Virtual Determination of Sex:Estimating Cut off Value of Digital Metric Traits of Foramen Magnum on Three-dimensional Computed Tomography with Receiver Operating Characteristic and Logistic Regression Analysis

Background:Radiological imaging plays a pivotal role in forensic anthropology.As have the imaging techniques advances,so have the digital skeletal measurements inched towards precision.Secular trends of the population keep on changing in modem times.Hence,finding the precise technique of bone measurement,with greater reproducibility,in modem population is always needed in making population specific biological profile.Aim and Objective:The aim of this study was to estimate the accuracy of the foramen magnum measurement,obtained by three dimensional multi-detector computed tomography using volume rendering technique with the cut off value of each variable,in sex determination of an individual.Materials and Methods:Two metric traits,an antero-posterior diameter(APD)and transverse diameter(TD),were measured digitally in an analysis of 130 radiological images having equal proportion of male and female samples.Foramen magnum index and area of foramen magnum(Area by Radinsky's[AR],Area by Teixeira5s[AT])were derived from APD and TD.Results:Descriptive statistical analysis,using unpaired t-test,showed significant higher value in males in all the variables.Using Pearson correlation analysis,maximum correlation was observed between area(AT and AR r=0.999)and between area and TD(AR r=0.955 and AT r=0.945 respectively).When used individually,TD had the highest predictive value(67.7%)for sex detennination among all the parameters followed by AT(65.4%)and AR(64.6%).Cutoff value of variables TD,AR and AT were 29.9 mm,841.80 mm2 and 849.70 mm2 respectively.Receiver operating characteristic curve predicted male and female sex with 96.2%and 89.2%accuracy respectively.The overall accuracy of the model was 92.7%.Conclusion:Measurements from 3D CT using volume rendering technique were precise,and the application of logistic regression analysis predicted the sex with more accuracy.

A Study on the Determinants of Location Choice in Inward Foreign Direct Investment （FDI）： Focusing on the Incheon Free Economic Zone

Inward foreign direct investment （FDI） is expected to grow further by virtue of economic globalization. A thorough understanding of the locational determinants of inward FDI will be conducive to enhanced efficiency in attracting direct and SOC-related investments from foreign entities. This study analyzes 51 cases of inward direct foreign investment made in the Incheon free economic zone （IFEZ） from 2002 to 2009 to determine the factors influencing FDI volume, the relevance of locations and the correlation between investment size and location. First, the relationship between the loeational determinants of FDI and the total investment size （total expected project cost） is analyzed. Second, the relationship between the locational determinants of FDI and the FDI is analyzed. Third, the relationship between the locational determinants of FDI and the location choice is analyzed. The results indicate the determinants that influence locations and investment size of FDI entities; whether these factors exercise influence in the zone; and the factors that have relatively significant effects. Ultimately, based on the analytical findings, a few implications for policy and practice are derived.

Factors influencing farmer willingness to plant forage triticale in winter fallow fields in Northern China:An example from central Shanxi Province

Background:Using winter fallow fields for plant forage is important to ensure food security.Forage triticale(×Triticosecale)has higher yields than other available forage crops and can be planted widely in winter fallow fields.Recently,the planted area of forage triticale in Shanxi Province,China,has exceeded 3500 ha;however,problems such as low farmer willingness to plant(WTP)winter forage still remain.Methods:A total of 219 farmers were surveyed in Taiyuan,Lvliang,and Jinzhong.We analyzed the factors influencing farmer WTP forage triticale,focusing on personal,family,land,and cognition characteristics.We used a binary logistic regression model to quantify the influence of various factors on farmer behavior and conducted a robustness check and heterogeneity analysis.Results:“Age”was negatively correlated with farmer WTP—farmers 50 years of age and older showed less WTP.“Land lease situation”was also negatively correlated with WTP.Factors that positively correlated with WTP were“land areas,”“raising of livestock,”“size of labor force,”and“development prospect.”Conclusions:Many farmers are over 50 years of age,land lessors,and have low WTP winter forage.Farmers who raise livestock and have large labor forces,huge land areas,and good cultivation prospects have a high WTP.This study identifies the factors influencing farmers'WTP to assist in the development of the forage triticale industry in the study region,improving land resource utilization and efficiency.The findings are likely to have wider relevance and application.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.