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In this paper, numerical approach for free vibration response of functionally graded material (FGM) circular and annular plate is examined using differential quadrature method (DQM), and first-order shear deformation theory. The effective material properties through the thickness of the plate are obtained via power-law distribution. The governing partial differential equations (GDEs) are obtained using Hamilton’s principle. The five GDEs are discretized via DQM. Several comparison studies were carried out by those published in the literature. The free vibration analysis is investigated to reveal the effects of outer radius to thickness ratio, grading index, radii ratio and boundary conditions.

In domains with high stakes, like healthcare and medicine, trustworthy and robust decision-making is crucial due to the potential risks associated with misclassification. However, many traditional machine learning classifiers lack calibrated predictions, and reliable uncertainty estimates for new unseen data. This paper addresses the challenge of uncertainty quantification in text classification in healthcare and proposes a three-fold approach to \textit{support} robust and trustworthy decision-making by medical practitioners. To evaluate our solution, we implement it on a multi-label medical transcription dataset from Kaggle. Our study demonstrates three significant results: the ability of our model to reject uncertain predictions by providing a null set, the provision of set predictions with guaranteed coverage for further investigation, and the prioritization of decision-making based on confidence levels of predictions with the same label. Additionally, we tackle the issue of imbalanced datasets in the medical domain by employing the Mondrian Conformal Predictor with a Naïve Bayes classifier. Our findings are expected to enhance the risk-aware decision-making process in the medical field., KDD'23 Southern California Data Science Day, Applied Data Science (ADS) Track, Long Beach, CA

Mobility is no longer just a necessity for travelers, but choices among several possible routes and transportation modes. Urban passenger rail transport plays an essential role because it is affordable, convenient, safe, and fast. On the other hand, rail lines are limited to high passenger density corridors. Inevitably, rail has to be placed together with different transport modes, forming a multimodal network. However, to enable this integration with other modes of transport, numerous practical problems remain, such as making a smooth transition from the existing siloed, mode specific operational structure towards an interconnected system of transportation modes and business models for a seamless connected journey. The current isolated operational structure lacks a single truth and accurate visibility, which further discourages participation from augmenting transportation modes and leads to the extended reaction time for new technology integration. This research article introduces a Multimodal Mobility (MMM) solution framework that provides a functional interface to integrate and synchronize the railroad operations with other public transit networks (including train-bus-rapid transits) and micro-mobility services. The known approach to addressing the users’ seamless mobility experience entails a centralized, prearranged, a priori knowledge and mechanism for operating intermodal transport systems. In contrast, the method defined in this paper focuses on a market-driven demand-responsive system that allows for dis-intermediation in a network of peer-level transportation modes operations. The framework facilitates blockchain-based decentralized and multi-organizational engagement. The focus here is the role of railroad in the multimodal ecosystem and its performance advancements in this integrated solutions framework. Leveraging a combination of graph analytics and machine learning algorithms, we provide methods to address challenges in encoding spatial and temporal dependencies of multimodal transit networks and handle complex optimization problems such as mixed time window and volume variation for resource allocation and transit operational analytics. This enables operation of different transit modes with varied resolution and flexibility for operational parameters like time, capacity, ridership, revenue management, etc. The analytics enable solutions for recommendations on synchronizing and integrating operations of transportation systems. Further, the network’s decentralization and modular handling enable market-driven co-optimization of operational resources across various transportation modes to ensure seamless transit experience for users.

Carpets are the three-dimensional product used as a floor covering in homes, offices, commercial centers, decorative purposes, etc. The average life span of the carpet is four to seven years and after that, it becomes solid waste. The discarded carpets are causing a significant hazardous effect on the environment, climate, soil, and various health issues. To overcome the increasing carpet waste, the re-utilization of carpet is essentially desired. This article focuses on the development of polymer composites developed from discarded nylon carpets for lightweight applications. A modified technique of Vacuum-assisted resin transfer molding (VARTM) was used to fabricate epoxy composites. The tensile and flexural tests evaluated the mechanical performance of the proposed composite. The modified composite is manufactured in two different configurations, namely, face- back-to-back-face (FBBF) and back- face to face- back (BFFB) with the help of the VARTM setup. The result demonstrated that the fabricated BFFB composite has a higher strength. The high-resolution microscopy test of the developed samples shows the feasibility of the composites produced from discarded carpet for lightweight functions. An attempt has been made to resue the waste for the fabrication of cost-effective products.

The growing urban population and the changes in travel behavior with the help of IoT has created a market for new generations of transportation systems. Multi-Modal Mobility (MMM) is one of the major trends that is driving this transformation toward seamless and reliable transportation. However, due to the disconnected infrastructure of different modes of transportation, there is a need to develop advanced analytical tools to optimize the operational and service functions of different modes. Our goal is to build methods for integration and interaction of different modes. We propose a segmentation method to have a holistic view of the MMM network, but yet independent operation of different modalities. To this end, we encode the area into a uniform set of hexagonal-shaped cells and then extract features based on various modes of transportation. Then, we leverage cell features and the segmented network structure for analysis. As a case study, we specifically focus on demand-based analysis as it is an essential task for transportation systems. The demand pattern knowledge can improve the transportation system in both micro and macro level decision making. In this paper, we propose a graph analytic framework for the segmented MMM ecosystem. We utilize graph neural networks (GNNs) to perform demand-based clustering on the segmented area. Our focus is to cluster an area into categories based on the MMM demand patterns using the ridership data. We use Boston Bay open dataset with multiple modes of public transportation for analysis.

This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020, Comment: 7 pages, 13 figures, 4 tables, Invited submission to IEEE BigData Cup Global Road Damage Detection Challenge 2020

The scientific objective of this work is to reuse the polymer materials generated from carpet waste. The nylon obtained from the discarded carpet was used as reinforcement material into the epoxy matrix phase. A modified approach of Vacuum-assisted resin transfer molding (VARTM) was used to infuse the mixture of epoxy and hardener into the nylon carpet polymer composites. This investigates the water absorption behavior of carpet waste composite. It will justify the application of proposed composites for lightweight structural applications in a moisture environment. The nylon fiber-based-epoxy composite was dipped in water as per ASTM standard for a different duration, namely, 24, 48, 72, 144 hours. The findings demonstrate that it can effectively withstand in moisture environment, and a maximum 4.5% weight of samples increased among all the samples for a different duration. Also, the developed composite showed a lower water diffusion co-efficient than the nail head structure and linden wood. Also, the carpet waste composite samples were investigated for thermal degradation and chemical behavior using X-Ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy, respectively. The finding shows that the proposed composite could be used for lightweight components such as dashboard panels, sound absorbers, panel sheets, wall tiles, etc.

In this paper, design of an element for an antenna array covering lower 5G frequency bands is proposed. The antenna element consists of FR-4 (lossy) dielectric having thickness of 1.6 mm with 0.025 loss tangent and dielectric constant 4.3 as substrate. On the top of substrate, metal patch is extended by metallic strips of successively reduced widths and lengths to get wideband characteristic. On the bottom of the design, partial ground plane is used. To improve its radiation characteristics in desired direction, the above-mentioned patch antenna is suspended over full ground plate. The proposed antenna is having bandwidth good enough to cover entire 3 to 5 GHz 5G frequency bands. More than 5 dBi realized gain is obtained for almost all the required 5G frequencies with more than 80% total efficiency, which is essential to be a good broadcasting element. The proposed antenna design is simulated in microwave studio tool of CST-2016 software.

Wireless Body Area Network (WBAN) is one of the most rapidly growing technologies in the field of healthcare. However, providing a secure communication system over WBAN is a challenging task due to the sensitive data exchanged in resource-constrained WBAN. In this paper, we propose an efficient three-party authentication protocol for WBAN using Elliptic Curve Cryptography (ECC). We are using two-hop star network topology based on WBAN standard. The analysis results of BAN Logic and AVIS PA tool provide the security proofs of the proposed protocol. Moreover, the implementation results prove the efficiency of our proposed protocol.

This paper suggests a message encoding scheme for small text files in nucleotide strands for ultra high data density storage in DNA. The proposed scheme leads to high volume data density and depends on adoption of sequence transformation algorithms. Compression of small text files must fulfill special requirement since they have small context. The use of transformation algorithm generates better context information for compression with Huffman encoding. We tested the suggested scheme on collection of small text size files. The testing result showed the proposed scheme reduced the number of nucleotides for representing text message over existing method and realization of high data density storage in DNA

Semiconducting nano-phosphor pellets have been fabricated by hydraulic pressure, using ZnS and Mn doped ZnS semiconducting materials, which is synthesized by physical evaporation technique. Structural characterizations of synthesized semiconducting nanophosphor materials and pellets have been done by X-ray diffraction measurement and SEM while optical characterizations are done by UV-Visible absorption measurement. XRD pattern showed that the synthesized ZnS, Mn doped ZnS materials and pellet have cubic structure with preferential orientation along (111) planes. Optical absorption measurements indicated that the absorption decreases with increase of percent of Mn in pellet. The average maximum grain size (25.13 nm), Minimum dislocation density (1.57 × 10 11 /cm 3 ), lattice constant (5.398A) and minimum band gap (3.2 eV) have been obtained. Suitable explanation is given in this paper.

This paper suggests a message encoding scheme in nucleotide strand for small text les. The proposed scheme leads to ultra high volume data density and depends on adoption of transformation algorithms like, Burrow-Wheeler transformation and Move to Front for generating better context information. Huffman encoding further compresses the transformed text message. We used a mapping function to encode message in nucleotide from the binary strand and tested the suggested scheme on collection of small text size les. The testing result showed the proposed scheme reduced the number of nucleotides for representing text message over existing methods.

The nanocrystalline ZnS semiconducting thin films of 500 nm thickness have been deposited on glass substrate at different substrate temperatures (T s) by thermal evaporation technique. The structural property of deposited thin films has been measured by X-ray diffraction, scanning electron microscopy, and Energy dispersive analysis of X-ray. The electrical and optical properties of thin films have been determined by D.C. two point probe and ultra-violet visible spectroscopy measurements. The X-ray diffraction patterns show that thin films have cubic structure. The electrical resistivity of thin films has decreased from 0.36 × 106 to 0.15 × 106 Ω cm as substrate temperature increases from 300 to 400 K. It shows that films have semiconducting in nature. The grain size and electrical conductivity of the thin films have increased as the deposition temperature increased while dislocation density, activation energy, and band gap decreased. The minimum band gap 3.43 eV has been found.