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Abstract:We assessed the contribution of selected built environment factors to body weight in a pilot study in urban Visakhapatnam, South India. Participants were 123 men and 60 women (age 16 to 69 years; BMI 17.3-30.5) who had lived in the area for at least 3 years. Individuals with lower BMI tended to be (a) working people (non-home based-working away from home), (b) non-vegetarians, (c) physically active (activity mostly related to work), and (d) taking afternoon siestas. Psychological stress, quality of life and wellbeing data were used from an earlier study of individuals with diabetes mellitus. The measures included were depression, anxiety, energy, positive wellbeing, satisfaction, impact, and social worry and diabetes worry (Diabetes quality of life). Guttman's Smallest Space Analysis (SSA) suggested the relationships among the psychosocial measures can be accounted for by one facet with three axial sets of variables (a) positive wellbeing and energy, (b) satisfaction, impact, and social worry and diabetes worry, and (c) anxiety and depression. SSAs on male participants suggested that fasting blood glucose and weight were most closely associated with anxiety and energy levels. In female participants, weight and fasting glucose were most closely associated with energy and to a somewhat lesser extent with anxiety. In both sexes, age was closely associated with positive wellbeing. Also in both sexes, age, weight, and fasting glucose levels were closely associated with each other. The results support the importance of understanding the impact of built environment and psychosocial factors on body weight in diabetic individuals for designing prevention strategies.

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The optimization problems naturally found in various engineering, scientific, and business domains with different natures. In the real world, it is common to administer optimization problems that have three or more objectives; such problems associated with the "Many-objective Optimization Problems (MaOPs)" group, obtaining the optimal solution for such problems is a challenging task.The main components that are required to design effective many-objective optimization algorithms are the aggravation of conflicting objectives, convergence, and diversity along with the requirement of

Thyroid is one of the vital diseases that influence individuals of any age group now a day. Infections of the thyroid, incorporate conditions related with extreme release of thyroid hormones (Hyper thyroidism) which is likewise called thyrotoxicosis and those related with thyroid hormone insufficiency (Hypothyroidism). Expectation of these two sorts of thyroid disease is critical for thyroid analysis. In this paper, support vector machines and logistic regression are proposed for predicting patients with thyrotoxicosis and without thyrotoxicosis. The outcomes demonstrate that, logistic regression perform well over support vector machine with 98.92% exactness.

Predicting the Software reliability is a pertinent issue and it is a major concern of software developers and engineers in changing environment considerations. Software reliability models are developed to estimate the probability of failure free operation of the software for a long time. Many Software Reliability Growth Models (SRGM) were developed to give the latent number of faults in the software product. However none of these models performing to the expectations of the developers of the software. In this paper, A research is made using artificial neural network models to monitor the performance of the software that leads to predict the software reliability. The MLP model outperforms SVR model, and based on the results, these models can be considered to be a reasonable alternative for software quality prediction.

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