Project Management Issues for The Improvement of Medical Industry of Bangladesh by Industry 4.0

In this article, project management in Industry 4.0 is analyzed from the perspective of improving the health sector of Bangladesh. There is a potential scope of Industry 4.0 in the growth of the healthcare industry, which we have identified using Project Management 4.0 in this article. This proposal is defined as Improving Bangladesh`s health sector through Industry 4.0. We analyzed the necessary qualities of a project manager to tailor it. The features of Project Management 4.0 revealed in improving the health system are closely connected with the key components of project management: time management, cost management, quality management, project team management, communication management, project risk management, procurement, and resource management. Industry 4.0 is a necessary strategy to deliver new and evolving technologies in the medical field through a combination of technology, sensitive machines, and software. With the help of industrial revolution 4.0, it is creating a new virtual healthcare world

On Achieving Diversity in the Presence of Outliers in Participatory Camera Sensor Networks

This paper addresses the problem of collection and delivery of a representative subset of pictures, in participatory camera networks, to maximize coverage when a significant portion of the pictures may be redundant or irrelevant. Consider, for example, a rescue mission where volunteers and survivors of a large-scale disaster scout a wide area to capture pictures of damage in distressed neighborhoods, using handheld cameras, and report them to a rescue station. In this participatory camera network, a significant amount of pictures may be redundant (i.e., similar pictures may be reported by many) or irrelevant (i.e., may not document an event of interest). Given this pool of pictures, we aim to build a protocol to store and deliver a smaller subset of pictures, among all those taken, that minimizes redundancy and eliminates irrelevant objects and outliers. While previous work addressed removal of redundancy alone, doing so in the presence of outliers is tricky, because outliers, by their very nature, are different from other objects, causing redundancy minimizing algorithms to favor their inclusion, which is at odds with the goal of finding a representative subset. To eliminate both outliers and redundancy at the same time, two seemingly opposite objectives must be met together. The contribution of this paper lies in a new prioritization technique (and its in-network implementation) that minimizes redundancy among delivered pictures, while also reducing outliers.unpublishedis peer reviewe

Exploitation of information propagation patterns in social sensing

Online social media presents new opportunity for sensing the physical world. The sensors are essentially human, who share information in the broadcast social media. Such human sensors impose challenges like influence, bias, polarization, and data overload, unseen in the traditional sensor network. This dissertation addresses the aforementioned challenges by exploiting the propagation or prefential attachment patterns of the human sensors to distill a factual view of the events transpiring in the physical world. Our first contribution explores the correlated errors caused by the dependent sources. When people follow others, they are prone to broadcast information with unknown provenance. We show that using admission control mechanism to select an independent set of sensors improves the quality of reconstruction. The next contribution explores a different kind of correlated error caused by polarization and bias. During events related to conflict or disagreement, people take sides, and take a selective or preferential approach when broadcasting information. For example, a source might be less credible when it shares information conforming to its own bias. We present a maximum-likelihood estimation model to reconstruct the factual information in such cases, given the individual bias of the sources are already known. Our next two contributions relate to modeling polarization and unveiling polarization using maximum-likelihood and matrix factorization based mechanisms. These mechanisms allow us to automate the process of separating polarized content, and obtain a more faithful view of the events being sensed. Finally, we design and implement `SocialTrove', a summarization service that continuously execute in the cloud, as a platform to compute the reconstructions at scale. Our contributions have been integrated with `Apollo Social Sensing Toolkit', which builds a pipeline to collect, summarize, and analyze information from Twitter, and serves more than 40 users

Traffic Participants Detection and Classification Using YOLO Neural Network

One of the most important requirements for the next generation of traffic monitoring systems, autonomous driving technology, and Advanced Driving Assistance Systems (ADAS) is the detection and classification of traffic participants. Although in the areas of object detection and classification research, tremendous progress has been made, we focused on a specific task of detecting and classifying traffic participants from traffic scenarios. In our work, we have chosen a Deep Convolutional Neural Networks-based object detection algorithm – YOLOv4 (You Only Look Once Version 4) to detect and classify traffic participants accurately with fast speed. The main contribution of our work included: firstly, we built a custom image dataset of traffic participants (Car, Bus, Truck, Pedestrian, Traffic light, Traffic sign, Vehicle registration plate, Motorcycle, Ambulance, Bicycle wheel). After that, we run K-means clustering on the dataset to design anchor box, which is utilized to adapt to various small and medium scales. Finally, trained the network for the mentioned objects and tested our network in several driving conditions (daylight, low light, high traffic, foggy, rainy, etc.). We got the results reached a mean Average Precision (mAP) up to 65.95% and the speed was around 0.054 s

Effect of Substrate Surface on the Wide Bandgap SnO2 Thin Films Grown by Spin Coating

Tin (IV) oxide (SnO2) sols have been synthesized from SnCl2.2H2O precursor solution by applying two different processing conditions. The prepared sols were then deposited on UV-Ozone treated quartz and soda lime glass (SLG) substrates by spin coating. The as-synthesized film was soft-baked at about 100 deg. C. for 10 min. This process was repeated five times to get a compact film, followed by air-annealing at 250 deg. C. for 2 h. The pristine and annealed films were characterized by UV-Vis-NIR spectroscopy, Grazing Incident X-Ray Diffraction (GIXRD), and Field Emission Scanning Electron Microscope (FESEM). The effect of substrate surface was investigated by measuring the contact angles with De-Ionized (DI) water. UV-Ozone treatment of substrate provides a cleaner surface to grow a homogeneous film. The electrical resistivity of annealed thin films was carried out by a four-point-collinear probe employing the current reversal technique and found in the range of approx. 2x10^3 to 3x10^3 Ohm.cm. Film thickness was found in the range of approx. 137-285 nm, measured by a stylus profilometer. UV-Vis-NIR Transmission data revealed that all the thin film samples showed maximum (82-89) % transmission in the visible range. The optical bandgap of the thin films was estimated to be approx. 3.75 to 4.00 eV and approx. 3.78 to 4.35 eV for the films grown on SLG and quartz substrates, respectively.Comment: 4 pages, 7 figure

Spherical and Rod-shaped Gold Nanoparticles for Surface Enhanced Raman Spectroscopy

Raman Spectroscopy offers an in-situ, rapid, and non-destructive characterization tool for chemical analysis of diverse samples with no or minimal preparation. However, due to the inherent weak signal of conventional Raman spectroscopy, surface plasmon resonance features of noble metal nanoparticles have been utilized to conduct Surface Enhanced Raman Spectroscopy (SERS) in detecting trace label contaminants in foods and foodstuffs. In this effort, we synthesized gold nanoparticles (AuNPs) by reduction of chloroauric acid (HAuCl4) with sodium citrate dehydrate. We prepared different sizes of AuNPs at a fixed temperature (100 oC) but with varying pHs of 4 and 8. The as-synthesized AuNPs were characterized by UV-Vis spectroscopy, dynamic light scattering (DLS), and Field Emission Scanning Electron Microscopy (FE-SEM). FE-SEM micrographs revealed spherical AuNPs with an average diameter of approx. 55 nm and rod-shaped AuNPs with an average length of approx. 170 nm for sample synthesis at pH 8 and 4, respectively. The effectiveness of the as-prepared AuNPs for SERS is tested by detecting Rhodamine 6G diluted at a trace level. This study suggests that plasmonic nanoparticles coupled with SERS have great potential for broad applications in detecting other trace amounts of hazardous chemicals in foods and foodstuffs.Comment: 4 pages, 5 figure

Unveiling Polarization in Social Networks: A Matrix Factorization Approach

This paper presents unsupervised algorithms to uncover polarization in social networks (namely, Twitter) and identify polarized groups. The approach is language-agnostic and thus broadly applicable to global and multilingual media. In cases of conflict, dispute, or situations involving multiple parties with contrasting interests, opinions get divided into different camps. Previous manual inspection of tweets has shown that such situations produce distinguishable signatures on Twitter, as people take sides leading to clusters that preferentially propagate information confirming their individual cluster-specific bias. We propose a model for polarized social networks, and show that approaches based on factorizing the matrix of sources and their claims can automate the discovery of polarized clusters with no need for prior training or natural language processing. In turn, identifying such clusters offers insights into prevalent social conflicts and helps automate the generation of less biased descriptions of ongoing events. We evaluate our factorization algorithms and their results on multiple Twitter datasets involving polarization of opinions, demonstrating the efficacy of our approach. Experiments show that our method is almost always correct in identifying the polarized information from real-world twitter traces, and outperforms the baseline mechanisms by a large margin.Army Research Laboratory, Cooperative Agreement W911NF-09-2-0053DTRA grant HDTRA1-10-1-0120NSF grant CNS 13-29886Ope

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.Peer reviewe

SocialTrove: A Self-summarizing Storage Service for Social Sensing

The increasing availability of smartphones, cameras, and wearables with instant data sharing capabilities, and the exploitation of social networks for information broadcast, heralds a future of real-time information overload. With the growing excess of worldwide streaming data, such as images, geotags, text annotations, and sensory measurements, an increasingly common service will become one of data summarization. The objective of such a service will be to obtain a representative sampling of large data streams at a configurable granularity, in real-time, for subsequent consumption by a range of data-centric applications. This paper describes a general-purpose self-summarizing storage service, called SocialTrove, for social sensing applications. The service summarizes data streams from human sources, or sensors in their possession, by hierarchically clustering received information in accordance with an application-specific distance metric. It then serves a sampling of produced clusters at a configurable granularity in response to application queries. While SocialTrove is a general service, we illustrate its functionality and evaluate it in the specific context of workloads collected from Twitter. Results show that SocialTrove supports a high query throughput, while maintaining a low access latency to the produced real-time application-specific data summaries. As a specific application case-study, we implement a fact-finding service on top of SocialTrove.Army Research Laboratory, Cooperative Agreement W911NF-09-2-0053DTRA grant HDTRA1-10-1-0120NSF grants CNS 13-29886, CNS 09-58314, CNS 10-35736Ope