Neutron Flux Signal Acquisition from Plant Instrumentation Channel of Research Reactor for Reactivity Calculation

A design for instantaneous neutron flux signal acquisition system is being carried out for reactivity measurement of the nuclear research reactor. It is a computer-based digital data acquisition system that can perform continuous monitor and measurement of reactivity inserted into or removed from the research reactor. The acquisition system accomplishes with two major parts. The first part is an interfacing PCI based data acquisition card and the corresponding driver software intending to on-line acquisition of neutron flux signals from plant instrumentation channel. The second part incorporates the high-level Visual Basic real time program, indigenously developed for computation of reactivity by the solution of neutron point kinetic equations and other relevant functional modules like input file logging, reactivity calculation, graphics demonstration etc.

A Study on the Focuses and Fronts of International Second Language Acquisition Researches -Statistical Analysis of the Articles Published in Language Learning(2012-2016)

Keywords word frequency and co-word analysis are adopted in an attempt to to investigate the focuses and fronts of inter-national second language acquisition(SLA) researches by analyzing the key words in articles published in the internationally re-nowned SLA academic journal Language Learning from 2012 to 2016. It is found that the best researched topics in SLA are vocabu-lary acquisition, explicit knowledge, form-focused teaching, language use, and so on, among which learner's language attracts themost attention. In terms of research methods, they become more diversified and interdisciplinary, as empirical studies take a domi-nant position and experiments still play a leading role, displaying an interdisciplinary feature.

A Review of Research on L2 Vocabulary Acquisition through Reading

The crucial role of lexis in both first and second language acquisition has long been acknowledged by researchers.With that notion, many researchers and instructors has paid much attention to L2 vocabulary acquisition. The paper here attempts to give a brief review of some related research and is supposed to spark some ideas about L2 vocabulary acquisition on a theoretical and pedagogical basis.

A Critical Review of Motivation in Second Language Acquisition Research

As one of the major factors affecting second language learners' success to their acquisition achievement,motivation has been examined in a wide variety of research papers.It is thus instructive to conduct a critical review of both theoretical and empirical developments in SLA research from over the last few decades.This approach will provide a broad,and integrated perspective onto the current understanding of the complextopic of motivation.Such an undertaking is valuable for teachers,and researchers alike,in developing useful teaching methods,and finding futureavenues of SLA motivation research respectively.The following paper provides an overview of recent theoretical and empirical findings,examines some of the problems and contradictions found in current SLA research,and gives an initial departure point for future directions of research in the area of motivation.The paper concludes with a discussion of some of the implications for teachers,and possible classroom strategies that are drawn from the current body of motivation research.

A Review of Universal Grammar Concerning Second Language Acquisition Research

Chomsky’s Universal Grammar(UG)theory not only answers"the logical problem of language acquisition",but also provides insights and thoughts for second language acquisition(SLA)research on both theoretical and empirical level.However,there are many problems of UG-based research.On the theoretical aspect,UG-based approach left untouched many areas apart from"core grammar"and the continual development of the theory is problematic to second language researchers.On the empirical aspects,most experiments reveal problems with the design and data collection process.In short,the evaluation of UG concerning SLA should be developmental and comprehensive.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

An Overview of Research and Forecasting on Rainfall Associated with Landfalling Tropical Cyclones

The ability to forecast heavy rainfall associated with landfalling tropical cyclones （LTCs） can be improved with a better understanding of the mechanism of rainfall rates and distributions of LTCs. Research in the area of LTCs has shown that associated heavy rainfall is related closely to mechanisms such as moisture transport, extratropical transition （ET）, interaction with monsoon surge, land surface processes or topographic effects, mesoscale convective system activities within the LTC, and boundary layer energy transfer etc.. LTCs interacting with environmental weather systems, especially the westerly trough and mei-yu front, could change the rainfall rate and distribution associated with these mid-latitude weather systems. Recently improved technologies have contributed to advancements within the areas of quantitative precipitation estimation （QPE） and quantitative precipitation forecasting （QPF）. More specifically, progress has been due primarily to remote sensing observations and mesoscale numerical models which incorporate advanced assimilation techniques. Such progress may provide the tools necessary to improve rainfall forecasting techniques associated with LTCs in the future.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model

A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

Research on the short-term fishery forecasting of Spanish mackerel (Scomberomerus niphonius)

-This paper presents the use of the hydrographic factors in short-term fishery forecasting of the spawning migration stock of the Spanish mackerel and salinity describes more concretely the correlativity of water temperature, salinity and air temperature with the fishing season in spring. The data have been collected from the hydrographic environmental investigation at the fixed position on the sea and the telegraph recordings of the drift net operation in the spring fishing season during the period of April and May from 1972 to 1980. The correlation coefficients of various factors with the data of the fishing season have been calculated by using the monadic regression method.The main reference targets of the forecasting are: (1) By using the upper-layer water temperature as the forecasting factor at the beginning of the fishing season, the accuracy is high; (2) the distribution and location of the isotherm of the upper-layer water at 10°C at the beginning of April are used as an important factor for determining the location and the range of the central fishing area of the Spanish mackerel; (3) whether a low temperature area at 8°C existing at the Estuary of the Changjiang River can be used as an important factor for forecasting the migration distribution of the Spanish mackerel moving to the north.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

Forecasting Shark Attack Risk Using AI: A Deep Learning Approach

This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.

Beyond authorship: Analyzing contributions in PLOS ONE and the challenges of appropriate attribution

Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.

三语音系习得研究的国际前沿及启示

三语音系习得是三语习得的重要组成部分,也是多语主义研究的重要议题。通过对2000年以来国际上已有研究成果梳理后发现:研究主题包括多语者的跨语言影响和多语优势;研究视角涵盖语音感知和产出两个方面;研究对象分为音段和超音段两类;研究方法以实证研究为主;研究理论主要包括由二语音系习得及三语句法习得领域引入的有关理论和模型。通过分析梳理,三语音系习得研究的趋势大致为:增加逆向研究,加强主观因素研究,侧重超音段研究,强化语音感知研究,倡导纵向研究,扩大研究对象范围等。

聋生汉语特殊句式习得研究的新成果

本期重点号专题围绕基于语料库的听障学生汉语特殊句式研究,由多位聋校一线教师针对教学中的具体问题研究撰写。研究密切结合聋生习得汉语书面语的实际情况和特点,细致而深入地进行了阐述与分析,取得了有意义的研究结论,进一步深化了对研究论题的认识,对教学具有很强的指导意义和参考价值。

机载双极化气象雷达雷暴回波仿真与验证

雷暴是一种短暂而剧烈的强对流天气,常伴有闪电、冰雹、强降水等危险天气,对民航飞机的飞行安全造成巨大威胁。机载气象雷达作为保证飞行器飞行安全必备的装备,用于探测与显示航路附近的实时气象信息,辅助机组人员规避危险气象。由于极化技术在气象探测方面的优势,双极化雷达成为机载气象雷达的发展方向。但是雷暴天气具有发展迅速、变化复杂,危险性高等特点,使得获取实测机载双极化气象雷达雷暴回波数据困难。为了解决这一问题,本文基于机载双极化气象雷达提出一种雷暴回波仿真方法并进行验证。方法首先利用数值预报模式WRF模式(Weather Research and Forecasting)对雷暴气象场景进行模拟;然后使用T-Matrix方法计算气象粒子的单个粒子散射振幅矩阵,同时结合场景内粒子的微物理特性,计算雷暴目标的反射率因子;最后应用雷达气象方程,基于机载气象雷达系统参数建立雷暴回波信号模型,实现机载双极化气象雷达雷暴回波信号仿真。最后,为检验方法的正确性和准确性,基于雷暴单体识别算法对回波仿真结果进行验证。通过仿真不同仰角下雷暴回波,实验结果表明,基于WRF模式的机载双极化气象雷暴回波仿真方法对雷暴天气具有良好的模拟能力,经单体识别算法验证,结果表明可准确体现雷暴单元的质心分布,结构属性和立体特征,对比实测数据,雷暴回波仿真结果与实测数据相吻合,实验结果具有真实性和准确性。

两种再分析资料和Nudging方法在WRF模式降水模拟中的适用性

采用Grid Nudging(GN)和Spectral Nudging(SN)方法,用再分析资料ERA5和FNL驱动中尺度数值天气预报模式(WRF),探究不同再分析资料和Nudging方法对降水模拟效果的改进效果及机理.对2021年3月15日中国南方地区降水过程设计6组试验进行数值模拟,分析不同试验方案对降水及相关物理量的影响.结果表明,WRF模式能较好地模拟出本次降水事件,进行Nudging驱动后显著提升了降水分布、降水中心落区和降水量的模拟效果.与观测数据综合对比,GN的模拟效果优于SN,尤其是使用ERA5资料结合GN模拟效果最佳,能够准确地模拟出发生在安徽省南部的降水中心以及超过33 mm/d的降水强度.模式结果与两个观测站点记录的降水发生时间和降水强度变化较为一致.GN方法使模式有效提高了西南低空急流的强度,校正了风向,对水汽通量和水汽通量散度的刻画更符合实际情况.

中国首次南极航空气象保障工作回顾及展望

我国航空气象保障在南极地区尚属于空白区。为给相关的业务和科研工作提供参考,回顾中国第39次南极科学考察队航空气象保障工作,介绍具体的保障流程及方法,提出对未来工作的展望。研究表明:极地地区航空气象保障要注意选择飞行窗口期,参考多家数值预报产品来制作航空天气预报,并根据实况结果分析预报偏差;另外要掌握局地天气变化规律,尤其是下降风的影响,与机长充分交换意见,参考工作现场多种特殊情况并提出合理的气象保障建议。

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

日本近年来关于地震预测研究的科学计划和科学理念

尽管日本国土面积只占世界陆地的1%,但发生的地震却占全球地震总数的10%左右。尤其是致灾型大地震的发生率很高。为应对这一问题,日本政府自1965年开始推进地震预测研究计划,并经历了多个阶段的计划和修订。近年来,随着信息科学领域的快速发展,日本政府将信息科学引入到了地震科学研究,开展了《利用信息科学的地震研究》计划,旨在促进面向新地震预测科学的创新性研究。目前,日本地震调查本部对地震预测的总体观点“一定程度上可预测板间地震的发生时间,但现有研究仍无法预测未来地震,当前工作的重点是预测未来地震的震动规模”。地震预测的科学研究理念正从原先的“灾害发生的预测”向“灾害发展过程的预测”转变。