(R1464) Stability of the Artificial Equilibrium Points in the Low-Thrust Restricted Three-Body Problem with Variable Mass

In this article, we have investigated the existence and stability of the artificial equilibrium points (AEPs) in the low-thrust restricted three-body problem with variable mass. In this model of the low-thrust restricted three-body problem, we have considered both the primaries as point masses. The mass of the spacecraft varies with time according to Jeans’ law (1928). We have introduced a new concept for creating the AEPs in the restricted three-body problem with variable mass using continuous constant acceleration. We have derived the equations of motion of the spacecraft after using the space-time transformations of Meshcherskii. The AEPs have been created by cancelling the gravitational and centrifugal forces with the constant continuous low-thrust at the non-equilibrium points. The positions of these AEPs will depend not only on magnitude but also on the constant directions of the low-thrust acceleration. We have analyzed the linear stability of the AEPs and found that all the AEPs are unstable. Finally, we have drawn the zero velocity curves (ZVCs) to determine the possible regions of motion in which the spacecraft is free to move

Heuristic Approach to Satisfaction Level of Demand Maker in Three Stage Scheduling with Fuzzy Due Time

This paper discusses three stage flow shop scheduling including transportation time in frame work of fuzzy due date with bi-objective criteria. On one side to minimize the tardiness of jobs while on other way to determine the satisfaction level of demand maker with the help of fuzzy due date to each job. The analysis has been made through heuristic algorithm based on mathematical theorem regarding tardiness of the jobs. The objective of the paper is to find due time to each job and optimal or near optimal sequence of jobs in order to minimize the tardiness of jobs so that they can be performed within time. The paper ends with a numerical illustration and analyse graphically the satisfaction level of demand maker. Keywords: Fuzzy processing time, trapezoidal, tardiness, <AHR>, linguistic variable, fuzzy quantifier

CREDIT POINTS EXCHANGE

The present disclosure relates to a method and a system for establishing a credit exchange platform that allows users to effectively utilize the unused credit points. In an embodiment, the present disclosure discloses aggregating the credit points accumulated on multiple credit cards using the credit exchange platform. Further, the disclosure suggests allowing the users to redeem the aggregated credit points or sell them to a different user, resulting in effective and best use of the credit points

ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours

Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-based architectures that were proposed to align image structures tend to fail with contour representation of shapes, mostly due to the use of proximity-insensitive pixel-wise similarity measures as loss functions in their training processes. This work presents a novel ConvNet, "ProAlignNet" that accounts for large scale misalignments and complex transformations between the contour shapes. It infers the warp parameters in a multi-scale fashion with progressively increasing complex transformations over increasing scales. It learns --without supervision-- to align contours, agnostic to noise and missing parts, by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric that uses classical Morphological Chamfer Distance Transform. We evaluate the reliability of these proposals on a simulated MNIST noisy contours dataset via some basic sanity check experiments. Next, we demonstrate the effectiveness of the proposed models in two real-world applications of (i) aligning geo-parcel data to aerial image maps and (ii) refining coarsely annotated segmentation labels. In both applications, the proposed models consistently perform superior to state-of-the-art methods.Comment: Accepted at CVPR 202

In vitro release behavior of paclitaxel and carboplatin from poly(l-lactide) microspheres dispersed in thermosensitive biodegradable gel for combination therapy

The objective of the current work was to design an injectable, sustained release formulation of a combination of anticancer drugs, carboplatin and paclitaxel, for localized delivery. In this combination formulation, carboplatin was encapsulated into poly(L-lactide) (PLA) microspheres and paclitaxel was dissolved in thermosensitive biodegradable gel of PLGA-PEG-PLGA (poly (DL-lactide-co-glycolide- polyethylene glycol- poly (DL-lactide-co-glycolide)); no external solvent like cremophorEL was used in the formulation, further, these carboplatin microspheres were dispersed in the gel containing paclitaxel to achieve a single delivery system. The combined formulation was assessed for various parameters for sustained release of both the drugs. Release profiles of carboplatin from PLA microspheres; paclitaxel from hydrogel alone and in combination with carboplatin and carboplatin microspheres dispersed in paclitaxel loaded gel were studied. In vitro release of both the drugs from PLGA-PEG-PLGA hydrogel showed that carboplatin was released with 40-50% burst release and paclitaxel was released in biphasic manner for 50-60 days. Initial burst of carboplatin was controlled by incorporating it in PLA microspheres which were then dispersed in paclitaxel loaded hydrogel and the new formulation did not exhibit any burst release of the drug. Release pattern of combination formulations revealed that the two drugs were co-eluting from a single delivery system and the rate of release of each of the individual drugs was significantly affected.Thus, a novel injectable combination formulation for sustained and simultaneous delivery of carboplatin and paclitaxel was developed which provided sustained release of each of the drugs and could be further explored in tumor models.Keywords: Thermosensitive hydrogels, Microspheres, controlled release, combination chemotherapy

Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes

E-commerce websites (e.g. Amazon) have a plethora of structured and unstructured information (text and images) present on the product pages. Sellers often either don't label or mislabel values of the attributes (e.g. color, size etc.) for their products. Automatically identifying these attribute values from an eCommerce product page that contains both text and images is a challenging task, especially when the attribute value is not explicitly mentioned in the catalog. In this paper, we present a scalable solution for this problem where we pose attribute extraction problem as a question-answering task, which we solve using \textbf{MXT}, consisting of three key components: (i) \textbf{M}AG (Multimodal Adaptation Gate), (ii) \textbf{X}ception network, and (iii) \textbf{T}5 encoder-decoder. Our system consists of a generative model that \emph{generates} attribute-values for a given product by using both textual and visual characteristics (e.g. images) of the product. We show that our system is capable of handling zero-shot attribute prediction (when attribute value is not seen in training data) and value-absent prediction (when attribute value is not mentioned in the text) which are missing in traditional classification-based and NER-based models respectively. We have trained our models using distant supervision, removing dependency on human labeling, thus making them practical for real-world applications. With this framework, we are able to train a single model for 1000s of (product-type, attribute) pairs, thus reducing the overhead of training and maintaining separate models. Extensive experiments on two real world datasets show that our framework improves the absolute recall@90P by 10.16\% and 6.9\% from the existing state of the art models. In a popular e-commerce store, we have deployed our models for 1000s of (product-type, attribute) pairs.Comment: ACL 2023 Industry Track, 8 Page

Acute necrotising pancreatitis as the first and sole presentation of undiagnosed primary hyperparathyroidism

Primary hyperparathyroidism is a recognized, but rare, cause of acute pancreatitis. The pathophysiology of hypercalcemia-induced acute pancreatitis is not well known, but when this combination occurs, pancreatitis is likely to be severe and the degree of hypercalcemia may play an important role in this association. Therefore, the cause of hypercalcemia should be identified early. Surgical resection of the parathyroid adenoma is the ultimate therapy. We report two cases with severe acute necrotizing pancreatitis associated with hypercalcemia. The cause of hyperparathyroidism was a benign parathyroid adenoma. We highlight the drawbacks in delaying the diagnosis of primary hyperparathyroidism in patients with acute pancreatitis as the sole clinical presentation