River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

In this study, an extended version of variable parameter McCarthy–Muskingum (VPMM) method originally proposed by Perumal and Price (J Hydrol 502:89–102, 2013) was compared with the widely used data-based model, namely support vector machine (SVM) and hybrid wavelet-support vector machine (WASVM) to simulate the hourly discharge in Neckar River wherein significant lateral flow contribution by intermediate catchment rainfall prevails during flood wave movement. The discharge data from the year 1999 to 2002 have been used in this study. The extended VPMM method has been used to simulate 9 flood events of the year 2002, and later the results were compared with SVM and WASVM models. The analysis of statistical and graphical results suggests that the extended VPMM method was able to predict the flood wave movement better than the SVM and WASVM models. A model complexity analysis was also conducted which suggests that the two parameter-based extended VPMM method has less complexity than the three parameter-based SVM and WASVM model. Further, the model selection criteria also give the highest values for VPMM in 7 out of 9 flood events. The simulation of flood events suggested that both the approaches were able to capture the underlying physics and reproduced the target value close to the observed hydrograph. However, the VPMM models are slightly more efficient and accurate, than the SVM and WASVM model which are based only on the antecedent discharge data. The study captures the current trend in the flood forecasting studies and showed the importance of both the approaches (physical and data-based modeling). The analysis of the study suggested that these approaches complement each other and can be used in accurate yet less computational intensive flood forecasting

Japanese encephalitis virus latency following congenital infection in mice

Latent Japanese encephalitis virus (JEV) infection was shown in inapparently congenitally infected Swiss albino mice after their mothers had been given JEV intraperitoneally during pregnancy. Only one of 37 (2.7%) of the baby mice showed persistence of infectious virus at 5 weeks of age. Reactivation of JEV in Swiss albino mice was demonstrated by stimulation with allogeneic spleen cells from Parks strain mice at 21 weeks of age; reactivation was demonstrated in 41% of the inapparently infected mice. The spleen cells of congenitally infected mice had depressed [3H] thymidine uptake following stimulation with concanavalin A, and depressed ability to induce a graft-versus-host response

Rhabdomyosarcoma of the posterior chest wall in a newborn: a case report

Rhabdomyosarcoma is the most common soft tissue malignancy of childhood, but may occur extremely rarely in the neonatal period. There are only a few reports of rhabdomyosarcoma in neonates. Although, it may arise anywhere in the body, the head and neck, and genitourinary regions are the most frequent sites. Truncal and chest wall rhabdomyosarcoma is relatively rare occurrence. We report a neonate with embryonal rhabdomyosarcoma arising from the posterior chest wall muscles at birth. Computer Tomography scan raised the possibility of rhabdomyosarcoma or neurofibroma, fine-needle aspiration cytology was inconclusive. Total excision was done and chemotherapy given. At 6 months child is without recurrence

Microfilariae in Breast Fine Needle Aspiration-an Unusual Finding

Abstract Filariasis is a major public health problem particularly in tropical countries like India. The presence of microfilaria using fine needle aspiration cytology has been reported from various sites. However, the presence of filarial worm on breast aspirates has rarely been reported. Here, we report an unusual case in which aspiration cytology revealed presence of numerous microfilariae in breast lump

Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting