Underestimated contribution of HONO to indoor OH radicals: an emerging concern

For a long time,the hydroxyl (OH)radicals has attracted particular attention not only because it is the most important species in the photooxidation cycles in the atmosphere but also because it can oxidize volatile organic compounds (VOCs)to form secondary oxygenated gas species and aerosols,some of which can be toxic and carcinogenic.Since most people spend 80%-90% of their life-time indoors,a better understanding of the formation,occurrence and reaction of OH radicals indoors is crucial to assess the potential impact on human health.Moreover,the secondary pollutants can be substantially reduced in indoor environments only with comprehensive knowledge of the oxidative chemistry and subsequent chains of chemical reactions that pollutants undergo.

Mesh‑free semi‑quantitative variance underestimation elimination method in Monte Caro algorithm

The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models.

计算机化自适应测验(CAT)的发展和前景展望(续)

三、CAT中对的估计（一）MLE（极大似然估计法）假设一个能力水平为θ的被试对n道项目X1,X2,…,Xn作答。θ的估计可以通过使（8）式所示的似然函数最大化的方式来得到。令（?）n为此时所得的θ估计。显然（?）n也是（9）式的极大似然估计。已知在一定的条件下,（?）n符合渐进正态,其均值为θ,方差近似为I-1n（（?）n）。目前的CAT设计大多通过递归方式在被试回答一个新的项目之后得到最新的θ估计,并根据信息最大化法抽取下一个项目。

CO_2 Flux Estimation by Different Regression Methods from an Alpine Meadow on the Qinghai-Tibetan Plateau

CO2 efflux was estimated using different regression methods in static chamber observation from an alpine meadow on the Qinghai-Tibetan Plateau. The CO2 efflux showed a seasonal pattern, with the maximun flux occurring in the middle of July. The temperature sensitivity of CO2 efflux （Q10） was 3.9, which was at the high end of the range of global values. CO2 emissions calculated by linear and nonlinear regression were significantly different （p 〈0.05）. Compared with the linear regression, CO2 emissions calculated by exponential regression and quadratic regression were 12.7% and 11.2% larger, respectively. However, there were no significant differences in temperature sensitivity values estimated by the three methods. In the entire growing season, the CO2 efflux estimated by linear regression may be underestimated by up to 25% compared to the real CO2 efflux. Consequently, great caution should be taken when using published flux data obtained by linear regression of static chamber observations to estimate the regional CO2 flux in alpine meadows on the Qinghai-Tibetan Plateau.

A Hybrid Dynamic Programming Method for Concave Resource Allocation Problems

Concave resource allocation problem is an integer programming problem of minimizing a nonincreasing concave function subject to a convex nondecreasing constraint and bounded integer variables. This class of problems are encountered in optimization models involving economies of scale. In this paper, a new hybrid dynamic programming method was proposed for solving concave resource allocation problems. A convex underestimating function was used to approximate the objective function and the resulting convex subproblem was solved with dynamic programming technique after transforming it into a 0-1 linear knapsack problem. To ensure the convergence, monotonicity and domain cut technique was employed to remove certain integer boxes and partition the revised domain into a union of integer boxes. Computational results were given to show the efficiency of the algorithm.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon:A data-driven modelling

Controlling the COVID-19 outbreak remains a challenge for Cameroon,as it is for many other countries worldwide.The number of confirmed cases reported by health authorities in Cameroon is based on observational data,which is not nationally representative.The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear.This study aimed to estimate and model the actual trend in the number of COVID-19 new infections in Cameroon from March 05,2020 to May 31,2021 based on an observed disaggregated dataset.We used a large disaggregated dataset,and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05,2020 to May 31,2021.Subsequently,seasonal autoregressive integrated moving average(SARIMA)modeling was used for forecasting purposes.Based on the prospective MRP modeling findings,a total of about 7450935(30%)of COVID-19 cases was estimated from March 05,2020 to May 31,2021 in Cameroon.Generally,the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times.The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31,2021.If no action is taken,there could be many waves of the outbreak in the future.To avoid such situations which could be a threat to global health,public health Abbreviations:ACF,Autocorrelation Function;AIC,Akaike information criterion;COVID-19,Coronavirus Disease 2019;MAE,Mean Absolute Error;MAPE,Mean Absolute Percentage Error;MASE,Mean Absolute Scaled Error;ME,Mean Error;MPE,Mean Percentage Error;MRP,Multilevel Regression and Post-stratification;PACF,Partial Autocorrelation Function;PLACARD,Platform for Collecting,Analyzing and Reporting Data;SARIMA,Seasonal Autoregressive integrated moving average;SARS-CoV-2,Severe Acute Respiratory Syndrome Coronavirus 2.

Underestimation of Oceanic Warm Cloud Occurrences by the Cloud Profiling Radar Aboard Cloud Sat

The Cloud Profiling Radar （CPR） onboard CloudSat is an active sensor specifically dedicated to cloud detection. Compared to passive remote sensors, CPR plays a unique role in investigating the occurrence of multi-layer clouds and depicting the internal vertical structure of clouds. However, owing to contamination from ground clutter, CPR reflectivity signals are invalid in the lowest 1 km above the surface, leading to numerous missed detections of warm clouds. In this study, by using 1-yr CPR and MODIS （Moderate Resolution Imaging Spectroradiometer） synchronous data, those CPR-missed oceanic warm clouds that are identified as cloudy by MODIS are examined. It is demonstrated that CPR severely underestimates the occurrence of oceanic warm clouds, with a global-average miss rate of about 0.43. Over the tropical and subtropical oceans, the CPR-missed clouds tend to occur in regions with relatively low sea surface temperature. CPR misses almost all warm clouds with cloud tops lower than 1 km, and the miss rate reduces with increasing cloud top. As for clouds with cloud tops higher than 2 kin, the negative bias of CPR-captured warm cloud occurrence falls below 3%. The cloud top height of CPR-missed warm clouds ranges from 0.6 to 1.2 kin, and these clouds mostly have evidently small optical depths and droplet effective radii. The vertically integrated cloud liquid water content of CPR-missed warm clouds is smaller than 50 g m 2 It is also revealed that CPR misses some warm clouds that have small optical depths or small droplet sizes, besides those limited in the boundary layer below about 1 km due to ground clutter.

Exploring the percentage of COVID-19 cases reported in the community in Canada and associated case fatality ratios

While surveillance can identify changes in COVID-19 transmission patterns over time and space,sections of the population at risk,and the efficacy of public health measures,reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases.Our primary objective was to estimate the percentage of cases reported in the general community,considered as those that occurred outside of long-term care facilities(LTCFs),in specific provinces and Canada as a whole.We applied a methodology using the delay-adjusted case fatality ratio(CFR)to all cases and deaths,as well as those representing the general community.Our second objective was to assess whether the assumed CFR(mean=1.38%)was appropriate for calculating underestimation of cases in Canada.Estimates were developed for the period from March 11th,2020 to September 16th,2020.Estimates of the percentage of cases reported(PrCR)and CFR varied spatially and temporally across Canada.For the majority of provinces,and for Canada as a whole,the PrCR increased through the early stages of the pandemic.The estimated PrCR in general community settings for all of Canada increased from 18.1%to 69.0%throughout the entire study period.Estimates were greater when considering only those data from outside of LTCFs.The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07%on March 11th,2020 to 2.00%on September 16th,2020.Therefore,the true CFR in the general community in Canada was likely less than 2%on September 16th.According to our analysis,some provinces,such as Alberta,Manitoba,Newfoundland and Labrador,Nova Scotia,and Saskatchewan reported a greater percentage of cases as of September 16th,compared to British Columbia,Ontario,and Quebec.This could be due to differences in testing rates and criteria,demographics,socioeconomic factors,race,and access to healthcare among the provinces.Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study.The estimates provide context to the summative state of the pandemic in Canada,and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method

Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers.To meet this goal,the job scheduler of high-performance computing systems often uses backfilling scheduling to fill short-time jobs into job gaps at the front of the queue.Backfilling scheduling needs to obtain the running time of the job.In the past,the job running time is usually given by users and often far exceeded the actual running time of the job,which leads to inaccurate backfilling and a waste of computing resources.In particular,when the predicted job running time is lower than the actual time,the damage caused to the utilization of the system’s computing resources becomes more serious.Therefore,the prediction accuracy of the job running time is crucial to the utilization of system resources.The use of machine learning methods can make more accurate predictions of the job running time.Aiming at the parallel application of aerodynamics,we propose a job running time prediction framework SU combining supervised and unsupervised learning and verify it on the real historical data of the high-performance computing systems of China Aerodynamics Research and Development Center(CARDC).The experimental results show that SU has a high prediction accuracy(80.46%)and a low underestimation rate(24.85%).