Modelling and analysis of recruitment distributions

Recruitment data for 18 marine fish stocks are smoothed using 10 parametric families of probability distributions. Comparative fit of the 10 families is assessed by means of the maximized log-likelihood. Results indicate that the gamma distribution provides an overall good fit in the right-hand tail of the data, but that some adjustment to the gamma distribution is called for in the left-hand tail. Weight functions and weighted distributions are suggested as one means of achieving the needed adjustment.     

Transport of Pollutant in Open Channel with Short Waves

This study examines the effect of short period water waves on the longitudinal mixing of pollutants in open channel flow. These waves create orbital motions and therefore increase the magnitude of the dispersion coefficient. Experiments are conducted for non-wavy and wavy flow. The values of the longitudinal dispersion coefficients are determined by applying the method of least squares to the measured solute concentrations at various time intervals. For non-wavy flow, the measured values of longitudinal dispersion coefficient match closely with those computed from the empirical equation given by Seo [1]. For wavy flow, a new factor called the wave parameter (a/TU _*, a=wave amplitude, T=wave period, U _*=shear velocity) is found important and a nonlinear multiple regression analysis is used to derive a new expression for the wave induced longitudinal dispersion coefficient (WILDC). An uncertainty analysis is conducted as per IS Code 5168 and the confidence interval is determined. Linear water wave theory is applied to modify the existing expression of the longitudinal dispersion coefficient of Seo [1] by including the effect of short waves. A mathematical model for WILDC is then developed. Comparative study between wavy and non-wavy flow cases has been conducted. The results clearly show an increase in the magnitude of longitudinal dispersion coefficient in the presence of waves.     

PULSE,progressive upper level set scan statistic for geospatial hotspot detection

This paper presents a scan statistic, progressive upper level set (PULSE) scan statistic, for geospatial hotspot detection and its software implementation. Like ULS, the PULSE scan statistic is based on the arbitrarily shaped scan window and can be adapted for a network setting. PULSE is a refinement of the upper level set (ULS) scan statistic. Like some other likelihood based scanning devices, the ULS scan statistic identifies maximum likelihood estimate (MLE) zones that tend to be ‘stringy’ and sprawling. Its search path increases possibility of inclusion of extraneous cells in its MLE zones and, to a smaller extent, of exclusion of cells that belong to a true hotspot from its MLE zone. The PULSE scan statistic achieves improvement over the ULS scan statistic in two ways. First, it begins its search for a most likely zone with a large population of candidate zones obtained by modifying the ULS tree structure and continues its search using a genetic algorithm. Secondly, to reduce chances of generating an MLE that is excessively stringy and that includes extraneous cells in the MLE zone, PULSE uses cardinality and compactness of zones along with their likelihoods as the fitness function in the genetic algorithm and uses several pertinent criteria including evenness of intra-zone cellular response ratios to determine the MLE zone. To reduce computation, Gumbel distribution of extreme values is used to determine the p-value of the MLE zone. Better results come at the cost of increased processing time. An evaluative performance study is presented.     

Next-generation sequencing in the biodiversity conservation of endangered medicinal plants

Environmental Science and Pollution Research - Medicinal plants have been used as traditional herbal medicines in the treatment of various types of diseases. However, the increased demand for these...     

Biosorption of chromium (VI) from aqueous solutions and ANN modelling

The use of sustainable, green and biodegradable natural wastes for Cr(VI) detoxification from the contaminated wastewater is considered as a challenging issue. The present research is aimed to assess the effectiveness of seven different natural biomaterials, such as jackfruit leaf, mango leaf, onion peel, garlic peel, bamboo leaf, acid treated rubber leaf and coconut shell powder, for Cr(VI) eradication from aqueous solution by biosorption process. Characterizations were conducted using SEM, BET and FTIR spectroscopy. The effects of operating parameters, viz., pH, initial Cr(VI) ion concentration, adsorbent dosages, contact time and temperature on metal removal efficiency, were studied. The biosorption mechanism was described by the pseudo-second-order model and Langmuir isotherm model. The biosorption process was exothermic, spontaneous and chemical (except garlic peel) in nature. The sequence of adsorption capacity was mango leaf > jackfruit leaf > acid treated rubber leaf > onion peel > bamboo leaf > garlic peel > coconut shell with maximum Langmuir adsorption capacity of 35.7 mg g^?1 for mango leaf. The treated effluent can be reused. Desorption study suggested effective reuse of the adsorbents up to three cycles, and safe disposal method of the used adsorbents suggested biodegradability and sustainability of the process by reapplication of the spent adsorbent and ultimately leading towards zero wastages. The performances of the adsorbents were verified with wastewater from electroplating industry. The scale-up study reported for industrial applications. ANN modelling using multilayer perception with gradient descent (GD) and Levenberg-Marquart (LM) algorithm had been successfully used for prediction of Cr(VI) removal efficiency. The study explores the undiscovered potential of the natural waste materials for sustainable existence of small and medium sector industries, especially in the third world countries by protecting the environment by eco-innovation.     

The rationales behind the unit-segment theory and its analysis methodology for modeling vehicular movement networks

The theory of space accessibility analysis, space syntax, has been considered for capturing trip-makers’ route-choice decisions with an understanding that, while selecting routes, trip-makers tend to follow forward-facing fields traditionally represented by axial lines. But the idea of treating axial lines as a set of roadway segments representing an urban texture has remained under scrutiny when distinct procedures have been tested without much success. Nor has the axial-line theory seemed to gain much popularity within the orthodox transportation planning community. Studies show that a long axial line is found gaining configurational centrality, in general, for being well connected to its incident lines. Fragmenting a long axial line results in weakening it (in centrality terms), but also in generating a locally oriented model. Recent investigations, dealing with this problem, have resulted in the emergence of the unit-segment theory, which has turned out to be more sensitive to the local structures located within a large urban texture than the axial counterpart for the purpose of modeling vehicular movement networks without using origin–destination trip-counts. Essentially, the question now is: Is the unit-segment analysis also capable of generating syntax configurations that are equally sensitive to large and complex urban textures? What should be the methodology of such an analysis? This paper throws deeper insights into these questions by producing theoretical and empirical research findings and seeks to develop a common ground for both transportation planning and space syntax communities to work together for gaining a better understanding of how urban configurations relate to urban vehicular movement networks.     

Infrastructure development through PPPs in India: criteria for sustainability assessment

Public private partnerships (PPPs) allow the Indian Government to leverage private capital for meeting the widening demand-supply gap in the provision of infrastructure services. The private sector, however, prefers to limit the participation to financially attractive projects only, thereby resulting in patterns of infrastructure creation impeding the progress towards sustainable development. In order to promote sustainable development, the PPP procurement process should focus on incentivising the private sector for sustainable infrastructure development rather than concentrating on ensuring financial sustainability only. This paper discusses the principles-based PPP-specific framework that has been developed to facilitate assessment of PPP projects' progress towards sustainable development. The framework development was based on a holistic approach to sustainability assessment and subsequently validated through questionnaire survey with key stakeholders in the Indian PPP programme. This framework will provide the decision makers with appropriate decision aid for integration of sustainable development principles in the PPP procurement process.     

Water Quality Modeling of Municipal Discharges from Sea Outfalls,Mumbai

The island city of Mumbai with a population of about 10 million generates about 2000 million liters per day (mld) of sewage from the seven service areas of the city sewerage network and discharges it into the adjoining west coast and the two creeks in the Arabian Sea. This has resulted in degradation of coastal water quality, contamination of the adjoining beaches and seafronts. The Municipal Corporation of Greater Mumbai has therefore, undertaken the task of delineating appropriate sewage disposal system to achieve cleaner marine ecosystem through marine outfalls at specific locations. This paper presents the results of the mathematical simulations on the impacts of discharge is-a-vis the length of the outfall and level of land treatment apriori. The results of the simulation indicate the level of bacterial pollution to be higher near the diffuser locations as compared to nearshore regions. 48 hsimulation result analysis shows that FC counts nearthe diffuser location will be in the range of 2000–8000 counts per 100 ml.