Mobile diaries – Benchmark against metered measurements: An empirical investigation

Researchers seeking to study the relationships between consumers' communications, attitudes, and behaviors could benefit from monitoring consumers over time, across multiple locations and channels, and in a way that reflects consumers' subjective perceptions. Diaries on smartphones (mobile diaries) can be used as a research tool for such purposes. A mobile diary is a self-report instrument whereby people use their mobile handset to repeatedly report experiences of interest. Mobile diaries are increasingly used in psychology, geography, medicine, and commercial marketing. Yet they have rarely been used for quantitative marketing research, and were not benchmarked against best-practice metrics in marketing.In this study, we aim to set the ground for using mobile diaries in quantitative marketing research. We first lay out the theoretical infrastructure for the usage of mobile diaries, and describe possible respondent reporting concerns, including concerns related to non-reporting, reporting over time, and concerns stemming from individual-level heterogeneity.We demonstrate the potential of mobile diaries, as well as the importance of the various concerns, using a benchmark test case in the context of primetime TV viewing. Our benchmark uses a sample of respondents with both mobile diary viewing reports and Nielsen People Meter (NPM) records. Our analysis reveals that averaging across all conditions, 47.4%–64.7% of the NPM records are reported by the diary. The major sources for mismatch are random time periods without alarms, short viewings, and periodic reporting inactivity (pulsing). Concerns such as a decrease in reporting rates over time (e.g., fatigue), smartphone ownership, and demographic variation across individuals have relatively small effects on reporting likelihood. Analyzing the cases in which diary reports do not have a matching NPM record, we find many of them can be attributed to out-of-home viewing and viewing on non-metered devices. This finding demonstrates how mobile diaries can complement metered measurements. Overall, aggregate diary-based ratings have a 0.90 correlation with NPM ratings.We discuss implications for designing and using mobile diary studies in marketing.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

强化食品抽样检验推动食品安全深度监管

做好食品抽样检验工作是确保食品质量和安全的关键,本研究对目前国内食品抽样检验管理现状进行了分析,针对食品安全抽样检验管理存在的问题提出要强化制度建设、强化技术团队建设、制定统一检测标准等方法,并提出进一步深化改革食品安全抽样检验管理办法,旨在保障公众食品安全,提高食品抽样监管力度。     

A proposed configurable approach for recommendation systems via data mining techniques

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.     

High-frequency credit spread information and macroeconomic forecast revision

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.     

双平行线阵的快速一维DOA估计

针对传统子空间算法需要进行特征值分解或奇异值分解等复杂计算的问题，提出一种双平行线阵（Double Parallel Linear Array,DPLA）的快速一维波达方向（Direction of Arrival，DOA）估计算法。算法通过处理互协方差矩阵的第一列元素构造出等效的噪声子空间，再通过求根MUSIC（Multiple Signal Classification）算法得到DOA估计，有效避开了特征值分解或奇异值分解，降低了计算复杂度，提高了运算速度。仿真结果表明，该算法在提高了估计精度的同时减少了估计时间。     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

Pharmaceutical cost‐sharing systems and savings for healthcare systems from parallel trade

This paper studies the consequences of parallel trade in a two‐country model. It compares a coinsurance scheme (consumers pay a percentage of the drug price) and an indemnity insurance scheme (reimbursement is independent of the drug price) with respect to changes in copayments and public health expenditure. In the destination country, copayments for patients decrease to a larger extent under indemnity insurance, whereas reductions in public health expenditure occur only under coinsurance. In the source country, copayments increase less under coinsurance, whereas health expenditure is reduced more under indemnity insurance. In both countries, total expenditure under parallel trade is lower.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

2020年河北省蔬菜监督抽检数据分析

目的:分析2020年河北省市场监督管理局发布的蔬菜监督抽检数据.方法:下载2020年河北省市场监督管理局发布的食品抽检相关通告,从蔬菜品种、蔬菜细类、不合格蔬菜检出详情及不合格项目等方面进行分析.结果:1199批次蔬菜10个蔬菜品种中,检出62批次不合格,平均不合格率为5.17％.鳞茎类蔬菜、芽菜类蔬菜等不合格率较高,不合格突出的蔬菜细类为韭菜、豆芽、芹菜等,不合格项目主要为农药残留,且禁用农药残留占总不合格项次的50％.结论:河北省蔬菜质量安全水平相对较好,但低于全国平均水平,超剂量使用农药、违规使用禁用农药仍是蔬菜质量安全的主要问题,仍需加强韭菜、豆芽、芹菜等高风险蔬菜的监管力度.