Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching

Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels.

Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training:A Flipped Classroom and Case-based Learning Exercise

Objective:The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences.This study aimed to determine the deciding factors that drive students'perceived advantages in class to improve precision education and the teaching model.Methods:A mixed strategy of an existing flipped classroom(FC)and a case-based learning(CBL)model was conducted in a medical morphology curriculum for 575 postgraduate students.The subjective learning evaluation of the individuals(learning time,engagement,study interest and concentration,and professional integration)was collected and analyzed after FC-CBL model learning.Results:The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model.Students felt more engaged by instructors in person and benefited in terms of time-saving,flexible arrangements,and professional improvement.Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories:1)advancing a guideline of precision teaching according to individual characteristics;2)revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course;3)understanding the perceived advantages and their interfaces;and 4)barriers and/or improvement to implement the FC-CBL model in the Research Design class,such as a richer description of e-learning and hands-on practice.Conclusion:Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

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.

A case-based reasoning method of recognizing liquefaction pits induced by 2021 M_(W) 7.3 Madoi earthquake

Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits based on high-resolution satellite images.Therefore,we provide a framework for extracting liquefaction pits based on a case-based reasoning method.Furthermore,five covariates selection methods were used to filter the 11 covariates that were generated from high-resolution satellite images and digital elevation models(DEM).The proposed method was trained with 450 typical samples which were collected based on visual interpretation,then used the trained case-based reasoning method to identify the liquefaction pits in the whole study area.The performance of the proposed methods was evaluated from three aspects,the prediction accuracies of liquefaction pits based on the validation samples by kappa index,the comparison between the pre-and post-earthquake images,the rationality of spatial distribution of liquefaction pits.The final result shows the importance of covariates ranked by different methods could be different.However,the most important of covariates is consistent.When selecting five most important covariates,the value of kappa index could be about 96%.There also exist clear differences between the pre-and post-earthquake areas that were identified as liquefaction pits.The predicted spatial distribution of liquefaction is also consistent with the formation principle of liquefaction.

Temperature Prediction of Aluminum Alloy Work-Pieces in Aging Furnaces Based on Improved Case-Based Reasoning

The temperature of aluminum alloy work-pieces in the aging furnace directly affects the quality of aluminum alloy products. Since the temperature of aluminum alloy work-pieces cannot be measured directly, a temperature prediction model based on improved case-based reasoning (CBR) method is established to realize the online measurement of the work-pieces temperature. More specifically, the model is constructed by an advanced case-based reasoning method in which a state transition algorithm (STA) is firstly used to optimize the weights of feature attributes. In other words, STA is utilized to find the suitable attribute weights of the CBR model that can improve the accuracy of the case retrieval process. Finally, the CBR model based on STA (STCBR) was applied to predict the temperature of aluminum alloy work-pieces in the aging furnace. The results of the experiments indicated that the developed model can realize high-accuracy prediction of work-pieces temperature and it has good application prospects in the industrial field.

Formulation of Work-Study Combined and Result-Oriented Integrated Curriculum Standards

According to the Annex Technical Regulations for Integrated Curriculum Development(Trial)in Document No.30 of the General Office of the Ministry of Human Resources and Social Security(2012),this paper studies the formulation of the curriculum standards for the integration of Chinese medicinal materials production.We focus on the formulation ideas of the curriculum standards for the integration of Chinese medicinal materials production,the formulation process of the curriculum standards for the integration of Chinese medicinal materials production,including the description of typical work tasks,the determination of curriculum objectives,the analysis of study content,the description of referential study tasks,teaching implementation suggestions,assessment and evaluation suggestions,which can provide a reference for the development and research of other related integrated courses.

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.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

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.

Research of Genetic Training Algorithm for Identifying Mechanical Failure Modes within the Framework of Case-Based Reasoning

The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.

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.

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.

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.

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.

Determinants of Satisfaction at Work and Its Reflections on Performance

This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.

The action mechanism of the work done by the electric field force on moving charges to stimulate the emergence of carrier generation/recombination in a PN junction

It is discovered that the product of the current and the electric field in a PN junction should be regarded as the rate of work(power)done by the electric field force on moving charges(hole current and electron current),which was previously misinterpreted as solely a Joule heating effect.We clarify that it is exactly the work done by the electric field force on the moving charges to stimulate the emergence of non-equilibrium carriers,which triggers the novel physical phenomena.As regards to Joule heat,we point out that it should be calculated from Ohm’s law,rather than simply from the product of the current and the electric field.Based on this understanding,we conduct thorough discussion on the role of the electric field force in the process of carrier recombination and carrier generation.The thermal effects of carrier recombination and carrier generation followed are incorporated into the thermal equation of energy.The present study shows that the exothermic effect of carrier recombination leads to a temperature rise at the PN interface,while the endothermic effect of carrier generation causes a temperature reduction at the interface.These two opposite effects cause opposite heat flow directions in the PN junction under forward and backward bias voltages,highlighting the significance of managing device heating phenomena in design considerations.Therefore,this study possesses referential significance for the design and tuning on the performance of piezotronic devices.

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.

Work Ability Index and Work Ability Score: A Comparation between both Scores in a Persistent COVID-19 Cohort

Aims: The present study aims to compare the assessment of work ability based on the use of the Work Ability Index (WAI) with another questionnaire base only on the use of WAI’s first item, termed as the “Work Ability Score” (WAS). Study design: A cohort of 384 Spanish workers included in a Post COVID-19 condition or persistent COVID-19 multicenter research was utilized. Place and Duration of Study: This cohort was enlisted in four hospitals (Hospital Universitario 12 de Octubre, Madrid;Hospital Universitario Virgen Macarena, Sevilla, Andalucía;Hospital Universitario Gregorio Marañón, Madrid and Complejo Asistencial Universitario de Salamanca, Castilla y León), since 2021 until 2022. Methodology: 384 Spanish workers (176 men and 208 women;aged 20 to 70 years) with Post COVID-19 condition or persistent COVID-19 were included. Descriptive analysis of primary scores was conducted. Given the non-normal distribution of data, the Mann-Whitney and Kruskal-Wallis tests were employed. Spearman and Kendall correlations were employed to assess the relationship between WAI and WAS, also used weighted Kappa to estimate the degree agreement between WAI and WAS. Logistic regression models were utilized to study determinants influencing WAI and WAS, categorized as poor or moderate. Results: WAI had an average score of 32.98 (SD = 10.28), whereas WAS had an average of 5.95 (SD = 2.77). Significant differences were observed in both WAI and WAS across the same variables. Strong and statistically significant correlations were evident between WAI and WAS (rs = 0.83, p < 0.001). All the variables used in the logistic regression model (gender, the sector employment, and previous chronic diseases) were statistically significant in both questionnaires. Conclusion: WAS questionnaire could be used as a tool for reliable assessment of work ability among Spanish workers with Post COVID-19 condition or Persistent CO-VID-19.

Eight of ten patients return to daily activities, work, and sports after total knee arthroplasty

BACKGROUND Besides return to work(RTW)and return to sports(RTS),patients also prefer to return to daily activities(RTA)such as walking,sleeping,grocery shopping,and domestic work following total knee arthroplasty(TKA).However,evidence on the timelines and probability of patients’RTA is sparse.AIM To assess the percentage of patients able to RTA,RTW,and RTS after TKA,as well as the timeframe and influencing factors of this return.METHODS A retrospective cohort study with prospectively collected data was conducted at a medium-sized Dutch orthopedic hospital.Assessments of RTA,RTW,and RTS were performed at 3 mo and/or 6 mo following TKA.Investigated factors en-compassed patient characteristics,surgical characteristics,and preoperative patient-reported outcomes.RESULTS TKA patients[n=2063;66 years old(interquartile range[IQR]:7 years);47%male;28 kg/m2(IQR:4 kg/m2)]showed RTA ranging from 28%for kneeling to 94%for grocery shopping,with 20 d(IQR:27 d)spent for putting on shoes to 74 d(IQR:57 d)for kneeling.RTW rates varied from 62%for medium-impact work to 87%for low-impact work,taking 33 d(IQR:29 d)to 78 d(IQR:55 d).RTS ranged from 48%for medium-impact sports to 90%for low-impact sports,occurring within 43 d(IQR:24 d)to 90 d(IQR:60 d).One or more of the investigated factors influenced the return to each of the 14 activities examined,with R²values ranging from 0.013 to 0.127.CONCLUSION Approximately 80%of patients can RTA,RTW,and RTS within 6 mo after TKA.Return is not consistently in-fluenced by predictive factors.Results help set realistic pre-and postoperative expectations.