The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several in...The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.展开更多
Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the un...Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.展开更多
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to i...BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity.Using interventions by way of virtual reality(VR)could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.This could also aid staff to and change current RPs.AIM To assess the feasibility and effectiveness of using a VR platform to provide reduction in RP training.METHODS A randomised controlled feasibility study,accompanied by evaluations at 1 month and 6 months,was conducted within inpatient psychiatric wards at Southern Health National Health Service Foundation Trust,United Kingdom.Virti VR scenarios were used on VR headsets to provide training on reducing RPs in 3 inpatient psychiatric wards.Outcome measures included general self-efficacy scale,generalised anxiety disorder assessment 7(GAD-7),Burnout Assessment Tool 12,the Everyday Discrimination Scale,and the Compassionate Engagement and Action Scale.RESULTS Findings revealed statistically significant differences between the VR and treatment as usual groups,in the Everyday Discrimination Scale items Q8 and Q9:P=0.023 and P=0.040 respectively,indicating higher levels of perceived discrimination in the VR group.There were no significant differences between groups in terms of general self-efficacy,generalised anxiety disorder assessment 9,and Burnout Assessment Tool 12 scores.A significant difference was observed within the VR group for compassionate engagement from others(P=0.005)over time.Most respondents recorded System Usability Scale scores above 70,with an average score of 71.79.There was a significant reduction in rates of RPs in the VR group vs treatment as usual group with a fluctuating variability observed in the VR group likely due to external factors not captured in the study.CONCLUSION Ongoing advancement of VR technology enables the possibility of creating scenarios and simulations tailored to healthcare environments that empower staff by providing more comprehensive and effective training for handling situations.展开更多
In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>...In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>(N+α)/N,λis a postive parameter and Rαstands for the Green's function of the discrete fractional Laplacian,which has no singularity at the origin but has same asymptotics as the Riesz potential at infinity.Under some assumptions onα(x),we establish the existence and asymptotic behavior of ground state solutions for the nonlinear Choquard equation by the method of Nehari manifold.展开更多
For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The propose...For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.展开更多
BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,wh...BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,who are expected to have a better prognosis,often experience recurrence.Thus,it is important to identify the risk factors for early recurrence and to develop an adequate treatment plan.AIM To evaluate the predictive factors associated with the early recurrence of earlystage PDAC.METHODS This study enrolled 407 patients with stage I PDAC undergoing upfront surgical resection between January 2000 and April 2016.Early recurrence was defined as a diagnosis of recurrence within 6 mo of surgery.The optimal cutoff values were determined by receiver operating characteristic(ROC)analyses.Univariate and multivariate analyses were performed to identify the risk factors for early recurrence.RESULTS Of the 407 patients,98 patients(24.1%)experienced early disease recurrence:26(26.5%)local and 72(73.5%)distant sites.In total,253(62.2%)patients received adjuvant chemotherapy.On ROC curve analysis,the optimal cutoff values for early recurrence were 70 U/mL and 2.85 cm for carbohydrate antigen 19-9(CA 19-9)levels and tumor size,respectively.Of the 181 patients with CA 19-9 level>70 U/mL,59(32.6%)had early recurrence,compared to 39(17.4%)of 226 patients with CA 19-9 level≤70 U/mL(P<0.001).Multivariate analysis revealed that CA 19-9 level>70 U/mL(P=0.006),tumor size>2.85 cm(P=0.004),poor differentiation(P=0.008),and non-adjuvant chemotherapy(P=0.025)were significant risk factors for early recurrence in early-stage PDAC.CONCLUSION Elevated CA 19-9 level(cutoff value>70 U/mL)can be a reliable predictive factor for early recurrence in early-stage PDAC.As adjuvant chemotherapy can prevent early recurrence,it should be recommended for patients susceptible to early recurrence.展开更多
Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offer...Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.展开更多
We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models...We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.展开更多
The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on...The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.展开更多
BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.T...BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.The use of CBT for severe mental illness is now more common globally.AIM To investigate the feasibility and acceptability of a culturally adapted,CBT-based,third-wave therapy manual using the Comprehend,Cope,and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.METHODS A pilot study was used to assess the feasibility and acceptability of the manualised intervention.Outcome measures were evaluated at baseline,post-intervention and 12 wk-follow up.32 participants with mental health conditions aged 20-53 years were recruited.Assessments were completed at three time points,using Clinical Outcomes in Routine Evaluation(CORE),Hospital Anxiety and Depression Scale(HADS),Bradford Somatic Inventory and World Health Organization Disability Assessment Schedule 2.0(WHODAS).The Patient Experience Questionnaire was completed post-treatment.RESULTS Repeated measures of analysis of variance associated with HADS depression,F(2,36)=12.81,P<0.001,partialη^(2)=0.42 and HADS anxiety scores,F(2,26)=9.93,P<0.001,partialη^(2)=0.36;CORE total score and WHODAS both showed significant effect F(1.25,18.72)=14.98,P<0.001,partialη^(2)=0.5.and F(1.29,14.18)=6.73,P<0.001,partialη^(2)=0.38 respectively.CONCLUSION These results indicate the effectiveness and acceptability of the culturally adapted,CBT-based,third-wave therapy manual intervention among minoritized groups with moderate effect sizes.Satisfaction levels and acceptability were highly rated.The viability and cost-effectiveness of this approach should be explored further to support universal implementation across healthcare systems.展开更多
The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sum...The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment condition.Our proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other applications.Our main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.展开更多
Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the fie...Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.展开更多
Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically ...Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.展开更多
1st cases of COVID-19 were reported in March 2020 in Bangladesh and rapidly increased daily. So many steps were taken by the Bangladesh government to reduce the outbreak of COVID-19, such as masks, gatherings, local m...1st cases of COVID-19 were reported in March 2020 in Bangladesh and rapidly increased daily. So many steps were taken by the Bangladesh government to reduce the outbreak of COVID-19, such as masks, gatherings, local movements, international movements, etc. The data was collected from the World Health Organization. In this research, different variables have been used for analysis, for instance, new cases, new deaths, masks, schools, business, gatherings, domestic movement, international travel, new test, positive rate, test per case, new vaccination smoothed, new vaccine, total vaccination, and stringency index. Machine learning algorithms were used to predict and build the model, such as linear regression, K-nearest neighbours, decision trees, random forests, and support vector machines. Accuracy and Mean Square error (MSE) were used to test the model. A hyperparameter was also applied to find the optimum values of parameters. After computing the analysis, the result showed that the linear regression algorithm performs the best overall among the algorithms listed, with the highest testing accuracy and the lowest RMSE before and after hyper-tuning. The highest accuracy and lowest MSE were used for the best model, and for this data set, Linear regression got the highest accuracy, 0.98 and 0.97 and the lowest MSE, 4.79 and 4.04, respectively.展开更多
In a one-way analysis-of-variance(ANOVA) model,the number of pairwise comparisons can become large even with a moderate number of groups.Motivated by this,we consider a regime with a growing number of groups and prove...In a one-way analysis-of-variance(ANOVA) model,the number of pairwise comparisons can become large even with a moderate number of groups.Motivated by this,we consider a regime with a growing number of groups and prove that,when testing pairwise comparisons,the Benjamini-Hochberg(BH) procedure can asymptotically control false discoveries,despite the fact that the involved t-statistics do not exhibit the wellknown positive dependence structure required for exact false discovery rate(FDR) control.Following Tukey's perspective that the difference between the means of any two groups cannot be exactly zero,our main result provides control over the directional false discovery rate and directional false discovery proportion.A key technical contribution of our work is demonstrating that the dependence among the t-statistics is sufficiently weak to establish the convergence result typically required for asymptotic FDR control.Our analysis does not rely on conventional assumptions such as normality,variance homogeneity,or a balanced design,thereby offering a theoretical foundation for applications in more general settings.展开更多
Let X_(1),X_(2),...,Xn be independent and identically distributed random vectors,T_n=T_n(X_(1),X_(2),...,X_(n))be a degenerate U-statistic,and△_(n)=△_(n)(X_(1),X_(2),...,X_(n))be a remainder term.In this paper,we es...Let X_(1),X_(2),...,Xn be independent and identically distributed random vectors,T_n=T_n(X_(1),X_(2),...,X_(n))be a degenerate U-statistic,and△_(n)=△_(n)(X_(1),X_(2),...,X_(n))be a remainder term.In this paper,we establish a Berry-Esseen-type theorem for T_(n)+△_(n)by an exchangeable pair approach.As an application,a sharp error bound of normal distribution approximation for the distance correlation is obtained,which improves some results in Gao et al.(2021).展开更多
The Waring distribution is an important two-parameter discrete distribution,commonly used in fields such as ecology,linguistics,and information science,where heavy tails are often observed.In this paper,we propose a n...The Waring distribution is an important two-parameter discrete distribution,commonly used in fields such as ecology,linguistics,and information science,where heavy tails are often observed.In this paper,we propose a new goodness-of-fit test for the Waring distribution,which is established through the hazard rate and a linear equivalent definition of the Waring distribution.We establish an asymptotic Chi-square null distribution for the proposed test and show that it is more powerful than classical methods in simulation studies.Finally,we apply the test to analyze the authorships of published papers on computer science.展开更多
In practice,when dealing with censored data,survival analysis must be employed.In this case,parametric and non-parametric models are appropriate to analyze survival data to obtain optimal estimates of the parameters o...In practice,when dealing with censored data,survival analysis must be employed.In this case,parametric and non-parametric models are appropriate to analyze survival data to obtain optimal estimates of the parameters of interest.To identify significant determinants of infant mortality in rural Bangladesh,survival data have been extracted from the Bangladesh Demographic and Health Survey(BDHS),2017-2018.In this study,an event involving an infant death within the past 12 months;otherwise,0 will be used for censoring purposes.The main aim of this study is to find out the relationship between infant death and demographic factors.Used the Cox proportional hazard model(COXPH)to determine the responsible factors and found that age group,religion,and residence area significantly affected mortality.This study found that urban areas had a higher survival rate than rural areas.On the other hand,the age group 20-34 has a higher survival probability than other groups.And also,the“Others”have a higher mortality rate than the Muslim religion.Notably,background factors are effective on health facilities which help to increase the survival rate.展开更多
Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/metho...Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.展开更多
文摘The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.
基金This work was supported by grants from the National Natural Science Foundation of China,with No.NSFC62006109 and NSFC12031005.
文摘Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
文摘BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity.Using interventions by way of virtual reality(VR)could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.This could also aid staff to and change current RPs.AIM To assess the feasibility and effectiveness of using a VR platform to provide reduction in RP training.METHODS A randomised controlled feasibility study,accompanied by evaluations at 1 month and 6 months,was conducted within inpatient psychiatric wards at Southern Health National Health Service Foundation Trust,United Kingdom.Virti VR scenarios were used on VR headsets to provide training on reducing RPs in 3 inpatient psychiatric wards.Outcome measures included general self-efficacy scale,generalised anxiety disorder assessment 7(GAD-7),Burnout Assessment Tool 12,the Everyday Discrimination Scale,and the Compassionate Engagement and Action Scale.RESULTS Findings revealed statistically significant differences between the VR and treatment as usual groups,in the Everyday Discrimination Scale items Q8 and Q9:P=0.023 and P=0.040 respectively,indicating higher levels of perceived discrimination in the VR group.There were no significant differences between groups in terms of general self-efficacy,generalised anxiety disorder assessment 9,and Burnout Assessment Tool 12 scores.A significant difference was observed within the VR group for compassionate engagement from others(P=0.005)over time.Most respondents recorded System Usability Scale scores above 70,with an average score of 71.79.There was a significant reduction in rates of RPs in the VR group vs treatment as usual group with a fluctuating variability observed in the VR group likely due to external factors not captured in the study.CONCLUSION Ongoing advancement of VR technology enables the possibility of creating scenarios and simulations tailored to healthcare environments that empower staff by providing more comprehensive and effective training for handling situations.
文摘In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>(N+α)/N,λis a postive parameter and Rαstands for the Green's function of the discrete fractional Laplacian,which has no singularity at the origin but has same asymptotics as the Riesz potential at infinity.Under some assumptions onα(x),we establish the existence and asymptotic behavior of ground state solutions for the nonlinear Choquard equation by the method of Nehari manifold.
基金Project supported by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZC20231050)the National Natural Science Foundation of China(Grant Nos.12175193 and 11905183)the 13th Five-year plan for Education Science Funding of Guangdong Province(Grant No.2021GXJK349)。
文摘For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.
文摘BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,who are expected to have a better prognosis,often experience recurrence.Thus,it is important to identify the risk factors for early recurrence and to develop an adequate treatment plan.AIM To evaluate the predictive factors associated with the early recurrence of earlystage PDAC.METHODS This study enrolled 407 patients with stage I PDAC undergoing upfront surgical resection between January 2000 and April 2016.Early recurrence was defined as a diagnosis of recurrence within 6 mo of surgery.The optimal cutoff values were determined by receiver operating characteristic(ROC)analyses.Univariate and multivariate analyses were performed to identify the risk factors for early recurrence.RESULTS Of the 407 patients,98 patients(24.1%)experienced early disease recurrence:26(26.5%)local and 72(73.5%)distant sites.In total,253(62.2%)patients received adjuvant chemotherapy.On ROC curve analysis,the optimal cutoff values for early recurrence were 70 U/mL and 2.85 cm for carbohydrate antigen 19-9(CA 19-9)levels and tumor size,respectively.Of the 181 patients with CA 19-9 level>70 U/mL,59(32.6%)had early recurrence,compared to 39(17.4%)of 226 patients with CA 19-9 level≤70 U/mL(P<0.001).Multivariate analysis revealed that CA 19-9 level>70 U/mL(P=0.006),tumor size>2.85 cm(P=0.004),poor differentiation(P=0.008),and non-adjuvant chemotherapy(P=0.025)were significant risk factors for early recurrence in early-stage PDAC.CONCLUSION Elevated CA 19-9 level(cutoff value>70 U/mL)can be a reliable predictive factor for early recurrence in early-stage PDAC.As adjuvant chemotherapy can prevent early recurrence,it should be recommended for patients susceptible to early recurrence.
基金This research was supported by the following funds:Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173400001,KCXFZ20201221173411032,SGDX20210823103805042)Natural Science Fund of Guangdong Province(2021A1515010983)Shenzhen Key Medical Discipline Construction Fund(no.SZXK005).
文摘Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.
文摘We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
基金Supported by the National Natural Science Foundation of China(71631004, 72033008)National Science Foundation for Distinguished Young Scholars(71625001)Science Foundation of Ministry of Education of China(19YJA910003)。
文摘The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.
文摘BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.The use of CBT for severe mental illness is now more common globally.AIM To investigate the feasibility and acceptability of a culturally adapted,CBT-based,third-wave therapy manual using the Comprehend,Cope,and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.METHODS A pilot study was used to assess the feasibility and acceptability of the manualised intervention.Outcome measures were evaluated at baseline,post-intervention and 12 wk-follow up.32 participants with mental health conditions aged 20-53 years were recruited.Assessments were completed at three time points,using Clinical Outcomes in Routine Evaluation(CORE),Hospital Anxiety and Depression Scale(HADS),Bradford Somatic Inventory and World Health Organization Disability Assessment Schedule 2.0(WHODAS).The Patient Experience Questionnaire was completed post-treatment.RESULTS Repeated measures of analysis of variance associated with HADS depression,F(2,36)=12.81,P<0.001,partialη^(2)=0.42 and HADS anxiety scores,F(2,26)=9.93,P<0.001,partialη^(2)=0.36;CORE total score and WHODAS both showed significant effect F(1.25,18.72)=14.98,P<0.001,partialη^(2)=0.5.and F(1.29,14.18)=6.73,P<0.001,partialη^(2)=0.38 respectively.CONCLUSION These results indicate the effectiveness and acceptability of the culturally adapted,CBT-based,third-wave therapy manual intervention among minoritized groups with moderate effect sizes.Satisfaction levels and acceptability were highly rated.The viability and cost-effectiveness of this approach should be explored further to support universal implementation across healthcare systems.
基金supported by the Singapore Ministry of Education Academic Research Fund Tier 2(Grant No.MOE2018-T2-2-076)。
文摘The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment condition.Our proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other applications.Our main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.
基金supported by grants from the National Natural Science Foundation of China No.NSFC62006109 and NSFC12031005the 13th Five-year plan for Education Science Funding of Guangdong Province No.2021GXJK349,No.2020GXJK457the Stable Support Plan Program of Shenzhen Natural Science Fund No.20220814165010001.
文摘Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.
文摘Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.
文摘1st cases of COVID-19 were reported in March 2020 in Bangladesh and rapidly increased daily. So many steps were taken by the Bangladesh government to reduce the outbreak of COVID-19, such as masks, gatherings, local movements, international movements, etc. The data was collected from the World Health Organization. In this research, different variables have been used for analysis, for instance, new cases, new deaths, masks, schools, business, gatherings, domestic movement, international travel, new test, positive rate, test per case, new vaccination smoothed, new vaccine, total vaccination, and stringency index. Machine learning algorithms were used to predict and build the model, such as linear regression, K-nearest neighbours, decision trees, random forests, and support vector machines. Accuracy and Mean Square error (MSE) were used to test the model. A hyperparameter was also applied to find the optimum values of parameters. After computing the analysis, the result showed that the linear regression algorithm performs the best overall among the algorithms listed, with the highest testing accuracy and the lowest RMSE before and after hyper-tuning. The highest accuracy and lowest MSE were used for the best model, and for this data set, Linear regression got the highest accuracy, 0.98 and 0.97 and the lowest MSE, 4.79 and 4.04, respectively.
基金Weidong Liu was supported by National Natural Science Foundation of China(Grant No.11825104)Qi-Man Shao was supported by National Natural Science Foundation of China(Grant No.12031005)Shenzhen Outstanding Talents Training Fund of China.
文摘In a one-way analysis-of-variance(ANOVA) model,the number of pairwise comparisons can become large even with a moderate number of groups.Motivated by this,we consider a regime with a growing number of groups and prove that,when testing pairwise comparisons,the Benjamini-Hochberg(BH) procedure can asymptotically control false discoveries,despite the fact that the involved t-statistics do not exhibit the wellknown positive dependence structure required for exact false discovery rate(FDR) control.Following Tukey's perspective that the difference between the means of any two groups cannot be exactly zero,our main result provides control over the directional false discovery rate and directional false discovery proportion.A key technical contribution of our work is demonstrating that the dependence among the t-statistics is sufficiently weak to establish the convergence result typically required for asymptotic FDR control.Our analysis does not rely on conventional assumptions such as normality,variance homogeneity,or a balanced design,thereby offering a theoretical foundation for applications in more general settings.
基金supported by National Natural Science Foundation of China(Grant No.12301182)supported by National Natural Science Foundation of China(Grant No.12031005)Shenzhen Outstanding Talents Training Fund,China。
文摘Let X_(1),X_(2),...,Xn be independent and identically distributed random vectors,T_n=T_n(X_(1),X_(2),...,X_(n))be a degenerate U-statistic,and△_(n)=△_(n)(X_(1),X_(2),...,X_(n))be a remainder term.In this paper,we establish a Berry-Esseen-type theorem for T_(n)+△_(n)by an exchangeable pair approach.As an application,a sharp error bound of normal distribution approximation for the distance correlation is obtained,which improves some results in Gao et al.(2021).
基金supported by the National Natural Science Foundation of China 12371265Natural Science Foundation of Shanghai 21ZR1420700Fundamental Research Funds for the Central Universities(2022QKT001).
文摘The Waring distribution is an important two-parameter discrete distribution,commonly used in fields such as ecology,linguistics,and information science,where heavy tails are often observed.In this paper,we propose a new goodness-of-fit test for the Waring distribution,which is established through the hazard rate and a linear equivalent definition of the Waring distribution.We establish an asymptotic Chi-square null distribution for the proposed test and show that it is more powerful than classical methods in simulation studies.Finally,we apply the test to analyze the authorships of published papers on computer science.
文摘In practice,when dealing with censored data,survival analysis must be employed.In this case,parametric and non-parametric models are appropriate to analyze survival data to obtain optimal estimates of the parameters of interest.To identify significant determinants of infant mortality in rural Bangladesh,survival data have been extracted from the Bangladesh Demographic and Health Survey(BDHS),2017-2018.In this study,an event involving an infant death within the past 12 months;otherwise,0 will be used for censoring purposes.The main aim of this study is to find out the relationship between infant death and demographic factors.Used the Cox proportional hazard model(COXPH)to determine the responsible factors and found that age group,religion,and residence area significantly affected mortality.This study found that urban areas had a higher survival rate than rural areas.On the other hand,the age group 20-34 has a higher survival probability than other groups.And also,the“Others”have a higher mortality rate than the Muslim religion.Notably,background factors are effective on health facilities which help to increase the survival rate.
基金supported by grants from Shenzhen Polytechnic University Research(Fund No.6025310042 K)the National Natural Science Foundation of China(No.NSFC62006109 and NSFC12031005).
文摘Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.
