Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.

Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants

In order to solve the problem of poor interpretability of support vector re- gression （SVR） applied in quantitative structure-property relationship （QSPR）, a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR.

Competitiveness of Characteristic Agriculture in Guangxi Zhuang Autonomous Region Based on Explanatory Indicators

The five explanatory indicators of the competitiveness of characteristic agriculture are the agricultural science and technology,the cultural quality of agricultural labor force,the agricultural infrastructure,the resource endowment,and the agricultural management scale.According to these explanatory indicators,competitiveness of characteristic agriculture is relatively strong in Guangxi Zhuang Autonomous Region of China,which is mainly reflected in the resource advantage,irrigation degree,and road construction level.However,the agricultural technology level,the cultural quality of agricultural labor force,the agricultural mechanization,and the agricultural management scale have relatively poor competitiveness.Therefore,more attention should be paid in these aspects,in order to improve the competitiveness of characteristic agriculture in Guangxi.

Cultural Context in Literary Translation——from the Perspective of Explanatory Function and Restrictive Function

Literary translation is not merely the transformation of words,but it is a cross-cultural activity from cultural perspective.The paper mainly explores the concept of cultural context and the relationship between cultural context and literary translation,and analyzes explanatory function and restrictive function of cultural context in literary translation.If these functions can be involved in the literary translation,it is necessary for translators to resort to different translation strategies according to the different target cultural context.The selection of translation strategies is never random,but constrained by the cultural context.

Older Adults’ Explanatory Models of Colds and Flu

Systematic data collection and analysis techniques were used in Los Angeles to discover older adults’ shared explanatory models (EM) of the causes, prevention, symptoms, treatment and consequences of late life illnesses, including influenza and the common cold. Recorded narratives also were analyzed to illustrate similarities and differences in shared cultural knowledge of these illnesses. Consensus analyses results suggest that shared EM of influenza and the common cold are similar. Participants identified both illnesses as contagious, caused or exacerbated by bad weather, but not the result of lifestyle, aging or heredity. Other shared cultural knowledge includes that both illnesses can be treated with home remedies, over-the-counter medications and medical care;both illnesses cause discomfort but are not serious, life-threatening or disabling. Despite the similarities and the apparent merging of the two illnesses in popular thought, many older adults do distinguish them, based on symptom patterns and severity, as revealed in their transcribed narratives. Consistent with other studies, participants attribute gastrointestinal symptoms to influenza but not to colds. They do not understand the potential role of lifestyle, age and chronic conditions in etiology and onset, and they are not concerned with their vulnerability to the potential sequelae of influenza. Public health education explaining the effects of lifestyle on susceptibility and vulnerability to the flu, how to distinguish and appropriately treat colds and the flu, and when to contact physicians, is recommended for older adults. Mixed method studies can prove useful at the planning stages of such interventions.

Explanatory Factors for the Variation in HIV Prevalence between Regions of Côte d’Ivoire: An Ecological Study

Introduction: No study has analyzed the reasons for the difference in HIV prevalence between Ivorian regions ranging from 1.3% in the central-western region to 4.1% in Abidjan among men. Objective: To analyze explanatory factors for the difference in HIV prevalence observed in men in Côte d’Ivoire’s regions. Methodology: Assessment of the relationship between HIV prevalence per region and risk factors explored in the 2012 Côte d’Ivoire Demographic and Health Surveys (DHS). A multivariate analysis was conducted to assess the relationship between HIV prevalence and each variable. Results: The explanatory power of the variation of HIV prevalence between regions was 98%. There was a significant association between HIV prevalence and union (r = −0.38;p = 0.008;95% CI (−0.53 to −0.23)), condom use (r = −0.01;p = 0.19;95% CI (−0.03 to −0.01)), practice of Christian religion (r = −0.1;p = 0.017;95% CI (−0.16 to −0.05)), and schooling (r = −0.01;p = 0.25;95% CI (−0.04 to 0.02)). There was a paradoxical association between HIV prevalence and mean age at first sexual intercourse (r = −0.1;p = 0.017;95% CI (−0.16 to −0.05)) and sexual infections (r = −0.48;p = 0.016;95% CI (−0.75 to −0.22)). Conclusion: The explanatory factors for the difference in HIV prevalence observed in men in the regions of the country were union, condom use, mean age at first sexual intercourse, sexual infection, sexual activity, and multiple sexual partnerships. However, only union and condom use were effective in reducing HIV prevalence by preventing new infections.

Older Adults’ Explanatory Models of High Blood Pressure

Shared Explanatory Models (EM) of High Blood Pressure (HBP)/Hypertension (HTN) were explored using systematic data collection and analysis methods from cognitive anthropology. Older adults who were members of a Medicare HMO in Los Angeles were asked to list all the illnesses experienced by older adults that they could recall, and those listing HBP or HTN were asked to further list and discuss its symptoms, causes, treatments and prevention. Responses were tape recorded, transcribed, and analyzed to develop a systematic “sentence completion by card sort” follow-up procedure. Consensus Analysis (CA) of the systematically collected data identified shared EM for HBP/HTN. The model presented here is similar to models of HBP/HTN described by researchers working with patients from different regions and different ethnic groups, suggesting that there is a widely shared lay or popular model for this disease. Stress, lifestyle (diet, exercise, weight, and substance use), heredity and aging are thought to be the major causes of HBP/HTN. Physicians are thought to be the appropriate source of care, as HTN/HBP is serious, life threatening, and potentially disabling. The study of cultural understandings and shared EM of disease has direct relevance for clinical practice and public health education. For a disease such as HTN/HBP, knowing where and how such explanations differ systematically between patients and clinicians, and what impact this may have on patterns of adherence to prescribed treatment is a crucial area of concern.

Explanatory Models of Inmate HIV Risk Behaviors: Does a Fatalistic Model Exist?

The deprivation, importation, situational, and administrative control models have been used to explain inmate violence. More recently, HIV risk behaviors of inmates have been explained with the deprivation and importation models. The goal of this study is to assess the utility of these models in describing inmate HIV risk behaviors and to identify additional models that may exist. Forty seven ex-offenders released from prison within three months of the study were recruited from a community based organization. They participated in focus group discussions that explored the contexts surrounding inmate engagement in HIV risk behaviors in prison. Data were analyzed using NVivo 7 and results were organized into themes. Inmates engaged in sex in exchange for money and for affection. Inmates who were drug users before incarceration were more likely to abuse drugs in prison. Security measures, if effective, deterred the entrance of illegal substance into prison, but when security is lax, inmates take the opportunity to engage in sex, and illegal substances are brought into prison. Our results reveal that deprivation, importation, situational, and administrative control factors are associated with HIV risk behaviors among inmates and they can be used in explaining these behaviors. The association of risk behaviors with long or life sentences suggests that fatalism may play a role in risk behaviors among inmates. Fatalism is a factor which requires future examination.

Disparities between Explanatory Models of Health Clients, Healthcare Providers and Health Insurer

We present qualitative data from a study in Ghana （2011）, where the National Health Insurance Scheme （NHIS） was introduced to improve access to health care. In 2011 membership enrolment and retention in the scheme was stalling. To obtain better insights into socio-cultural factors that influence utilization of healthcare services and the NHIS this study compared Explanatory Models of healthcare clients with those of primary healthcare providers and the NHIS regarding illness, the need for, the quality of, and the control over heaithcare and health insurance services. We found critical disparities in socio-cultural beliefs and perceptions of healthcare and health insurance between these three stakeholder groups, such as the clients＇ holistic view on illness versus healthcare providers＇ bio-medical view; the clients＇ inter-relational focus in perceiving quality of services versus the providers＇ medical technical focus. These differences are leading to misconceptions, blame practice, poor services, non-adherence and low trust. The findings increase our understanding of clients＇ behavior and that of their service providers. We conclude with key messages for policy leaders and operational managers that can guide them in improving services and facilitating client trust and interest to participate in health insurance and utilize healthcare services.

Elaboration of Explanatory Factors of Accidents in Cameroon by Factorial Correspondence Analysis

The aim of this paper is to examine the causes of road accidents in Cameroon. The Douala-Yaoundé highway was chosen as the case of study. Available field data recorded from the year 2006 to 2011, have enabled the analysis of each accident. The method used here is the factorial correspondence analysis;which aims to bring in a small number of dimensions, most of the initial information, focusing not on the absolute values, but the correspondence between the variables, that is to say the relative values. From this analysis, it appears that, of the 906 accidents recorded during this period, top five causes account for nearly 83% of the information provided by the set of variables on the occurrence of road accidents. These causes are: driver inattention, lack of control, over speeding, improper overtaking and tire puncture. These results require involvement in the construction of road safety policies through training, sensitization and adequate repressions as well as administrative reforms and research policy in road safety.

An Explanatory Strategy for Reducing the Risk of Privacy Leaks

As machine learning moves into high-risk and sensitive applications such as medical care,autonomous driving,and financial planning,how to interpret the predictions of the black-box model becomes the key to whether people can trust machine learning decisions.Interpretability relies on providing users with additional information or explanations to improve model transparency and help users understand model decisions.However,these information inevitably leads to the dataset or model into the risk of privacy leaks.We propose a strategy to reduce model privacy leakage for instance interpretability techniques.The following is the specific operation process.Firstly,the user inputs data into the model,and the model calculates the prediction confidence of the data provided by the user and gives the prediction results.Meanwhile,the model obtains the prediction confidence of the interpretation data set.Finally,the data with the smallest Euclidean distance between the confidence of the interpretation set and the prediction data as the explainable data.Experimental results show that The Euclidean distance between the confidence of interpretation data and the confidence of prediction data provided by this method is very small,which shows that the model's prediction of interpreted data is very similar to the model's prediction of user data.Finally,we demonstrate the accuracy of the explanatory data.We measure the matching degree between the real label and the predicted label of the interpreted data and the applicability to the network model.The results show that the interpretation method has high accuracy and wide applicability.

Extracting Chinese Explanatory Expressions with Discrete and Neural CRFs

Recent work on opinion mining typically focuses on subtasks such as aspect mining or polarity classification, ignoring the detailed explanatory evidences that account for one certain user opinion. In this paper, we study the extraction of explanatory expressions, by modeling the problem based on conditional random field (CRF). We compare the effectiveness of both discrete and neural features, and further integrate them.We evaluate the models on two datasets from two different domains which have been annotated with ground-truth explanatory expression.Results show that the neural CRF model performs better than the discrete CRF. After a combination of the discrete and neural features, our final CRF mode achieves the top-performing results.

Chinese Explanatory Segment Recognition as Sequence Labeling

How to mine the underlying reasons for opinions is a key issue on opinion mining. In this paper, we propose a CRF-based labeling approach to explanatory segment recognition in Chinese product reviews. To this end, we first reformulate explanatory segments recognition as a labeling task on a sequence of words, and then explore various features from three linguistic levels, namely character, word and semantic under the framework of conditional random fields. Experimental results over product reviews from mobilephone and car domains show that the proposed approach significantly outperforms existing state-of-the-art methods for explanatory segment extraction.

The Effect of A100 Gel, on Hair Growth and Hair Quality: An Explanatory Study

Male pattern baldness or androgenic alopecia is a great problem for many individuals’ especially young people. A100 is composed of two active ingredients, a pollen extract and pentane-1,5-diol. The pollen extract provides a source of natural nutrients and pentane-1,5-diol acts as a solvent to unplug the hair follicle as well as acts as an enhancer for uptake of nutrients. Other components are claimed to increase blood flow to the hair papilla. A100 has been effective in earlier studies. The aim of this open explanatory study was to investigate the effect of 4 months twice daily application with this commercial pollen gel, A100, in subjects with male androgenic alopecia. Twenty male subjects, between 18 and 40 years with androgenic alopecia were included. A100 gel was applied to the area of the scalp with poor hair growth twice daily for 4 months. The subjects were seen at the start of treatment and then every month. Sixteen subjects fulfilled the whole 4 months of treatment and 2 fulfilled 3 months of treatment. A statistically significant increase in number of hairs was seen after 4 months of treatment with A100 (p < 0.001). This effect was seen for all types of hair. Fifty-six percent of the 16 subjects who fulfilled the 4 months treatment had an increase in hair growth of more than 50%, and 31% had an increase over 100%. No side effects were seen and the subjects found A100 gel a cosmetically attractive treatment. A100 was in this explanatory study an effective and safe treatment for androgenic alopecia or male pattern baldness.

Explanatory Multi-Scale Adversarial Semantic Embedding Space Learning for Zero-Shot Recognition

The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space learning plays an important role in zero-shot recognition. Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors. However, the discriminative information included in the user-defined attribute vector is limited. In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space. To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders. We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information. The proposed method is evaluated on the AwA2 and CUB dataset. These results show that our proposed method achieves superior performance.

A Sequential Regression Model for Big Data with Attributive Explanatory Variables

As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data is sequentially collected and used for online statistical modeling.For modeling sequential data,we propose a sequential linear regression method that extracts essential information from historical data.This carefully selected information is then utilized to update a model according to a sequential estimation scheme.With this technique,the earlier data no longer needs to be stored,and the sequential updating is computationally efficient in speed and storage.A weighted strategy is introduced on the current model to determine the impact of data from different periods.When compared with estimation methods that use historical data,our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.

Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economic aggregates, so as to facilitate comparison of aggregates over time. Two methods are used for calculating economic aggregates at comparable prices: 1. Multiplying the output of products by their constant prices of certain year; 2. Deflation of data at current prices by relevant price index.

Explanatory Notes on Main Statistical Indicators

Gross Domestic Product (GDP) refers to the final products at market prices produced by all reside nt units in a country (or a region) during a certain period of time. Gross domestic product is expressed in three differe nt forms, i.e. value, in come, and products respectively. GDP in its value form refers to the total valueof all goods and services produced by all reside nt units duri ng a certai n period of time, minus the total value of in put of goods and services of the n ature of non -fixed assets;in other term, it is the sum of the value-added of all reside nt units. GDP in the form of income in eludes the in come created by all resident units and distributed to reside nt and non-reside nt units. GDP in the form of products refers to the value of all goods and services for final consumption by all resident units minus the net exports of goods and services during a given period of time. In the practice of national accounting, gross domestic product is calculated with three approaches, i.e. production approach, income approach and expenditure approach, which reflect gross domestic product and its composition from different aspects.

Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economic aggregates,soas to facilitate comparison of aggregates over time.Two methods are used for calculating economic aggregates at comparableprices:1.Multiplying the output of products by their constant prices of certain year;2.Deflation of data at current prices byrelevant price index.Constant Price refers to the average price of a given product in certain year,which is used for comparison of output valueover time.As the output value at constant prices removes the factor of price changes,it reflects the trend of productiondevelopment over time.Since 1949,with the changes in general price level,National Bureau of Statistics has

Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economicaggregates, so as to facilitate comparison of aggregates over time. Two methods are used for calculating economicaggregates at comparable prices: 1. Multiplying the output of products by their constant prices of certain year;2. Deflation of data at current prices by relevant price index.