Analysis of Metallic Shielding for Reduction of RF Induced Heating of Electrode During MRI for Active Implants

The options available to patients with implantable devices are limited. It is because there are multiple interactions between the MRI environment and the implantable medical devices. The three main components of MRI systems- static magnet, RF coil, and a gradient coil- interact with the implantable medical devices. These interactions can cause force, torque, device vibrations and RF-induced heating. Among all these potential hazards is the heating caused by the RF electromagnetic field. The lead wires of the implants can act as antennas and pick up the electric field generated by the RF coil. This results in the induced current traveling along the length of the device that will dissipate as heat where it is coupled to tissue. The combination of critically sensitive tissues and high heat makes this interaction the most significant risk for patient safety. Hence, there arises a need to design effective techniques that can minimize RF heating induced during an MRI. The technique of shielding has been proven to reduce RF-induced heating. The focus of current research is to provide analysis of shielding technique for reduction of RF-induced heating of electrodes during MRI. Shielded leads have been developed as a method to reduce RF-heating responsible for temperature rise at the electrodes. The purpose of this work is to provide a quantitative understanding of how a conducting metallic shield over a lead will reduce RF heating at the electrode during MRI scans. A physical model and equations for reduction of RF heating by a shielded lead are presented. Temperature rises are calculated for different lengths of shielded and unshielded leads. Confirming measurements are made for a quarter-wavelength coaxial cable model of the lead. Measured temperature rise and transfer function depended on terminations conditions, with the shorted lead exhibiting the temperature rise sixteen times less than an open-ended lead. The information provided by this work is expected to facilitate the development of lead wires with reduced RF-induced heating. The availability of lead wires with reduced heating will allow expanded access to MRI by patients with implantable devices

Dynamic Body VSLAM with Semantic Constraints

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modeling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by a significant amount for moving object trajectory reconstruction relative to state-of-the-art methods like VISO 2, as well as standard bundle adjustment algorithms

Transient Analysis of Primary Feed Pump Trip for 700 MWe IPHWR

700 MWe Indian Pressurized Heavy Water Reactor (IPHWR) is a horizontal channel type reactor with two loops of Primary Heat Transport (PHTS) system. Three (two operating and one stand by) main boiler feed water pumps (BFP) supply feed water to Steam Generators (SGs). In the event of one of the running BFP trip, standby comes on line on auto. Transient analysis for this event is performed using in- house computer code ATMIKA.T .The transient has been initiated by tripping one of the pumps. Two cases are postulated: 1: BFP Trip and Standby BFP available on auto 2: BFP Trip and Standby pump not available. This paper provides timelines of following sequence of events which is important for operator’s action to maneuver the event, and the main findings of the study are: Following the tripping of one BFP, feed flow reduces and SGs level start falling. As SGs level fall, feed control valves open up in level control mode and system resistance in feed water circuit reduces. If the standby pump comes on auto, the SGs level recovers with a slight dip in level. The feed flow increases and settles down to normal value. Subsequently all the parameters converge to steady state value. Reactor continues to operate at 100% FP. In the event of main BFP trip without the availability of standby BFP, feed flow rate drops. SGs pressure rise slightly due to reduction in sub cooled feed flow and SGs level start to decrease. Reactor setback starts as SG level goes below set back limit. SG level continues to fall and reactor trips on SG Level very very low trip setting

IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery

Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classified into predefined categories or as a clustering task when new and previously unknown intent categories need to be discovered from these utterances. Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup. While typically these tasks are modeled as separate tasks, we propose IntenDD, a unified approach leveraging a shared utterance encoding backbone. IntenDD uses an entirely unsupervised contrastive learning strategy for representation learning, where pseudo-labels for the unlabeled utterances are generated based on their lexical features. Additionally, we introduce a two-step post-processing setup for the classification tasks using modified adsorption. Here, first, the residuals in the training data are propagated followed by smoothing the labels both modeled in a transductive setting. Through extensive evaluations on various benchmark datasets, we find that our approach consistently outperforms competitive baselines across all three tasks. On average, IntenDD reports percentage improvements of 2.32%, 1.26%, and 1.52% in their respective metrics for few-shot MC, few-shot ML, and the intent discovery tasks respectively.Comment: EMNLP 2023 Finding

Morphine modulates proliferation of kidney fibroblasts

Morphine modulates proliferation of kidney fibroblasts. Renal interstitial scarring is an important component of heroin-associated nephropathy. Kidney fibroblasts have been demonstrated to play a role in the development of renal scarring in a variety of renal diseases. We studied the effect of morphine, an active metabolite of heroin, on the proliferation of kidney fibroblasts. Morphine at a concentration of 10−12M enhanced (P < 0.001) the proliferation of kidney fibroblasts (control, 67.5 ± 2.0 vs. morphine, 112.2 ± 10.1 × 104 cells/well). [3H]thymidine incorporation studies further confirmed these results. Morphine at concentrations of 10−12M to 10−10M also modulated mRNA expression of early growth related genes (c-fos, c-jun and c-myc). Morphine at concentrations of 10−8 to 10−4M promoted apoptosis of kidney fibroblasts and also enhanced the synthesis of p53 by kidney fibroblasts. We speculate that morphine-induced kidney fibroblast proliferation may be mediated through the activation of early growth related genes, whereas morphine induced kidney fibroblast apoptosis may be mediated through the generation of p53. The present in vitro study provides a hypothetical basis for the role of morphine in the development of renal interstitial scarring in patients with heroin-associated nephropathy

Women Entrepreneurs of Rajasthan: Decoding Managerial Skills

Employment has been an obvious marvel in the development of new women entrepreneurs. Men or women are equally endowed with psychological and physical abilities along with managerial abilities that are essential for being a successful entrepreneur. Women are certainly not inferior as many of them are ready to undertake the various type of work if opportunities are provided (Singh N. P., 1985). Nearly a decade back, the International Labour Organization (ILO) 1998 World Employment Report characterized the informal sector in the following words: "Informal units comprise small enterprises with hired workers, household enterprises using mostly family labour, and the self- employed. Women entrepreneurship development can be identified by the motivation amongst women, knowledge and awareness, skill enhancement and training, Decision making and Risk-taking abilities. The objective of the paper is to identify the major challenges and cultural and economic barriers faced by women entrepreneurs in Rajasthan that creates a hindrance in the growth and development of Women entrepreneurship

MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device

Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that use pixel-dense warping operations for temporal modeling, making them too computationally expensive for deployment on mobile devices. Recent work has demonstrated that running a neural decoder in real time on mobile is feasible, but shows this only for 720p RGB video. This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device. Our codec relies on two major contributions. First, we design an efficient codec that uses a block-based motion compensation algorithm available on the warping core of the mobile accelerator, and we show how to quantize this model to integer precision. Second, we implement a fast decoder pipeline that concurrently runs neural network components on the neural signal processor, parallel entropy coding on the mobile GPU, and warping on the warping core. Our codec outperforms the previous on-device codec by a large margin with up to 48% BD-rate savings, while reducing the MAC count on the receiver side by $10 \times$. We perform a careful ablation to demonstrate the effect of the introduced motion compensation scheme, and ablate the effect of model quantization.Comment: Matches version published at WACV 202