New criterion for rock joints based on three-dimensional roughness parameters

The shear behavior of rock joints is important in solving practical problems of rock mechanics. Three group rock joints with different morphologies are made by cement mortar material and a series of CNL(constant normal loading) shear tests are performed. The influences of the applied normal stress and joint morphology to its shear strength are analyzed. According to the experimental results, the peak dilatancy angle of rock joint decreases with increasing normal stress, but increases with increasing roughness. The shear strength increases with the increasing normal stress and the roughness of rock joint. It is observed that the modes of failure of asperities are tensile, pure shear, or a combination of both. It is suggested that the three-dimensional roughness parameters and the tensile strength are the appropriate parameter for describing the shear strength criterion. A new peak shear criterion is proposed which can be used to predict peak shear strength of rock joints. All the used parameters can be easily obtained by performing tests.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

Impact of Workspace Design in Indian Context on Occupant＇s Behavior, to Enhance Efficiency and Comfort

The environmental psychology ofa workspace is a rich and diverse field of study that is growing fast and gaining popularity. The indoor environmental factors in workspaces should follow functional as well as behavioral requirements as the spaces are designed as per the user＇s requirements, needs and aspirations. These play a major role in achieving behavioral impact on the user, as sense of well-being in the environment and physical comfort in the space enhances the productivity and efficiency of the user of the space. The slow and gradual importance of this issue in workspace design with Indian context, post occupancy evaluation can be adjudged as a permanent tool for improving indoor environment in buildings. Occupants should be used as a benchmark of evaluation, showing correlation of physical measurements and subjective parameters. It also suggests recommendations to improve quality of indoor environment in office buildings with analytical study, an important step in post occupancy evaluation. The objective of the paper is to inquire how people experience environmental psychology of the workspace as the determinants are efficiency, productivity and comfort. The success of any design depends upon the degree to which it creates an interface between the user and the environment.

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.