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Mahasweta Devi is a literary stalwart who has written vehemently for a social cause. As an activist writer, she has explored, studied and presented an authentic portrait of the tumults of the lives of tribal community in India. Her texts present a rare critique of our socio-political-economic structure that is blind towards its own discrepancies. Douloti, the bountiful is yet another piece of work by the writer that seems to explain ‘all about tribal life’− the cycle of exploitation, chronic poverty, hunger, deprivation, bonded slavery, prostitution, marginalisation, the pernicious nexus amongst the rich, powerful and power-mongering elites and so on. The present paper is an attempt to make a deep study of the work and to trace the modus operandi of exploitation, as shown by the writer, with special emphasis on how women are made to suffer differently.

The Official response to Juvenile crime in many countries is underlain by both hypothesis and evidence. These approaches are assessed for similarities and differences in terms of their contribution to fairness, justice, crime control and crime prevention. The nature of formal procedures for the management and management of juvenile offenders and specific government and multilateral responses to child crime that provide the basis for a more uniform treatment of youth in the justice system follow a short historical examination of recognitions of childhood innocence.

To move from 4G to 5G technology spectrum deficiency is an important criteria which can be overcome by an efficient technology called Cognitive Radio (CR). This can be achieved by continuous sensing the spectrum band, and detecting the unused frequency bands which would be not used by licensed band and without any unwanted interference to the primary user or licensed user (PU). The demand of spectrum resources has been increased greatly in modern wireless communication system. Network Selection is one of the difficult mechanisms when moving from 4G to 5G technology to address spectrum shortage issue and by achieve Fast delivery handover route with high speed data rate access, and also maintain the QOS. We Proposed NN-PSO method for network selection and Fast Delivery handover route mechanism in order to increase system efficiency by consider more number of SUs in network and providing to their preferences, and respect the criteria of primary network operators, at the same time. An optimization used to reduced unwanted interference suffer by licensed users (PU), due to presence of SU’s and also the subscription fees that SUs have to pay for using the licensed network or band (PN). The goal is to provide SUs good network and fast delivery handover route with a high QoS based on the criteria of SU, subject to the interference boundation of each existing network with other channels. The proposed technique Neural Network and Particle swarm optimization would be use to solve the Network selection and optimization problem. Finally, experimental results and numerical parameters represent the effectiveness of the proposed NN-PSO methods to finding a near-optimal solution for network selection.

Summary In presence of environmental noise, speakers tend to emphasize their vocal effort to improve the audibility of voice. This involuntary adjustment is known as Lombard effect (LE). Due to LE the signal to noise ratio of speech increases, but at the same time the loudness, pitch and duration of phonemes changes. Hence, accuracy of automatic speech recognition systems degrades. In this paper, the effect of unsupervised equalization of Lombard effect is investigated for Hindi vowel classification task using Hindi database designed at TIFR Mumbai, India. Proposed Quantile-based Dynamic Cepstral Normalization MFCC (QCN-MFCC) along with baseline MFCC features have been used for vowel classification. Hidden Markov Model (HMM) is used as classifier. It is observed that QCN-MFCC features have given a maximum improvement of 5.97% and 5% over MFCC features for context-dependent and context-independent cases respectively. It is also observed that QCN-MFCC features have given improvement of 13% and 11.5% over MFCC features for context-dependent and context-independent classification of mid vowels.

A number of noninvasive imaging techniques have been used for the evaluation of bone marrow, including magnetic resonance imaging (MRI) and bone marrow scintigraphy. The appearance of bone marrow on MRI varies considerably depending on the proportion of red and yellow marrow, and the composition of the red marrow and its distribution with relation to age and sex. The composition of bone marrow also can vary under physiological and pathological conditions. MRI is a highly sensitive technique for evaluating the bone marrow, but it is limited in its practical use for whole-body bone marrow screening. Bone marrow scintigraphy with radiolabeled compounds such as technetium-99m-labeled nanocolloid and monoclonal antibodies has the advantage of evaluating the entire bone marrow, and has been used for the diagnosis of various bone marrow disorders. In addition, (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging can be used to evaluate bone marrow metabolism and disease and to provide information about the state of the primary tumor, lymph nodes, and distant metastases. Understanding of the appearance of normal bone marrow, including age- and sex-specific differences with each of these imaging modalities, is essential to permit accurate diagnosis of benign and malignant bone marrow disorders. We present a review of MRI and scintigraphy of normal bone marrow with some emphasis on FDG-PET imaging in assessing marrow activity in normal and abnormal states and also present preliminary data regarding normal age-related changes in bone marrow through use of FDG-PET, as well as the role of segmentation of bone marrow on MRI for quantitative calculation of the metabolic volumetric product for red marrow metabolism using FDG-PET.

It is useful to understand the normal changes in structure and function in the thorax that occur with age. Thus, we present the following quantitative preliminary data obtained from retrospective quantitative analysis of computed tomography (CT) and positron emission tomography (PET) examinations in subjects 0 to 90 years of age: Mean lung standard uptake values were found to significantly increase with increasing age and with increasing body mass index (BMI). Mean lung attenuation was seen to statistically significantly decrease with increasing age in subjects who had a CT scan, had a nonsignificant tendency to decrease with increasing age in subjects with a PET/CT scan, had a nonsignificant tendency to increase with increasing BMI, and was seen to significantly increase with increasing mean lung standard uptake values. Mean lung volumes were not noted to significantly change with increasing age in adult subjects whether or not they were normalized to the craniocaudal thoracic lengths, although mean lung volumes significantly increased with increasing age in pediatric subjects. Mean lung volumes had a nonsignificant tendency to decrease with increasing BMI, although normalized mean lung volumes significantly decreased with increasing BMI. Lung metabolic volumetric products were not noted to significantly change with increasing BMI or with increasing age. In this work, we also review the literature regarding normal structural and functional changes in the thorax with age.

The development of clinically-applicable quantitative methods for the analysis of brain fluorine-18 fluoro desoxyglucose-positron emission tomography ((18)F-FDG-PET) images is a major area of research in many neurologic diseases, particularly Alzheimer's disease (AD). Region of interest visualization, evaluation, and image registration (ROVER) is a novel commercially-available software package which provides automated partial volume corrected measures of volume and glucose uptake from (18)F-FDG PET data. We performed a pilot study of ROVER analysis of brain (18)F-FDG PET images for the first time in a small cohort of patients with AD and controls. Brain (18)F-FDG-PET and volumetric magnetic resonance imaging (MRI) were performed on 14 AD patients and 18 age-matched controls. Images were subjected to ROVER analysis, and voxel-based analysis using SPM5. Volumes by ROVER were 35% lower than MRI volumes in AD patients (as hypometabolic regions were excluded in ROVER-derived volume measurement ) while average ROVER- and MRI-derived cortical volumes were nearly identical in control population. Whole brain volumes when ROVER-derived and whole brain metabolic volumetric products (MVP) were significantly lower in AD and accurately distinguished AD patients from controls (Area Under the Curve (AUC) of Receiver Operator Characteristic (ROC) curves 0.89 and 0.86, respectively). This diagnostic accuracy was similar to voxel-based analyses. Analysis by ROVER of (18)F-FDG-PET images provides a unique index of metabolically-active brain volume, and can accurately distinguish between AD patients and controls as a proof of concept. In conclusion, our findings suggest that ROVER may serve as a useful quantitative adjunct to visual or regional assessment and aid analysis of whole-brain metabolism in AD and other neurologic and psychiatric diseases.

Low power memory is required today most priority with also high stability. The power is most important factor for today technology so the power reduction for one cell is vital role in memory design techniques. In this paper we introduced some design circuit techniques for low power design. Leakage current in standby mode is the major part of power loss. We concentrate on the technique that to reduced the leakage current in standby mode.

The aim of this study was to introduce a new concept for accurate measurement of the global metabolic activity of the red marrow by combining segmented volumetric data from structural imaging techniques such as magnetic resonance imaging (MRI) and quantitative metabolic information provided by functional modalities such as positron emission tomography (PET).Imaging studies from five subjects who had undergone both MRI and 2-deoxy-2-[F-18]fluoro-D-glucose(FDG)-PET were selected for this analysis to test the feasibility of this approach. In none of the subjects, there were any marrow abnormalities as determined either by the MRI or by FDG-PET studies. The mean blood glucose level was 96+/-25 mg/dl. The first step was to calculate vertebral volume at L1, L3, and L5 from the available MRI studies. The red and yellow marrows were then segmented within the lumbar vertebrae using the 3DVIEWNIX software system from which the respective volumes were also calculated for each. This also allowed calculating the bone volume in each of the vertebral bodies examined. By employing the standard techniques, the mean of the maximum standardized uptake values (mean SUVmax) for the bone marrow were calculated in L1, L3 and L5 of the lumbar spine, and then global red marrow activity was calculated using the following approach: (1) Whole vertebral metabolic activity (WVMA)=vertebral volume x mean SUVmax of the entire marrow, (2) whole vertebral metabolic activity for yellow marrow (WVMAYM)=yellow marrow volume x mean SUVmax of fat (obtained from measurements of subcutaneous fat), (3) whole vertebral metabolic activity for red marrow (WVMARM)=WVMA-WVMAYM; and finally, (4) SUVmax for pure red marrow=whole vertebral metabolic activity for red marrow (WVMARM)/red marrow volume (obtained from the segmentation data).The mean volume of the lumbar vertebral body was 15.6+/-1.4 cm3, the bone marrow mean SUVmax was 1.5+/-0.3, and the MVP for the lumbar vertebral body was 23.4+/-5.9. The mean volume of the yellow marrow in the lumbar vertebral body was 7.7+/-1.1 cm3, the yellow marrow mean SUVmax was estimated to be 0.38+/-0.1 and the MVP for the yellow marrow in the lumbar vertebral body was 2.9+/-0.9. The mean volume of the red marrow in lumbar vertebral body was 7.9+/-1.1 cm3, the red marrow mean SUVmax was estimated to be 2.6+/-0.6, and the MVP for the red marrow in the lumbar vertebral body was 20.5+/-5.9.Estimation of the individual component of the bone marrow is plausible using medical image segmentation with combined structure-function approach. This can have potential research and clinical applications concerning the study of global metabolic activity of the individual component and diagnosis of benign and malignant bone marrow disorders.

To examine the relation between low-contrast letter acuity, an emerging visual outcome for multiple sclerosis (MS) clinical trials, and brain MRI abnormalities in an MS cohort.T2 lesion volume and brain parenchymal fraction were determined for whole brain and within visual pathway regions of interest. Magnetization transfer ratio histograms were examined. Vision testing was performed binocularly using low-contrast letter acuity (2.5%, 1.25% contrast) and high-contrast visual acuity (VA). Linear regression, accounting for age and disease duration, was used to assess the relation between vision and MRI measures.Patients (n = 45) were aged 44 +/- 11 years, with disease duration of 5 years (range1 to 21), Expanded Disability Status Scale score of 2.0 (0 to 6.0), and binocular Snellen acuity of 20/16 (20/12.5 to 20/25). The average T2 lesion volume was 18.5 mm(3). Patients with lower (worse) low-contrast letter acuity and high-contrast VA scores had greater T2 lesion volumes in whole brain (2.5% contrast: p = 0.004; 1.25%: p = 0.002; VA: p = 0.04), Area 17 white matter (2.5%: p0.001; 1.25%: p = 0.02; VA: p = 0.01), and optic radiations (2.5%: p = 0.001; 1.25%: p = 0.02; VA: p = 0.007). Within whole brain, a 3-mm(3) increase in lesion volume corresponded, on average, to a 1-line worsening of low-contrast acuity, whereas 1-line worsening of high-contrast acuity corresponded to a 5.5-mm(3) increase.Low-contrast letter acuity scores correlate well with brain MRI lesion burden in multiple sclerosis (MS), supporting validity for this vision test as a candidate for clinical trials. Disease in the postgeniculate white matter is a likely contributor to visual dysfunction in MS that may be independent of acute optic neuritis history.

The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance.

Preliminary results generated from digital mammography, computed tomography, magnetic resonance imaging, and 18 F-fluorodeoxyglucose positron emission tomography demonstrate concordant findings of decreasing glandular tissue and decreasing metabolic activity with increasing age. These results are presented in the context of a detailed literature review summarizing age-related changes in the breast, both from the histologic/physiologic and the imaging perspectives. We also discuss potential applications of this approach and emphasize the importance of new advanced imaging technologies to offer high levels of quantitative precision for tissue characterization for research and clinical purposes.

The Medical Image Processing Group (MIPG) at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available, open source, and is integrated with toolkits such as ITK and VTK. CAVASS runs on Windows, Unix, and Linux but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive COWs (Cluster of Workstations) for more time consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3D and higher dimensional medical imagery, so support for DICOM data and the efficient implementation of algorithms is given paramount importance.

This paper presents (1) an improved hierarchical method for segmenting the component tissue regions in fast spin echo T2 and PD images of the brain of Multiple Sclerosis (MS) patients, and (2) a methodology to characterize the disease utilizing the distributions of standardized T2 and PD intensities in the segmented tissue regions. First, the background intensity inhomogeneities are corrected and the intensity scales are standardized for all acquired images. The segmentation method imposes a feedback-like procedure on our previously developed hierarchical brain tissue segmentation method. With gradually simplified patterns in images and stronger evidences, pathological objects are recognized and segmented in an interplay fashion. After the brain parenchymal (BP) mask is generated, an under-estimated gray matter mask (uGM) and an over-estimated white matter mask (oWM) are created. Pure WM (PWM) and lesion (LS) masks are extracted from the all-inclusive oWM mask. By feedback, accurate GM and WM masks are subsequently formed. Finally, partial volume regions of GM and WM as well as Dirty WM (DWM) masks are generated. Intensity histograms and their parameters (peak height, peak location, and 25th, 50th and 75th percentile values) are computed for both T2 and PD images within each tissue region. Tissue volumes are also estimated. Spearman correlation coefficient rank test is then utilized to assess if there exists a trend between clinical states and the image-based parameters. This image analysis method has been applied to a data set consisting of 60 patients with MS and 20 normal controls. LS related parameters and clinical Extended Disability Status Scale (EDSS) scores demonstrate modest correlations. Almost every intensity-based parameter shows statistical difference between normal control and patient groups with a level better than 5%. These results can be utilized to monitor disease progression in MS.