Traffic Flow Prediction Using Convolutional Neural Network accelerated by Spark Distributed Cluster

Obtain information from historical data to forecast traffic flow in a city can be difficult because a precision forecasting demands large amount of data and accurate pattern analysis. Meanwhile, it is also meaningful because it provides a detailed and accurate point-to-point prediction for users. In this project, I use CNN (Convolutional Neural Network) to train the model based on the images captured by webcams in New York City. Then I deploy the training process on a Spark distributed Cluster so that the whole training process is accelerated. To efficiently combine CNN and Apache Spark, the prediction model is re-designed and optimized, and the distributed cluster is tuned. By using 5-fold validation, multiple test results are presented to provides a support for the analysis about the model optimization and distributed cluster tuning. The aim of this project is to find the most accurate prediction model for the traffic flow prediction with acceptable time cost

Multi-Objective Feature Selection With Missing Data in Classification

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study

Long-term use of antibiotics and risk of type 2 diabetes in women:a prospective cohort study

BACKGROUND: Accumulating evidence suggested that long-term antibiotic use may alter the gut microbiome, which has, in turn, been linked to type 2 diabetes. We undertook this study to investigate whether antibiotic use was associated with increased risk of type 2 diabetes. METHODS: This prospective cohort study included women free of diabetes, cardiovascular disease and cancer in the Nurses’ Health Study (NHS 2008–2014) and NHS II (2009–2017). We evaluated the overall duration of antibiotics use in the past 4 years and subsequent diabetes risk with Cox proportional-hazards regression adjusting for demography, family history of diabetes and lifestyle factors. RESULTS: Pooled analyses of NHS and NHS II (2837 cases, 703 934 person-years) revealed that a longer duration of antibiotic use in the past 4 years was associated with higher risk of diabetes [Trend-coefficient = 0.09, 95% confidence interval (CI) 0.04 to 0.13]. Participants who received antibiotics treatment for a medium duration of 15 days to 2 months [hazard ratio (HR) 1.23, 95% CI 1.10 to 1.39] or long duration of >2 months (HR 1.20, 95% CI 1.02 to 1.38) had higher risk of type 2 diabetes as compared with non-users. Subgroup analyses suggested that the associations were unlikely to be modified by age, family history of diabetes, obesity, smoking, alcohol drinking, physical activity and overall diet quality. CONCLUSIONS: A longer duration of antibiotic use in recent years was associated with increased risk of type 2 diabetes in women. Physicians should exercise caution when prescribing antibiotics, particularly for long-term use

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effectiveness of AI in Strategic Decision Making: An Empirical Study on Identifying High-Potential Talents

While the efficiency of AI in decision making has been confirmed in the literature, the effectiveness of AI decision making is less studied. To address this research void, we examine the effectiveness of AI using data collected from a leading technology company that applies both AI and human in the high-potential talent identification process. The cross-sectional comparison of these employees’ after-selection performance generates mixed results. While the AI-selected employees had higher contribution scores than their human-recommended peers, the human-recommended employees showed higher growth potential (proxied by promotion speed) and organizational commitment (proxied by turnover). Further analysis indicates that the AI-selected employees exhibited suboptimal performance on all three aspects when compared to the short-listed employees that were selected through an additional round of human evaluation. Jointly, these results suggest that AI can be an effective screening tool for identifying high-potential talents, but human instinct is essential for the final selection

Is AI Better Than Human in Identifying High-Potential Talents: A Quasi-Field Experiment

With the advent of HR 3.0, Artificial Intelligence (AI) has been increasingly used in human resource (HR) management, including high-potential talent identification. Despite the increased adoption of AI in HR management, empirical evidence about the effectiveness of AI in talent assessment and identification is still scant. Our research represents an initial attempt to address this research void. With empirical data collected from a leading high-tech company in China, we conduct a quasi-field experiment to explore how effective AI is in identifying high-potential talents. Our preliminary results indicate that AI can slightly outperform human managers in the initial screening of high-potential talents. However, the performance of the AI-identified employees is suboptimal to that of the final short-listed employees. These results provide support for AI’s effectiveness in initial talent screening but raise concerns on using AI alone in talent identification. We wrap the paper up with discussions on the potential contribution and the planned extension

Experimental Study on Cumulative Plastic Deformation of Coarse-Grained Soil High-Grade Roadbed under Long-Term Vehicle Load

According to the change characteristics of the subgrade moisture content and the mechanical calculation of several typical highways, the test scheme of the permanent deformation of coarse soil was formulated. The relationship between the permanent deformation of coarse-grained soil and the stress level, compaction degree, moisture content, and loading frequency was studied by cyclic loading triaxle testing. The results show that the permanent deformation of coarse-grained soil increases with the increase in partial stress and moisture content and decreases with the increase in compaction degree. The experimental data were fitted by the Tseng-Lytton model, and the correlation coefficients were 92%, which indicated that the model could be used to predict the permanent deformation of coarse soil. The relationships between the model coefficient and the moisture content and spring back modulus were obtained by the multiple regression method. Finally, the permanent deformation of the subgrade soil was calculated by using the layered summation method and a typical subgrade pavement structure

Micro-Change Process of Calcium–Magnesium Double Expansive Agent and Its Performance Characterization in Cement-Based Materials

With the increase of cement output, the demand for cement expansion agents increases, and composite expansion agents have become the development trend. The purpose of this study is to study the microscopic change process and expansion effect of calcium oxide and magnesium oxide double expansion agents. After calcination at different temperatures, the change process of microscopic morphology of calcined products was observed. Through calcining dolomite at 900 °C, the mixture D900 of calcium oxide and magnesium oxide was obtained. To prepare mixed cement, 10 wt %, 20 wt %, and 30 wt % of D900 were added into cement to prepare mixed cement. At the same time, the compressive strength, deformation, and porosity of mixed cement were measured. The results show that adding D900 improves the expansion rate of early cement paste and reduces the compressive strength. After 120 days, the compressive strength of 20 wt % cement paste is higher than that of blank cement paste, and the porosity of 20 wt % cement paste is the lowest among the three mixed cements. This shows that 20 wt % is a more suitable substitute