Human body images encode plenty of useful biometric information, such as pupil color, gender, and weight. Among these, body weight is a good indicator of health conditions. Motivated by recent health science studies, this paper investigates the feasibility of analyzing body weight from two-dimensional (2D) frontal view human body images. The widely used body mass index (BMI) is employed as a measure of body weight. To investigate the problems at different levels of difficulties, three feasibility problems, from easy to hard, are studied. More specifically, a framework is developed for analyzing body weight from human body images. Computation of five anthropometric features is proposed for body weight characterization. Correlation is analyzed between the extracted anthropometric features and the BMI values, which validates the usability of the selected features. A visual-body-to-BMI dataset is collected and cleaned to facilitate the study, which contains 5900 images of 2950 subjects along with the labels corresponding gender, height, and weight. Some interesting results are obtained, demonstrating the feasibility of analyzing body weight from 2D body images. In addition, the proposed method outperforms two state-of-art facial image-based weight analysis approaches in most cases.
This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier.
A number of methods are available to researchers for estimating molecular weights (molar masses) and molecular weight distributions of starches − or those in solution, using an appropriate solvent/solubilisation protocol. We outline the methods available and assess their relative merits and limitations. We focus on size‐exclusion chromatography or field flow fractionation coupled to multi‐angle light scattering, viscometry, and sedimentation velocity and sedimentation equilibrium in the analytical ultracentrifuge.
There are numerous influencing factors of the risk consequences of dam break. The scientific and reasonable index system and its weight distribution are some of the key elements for comprehensive evaluation of the dam break risk. Taking into consideration 20 factors, including hazards, exposure and vulnerability, the evaluation index system of the consequences of dam break risk is constructed. Using the Statistical Cloud Model (SCM) to improve the entropy method, we establish the weight calculation model of the influencing factors of dam break risk consequences. The results shows that the top five factors with the highest weight are risk population, flood intensity, alert time, risk understanding and distance from the dam. Compared to traditional algebraic weight calculation methods, the result is basically consistent with the algebraic weight distribution, and increases the range by 2.03 times, supporting a more scientific basis for recognizing and evaluating dam break risk consequences.
The metabolomic approach using LC-MS analyses suffers from substantial intensity variability which must be corrected before extracting useful biological information. In this paper, Common Components and Specific Weights Analysis (CCSWA) is proposed as a novel method for the correction of this analytical bias. This method was compared to LOESS normalisation for within-batch correction and to the median of the quality controls for between-batch correction. In the first case, the correction of a non-continuous effect in the batch was investigated using both LOESS signal correction and CCSWA on fish samples. In the second case, four batches were analysed and combined to create a larger cohort of honey samples. CCSWA was successfully applied to correct both within- and between-batch effects observed in the LC-MS signals.