The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thr...The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.展开更多
Background:With an increase in the evidence for the associations between park-based physical activity(PA)and physical environments(especially park and neighborhood environments),researchers face an important challenge...Background:With an increase in the evidence for the associations between park-based physical activity(PA)and physical environments(especially park and neighborhood environments),researchers face an important challenge in interpreting and summarizing the evidence to develop environ?mental change interventions.An updated review is needed to better inform policymaking and environmental interventions.The current study aimed to systematically review the research on the associations of park-based PA with park and neighborhood environmental characteristics.Methods:We targeted English peer-reviewed articles from 5 electronic databases using keywords related to park-based PA,park environments,and neighborhood environments.Of the 4071 identified papers,25 studies published between 2008 and 2016 met all the eligibility criteria and were included in this review.Results:The characteristics of physical environment that received consistent support included paths/trails,lighting,and incivilities(e.g.,broken glasses and litter).Mixed findings were revealed for 6 park environmental factors(unspecified active facilities,playgrounds and skating areas,fit?ness stations,picnic areas,greenness,and park size)and 2 neighborhood environmental factors(park density and park proximity).Conclusion:It can be concluded that paths/trails,lighting,and incivilities are 3 key physical environmental attributes of park-based PA.Given the inconsistent findings on park and neighborhood environmental factors,more robust designs such as prospective investigation are required.展开更多
Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性...针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性,通过用高支持度的评分估计取代传统的期望估计法来找出用户更喜欢的项目,从而提高推荐的准确度,并从理论上论述了该算法在稀疏数据集条件下相对其他算法具有更强的抗干扰能力。该模型还易于与其他推荐模型融合,具有很好的可拓展性。实验结果表明:LLSF算法显著提升了推荐的准确性,在MovieLens数据集上,F1分数可达到传统的kNN算法的3倍多,对于越是稀疏的数据集,准确率提升幅度越大,在Book-Crossing数据集上,当稀疏度由91%增加到99%时,F1分数的改进由22%提高到125%。同时该方法不会牺牲推荐覆盖率,可以保证长尾项目的挖掘效果。展开更多
基金primarily supported by the National 973 Fundamental Research Program of China(Grant No.2013CB430103)the Department of Transportation Federal Aviation Administration(Grant No.NA17RJ1227)through the National Oceanic and Atmospheric Administration+1 种基金supported by the National Science Foundation of China(Grant No.41405100)the Fundamental Research Funds for the Central Universities(Grant No.20620140343)
文摘The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.
基金supported by the Germany/Hong Kong Joint Research Scheme 2015/16 under Hong Kong SAR Governments’ RGC Grant (No.G-HKBU202/15)the Faculty Research Grant,Hong Kong Baptist University, Hong Kong (No.FRG2/13-14/065)
文摘Background:With an increase in the evidence for the associations between park-based physical activity(PA)and physical environments(especially park and neighborhood environments),researchers face an important challenge in interpreting and summarizing the evidence to develop environ?mental change interventions.An updated review is needed to better inform policymaking and environmental interventions.The current study aimed to systematically review the research on the associations of park-based PA with park and neighborhood environmental characteristics.Methods:We targeted English peer-reviewed articles from 5 electronic databases using keywords related to park-based PA,park environments,and neighborhood environments.Of the 4071 identified papers,25 studies published between 2008 and 2016 met all the eligibility criteria and were included in this review.Results:The characteristics of physical environment that received consistent support included paths/trails,lighting,and incivilities(e.g.,broken glasses and litter).Mixed findings were revealed for 6 park environmental factors(unspecified active facilities,playgrounds and skating areas,fit?ness stations,picnic areas,greenness,and park size)and 2 neighborhood environmental factors(park density and park proximity).Conclusion:It can be concluded that paths/trails,lighting,and incivilities are 3 key physical environmental attributes of park-based PA.Given the inconsistent findings on park and neighborhood environmental factors,more robust designs such as prospective investigation are required.
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
文摘针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性,通过用高支持度的评分估计取代传统的期望估计法来找出用户更喜欢的项目,从而提高推荐的准确度,并从理论上论述了该算法在稀疏数据集条件下相对其他算法具有更强的抗干扰能力。该模型还易于与其他推荐模型融合,具有很好的可拓展性。实验结果表明:LLSF算法显著提升了推荐的准确性,在MovieLens数据集上,F1分数可达到传统的kNN算法的3倍多,对于越是稀疏的数据集,准确率提升幅度越大,在Book-Crossing数据集上,当稀疏度由91%增加到99%时,F1分数的改进由22%提高到125%。同时该方法不会牺牲推荐覆盖率,可以保证长尾项目的挖掘效果。
