Potential public health significance of faecal contamination and multidrug-resistant Escherichia coli and Salmonella serotypes in a lake in India

To assess the prevalence of faecal coliform bacteria and multiple drug resistance among Escherichia coli and Salmonella serotypes from Vembanadu Lake. Study design: Systematic microbiological testing. Methods: Monthly collection of water samples were made from ten stations on the southern and northern parts of a salt water regulator constructed in Vembanadu Lake in order to prevent incursion of seawater during certain periods of the year. Density of faecal colifrom bacteria was estimated. E. coli and Salmonella were isolated and their different serotypes were identified. Antibiotic resistance analysis of E. coli and Salmonella serotypes was done and the MAR index of individual isolates was calculated. Results: Density of faecal coliform bacteria ranged from mean MPN value 2900 -7100/100ml. Results showed multiple drug resistance pattern among the bacterial isolates. E. coli showed more than 50% resistance to amickacin, oxytetracycline, streptomycin, tetracycline and kanamycin while Salmonella showed high resistance to oxytetracycline, streptomycin, tetracycline and ampicillin. The MAR indexing of the isolates showed that they have originated from high risk source such as humans, poultry and dairy cows. Conclusions: The high density of faecal coliform bacteria and prevalence of multi drug resistant E. coli and Salmonella serotypes in the lake may pose severe public health risk through related water borne and food borne outbreaks

MQFD: A Model for Synergizing TPM and QFD

This thesis presents the methodology of linking Total Productive Maintenance (TPM) and Quality Function Deployment (QFD). The Synergic power ofTPM and QFD led to the formation of a new maintenance model named Maintenance Quality Function Deployment (MQFD). This model was found so powerful that, it could overcome the drawbacks of TPM, by taking care of customer voices. Those voices of customers are used to develop the house of quality. The outputs of house of quality, which are in the form of technical languages, are submitted to the top management for making strategic decisions. The technical languages, which are concerned with enhancing maintenance quality, are strategically directed by the top management towards their adoption of eight TPM pillars. The TPM characteristics developed through the development of eight pillars are fed into the production system, where their implementation is focused towards increasing the values of the maintenance quality parameters, namely overall equipment efficiency (GEE), mean time between failures (MTBF), mean time to repair (MTIR), performance quality, availability and mean down time (MDT). The outputs from production system are required to be reflected in the form of business values namely improved maintenance quality, increased profit, upgraded core competence, and enhanced goodwill. A unique feature of the MQFD model is that it is not necessary to change or dismantle the existing process ofdeveloping house ofquality and TPM projects, which may already be under practice in the company concerned. Thus, the MQFD model enables the tactical marriage between QFD and TPM.First, the literature was reviewed. The results of this review indicated that no activities had so far been reported on integrating QFD in TPM and vice versa. During the second phase, a survey was conducted in six companies in which TPM had been implemented. The objective of this survey was to locate any traces of QFD implementation in TPM programme being implemented in these companies. This survey results indicated that no effort on integrating QFD in TPM had been made in these companies. After completing these two phases of activities, the MQFD model was designed. The details of this work are presented in this research work. Followed by this, the explorative studies on implementing this MQFD model in real time environments were conducted. In addition to that, an empirical study was carried out to examine the receptivity of MQFD model among the practitioners and multifarious organizational cultures. Finally, a sensitivity analysis was conducted to find the hierarchy of various factors influencing MQFD in a company. Throughout the research work, the theory and practice of MQFD were juxtaposed by presenting and publishing papers among scholarly communities and conducting case studies in real time scenario.

“Assessment of Heavy Metals in the Environmental Compartments of the Central and Northern Coast of Kerala, India”

The nearshore marine ecosystem is a dynamic environment impacted by many activities, especially the coastal waters and sediments contiguous to major urban areas. Although heavy metals are natural constituents of the marine environment, inputs are considered to be conservative pollutants and are potentially toxic, accumulate in the sediment, are bioconcentrated by organisms and may cause health problems to humans via the food chain. A variety of metals in trace amounts are essential for biological processes in all organisms, but excessive levels can be detrimental by acting as enzyme inhibitors. Discharge of industrial wastewater, agriculture runoff and untreated sewage pose a particularly serious threat to the coastal environment of Kerala, but there is a dearth of studies in documenting the contaminant metals. This study aimed principally to assess such contamination by examining the results of heavy metal (Cu, Pb, Cr, Ni, Zn, Cd and Hg) analysis in seawater, sediment and benthic biota from a survey of five transects along the central and northern coast of Kerala in 2008 covering a 10.0 km stretch of near shore environment in each transect. Trophic transfer of metal contaminants from aquatic invertebrates to its predators was also assessed, by employing a suitable benthic food chain model in order to understand which all metals are undergoing biotransference (transfer of metals from a food source to consumer).The study of present contamination levels will be useful for potential environmental remediation and ecosystem restoration at contaminated sites and provides a scientific basis for standards and protective measures for the coastal waters and sediments. The usefulness of biomonitor proposed in this study would allow identification of different bioavailable metals as well as provide an assessment of the magnitude of metal contamination in the coastal marine milieu. The increments in concentration of certain metals between the predator and prey discerned through benthic food chain can be interpreted as evidence of biotransference.

A Comparative Study of Convective Parameterization Schemes in WRF-NMM Model

Severe local storms, including tornadoes, damaging hail and wind gusts, frequently occur over the eastern and northeastern states of India during the pre-monsoon season (March-May). Forecasting thunderstorms is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent non-linearity of their dynamics and physics. In this paper, sensitivity experiments are conducted with the WRF-NMM model to test the impact of convective parameterization schemes on simulating severe thunderstorms that occurred over Kolkata on 20 May 2006 and 21 May 2007 and validated the model results with observation. In addition, a simulation without convective parameterization scheme was performed for each case to determine if the model could simulate the convection explicitly. A statistical analysis based on mean absolute error, root mean square error and correlation coefficient is performed for comparisons between the simulated and observed data with different convective schemes. This study shows that the prediction of thunderstorm affected parameters is sensitive to convective schemes. The Grell-Devenyi cloud ensemble convective scheme is well simulated the thunderstorm activities in terms of time, intensity and the region of occurrence of the events as compared to other convective schemes and also explicit scheme

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.