Heat transfer in MHD Ekman layer on a porous plate

A steady asymptotic solution for the temperature distribution in the case of flow past a porous plate in a rotating frame of reference is obtained. In particular, the temperature distribution for MHD Ekman layer on a porous flat plate is studied. It is seen that, while a steady asymptotic solution is possible in case of suction, no steady temperature field is possible in case of blowing. Further, from the results it is observed that suction and magnetic field have opposing influence on the rate of heat transfer. © 1978 Società Italiana di Fisica

Clinical profile of patients with hypertensive emergencies in a tertiary care hospital

Background: Hypertensive emergency is defined by systolic blood pressure (SBP) ≥180 mmHg and/or diastolic blood pressure (DBP) ≥120 mmHg with acute target organ damage. Hypertensive emergencies can be life threatening and fatal unless timely treated. In the present study we aim to study the clinical profile and outcome of patients admitted with hypertensive emergency in the medical intensive care unit in our hospital. Methods: A cross-sectional observational study of all cases with hypertensive emergency admitted in Medical Intensive care unit (ICU) in Government General Hospital, Srikakulam during the study period was conducted. The clinical profile and outcome of the patients were assessed. Results: Out of the fifty patients in the present study, about 36 (72%) were males and 14 were females (28%) and about one third of the patients (36%) belonged to the age group of 60-69 years. Diabetes mellitus (30%) and dyslipidemia (40%) were the commonly encountered comorbidities in the study population. Most frequent presenting symptoms were neurological deficits (50%) followed by dyspnoea (32%) and chest pain (24%). Intracerebral haemorrhage was the commonest target organ damage found in 30% of the patients. Patients presenting with hypertensive emergencies with neurological target organ damage had statistically significant increased chance of mortality (p=0.007). Conclusions: As hypertension emergencies are consequence of uncontrolled hypertension, it is important to educate and bring awareness among public regarding the screening, early detection, and adherence to prescribed medication for hypertension to avoid adverse clinical outcomes

Silicon cycle in Indian estuaries and its control by biogeochemical and anthropogenic processes

We study the silicon biogeochemical cycle and its associated parameters in 24 and 18 Indian estuaries during dry and wet periods respectively. We focus more specifically on dissolved Si (DSi), amorphous Si (ASi,) lithogenic Si (LSi), Particulate Organic Carbon (POC), Total Suspended Material (TSM), Dissolved Inorganic Nitrogen (DIN), salinity and fucoxanthin, a marker pigment for diatoms. Overall, we show that the estuaries have strong inter and intra variability of their biogeochemical parameters both seasonally and along salinity gradients. Based on Principal Component Analysis and clustering of categorised (upper and lower) estuaries, we discuss the four major processes controlling the Si variability of Indian estuaries: 1) lithogenic supply, 2) diatom uptake, 3) mixing of sea water and, 4) land use. The influence of lithogenic control is significantly higher during the wet period than during the dry period, due to a higher particle supply through monsoonal discharge. A significant diatom uptake is only identified in the estuaries during dry period. By taking into account the non-conservative nature of Si and by extrapolating our results, we estimate the fluxes from the Indian subcontinent of DSi, ASi, LSi to the Bay of Bengal (211 ± 32, 10 ± 4.7, 2028 ± 317 Gmol) and Arabian Sea (80 ± 15, 7 ± 1.1, 1717 ± 932 Gmol). We show the impact of land use in watersheds with higher levels of agricultural activity amplifies the supply of Si to the coastal Bay of Bengal during the wet season. In contrast, forest cover and steep slopes cause less Si supply to the Arabian Sea by restricting erosion when entering the estuary. Finally, Si:N ratios show that nitrogen is always in deficit relative to silicon for diatom growth, these high Si:N ratios likely contribute to the prevention of eutrophication in the Indian estuaries and coastal sea

A rough fuzzy approach to web usage categorization

This paper introduces a novel clustering scheme employing a combination of rough set theory and fuzzy set theory to generate meaningful abstractions from web access logs. Our experimental results show that the proposed scheme is capable of capturing the semantics involved in web access logs at an acceptable computational expense

Use of Multi-category Proximal SVM for Data Set Reduction

Abstract. We present a tutorial introduction to Support Vector Machines (SVM) and try to show, using intuitive arguments, why SVM’s tend to perform so well on a variety of challenging problems. We then discuss the quadratic optimization problem that arises as a result of the SVM formulation. We talk about a few computationally cheaper alternative formulations that have been developed recently. We go on to describe the Multi-category Proximal Support Vector Machines (MPSVM) in more detail. We propose a method for data set reduction by effective use of MPSVM. The linear MPSVM formulation is used in an iterative manner to identify the outliers in the data set and eliminate them. A k-Nearest Neighbor (k-NN) classifier is able to classify points using this reduced data set without significant loss of accuracy. We also present geometrically motivated arguments to justify our approach. Experiments on a few publicly available OCR data sets validate our claims

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set. 1

Scalable Rough Support Vector Clustering

In this paper a novel scalable soft support vector clustering algorithm is proposed. Here softness is imparted to Support Vector Clustering paradigm by employing rough set theory and scalability is achieved using Multi Sphere Support Vector Clustering method. Empirical results show that the proposed method gives meaningful cluster abstractions

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets

Rough support vector clustering

In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic flavour in support vector clustering paradigm to achieve soft clustering. Empirical studies show that this method can find soft clusters having arbitrary shapes