Modular electromagnetic railgun accelerator for high velocity impact studies.

A modular electromagnetic railgun accelerator facility named "RAFTAR" (i.e., Railgun Accelerator Facility for Technology and Research) has been commissioned and its performance has been characterized for high velocity impact testing on materials in a single-shot mode. In the first tests, RAFTAR demonstrated an acceleration of more than 1000 m/s for an 8 g solid aluminum-7075 armature projectile. The current fed was 220 kA, having a muzzle time of about 1.75 ms. It is a single pulse breech-fed rectangular bore (14 × 13 mm2) railgun, and its 1.15 m long barrel assembly consists of two parallel copper bars with an inter-gap of 13 mm that are encased within 50 mm thick high strength reinforced fiberglass sheets (Garolite G10-FR4) and bolted from both the sides. RAFTAR is powered by two capacitor bank modules that have a maximum stored energy of 160 kJ each (containing eight 178 µF/15 kV capacitors), two high power ignitron switches, and a pulse shaping inductor. To obtain consistent acceleration of the armature inside the barrel, reversal of driving current is prevented, and its pulse duration is stretched by tactical integration of the crowbar switch and bitter coil inductor in the circuit. Armature projectile velocity measurement in-bore and outside in free space was performed by the time-of-flight technique using indigenously made miniature B-dot sensors and a novel shorting-foil arrangement, respectively. The time resolved measurement of the in-bore armature evidenced a velocity-skin-effect in the high acceleration phase. There is good agreement between the experimentally measured and theoretically predicted efficiency, confirming the optimal choice of operating parameters. The conclusion summarizes important experimental findings and analyzes the underlying causes that limit the performance of railguns.

A Novel Approach of Diffusion Tensor Visualization Based Neuro Fuzzy Classification System for Early Detection of Alzheimer's Disease.

This study examined early detection of Alzheimer's disease (AD) by diffusion tensor visualization-based methodology and neuro-fuzzy tools. Initially, we proposed a model for the early detection of AD using the measurement of apparent diffusion coefficient, fractional anisotropy, and gray matter, which can determine neurological disorder patterns and abnormalities in brain white matter. These are used as input parameters into fuzzy tools, and using fuzzy rules, we evaluate the AD score as an output variable that provides a useful platform to physicians in determining the status of the disease. In the second stage, we present an investigative study on AD and used the neuro-fuzzy classification system for pattern recognition of either AD or healthy control. The experimental results are from 20 samples (14 for training, 3 for validation, and 3 for testing) used in an artificial neural network classification system. The neural network is trained with a training algorithm and the performance of the training algorithm is obtained by executing a fuzzy expert system. Out of 20 patients, 9 are AD patients and 11 are healthy control patients. We present a neuro-fuzzy tool as a better classifier for early detection of AD and obtain a satisfactory performance with 100% accuracy.

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

BACKGROUND: Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. METHODS: The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (µ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). RESULTS: We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. CONCLUSION: The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

Observation of nonconventional spin waves in composite-fermion ferromagnets.

We find unexpected low energy excitations of fully spin-polarized composite-fermion ferromagnets in the fractional quantum Hall liquid, resulting from a complex interplay between a topological order manifesting through new energy levels and a magnetic order due to spin polarization. The lowest energy modes, which involve spin reversal, are remarkable in displaying unconventional negative dispersion at small momenta followed by a deep roton minimum at larger momenta. This behavior results from a nontrivial mixing of spin-wave and spin-flip modes creating a spin-flip excitonic state of composite-fermion particle-hole pairs. The striking properties of spin-flip excitons imply highly tunable mode couplings that enable fine control of topological states of itinerant two-dimensional ferromagnets.

Mathematical modelling of toxicity-related trade-offs in metronomic chemotherapy.

For better quality of life (QoL) for the cancer patients, metronomic chemotherapy (MCT) would be the rational option instead of conventional chemotherapy. However, in view of the recent arguments regarding the accumulation of toxicity in MCT, it is worthwhile to examine the role of pathophysiological constraints in retarding the curative potential of MCT. Drug application is stopped upon attaining a certain high level of toxicity and subsequent resumption is possible once the toxicity drops below a certain low level. Large delays in subsequent drug administration can thus effectively handle toxicity and it may retard the therapeutic potential due to excessive tumour growth in the absence of drug. Small delays, on the other hand, may result in inoperable pathophysiological states resulting in poor QoL. It is argued that only the intervention of domain knowledge of an expert oncologist with respect to drug administration decision can in fact clinch the trade-off issues arising out of the situation in favour of a sustainable QoL. A mathematical model that may act as a general guideline to tackle the trade-off issues in cancer treatment is provided. Rigorous simulation exercises are required to establish the concept of MCT in the backdrop of conventional cancer treatment practices.

Disseminated tuberculosis presenting as hemobilia, successfully treated by arterial embolization.

Tuberculosis, specially disseminated tuberculosis, involves the liver frequently. Focal hepatic tuberculosis with local hemorrhage has been reported. We report on a twenty-one year female with disseminated tuberculosis presenting with initially non-localisable massive upper gastrointestinal bleeding, subsequently found to have pancreatitis, right sided pleural effusion and hemobilia which was treated successfully.

Possible approach for serodiagnosis of ascariasis by evaluation of immunoglobulin G4 response using Ascaris lumbricoides somatic antigen.

Somatic antigen of Ascaris lumbricoides was purified to homogeneity (molecular mass, 34 kDa) by ammonium sulfate fractionation and successive chromatographic procedures, namely, gel permeation, ion exchange, and high-performance gel permeation liquid chromatographies. The antigen showed strong binding with immunoglobulin G (IgG) in Ascaris-infested patients and was cross-reactive with IgE and IgG in patients infested with other nematodes. It reacted specifically with IgG4 (P < 0.001) in 63 Ascaris-infested patients, which represented 65% of the total IgG response, though cross-reactivity with IgG1, IgG2, and IgG3 subclasses was observed, indicating the unique specificity of this test system and its potential utility in the serodiagnosis of ascariasis.

Facilitation of functional compartmentalization of bone marrow cells in leukemic mice by biological response modifiers: an immunotherapeutic approach.

Biological Response Modifiers (BRMs) including interleukin-2 (IL-2), interferon-gamma (IFN-gamma) and sheep erythrocytes (SRBC) protected N,N'-ethylnitrosourea (ENU) induced leukaemic mice. Two cell types from the bone marrow were isolated in density specific gradient representing two distinct compartments, the low density cells being more CD34 positive than the high density group. Investigations with the functional efficacy of such compartments revealed significant improvement of cytotoxic efficacy and phagocytic burst at the high density compartment (HDC) level. The high density compartment was found to be more responsive towards the BRMs compared to the cells of the low density compartment (LDC). It was suggested that use of BRMs in vivo can stimulate a potent functional progenitor compartmentalization in normal as well as leukaemic mice. These observations are expected to help a logistic approach towards combined BRM therapy at the clinical level.

Antimicrobial susceptibility pattern among methicillin resistant Staphylococcus isolates in Assam.

Prevalence of methicillin resistant Staphylococcus aureus from a referral hospital in Assam was studied. Methicillin resistance among the Staphylococcus aureus isolates was 52.9% and 15% among the coagulase negative staphylococci. Resistance to all antibiotics tested among the methicillin resistant and methicillin sensitive staphylococci was found to be 23.2% and 6.6% respectively. Higher resistance to multiple antibiotics in methicillin resistant strains as compared to methicillin sensitive strains was found to be statistically significant. Ciprofloxacin resistance among the strains was still lower in comparison to the findings from other parts of the country.

Bacterial colony counting using distance transform.

A distance-transform based technique is presented for the segmentation of monochrome images of colonies grown on membrane filters. This is used to count the number of Escherichia coli in a given water sample, which is used as a parameter for determining water quality. The result is compared with fuzzy c-means clustering approach.

A connectionist model for category perception: theory and implementation.

A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other.