Stochastic Modeling for Coliform Count Assessment in Ground Water

Stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil characteristics. The study is based on about twenty location and soil characteristics, majority of them are observed through laboratory analysis of soil and water samples collected from nearly thee hundred locations of drinking water sources, wells and bore wells selected at random from the district of Kasaragod. The water contamination in wells are found to be relatively more as compared to bore wells. The study reveals that only 7 % of the wells and 40 o~ of the bore wells of the district are within the permissible limit of WHO standard of drinking water quality. The level of contamination is very high in the hospital premises and is very low in the forest area. Two separate multiple ordinal logistic regression models are developed to predict the level of coliform count, one for well and the other for bore well. The significant feature of this study is that in addition to scientifically proving the dependence of the water quality on the distances from waste disposal area and septic tanks etc., it highlights the dependence of two other very significant soil characteristics, the soil organic carbon and soil porosity. The models enable to predict the quality of water in a location based on the set of soil and location characteristics. One of the important uses of the model is in fixing safe locations for waste dump area, septic tank, digging well etc. in town planning, designing residential layouts, industrial layouts, hospital/hostel construction etc. This is the first ever study to describe the ground water quality in terms of the location and soil characteristics.

Numerical simulation of three-dimensional combustion flows

A finite-rate method is used to simulate the three-dimensional combustion process in a plasma generator with CH4 as the fuel. The simulation was run with RNG k-ε model to simulate turbulence, with eddy-dissipation-concept (EDC) model to simulate the combustion and with discrete ordinates model to simulate radiation. The numerical results show that the flow field characteristics and the parameter distributions are under the condition of rich fuels, and these results provide valuable information when optimizing the plasma generator design and organizing its flow fields.

Modified Contingent Valuation Method for Agricultural Wastes between the Areas of Peloponnese and Thessaly in Greece

This paper focuses on investigating the tendency of farmers in the greater area of Peloponnese as well as those on the greater area of Thessaly to participate in a program of environmental agricultural waste management.The aim of the present study is to investigate the level of amount WTA(Willingness to Accept)against which the farmers would be willing to allow anyone to gather and carry away the leftovers of their farming exploitation.To serve the above goals,the agricultural leftovers are evaluated by the CVM(Contingent Valuation Method)as an environmental non-market economic commodity.The specimen response on utilization of the rejected biomass was satisfactory,since a good percentage of the interviewees are willing to contribute to this procedure for a minimal of remuneration,although investigations of this type are novel for Greek reality.From this investigation,it is perceived by the society the importance of alternative utilization of the rejected biomass.It is concluded that the minimum amount each interviewee is willing to accept as minimal remuneration and is affected by the way these biomass leftovers are managed,the age of the interviewees and the area in which they live.Those interviewees who use such biomass leftovers in alternative applications wish to be remunerated higher compared to those who relinquish such leftovers.The ordinal logistic model is listed in a broader category of generalized linear models for arrangement data.

Multivariate Analyses for Finding Significant Track Irregularities to Generate an Optimal Track Maintenance Schedule

We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displacement (LLID). The results of applying the cluster analysis technique to the sampling data showed that maintenance operation is required for approximately 10% of the total lots, and these lots were further classified into three groups according to the degree of maintenance need. To analyze the background factors for detecting abnormal LLID lots, a principal component analysis was performed;the results showed that the first principal component represents LLIDs from the viewpoints of the rail structure, equipment, and operating conditions. Binomial and ordinal logit regression models (LRMs) were used to quantitatively investigate the determinants of abnormal LLIDs. Binomial LRM was used to characterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significant impact on LLIDs.

RETHINKING HOW WE LIGHT AT NIGHT: CUTTING LIGHT POLLUTION FOR MORE SUSTAINABLE NIGHTS

INTRODUCTION Nighttime lighting is an essential part of modern society,yet all too often inefficient practices are employed that lead to poor visibility,energy waste,and light pollution.The use of dark-sky friendly lighting fixtures and techniques can improve the lit environment to be less harsh and more uniform,reduce many of the problems of light pollution,and lead to tremendous energy savings.

Using random-parameter and fixed-parameter ordered models to explore temporal stability in factors affecting drivers' injury severity in single-vehicle collisions

Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers' injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers' injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers' injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver's gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers' injury severity in single-vehicle collisions.