

学生在学习杠杆平衡条件后,通常会对真实情境中的杠杆平衡条件的普适性产生疑问.通过对Working Model软件的学习,建立杠杆模型、科学推理、提出猜想、设计实验、收集与分析数据等,深度经历科学探究的过程,学生不仅能收获知识和实验探究的能力,还能够促进其质疑创新等核心素养的发展.

Working Model在高中物理教学中的应用——以力学模型为例

基于Working Model的仿真实验功能,结合几个常见的力学运动模型展示Working Model在中学物理教学中的用途.利用Working Model将运动过程直观呈现,同时以数值、矢量、动态图像等输出形式将位移、速度和加速度等物理量同步反馈,以此进一步探究物理规律.

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

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.

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.

Determinants of Breastfeeding Practices among Working Mothers in Solwezi District of Zambia

Background: As one of the strategies to reduce infant morbidity and mortality, the World Health Organization recommends early initiation of exclusive breastfeeding and timely introduction of complementary feeding with continued breastfeeding for up to two years or beyond. Although breastfeeding is a natural process, it is influenced by different socio-cultural factors, habits, standards, and behaviours. Rapid industrialization and changes in lifestyle have seen increasing numbers of women in the labour force, and associated poor Exclusive Breastfeeding practices among working mothers. The aim of this study was to identify the determinants of breastfeeding practices among working mothers in Solwezi District of Zambia. Methods: This was a cross-sectional comparative survey comparing determinants of breastfeeding practices among working mothers aged 15 - 49 years in urban and rural areas of Solwezi District. The study was carried out in the Under-five Clinics at Solwezi, Kimasala and Mushitala Urban Clinics, as well as St. Francis, Kapijimpanga and Kisalala Rural Health Centers. Eighty-seven participants were successfully recruited using convenience sampling. A researcher-assisted questionnaire was used to collect data. Data analysis was done using SPSS version 27.0. Descriptive statistical analysis was done using numerical measures such as measures of central tendency and dispersion. The Pearson Chi Square test, correlation and linear regression analysis were performed to test association between the variables at the 0.05 level of significance. Are you assessing exclusive breastfeeding alone or what? Results: The mean age of respondents was 27.44 years (SD = 6.042 years) and the majority of the respondents were married (71.3%, n = 62). Forty-three percent of the respondents were self-employed while the rest were in formal government or private sector employment. Overall, 97.7% of the respondents had heard of exclusive breastfeeding, with health facilities being the most frequent source of information (64.4%). Attitude towards exclusive breastfeeding was generally positive. Fifty-eight percent of respondents exclusively breastfed while the rest were on mixed feeding. Seventy-one percent of respondents reported that their workplaces did not have breastfeeding facilities at work. Only 11.5% of the respondents reported having cultural beliefs restricting breastfeeding. Statistically significant associations were obtained from cross-tabulation analysis between respondent’s occupation and baby feeding type (p 0.001);workload and feeding type (0.024);as well as knowledge of EBF and feeding type (p 0.001). Conclusion: Majority of the respondents demonstrated good knowledge and positive attitude towards exclusive breastfeeding. This study recommends sensitization campaigns in communities to raise awareness about the importance of breastfeeding, and foster behaviour change that promotes breastfeeding.

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.

Online Peer Advice: Giving Advice on Chinese Social Networking Sites

This study investigates advice-giving in peer-to-peer comment sections on Chinese social networking sites. The results show that advice, explanation, experience, and assessment are the four most frequent discursive moves in Chinese advice given by peers on social networking sites. Involvement, mitigation, and face-threatening strategies are also utilized frequently by those peers to give directives and express empathy and solidarity on Chinese online sites, thereby building supportive relationships among them in online communities. The online peer advice-giving in this study is influenced by multiple aspects of the platform, participants, and language use, presenting distinctive characteristics that differ from other studies.

Research on the dynamic response of connecting rod bearing bush wear of reciprocating machine under variable working conditions

As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.