Asymptotic distributions of non-central studentized statistics

Let X 1, ..., X n be independent and identically distributed random variables and W n = W n (X 1, ..., X n ) be an estimator of parameter ?. Denote T n = (W n ? ? 0)/s n , where s n 2 is a variance estimator of W n . In this paper a general result on the limiting distributions of the non-central studentized statistic T n is given. Especially, when s n 2 is the jacknife estimate of variance, it is shown that the limit could be normal, a weighted χ 2 distribution, a stable distribution, or a mixture of normal and stable distribution. Applications to the power of the studentized U- and L- tests are also discussed.

扩展目标跟踪Student’s t逆Wishart平滑算法

脉冲干扰和离群量测信息等因素通常会导致异常的厚尾噪声,这使得以高斯假设为前提的扩展目标跟踪(ETT)估计器的性能急剧降低,针对该问题该文提出一种基于扩展目标随机矩阵模型(RMM)的Student’s t逆Wishart平滑(StIWS)算法。首先,将目标的运动状态以及过程噪声和量测噪声建模为Student’s t分布以表征异常噪声对扩展目标概率分布的影响,将目标扩展状态建模为服从逆Wishart分布的随机矩阵。然后,在Student’s t贝叶斯平滑框架下,详细推导了能在扩展目标的多重特征动态演变的过程中有效估计目标状态的StIWS算法。最后,通过扩展目标跟踪的仿真实验结果和真实场景实验结果验证了所提算法的有效性。

基于变分贝叶斯算法的多模型车载组合导航算法

在行车过程中强机动引起全球卫星导航系统(GNSS)量测噪声产生野值,表现出厚尾特性导致常规状态估计精度下降的问题,为此提出一种基于变分贝叶斯的SINS/GNSS组合导航信息融合方法。构建车载组合导航系统模型,采用Student’s t分布对量测异常情况下噪声建模,并用变分贝叶斯的方法对系统状态和隐变量进行求解,实现对模型参数的后验估计。针对城市行车存在GNSS测量失效的问题,利用交互式多模型算法实现了GNSS量测中断情况下的SINS/GNSS和SINS/OD子系统的动态交互融合。通过跑车实验进行验证,实验结果表明,所提算法可有效抑制GNSS量测野值噪声对SINS/GNSS/OD组合导航系统的影响,与传统交互式多模型算法相比,具有较高的精度和鲁棒性。

Evaluating the feasibility and preliminary effects of an online compassion training program for nursing students:A pilot randomized controlled trial

Objectives:This study aimed to assess the feasibility of an online compassion training program for nursing students and preliminarily investigate its effects on mindfulness,self-compassion,and stress reduction.Methods:This study employed a randomized controlled trial design.Second-year students from a nursing college in Guangzhou,China,were recruited as research participants in August 2023.The intervention group participated in an 8-week online compassion training program via the WeChat platform,comprising three stages:mindfulness(weeks 1e2),self-compassion(weeks 3e5),and compassion for others(weeks 6 e8).Each stage included four activities:psychoeducation,mindfulness practice,weekly diary,and emotional support.Program feasibility was assessed through recruitment and retention rates,program engagement,and participant acceptability.Program effectiveness was measured with the Mindful Attention Awareness Scale,Self-Compassion Scale-Short Form,and Perceived Stress Scale.Results:A total of 28 students completed the study(13 in the intervention group,15 in the control group).The recruitment rate was 36.46%,with a high retention rate of 93.3%.Participants demonstrated high engagement:69.2%accessed learning materials every 1e2 days,93.3%practiced mindfulness at least weekly,with an average of 4.69 diary entries submitted per person and 23.30 WeChat interactions with instructors.Regarding acceptability,all participants expressed satisfaction with the program,with 92.4%finding it“very helpful”or“extremely helpful.”In terms of intervention effects,the intervention group showed a significant increase in mindfulness levels from pre-intervention(51.54±10.93)to postintervention(62.46±13.58)(P<0.05),while no significant change was observed in the control group.Although there were no statistically significant differences between the two groups in post-intervention self-compassion and perceived stress levels,the intervention group showed positive trends:selfcompassion levels increased(35.85±8.60 vs.40.85±5.54),and perceived stress levels slightly decreased(44.77±8.65 vs.42.00±5.77).Conclusions:This pilot study demonstrated the feasibility of an online compassion training program for nursing students and suggested its potential effectiveness in enhancing mindfulness,self-compassion,and stress reduction.Despite limitations such as small sample size and lack of long-term follow-up,preliminary evidence indicates promising prospects for integrating such training into nursing education.Further research is warranted to confirm thesefindings and assess the sustained impact of this approach on nursing education and practice.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

Comparison of vergence mechanisms between university students with good and poor sleep quality

AIM:To compare the vergence mechanisms between good and poor sleepers in university students.METHODS:A total of 64 university students were recruited in this study.The validated Malay version of Pittsburgh Sleep Quality Index questionnaire(PSQI-M)was used to measure the participants’sleep quality over the past month.Participants were categorized as good sleepers(n=32)and poor sleepers(n=32)based on the PSQI-M scores.Heterophoria and fusional vergences were measured at distance and near.Mann-Whitney U test was used to compare heterophoria,negative fusional vergence(NFV),and positive fusional vergence(PFV)at distance and near between good and poor sleepers.Spearman correlation analysis was used to study the relationship between PSQI-M score and PFV at distance.RESULTS:Both distance and near heterophorias were not significantly different between good and poor sleepers(P>0.05).There was a difference in distance PFV(P<0.05)between good and poor sleepers,but not in distance NFV,near NFV,and near PFV(P>0.05).Distance PFV was negatively correlated with PSQI-M score(rs=-0.33,P<0.05).CONCLUSION:University students with poor sleep quality demonstrates a reduced ability to maintain fusion with increasing convergence demand at distance.Sleep quality assessment during binocular vision examination in university students is recommended.

Global prevalence and gender inequalities in at least 60 min of self-reported moderate-to-vigorous physical activity 1 or more days per week:An analysis with 707,616 adolescents

Background:Guidelines recommend that adolescents should accumulate an average of 60 min per day of moderate-to-vigorous physical activity(MVPA).However,using only this cut-off could hide important information.For instance,from a population-level point of view,increasing physical activity for those with no or low physical activity could provide more health benefits than increasing physical activity for those with intermediate levels.Also,including a more sensitive cut-point of≥1 days per week could be an additional strategy for identifying those with low access/opportunities for physical activity practice.Thus,the current study aims to estimate the prevalence of≥60 min of MVPA≥1 days per week among adolescents globally,and to describe any relevant gender inequalities.Methods:We used representative datasets from 146 countries/territories collected between 2003 and 2019.MVPA was self-reported.Participants were grouped into younger(≤14 years old)and older(>14 years old)adolescents.Crude Poisson regression models were used to identify the relative differences in≥60 min of MVPA≥1 days per week between boys and girls,and random-effects meta-analysis models were used to identify the pooled estimates.Analyses were stratified by country and region.Results:Approximately 80%of both younger and older adolescents reported≥60 min of MVPA≥1 days per week.This prevalence was≥94%in Europe and Central Asia and North America,while the estimates for the other regions were<77%.The prevalence of≥60 min of MVPA≥1 days per week was higher among boys than girls,with the largest differences occurring among the oldest adolescents(Prevalence ratio_(≤14y)=1.04(95%confidence interval(95%CI)):1.03-1.04)vs.Prevalence ratio_(>14y)=1.09(95%CI:1.08-1.10)).Conclusion:Approximately 8 out of 10 adolescents reported accumulating≥60 min of MVPA≥1 days per week,with notable differences between regions.Gender differences were observed in several countries,especially among the oldest adolescents.Priorities for physical activity promotion among adolescents should include increasing access/opportunities for physical activity among those who do not achieve≥60 min of MVPA≥1 days per week and reducing gender inequalities.

Attitude and willingness on gamete donation among medical students:An experience from a state university in Sri Lanka

Objective:To assess the attitude and willingness of medical students of the Faculty of Medicine,University of Jaffna,regarding gamete donation.Methods:An institutional-based descriptive cross-sectional study was conducted at the Faculty of Medicine,University of Jaffna,from September 2022 to May 2023 among undergraduate medical students who gave their voluntary participation.A self-administered questionnaire was used as a study instrument to collect data regarding their attitude and willingness toward gamete donation.Results:A total of 345 participants were recruited and their sociodemographic data revealed that 56.8%of the participants were female,62.3%aged between 26 and 30 years,and 92.2%were unmarried.Many of them received information regarding gamete donations during their clinical appointments.Over half(67.8%)of them showed a negative attitude towards gamete donation.Regarding willingness,only 39.7%of participants had a positive approach for being a gamete donor;among them,84.7%preferred anonymous donations.Religion and ethnicity had a significant influence on their attitudes and willingness.In addition,male was also found to be more willing to donate gametes.Conclusions:Most medical students have negative views about gamete donation.Imparting awareness and knowledge of assisted reproductive technology and gamete donation within medical students'sociocultural and ethical backgrounds might facilitate a change in attitude towards gamete donation amongst future medical practitioners.

Frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students

AIM:To investigate the frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students.METHODS:Totally 158 student volunteers underwent routine vision examination in the optometry clinic of Guangxi Medical University.Their data were used to identify the different types of accommodation and nonstrabismic binocular vision dysfunction and to determine their frequency.Correlation analysis and logistic regression were used to examine the factors associated with these abnormalities.RESULTS:The results showed that 36.71%of the subjects had accommodation and non-strabismic binocular vision issues,with 8.86%being attributed to accommodation dysfunction and 27.85%to binocular abnormalities.Convergence insufficiency(CI)was the most common abnormality,accounting for 13.29%.Those with these abnormalities experienced higher levels of eyestrain(χ2=69.518,P<0.001).The linear correlations were observed between the difference of binocular spherical equivalent(SE)and the index of horizontal esotropia at a distance(r=0.231,P=0.004)and the asthenopia survey scale(ASS)score(r=0.346,P<0.001).Furthermore,the right eye's SE was inversely correlated with the convergence of positive and negative fusion images at close range(r=-0.321,P<0.001),the convergence of negative fusion images at close range(r=-0.294,P<0.001),the vergence facility(VF;r=-0.234,P=0.003),and the set of negative fusion images at far range(r=-0.237,P=0.003).Logistic regression analysis indicated that gender,age,and the difference in right and binocular SE did not influence the emergence of these abnormalities.CONCLUSION:Binocular vision abnormalities are more prevalent than accommodation dysfunction,with CI being the most frequent type.Greater binocular refractive disparity leads to more severe eyestrain symptoms.

A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Preparedness,knowledge,and perception of nursing students about climate change and its impact on human health in India

Higher education plays an important role in all aspects of sustainability.Infusing climate change and sustainability into the curriculum is important to prepare future health-care professionals for climate change and sustainability actions to build resilient health systems.In this study,we evaluated the preparedness,knowledge,and perception regarding climate change and its impact on human health among Indian nursing students.A quantitative descriptive survey was conducted among 644 undergraduate nursing students studying in three institutes in the southern,northern,and western parts of India.We used a structured online questionnaire with established validity and reliability to collect data.The data were analyzed using Statistical Package for the Social Sciences version 26 software.The results showed that more than half of the students reported experiencing excessive increase(362;56.2%)and decrease(374;58.1%)in temperature in recent years.Though more than three quarters of the students(494;76.7%)accepted the general truth that climate change is mainly caused by human activities,only less than half of the students(309;48.0%)perceived the impact of climate change to be high.Three quarters of the students(483;75.0%)had a high level of knowledge regarding climate change.The students who had a high level of knowledge about climate change exerted a high perceived impact of climate change(χ^(2)=75.47;P<0.01).More than half of the students(50.9%)felt that they needed information about climate change to be included in the nursing curriculum,and only one sixth of the students(16.1%)were highly confident about engaging in climate change-related conversation with patients.Most of the students who participated in the study had basic knowledge about climate change and its impact on human health.However,there is an observed gap between knowledge and their preparedness and confidence to engage in climate change actions.Hence,we propose to strengthen the nursing curriculum by integrating various curricular and co-curricular activities related to climate change,so that future nurses are empowered to become climate change advocates.

Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

Implementation of an international medical student pen-pal club:survey outcomes of perceptions in cultural competency and global health

Background:Pen-pal clubs(PPC)are used worldwide for students to learn about different cultures and other skillsets without the need for travel.Many medical students are interested in global health opportunities abroad but costs,scheduling,and other barriers allow few to participate in such experiences.It is important that medical students have nuanced global medical perspectives and can contribute to the global medical community.Objective:The purpose of this study is to demonstrate that an international medical student PPC improves medical students'perspectives of cultural competency and global health engagement.Methods:In 2021,a novel medical student PPC was established that began between an American and Japanese medical school.Following a shareholders meeting,it was decided that the number of medical schools involved globally be expanded through previous institutional affiliations and online presences.In total,the club connected 50 American medical students and 52 medical students from 17 high-and middle-income countries.The primary form of communication was online;pen-pals were encouraged to communicate monthly using provided topics,although frequency and way of communication was their discretion.In February 2022,American PPC members were emailed a qualitative survey to assess the PPC's impact.Results:The survey was completed by 42%of American PPC members,95%of which were 22-26 years.Participants were preclinical medical students,60%whom were female and the majority either white(47%)or Asian(43%).Overall,the PPC positively influenced American medical students'perception of global medicine,medical education,and their cultural competency after joining the PPC compared to prior(P=0.004).Conclusion:PPCs encourage medical students to think from a global perspective and foster open-mindedness within varying social and cultural contexts.Having a global communication platform for students during medical school education may be an additional way to train aspiring global leaders.

Quran memorisation and heart rate variability:How do they correlate?

BACKGROUND Heart rate variability(HRV)was shown to be affected by performing religious activities.AIM To examine the relationship between the level of Quran(the holy book of Muslims)memorisation and HRV among teenagers.METHODS This experimental study included 16 Tahfiz students and 16 non-Tahfiz students(n=32).The HRV was measured in three tasks:Recalling familiar verses,memorising new verses,and recalling the newly memorised verses of the Quran.HRV analysis was done using these parameters:Standard deviation of N-N(heartbeat peak)interval;low frequency(LF);high frequency(HF)and LF/HF ratio.RESULTS There were significant differences between tasks for all parameters(P<0.05).However,between the groups,only the LF/HF ratio had significant differences,with F=5.04,P<0.05.Pearson correlation showed a moderate positive correlation between the number of pages memorised and the LF/HF ratio(r=0.61,P<0.05).CONCLUSION Quran memorisation increased the HRV and our results suggested that this activity could be developed as an effective sympathovagal modulation training activity.

Could school programs based on social-emotional learning prevent substance abuse among adolescents?

In this editorial,we comment on the article Adolescent suicide risk factors and the integration of social-emotional skills in school-based prevention programs by Liu et al.While the article focused on the issue of suicide and social-emotional learning programs as a possible intervention,we here discuss evidence of other reported outcomes and if it could be an effective way to prevent substance abuse among adolescents.

Mindfulness training in medical education as a means to improve resilience,empathy,and mental health in the medical profession

The high rates of depression,burnout,and increased risk of suicide among medical students,residents,and physicians in comparison with other careers signal a mental health crisis within our profession.We contend that this crisis coupled with the inadequate acquisition of interpersonal skills during medical education results from the interaction between a challenging environment and the mental capital of individuals.Additionally,we posit that mindfulness-based practices are instrumental for the development of major components of mental capital,such as resilience,flexibility of mind,and learning skills,while also serving as a pathway to enhance empathy,compassion,self-awareness,conflict resolution,and relational abilities.Importantly,the evidence base supporting the effectiveness of mindfulness-based interventions has been increasing over the years,and a growing number of medical schools have already integrated mindfulness into their curricula.While we acknowledge that mindfulness is not a panacea for all educational and mental health problems in this field,we argue that there is currently an unprecedented opportunity to gather momentum,spread and study mindfulness-based programs in medical schools around the world as a way to address some longstanding shortcomings of the medical profession and the health and educational systems upon which it is rooted.

Large multimodal models assist in psychiatry disorders prevention and diagnosis of students

Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for us to explore new me-thods and approaches concerning the prevention and treatment of mental illne-sses.Large multimodal models(LMMs),as the most advanced artificial intelligen-ce models(i.e.ChatGPT-4),have brought new hope to the accurate prevention,diagnosis,and treatment of psychiatric disorders.The assistance of these models in the promotion of mental health is critical,as the latter necessitates a strong foundation of medical knowledge and professional skills,emotional support,stigma mitigation,the encouragement of more honest patient self-disclosure,reduced health care costs,improved medical efficiency,and greater mental health service coverage.However,these models must address challenges related to health,safety,hallucinations,and ethics simultaneously.In the future,we should address these challenges by developing relevant usage manuals,accountability rules,and legal regulations;implementing a human-centered approach;and intelligently upgrading LMMs through the deep optimization of such models,their algorithms,and other means.This effort will thus substantially contribute not only to the maintenance of students’health but also to the achievement of global sustainable development goals.

Using ChatGPT to promote college students’participation in physical activities and its effect on mental health

As one of the most famous large language models,ChatGPT has great potential for application in physical education.It can provide personalized exercise plans,a variety of exercise options,and interactive support.The integration of ChatGPT into the teaching process can promote college students’participation in physical activities and improve their mental health while expanding the traditional teaching environment and promoting the reform of traditional teaching methods.However,the application of ChatGPT faces challenges and obstacles in physical education.To make full use of ChatGPT in physical education,it can be combined with wearable devices and sports equipment to enhance the efficiency of interactions with users.Relevant policies are urgently needed to avoid the improper use of users’data.

The Effect of Procrastination on Physical Exercise among College Students—The Chain Effect of Exercise Commitment and Action Control

Background:Exercise procrastination is prevalent among college students,causing decline in physical fitness.It is imperative to investigate the mechanism affecting college students’physical activity behaviors.This study was aimed at investigating the effect of procrastination on college students’physical exercise behavior,and the chain mediation effects of exercise commitment and action control(AC),to provide a theoretical basis for interventions targeting physical exercise behavior among college students.Methods:A questionnaire survey was conducted using convenience sampling.The General Procrastination Scale,Exercise Commitment Scale,Action Control Scale,and Physical Activity Rating Scale-3 questionnaires were used.Participants were 581 college students(age 19.27±0.94 years;243 males and 338 females).Statistical methods of regression analysis and structural equation modeling(SEM)were applied.Results:Procrastination,exercise commitment,and action control were found to be significant predictors of physical exercise behavior.Among these predictors,exercise commitment and action control showed full mediation effects in the relationship between procrastination and physical exercise behavior,and explained 25.48%and 30.77%of the total variance,respectively.The chain mediation effect of exercise commitment-action control was significant,accounting for 22.60%of the total variance,and the total indirect effect was 79.33%.Conclusion:Therefore,higher procrastination was associated with less participation in physical exercise behavior among college students.Improvements in exercise commitment and volitional decision-making ability for physical exercise behavior promoted physical exercise behavior,and increased exercise commitment promoted volitional decision-making ability among the students.The chain reaction effect of exercise commitment and action control also buffered the negative effects of procrastination on physical exercise behavior,thereby increasing physical exercise behavior among college students.

量测随机丢失下基于容积卡尔曼滤波的厚尾噪声处理方法

针对量测随机丢失和厚尾量测噪声条件下的非线性状态估计易发散问题,提出了一种新的非线性卡尔曼滤波方法。引入服从Gamma分布的辅助参数,将厚尾量测噪声建模为Student’s t分布,以解决厚尾噪声导致的状态估计易发散问题,并采用服从Benroulli分布的随机变量来描述量测信号随机丢失的现象;在量测随机丢失下,基于目标状态和未知参数建立联合后验分布,并使用变分贝叶斯方法,联合估计系统状态、量测丢失概率和未知的厚尾噪声。非线性目标跟踪仿真实验表明,提出的算法可自适应估计未知的量测丢失概率,在野值概率为5%的条件下,算法目标跟踪的位置、速度和转动速率均方根误差分别为对比算法的37%、28%和60%;在野值概率为10%的条件下,其他算法均出现了发散现象,而提出的算法依然能够以较低的误差跟踪目标,体现了所提算法良好的鲁棒性和优越性。