What really attracts boys to girls
The purpose of this research was to challenge misconceptions about what attracts teenaged boys to girls. It has been observed that some teenaged girls hold stereotypes about boys and believe boys are only attracted to superficial qualities like beautiful hair. The aim is to investigate differences between what girls believe boys find attractive about them and what boys actually find attractive about girls. Hypothesis: There is a significant difference between what teenaged boys find attractive in girls and what teenaged girls think boys find attractive. Procedure Background reading was done to discover which characteristics are commonly used to measure attractiveness among teenagers. A survey questionnaire was used for gathering data. To gather information for the construction of the questionnaire, 4 focus groups were interviewed. The groups were asked to determine indicators of attraction and repulsion. Two separate questionnaires were designed (one for boys and one for girls), based on focus group information, with similar questions which could be used to compare results. Questions were related to personality and looks. The research was representative as 399 teenagers participated in the final survey. Results were collated in Excel tables. Percentages were calculated by using a scientific calculator and Excel. A bivariate analysis was created to examine the main question: ranking qualities that attract boys to girls. A comparison was made between boys’ and girls’ opinions from different age categories. Mean averages were calculated for this question. Other questions were analysed using mode averages translated into percentage of each gender. Graphs were designed using the values calculated. Observations/Data/Results 72% of boys prefer a girl with average looks and an awesome personality but only 37% of girls believed that to be true. The average boys from 12-14, 15-17 and 18-20 year old categories agree that confidence, humour, a nice body, politeness and intelligence are the five most attractive qualities in a girl they would consider dating. The order of these five change across the age groups but are constantly the top five, however confidence and humour always trump a “nice body”. The average 12-14 and 15-17 year old girl overrates a nice body and underrates politeness and intelligence but confidence and humour feature in their top five. The average 18-20 year old girl agrees with the boys priorities. When defining what makes a girl beautiful, 68% of boys highlight personality traits and 59% of boys mentioned physical features. When noting what repulses boys about pretty girls, more than 50% of both sexes agree that smelling bad was most off putting. 52% of boys mentioned taking drugs as unattractive yet only 23% of girls recognized this as repulsive. Conclusions The data supports the hypothesis. Confidence and humour are more important to the average boy than a nice body across all age groups. The younger the girl, the less she understands what attracts boys. 63% of teenaged girls believe that looks are more important to boys than personality. This is clearly not true. 72% of boys value a great personality more highly than good looks.
Migration Data-Driven Mathematical Model for New City Growth
The growth of a city and the population movement has many correlations. However, the complex interaction causes difficulties in developing a mathematical model needed for analyzing the growth factor of a city and the movement factor of population. The model involving traditional equations cannot explain many phenomenon. The newly introduced data-science suggests possibilities to overcome these difficulties. Particularly, the abundant amount of accumulated data proposes a new solution for the problem we have. Throughout these steps, data-utilizing methodology, such as machine learning for artificial intelligence, are researched and developed with attention. In this research, data about accumulated for previous population movement and city growth are collected, and a mathematical science model based on data is developed to explain population movement and city growth by utilizing data analyzing methods such as machine learning. Especially, artificial neural network and stratified advance learning(deep learning) proves possibilities in solving many problems. This research aims to construct an artificial neural network appropriate for population movement and consequently use it in developing population movement model. Using this model, growth of many existing cities can be explained and furthermore, examining the population movement factor of a city and social factor necessary for city growth become possible. This model is expected to become the tool for resolving overpopulation and predicting and deciding factors needed for a new future city. In spite of decreasing population, it is still important to develop a model for population movement that well explains city growth and environment change.