Eating habits appears to be an important determinant of dietary intake and may consequently influence overweight and obesity. Understanding the relationship between the nutritional status and eating habits is necessary for effective prophylaxis and intervention of overweight/obesity in adolescents. The purpose of this study is to analyze the association of some eating habits with overweight and obesity among adolescents at the age of 11-14 from 9 junior high schools in Hanoi city to help design a model for predicting overweight and obesity from eating habits. A case-control study was conducted on 222 overweight/obese adolescents and 616 normal-weight adolescents (according to International Obesity Taskforce standards, IOTF). Research results indicated that protective factors of overweight and obesity include snacking; snacking at least 2 hours before or after a main meal; consumption of milk and dairy products in snacks; sensory liking for fruit. Risk factors include sensory liking for fat, sensory liking for sweet, sensory liking for fast food, sensory liking for carbonated soft drinks, skipping breakfast, and snacking before bed. The best predictive model of overweight and obesity s built from logistic regression analysis including 8 of the above eating habits with AUC (Area Under the Curve) value of 0.931. Thus, eating habits are closely related to overweight and obesity among 11-14 year-old adolescents in Hanoi. Keywords Eating habits, overweight, obesity, adolescence, risk factor. References [1] A.S. French, M. Story and C.L. Perry, Self-esteem and obesity in children and adolescents: a literature review, Obesity Research 3 (1995) 479-490. https://doi.org/10.1002/j.1550-8528.1995.tb00179.x[2] E.A. Finkelstein, C.J. Ruhm, and K.M. Kosa, Economic causes and consequences of obesity, Annual Review of Public Health 26 (2005) 239-257. https://doi.org/10.1146/annurev.publhealth.26.021304.144628 
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The aim of this study is to compare and evaluate the dose distribution and physical characteristics of two algorithms Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) inEclipse v13.6 software in regions heterogeneous densities. Computed Tomography Simulation (CT –Sim) data of 48 treated cancer patients (20 head and neck cancer (H&N) patients, 15 esophageal cancer patients, 8 lung cancer patients with 3 Dimensions Conformal Radiation Therapy (3D-CRT)and 5 lung cancer patients treated with Volumetric Modulated Arc Therapy (VMAT)) were used to replan the Eclipse v13.6 software with two algorithm AAA and AXB. For all plans, the Quality of Coverage (Q), the Conformity Index (CI), the Homogeneity Index (HI) and the dose volume histograms (DVH) for the targets and the organs at risk (OARs) were compared and evaluated. Pretreatment quality assurance (QA) was performed using the Electronic Portal Imaging Device(EPID) for all VMAT plans, and the gamma index method was used to qualify the agreement between calculations and measurements. In addition, total Monitor Units (MUs) and the calculation time were investigated. The indicators obtained from the H&N VMAT plans calculated by AAA close to ideal values than AXB. The total MUs obtained from two algorithms are approximately equal. The lung cancer 3D – CRT plans, the indicators for target and OARs are approximately the same. However, the calculation time of the AAA is faster than the AXB from 7.5 to 14 times. The indicator obtained from the lung cancer VMAT plans calculated by two algorithms AAA and AXB are approximately equal. The total MUs and time calculation are approximate the same. However, the V5, V10, V20 and Mean Lung Dose (MLD) obtained from AAA is lower than AXB. For esophageal cancer VMAT plans, the indicators HIRTOG, HIWu, and Q calculated by AAA close to the ideal values than AXB. However, the indicators CIPaddick, CIICRU-62, V5, V10, V20 and MLD calculated by AXB are better than AAA. The dose distribution indicators obtained from AAA algorithm are better than AXB algorithm in H&N cancer and lung cancer plans. For the esophageal cancer plans, AXB algorithm gave the dose distribution indicator are better than AAA.