Practical operation and theoretical basis of difference-in-difference regression in science of science:The comparative trial on the scientific performance of Nobel laureates versus their coauthors

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.

Equilibrium dividend strategies in the dual model with a random time horizon

This paper investigates the dividend problem with non-exponential discounting in a dual model.We assume that the dividends can only be paid at a bounded rate and that the surplus process is killed by an exponential random variable.Since the non-exponential discount function leads to a time inconsistent control problem,we study the equilibrium HJB-equation and give the associated verification theorem.For the case of a mixture of exponential discount functions and exponential gains,we obtain the explicit equilibrium dividend strategy and the corresponding equilibrium value function.Besides,numerical examples are shown to illustrate our results.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

An Investigation of Purely Azimuthal Passive Localization and Position Adjustment in Attempted UAV Formation Flights

When a cluster of unmanned aerial vehicles (UAVs) is flying in formation, it is crucial to maintain the formation and not to be interfered by external electromagnetic wave signals. In order to maintain the formation, this paper proposes to use pure azimuth passive positioning to adjust the position of UAVs, i.e., certain UAVs in the formation transmit signals, the rest of the UAVs receive the signals passively, and extract the orientation information from them to adjust the position of the UAVs [1] [2] [3]. In this paper, the position adjustment problem of UAVs in “circular” formation flight under three models is investigated. To address the problem of “how to obtain the position of the receiving UAV when there are two UAVs with known numbers and evenly distributed on the circumference in addition to the UAV transmitting at the known center of the circle, and the rest of the UAVs with slight deviations in their positions are receiving the signals”, two purely mathematical geometric methods, namely, triangular localization method and polar co-ordinate method, are proposed respectively. We have determined the position of the receiving UAV;we have used the exhaustive method and the construction and disproof method to solve the problem of “how many UAVs are needed to transmit signals in order to realize the effective positioning of the UAVs when it is known that a certain UAV with a slight deviation in its position receives the signals emitted by two UAVs at the same time”, and the results show that: in addition to the known signals emitted by two UAVs, it is also necessary to transmit the signals emitted by two UAVs. The results show that in addition to the known two UAVs transmitting signals, two additional UAVs are required to transmit signals for precise po-sitioning. When the position of UAVs has deviation at the initial moment, the ideal approximation method and the target delimitation method are pro-posed, and the target of nine UAVs uniformly distributed on a circle of a spe-cific radius is achieved through several adjustments, after which the ad-vantages and disadvantages of each model are analyzed, and suggestions for improvement are put forward. The purely azimuthal passive localization method and the constructed model approach proposed in this paper can be extended to other fields, such as spacecraft formations in space and battle-ship formations at sea, as well as other formation flight position adjustment problems.

Exploratory systematic review and meta-analysis on period poverty

BACKGROUNDPeriod poverty is a global health and social issue that needs to be addressed.It has been reported that many females compromise their education,employment,and social commitments during their menstruation days due to a number of reasons,including lack of access to toilets or menstrual products.AIM To provide a comprehensive understanding on period poverty,including outcomes associated with menstruation.METHODS All observational and randomised clinical trials reporting menstruation challenges,menstrual poverty and menstrual products were included.Our search strategy included multiple electronic databases of PubMed,Web of Science,ScienceDirect,ProQuest and EMBASE.Studies published in a peer review journal in English between the 30th of April 1980 and the 30th of April 2022 were included.The Newcastle-Ottawa Scale was used to assess the risk of bias of the systematic included studies.Pooled odds ratios(ORs)together with 95%confidence intervals(CIs)are reported overall and for sub-groups.RESULTS A total of 80 studies were systematically selected,where 38 were included in the meta-analysis.Of the 38 studies,28 focused on children and young girls(i.e.,10-24 years old)and 10 included participants with a wider age range of 15-49 years.The prevalence of using disposable sanitary pads was 45%(95%CI:0.35-0.58).The prevalence of menstrual education pre-menarche was 68%(95%CI:0.56-0.82).The prevalence of good menstrual hygiene management(MHM)was 39%(95%CI:0.25-0.61).Women in rural areas(OR=0.30,95%CI:0.13-0.69)were 0.70 times less likely to have good MHM practices than those living in urban areas.CONCLUSION There was a lack of evidence,especially from low-and middle-income countries.Further research to better understand the scope and prevalence of period poverty should be considered.This will enable the development of improved policies to increase access to menstrual products and medical support where necessary.

Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming data.In the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms.The estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming data.In the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established.The authors conduct extensive numerical experiments to evaluate the performance of the proposed method.These experiments demonstrate the effectiveness of the proposed method and support the theoretical results.An application to the Beijing PM2.5 Dataset is also presented.

A GMM approach in coupling internal data and external summary information with heterogeneous data populations

Because of advances in data collection and storage,statistical analysis in modern scientific research and practice now has opportunities to utilize external information such as summary statistics from similar studies.A likelihood approach based on a parametric model assumption has been developed in the literature to utilize external summary information when the populations for external and main internal data are assumed to be the same.In this article,we instead consider the generalized estimation equation(GEE)approach for statistical inference,which is semiparametric or nonparametric,and show how to utilize external summary information even when internal and external data populations are not the same.Our approach is coupling the internal data and external summary information to form additional estimation equations and then applying the generalized method of moments(GMM).We show that the proposed GMM estimator is asymptotically normal and,under some conditions,is more efficient than the GEE estimator without using external summary information.Estimators of the asymptotic covariance matrix of the GMM estimators are also proposed.Simulation results are obtained to confirm our theory and quantify the improvements by utilizing external data.An example is also included for illustration.

Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance

Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation.However,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test set.Here,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test set.By utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate annotation.With extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type annotation.We also verify the effectiveness of incorporating reference data during the training process.In addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell types.Furthermore,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream analyses.All the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS data.We anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data analysis.The source codes are available at the GitHub website(BioX-NKU/RAINBOW).

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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.

The Time-Consistent Optimal Reinsurance Strategy of Insurance Group under the CEV Model

The article introduces proportional reinsurance contracts under the mean-variance criterion,studying the time-consistence investment portfolio problem considering the interests of both insurance companies and reinsurance companies.The insurance claims process follows a jump-diffusion model,assuming that the risk asset prices of insurance companies and reinsurance companies follow CEV models different from each other.In the framework of game theory,the time-consistent equilibrium reinsurance strategy is obtained by solving the extended HJB equation analytically.Finally,numerical examples are used to illustrate the impact of model parameters on equilibrium strategies and provide economic explanations.The results indicate that the decision weights of insurance companies and reinsurance companies do have a significant impact on both the reinsurance ratio and the equilibrium reinsurance strategy.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

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.

Recent advances in statistical methodologies in evaluating program for high-dimensional data

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.

Optimization and Process Design Tools for Estimation of Weekly Exposure to Air Pollution Integrating Travel Patterns during Pregnancy

A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.

Wavelet Analysis of Bitcoin Price and Twitter-Based Economic Uncertainty Index

In this paper,we analyze the time-series graphs of Bitcoin price and Twitter-based economic uncertainty index over the past two years and use a wavelet coherence graph to determine their relationship.We found a causal relationship between Bitcoin(BTC)and Twitter-based economic uncertainty(TEU)index in different frequency bands,which would help predict Bitcoin price movements in the future.Our study provides reference to academics and investors.

Robotic versus laparoscopic distal pancreatectomy for pancreatic ductal adenocarcinoma: A propensity score-matched analysis

Background: Minimally invasive surgery is becoming increasingly popular in the field of pancreatic surgery. However, there are few studies of robotic distal pancreatectomy(RDP) for pancreatic ductal adenocarcinoma(PDAC). This study aimed to investigate the efficacy and feasibility of RDP for PDAC. Methods: Patients who underwent RDP or laparoscopic distal pancreatectomy(LDP) for PDAC between January 2015 and September 2020 were reviewed. Propensity score matching analyses were performed. Results: Of the 335 patients included in the study, 24 underwent RDP and 311 underwent LDP. A total of 21 RDP patients were matched 1:1 with LDP patients. RDP was associated with longer operative time(209.7 vs. 163.2 min;P = 0.003), lower open conversion rate(0% vs. 4.8%;P < 0.001), higher cost(15 722 vs. 12 699 dollars;P = 0.003), and a higher rate of achievement of an R0 resection margin(90.5% vs. 61.9%;P = 0.042). However, postoperative pancreatic fistula grade B or C showed no significant intergroup difference(9.5% vs. 9.5%). The median disease-free survival(34.5 vs. 17.3 months;P = 0.588) and overall survival(37.7 vs. 21.9 months;P = 0.171) were comparable between the groups. Conclusions: RDP is associated with longer operative time, a higher cost of surgery, and a higher likelihood of achieving R0 margins than LDP.

Estimates on the eigenvalues of complex nonlocal Sturm-Liouville problems

The present paper deals with the eigenvalues of complex nonlocal Sturm-Liouville boundary value problems.The bounds of the real and imaginary parts of eigenvalues for the nonlocal Sturm-Liouville differential equation involving complex nonlocal potential terms associated with nonlocal boundary conditions are obtained in terms of the integrable conditions of coefficients and the real part of the eigenvalues.

Neuro-11:a new questionnaire for the assessment of somatic symptom disorder in general hospitals

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.

Evaluation of a culturally adapted cognitive behavior therapy-based,third-wave therapy manual

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.

Asymptotics for the joint tail probability of bidimensional randomly weighted sums with applications to insurance

This paper studies the joint tail behavior of two randomly weighted sums∑_(i=1)^(m)Θ_(i)X_(i)and∑_(j=1)^(n)θ_(j)Y_(j)for some m,n∈N∪{∞},in which the primary random variables{X_(i);i∈N}and{Y_(i);i∈N},respectively,are real-valued,dependent and heavy-tailed,while the random weights{Θi,θi;i∈N}are nonnegative and arbitrarily dependent,but the three sequences{X_(i);i∈N},{Y_(i);i∈N}and{Θ_(i),θ_(i);i∈N}are mutually independent.Under two types of weak dependence assumptions on the heavy-tailed primary random variables and some mild moment conditions on the random weights,we establish some(uniformly)asymptotic formulas for the joint tail probability of the two randomly weighted sums,expressing the insensitivity with respect to the underlying weak dependence structures.As applications,we consider both discrete-time and continuous-time insurance risk models,and obtain some asymptotic results for ruin probabilities.

Data-driven slicing for dimension reduction in regressions:A likelihood-ratio approach

To efficiently estimate the central subspace in sufficient dimension reduction,response discretization via slicing its range is one of the most used methodologies when inverse regression-based methods are applied.However,existing slicing schemes are almost all ad hoc and not widely accepted.Thus,how to define datadriven schemes with certain optimal properties is a longstanding problem in this field.The research described here is then twofold.First,we introduce a likelihood-ratio-based framework for dimension reduction,subsuming the popularly used methods including the sliced inverse regression,the sliced average variance estimation and the likelihood acquired direction.Second,we propose a regularized log likelihood-ratio criterion to obtain a data-driven slicing scheme and derive the asymptotic properties of the estimators.A simulation study is carried out to examine the performance of the proposed method and that of existing methods.A data set concerning concrete compressive strength is also analyzed for illustration and comparison.