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COVID-19 Pandemic has brought the world underwaters. All over the world, people were affected. The focus during this period was mostly on patients and frontline workers, with some attention also towards working adults. One cohort that has not gained much light during this pandemic is of housewives. Housewives had to manage household chores along with managing family relations – especially in India, where societal expectations lie on the female to provide family members with care and manage the household. Dealing with uncertainty, decreased availability of personal space, increased presence of and interaction with people in the household due to work from home scenarios, shifting to the online world and adapting to the change, economic disturbances, absence of domestic help, managing parental responsibility, increased stress about one’s own and family members’ health and lack of social interaction have contributed to their inconvenience. Existing evidence supports that housewives have been experiencing burnout in their homes. This qualitative study was conducted to see how the added pressure of COVID – 19 and social isolation has affected housewives mentally, leading to burnout. This narrative study includes participants of Indian origin, between the ages of 34 to 50 years. Participants were shortlisted on the basis of their scores obtained on the COVID-19 Burnout Scale, designed by Murat Yıldırım and Fatma Solmaz. The themes generated through this research study are related to understanding the impact of burnout on the mental health of housewives along the areas of physical health, financial well-being, digitization, uncertainty regarding COVID-19, parental responsibilities, social & emotional health, relationship management, and coping mechanisms. The findings of this study suggest that the mental health of housewives has significantly worsened during the COVID-19 pandemic due to constant exposure to certain stressors.

In recent years, inductive graph embedding models, \emph{viz.}, graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. The performance of such networks depends strongly on the input node features, which vary across networks and applications. Selecting appropriate node features remains application-dependent and generally an open question. Moreover, owing to privacy and ethical issues, use of personalized node features is often restricted. In fact, many publicly available data from online social network do not contain any node features (e.g., demography). In this work, we provide a comprehensive experimental analysis which shows that harnessing a transductive technique (e.g., Node2Vec) for obtaining initial node representations, after which an inductive node embedding technique takes over, leads to substantial improvements in link prediction accuracy. We demonstrate that, for a wide variety of GNN variants, node representation vectors obtained from Node2Vec serve as high quality input features to GNNs, thereby improving LP performance., 5 Pages, Accepted by CIKM 2021

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

Obesity is one of the common metabolic diseaseswhich is tremendously on the rise. According to the World Health Organization(WHO), the number of patients has increased three times since 1975. A personwith Body Mass Index (BMI) equal to or higher than 30.0 is considered obese.Previously considered only as a cosmetic concern, obesity has now been shown tobe an aggravating element for various diseases and disorders including diabetesand cardiovascular diseases (CVD). The most common drugs subscribed byphysicians include orlistat, rimonabant, liraglutide and sibutramine which havebeen proven to be effective to reduce body fat in patients. However, in thelong run, patients have complained about symptoms which are mild like nausea,insomnia and dizziness to severe complications such as increase in risk ofheart attack or stroke. The following review provides insights about syntheticdrugs, their classification and their side-effects on human health.Furthermore, a gist of natural products i.e. phytochemicals that can be used asan alternative to these synthetic drugs has also been discussed in the review., Obezite olağanüstü düzeyde bir atış gösterenyaygın görülen metabolik hastalıklardan biridir. Dünya Sağlık Örgütü'ne (WHO)göre, hasta sayısı 1975'ten bu yana üç kat artmıştır. Beden kitle indeksi (BKİ)30’a eşit veya 30’dan daha yüksek olan bir kişi obez olarak kabul edilmektedir.Daha önce sadece kozmetik bir sorun olarak kabul edilen obezitenin günümüzde diyabetve kardiyovasküler hastalıklar (KVH) gibi çeşitli hastalıklar ve bozukluklariçin ağırlaştırıcı bir unsur olduğu gösterilmiştir. Doktorlar tarafından en çokreçetelenen ilaçlar olan orlistat, rimonabant, liraglutid ve sibutramininhastalarda vücut yağ oranını azaltmada etkili oldukları kanıtlanmıştır. Bununlabirlikte, uzun vadede hastalar bulantı, uykusuzluk ve baş dönmesi gibi hafifsemptomlardan ve kalp krizi veya inme gibi ciddi komplikasyonlardan şikayetetmektedir. Mevcut derleme sentetik ilaçlar, bu ilaçların sınıflandırılması veinsan sağlığı üzerindeki yan etkileri hakkında bilgi vermektedir. Ek olarak, busentetik ilaçlara alternatif olarak kullanılabilecek doğal ürünler olanfitokimyasalların bir özeti de mevcut derlemede tartışılmıştır.

Aim The objective of the present investigation is to compare the accuracy of two methods of age estimation, Cameriere's European formula and Demirjian's method, in estimating the age of mixed-dentition children in Telangana. Materials and methods Digital orthopantomographs (OPGs) of 36 children between the age of 7 years and 12 years were subject to analysis. All the OPGs were analyzed for dental age using both Cameriere's and Demirjian's methods for mixed dentition. Chronological age was calculated by subtracting the date of births from the date on which the OPGs were taken. Statistical analysis descriptive analysis was performed. The accuracies of both the methods (Cameriere's and Demirjian's) were evaluated by calculating the mean prediction error. Wilcoxon signed-rank test was performed for both methods of dental age estimation with chronological age and for boys and girls. Significance threshold was set at 5%. Results Cameriere method resulted in a mean prediction error of 0.579 for girls and 0.483 for boys. Demirjian's method resulted in a mean prediction error of 2.228 for girls and 2.046 for boys. Conclusion In conclusion, as far as accuracy is concerned, Cameriere's European formula proved to be more accurate, according to the current investigation. Significance The significance of age estimation is reflected in various fields such as pediatric endocrinology, orthodontics, law, anthropology, archeology, and forensics where identification of unascertained human bodies is required (crime investigations, mass disasters). Demirjian's method is commonly employed for dental age estimation in pediatric dentistry, orthodontics, and forensic dentistry. Cameriere's method may serve as a more accurate and reliable method for dental age estimation. Key message: Cameriere's European formula proved to be better, in terms of accuracy, in determining the chronological age of mixed-dentition children in the region of Telangana, according to the current investigation.

This paper presents an RF-based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

DNA methylation can mediate epigenetic silencing of tumor suppressor and cancer protective genes. The protein ubiquitin-like containing PHD and ring finger domains 1 (UHRF1) is an essential component in cells for DNA methylation maintenance. The SET- and RING-associated (SRA) domain of UHRF1 can bind hemimethylated DNA, and mediate recruitment of DNA methyltransferases to copy the methylation pattern to the newly synthesized daughter strand. Loss of UHRF1 function can lead to demethylation and re-expression of epigenetically silenced tumor suppressor genes and can reduce cancer cell growth and survival. We created a high-throughput time-resolved fluorescence resonance energy transfer (TR-FRET) assay to screen for inhibitors capable of disrupting the interaction between the UHRF1-SRA domain and hemimethylated DNA. Using this assay (Z' factor of 0.74 in 384-well format) we screened the Library of Pharmacologically Active Compounds (LOPAC) for UHRF1-SRA inhibitors, and validated 7 hit compounds. These compounds included the anthracycline derivatives idarubicin and mitoxantrone, which are commonly used chemotherapeutic drugs known to mediate cytotoxicity by acting as class II topoisomerase (TOP2) poisons. In a panel of additional known topoisomerase poisons, only the anthracycline derivatives showed dose responsive inhibition of UHRF1-SRA. Additionally, mitoxantrone and doxorubicin showed dose-responsive global DNA demethylation and demonstrated a synergistic growth inhibition of multiple cancer cell lines when combined with the DNA methyltransferase (DNMT) inhibitor decitabine. These data validate a novel TR-FRET assay for identification of UHRF1 inhibitors, and revealed unexpected epigenetic properties of commonly used chemotherapeutic drugs that showed synergistic cytotoxicity of cancer cells when combined with a demethylating agent.

The modern era of artificial intelligence is mostly driven by Deep Neural Network (DNN). As a result most of the intelligent/smart apps running on edge devices (mobile phones, televisions etc.) use DNN for their predictive ability. DNN based prediction suffers with operational overheads, which is the summation of model loading latency and inference latency. Model loading latency affects the first response of the DNN powered apps, whereas inference latency affects the subsequent responses. As apps switching has become a common practice among edge device users, so it is of utmost interest to make the switching smooth by reducing the model loading latency. In this paper asymmetrical DNN modularization is proposed as a potential solution. The proposed method solves two distinct problems (a). Improves the model loading latency by parallel loading of all the modules (child models) of given DNN model. (b). Helps in on-device training by keeping the live gradients only for last child model. The decision about modularization index and their corresponding positions are taken by reinforcement learning unit (RLU). RLU takes into account the available hardware resources on-device (eg. h/w threads), loading latency of each layer on dedicated compute units. In response, it provides the best modularization index k and their corresponding positions $\vec{p}$ specific to the DNN model and device, where $P=(p_{1},p_{2},\ \ldots,p_{k})$ and $p_{i}$ is the end position of child $i$ . The proposed method has shown significant loading time improvement (up to 7X) on popular DNNs, used for camera use-cases. Along with improving the loading latency the proposed modularization method facilitates for On-device personalization by separating the module with trainable layers and loading them particularly while training on-device.

Background and purpose Highergrade glial neoplasms undergo standard treatment with surgery, radiotherapy, and alkylating agents. There is often a clinical/neuroimaging dilemma in the post-treatment setting to differentiate disease recurrence from treatment-related changes. FET (fluoro-ethyl-tyrosine) PET has emerged as a molecular imaging modality for cases where MR imaging is inconclusive. This study aims to develop a cutoff on FET PET for differentiating true recurrence from post-treatment changes. Methods We retrospectively analyzed72 patientswith post-treatment grade 3 or 4 brain gliomas. Five to six mCi of 18 F-FET was injected and static imaging of the brain was performed at 20 min. A tumor-to-white matter (T/Wm) ratio was used as semiquantitative parameter. A T/Wm cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed by either histopathologic diagnosis in a multidisciplinary joint clinic or based on follow-up of clinical and neuroimaging findings. Results Forty-one of 72 patients (57%) showed recurrent disease on FET PET. Thirty-five of them were confirmed to have tumor recurrence; six patients showed post-treatment changes. Thirty-one of 72 patients (43%) showed post-treatment changes on FET PET; 27 were confirmed as post-treatment change and four patients had tumor recurrence on subsequent MR imaging. An optimum T/Wm cutoff of 2.65 was derived based on receiver operating characteristic analysis with a sensitivity of 80% and specificity of 87.5%. Conclusion Static FET PET can be used as problem-solving imaging modality with a T/Wm cutoff of 2.65 to differentiate late recurrence from post-treatment changes in grade 3 or 4 brain gliomas with equivocal MR features.

In modern times, every device runs on electricity. Batteries are fulfilling electricity needs for portable devices. In all the latest portable electronic devices, lithium-ion batteries (LIBs) are the primary source of power: the reason being their high charge storage density. The primary issue with the LIB is the lack of information on its remaining useful life (RUL). Thus, we try to analyze and predict the RUL of LIB in this paper. We present a novel statistical approach for extracting various critical points from the discharge cycle by inspecting the LIB dataset. The inspection of LIB assisted in finding the pattern and relationship with the number of cycles. Thus, two feature sets are proposed in this paper based on critical points. The feature set is examined with various machine learning regression models using the LIB dataset. Also, the comparative analysis is performed for the proposed merging parameter to find the decent performance.

Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM2.5 emissions from rural roads and motorways while overestimating overall PM2.5 emissions. Particulate matter resuspension is the dominant form of PM2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.

A variety of radiolabeled amino acids have been used for imaging brain tumors. FET and MET have been the most widely used amino acid tracers. Amino acid tracers can be used for detection, tumor grading, targeting biopsy, and evaluation of response. Fluorothymidine (FLT) is a nucleoside thymidine analog. Assessment of treatment response in gliomas is very challenging. Amino acid tracers have been proven to be more appropriate. FET PET/CT offers high diagnostic power in this scenario. FET PET with additional dynamic data analysis was significantly superior to conventional MRI in discriminating tumor recurrence from post-therapeutic changes. The present literature in neuroimaging using PET highlights the ability of amino acid PET to quantify biological responses to treatment and allows its application to monitor patients to identify early disease relapse and response to treatment. However, the diagnostic impact of amino acid PET needs to be compared with a variety of advanced MR imaging techniques to develop the most accurate and useful multi-modal biomarkers possible.

Previously, healthcare management systems were particularly focused on physical health and disregarded the mental health. With technology that encompass all spheres of life, the benefits of technology in healthcare remain unfathomable. A person’s emotion, body temperature and heart rate can provide insightful information on both mind and body. This paper demonstrates a complete healthcare management system that considers both physical as well as mental health. IoT sensor network and Arduino UNO is used to collect a person’s body temperature and heart rate and simultaneously stored in a database. The paper also includes an emotion recognition unit that is built using the MiniXception convolutional neural network architecture to keep a track of the person’s emotion. A natural language processing chatbot has been employed to enable a virtual doctor for the user and help the user to provide a preliminary diagnosis. Furthermore, the tools like HTMI5 and CSS have been employed to build the user interface. The data obtained is then stored using MySQL database service. The results of this implementation indicates that the proposed system can reduce the workload for doctors, to be more aware of their physical and mental health and lastly the proposed methodology will make healthcare to be more accessible.

In this era, people are likely to spend most of their time shopping. Generally, people have to wait in long queues during the process of shopping. While shopping is fun but billing is where the customers feel tiring due to the convention billing method i.e. scanning the products with barcode scanner and then preparing the bill which leads to long queues. Therefore, this paper can be used to develop a model which can be placed in the shopping cart itself to reduce the billing time and make shopping a better experience for the customers. In the proposed system there is an RFID reader which will be operated by an 8051 microcontroller. Whenever a customer puts an item into the cart it is automatically detected by the RFID reader, after that the price of the item is displayed on the screen and added to the bill. As the customers keep on putting items into the cart, the price of those items will be added simultaneously and will be shown on the screen. In case if the customer would like to remove some already added item from the cart then they could return it easily by swiping it again in front of the reader module. After the purchase is done, the customer is supposed to swipe the MasterCard (a mall issued card) in front of an employee. After swiping, it can be seen that the purchase has been verified at the billing counter and the customer is free to pay in cash or through online payment. This will save time at the billing counter as the employee does not have to verify the number of items purchased for security reasons and can simply pay the amount mentioned on the screen. Therefore, the proposed system can be implemented in malls, super markets, etc. to improve the shopping experience for customers and save their time.

This project is a demonstration of a legged robot called “WICORS” which has four legs for movement. Legged robots have also been very useful in the military in performing the remote operations from a distant place. Legged robots are the robots that imitate the walking patterns of various animals. It is used for the data retrieval for the specific applications in different areas. This project is made for the purpose to collect data in the regions where it is dangerous for humans to enter such as the mining areas containing the toxic gases. It also serves the purpose of collecting the information about the injured and casualties in the disaster affected regions. The sole purpose of this project is to increase the real-time data transmission wirelessly by reducing the live interaction by humans with the surrounding environment.

The objective of the present investigation is to compare the accuracy of two methods of age estimation, Cameriere's European formula and Demirjian's method, in estimating the age of mixed-dentition children in Telangana.Digital orthopantomographs (OPGs) of 36 children between the age of 7 years and 12 years were subject to analysis. All the OPGs were analyzed for dental age using both Cameriere's and Demirjian's methods for mixed dentition. Chronological age was calculated by subtracting the date of births from the date on which the OPGs were taken.descriptive analysis was performed. The accuracies of both the methods (Cameriere's and Demirjian's) were evaluated by calculating the mean prediction error. Wilcoxon signed-rank test was performed for both methods of dental age estimation with chronological age and for boys and girls. Significance threshold was set at 5%.Cameriere method resulted in a mean prediction error of 0.579 for girls and 0.483 for boys. Demirjian's method resulted in a mean prediction error of 2.228 for girls and 2.046 for boys.In conclusion, as far as accuracy is concerned, Cameriere's European formula proved to be more accurate, according to the current investigation.The significance of age estimation is reflected in various fields such as pediatric endocrinology, orthodontics, law, anthropology, archeology, and forensics where identification of unascertained human bodies is required (crime investigations, mass disasters). Demirjian's method is commonly employed for dental age estimation in pediatric dentistry, orthodontics, and forensic dentistry. Cameriere's method may serve as a more accurate and reliable method for dental age estimation. Key message: Cameriere's European formula proved to be better, in terms of accuracy, in determining the chronological age of mixed-dentition children in the region of Telangana, according to the current investigation.

A Building Automation Systems (BAS) mainly focuses on automation of indoor conditions like Heating, Ventilation, lighting and Air-Conditioning. At the same time, its motive is to provide cost and energy efficient solutions and thereby making the buildings intelligent In making the Intelligent Building(IB), most important thing becomes the communication between the different devices. It is very important to choose the right communication protocol for the communication between the devices. In this paper, we investigate the problems in different communication protocols, their advantages and which protocols should be used in what situations. We will also conclude that one protocol is not enough for setting up the whole system.

Team Anant, a student team is developing a 3U Nanosatellite with a hyperspectral camera as the payload. The proposed satellite is divided into multiple subsystems. Linking and coordination amongst them have been done based on satellite constraints. The satellite follows a combination of centralized and distributed architecture. This paper details the architecture of the interface between the On-Board Computer(OBC) subsystem and the Telemetry Tracking and Command (TTC) subsystem. The Telemetry microcontroller handles data downlink and error control. OBC handles payload camera operations and image compression. To make the system full duplex, one of the cores on OBC is dedicated to the uplink circuitry. Hence, re-transmission requests and acknowledgements from the ground station are received by OBC. This necessitates a proper interface between On-Board SoC and telemetry microcontroller which is done using the UART protocol. The On-Board Computer of the satellite acts as the primary source of all the commands and monitors the status of the various subsystems. Its architecture performs hyperspectral image compression on a Field Programmable Gate Array (FPGA) before the image gets downlinked to the ground station. The algorithm used for the same is CCSDS 1.2.3. Storage and downlinking of payload and housekeeping data are aided by a subsystem specific interface architecture. The image is transferred from Payload to an on-board memory from where it is fetched by the FPGA in tiles for compression. The compressed data is pushed in the FIFO and stacked in its buffer. Subsequently, the telemetry microcontroller pops out the data whenever it is required to be packaged before downlinking. Therefore, FIFO hardware guarantees a sequential flow of data from OBC to telemetry without explicitly knowing the memory addresses of the compressed image. Moreover this FIFO memory is also used for sending housekeeping data. Thus, this paper will be elucidating the concepts of interfacing between the different subsystem's microcontrollers with the management of shared memory.

Aims and Objective: The aim of this study was to identify the presence of Helicobacter pylori in saliva of patients with and without gastritis by polymerase chain reaction (PCR) method. Materials and Methods: The study comprised 20 patients in Group I presenting with various symptoms of gastritis and 10 asymptomatic subjects in Group II. The intestinal endoscopy antral biopsies were collected from 20 symptomatic patients with gastroduodenal disorders. The saliva specimens were taken from all patients before endoscopy. PCR was performed using genomic DNA, isolated from the saliva and the biopsies of the patients as the template to detect the presence of the 16S ribosomal RNA gene in H. pylori. Results: In Group I, 10 (50%) cases of clinical gastritis were positive for H. pylori by endoscopy biopsy and 10 (50%) were negative. Of the 10 endoscopy biopsy positive cases for H. pylori, eight were PCR positive in saliva and two were negative. Of the 10 endoscopy biopsy negative cases, three were PCR positive for H. pylori in saliva and seven were negative. In Groups II, four were symptomatic for gastritis and six were negative. Of the six gastritis negative cases, three were PCR positive, four were gastritis positive, and three were PCR positive. Sensitivity and specificity of PCR were found to be 80% and 70%, respectively. The positive predictive and negative predictive values of PCR in saliva were 72.7% and 77.7%, respectively. Conclusion: PCR analysis of saliva may be handy in identification of H. pylori and serves as a noninvasive technique to diagnose and monitor the prognosis.

Decision making using Machine Learning can be efficiently applied to security. Malware has become a big risk in today’s times. In order to provide protection for the same, we present a machine-learning based technique for predicting Windows PE files as benign or malignant based on fifty-seven of their attributes. We have used the Brazilian Malware dataset, which had around 1,00,000 samples and 57 labels. We have made seven models, and have achieved 99.7% accuracy for the Random Forest model, which is very high when compared to other existing systems. Thus using the Random Forest model one can make a decision on whether a particular file is malware or benign.

Tendering is a procedure taken up by organizations for procurement of goods and services. Tender process when followed properly allows bids from multiple vendors and thereby facilitates healthy competition and fair outcome. Almost all the countries follow this process in standard and similar fashion. Lack of transparency in the tendering process and assignment of contracts to preferred agencies (rather than to a deserving bidder) has led to unwise use of money and the spirit of speculation. Blockchain is a decentralized ledger that makes records immutable, facilitates the secure exchange of digital currency, and performs deals and transactions. Each member of the blockchain network has access to the latest copy of encrypted ledger so that they can validate a new transaction. In this paper, we propose a blockchain-based tender procurement system with secure, fair, and reliable bidding architecture that can transparently manage the whole bidding process and perform an unbiased evaluation of bids. This system allows citizens to evaluate the process with a single click through an auditing application, which accounts for some stake to be put in the system against a false claim of task completion and thus actively help in making the democratic system stronger.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project. NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in the Department of Astronomy at the University of Washington; STFC studentshipScience & Technology Facilities Council (STFC) [ST/N504336/1]; STFC grantScience & Technology Facilities Council (STFC) [ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American Astronomical Society; Space program The following individuals recognize support for their personal contributions. B.M.S. is supported by the NSF grant AST-1715122 and acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. D.S. was supported by STFC studentship ST/N504336/1 and STFC grant ST/N000692/1.; We acknowledge financial contributions from Google as part of the Google Summer of Code program and from the Space program. We acknowledge financial contributions from NumFocus for improving the usability of SunPy's Data Downloader. Additionally, we acknowledge current and future funding from the Solar Physics Division of the American Astronomical Society for SunPy workshops and tutorials at annual meetings.; This work has made use of data from the European Space Agency (ESA) mission Gaia,80 processed by the Gaia Data Processing and Analysis Consortium (DPAC).81 Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement.

Due to the hike in fuel prices and the environmental threats associated with the combustion of fossil fuels, researchers are opting for an alternative energy source to propel the vehicle. One such solution is the adoption of electric vehicles, which accounts for high degree of sustainability as compared to the conventional fuel vehicles. The requirement of low power tractors has been on the rise since 2011. The increasing debts and deteriorating economic conditions of the farmers have led them to opt for smaller machines at the cost of productivity. This work presents the design and development of electric motor powered tractor for mild ploughing operation with a 3kw BLDC motor sustaining a 2 hours constant operation using Lead acid traction batteries. Description of the subsystems which includes the steering system, braking system, electric powertrain, and the chassis frame has been delineated precisely. The vehicle modelling was done in Solidworks and the analysis of the framework has been carried in ANSYS software. Safe design of the framework was ensured during maximum loading condition. Design calculations were carried out to obtain an optimized powertrain respectively. A prototype was developed and weight of the prototype was found to be 320 kg. Testing was carried out and the tractor could plough with a depth of 5cm. The successful testing of the concept tractor can be extrapolated to high power motor for real-time ploughing applications.

Context Cigarette smoking is a well-established risk factor for periodontitis and carries an increased risk for loss of periodontal attachment as well as bone loss. Aims The purpose of the present study was to investigate whether disease severity differs between smokers and nonsmokers in a group of chronic periodontitis patients by assessing the periodontal probing depth (PPD) and bleeding on probing (BOP). Materials and methods The study included 150 individuals, 75 smokers and 75 nonsmokers, in the age group of 35-60 years. Subjects with chronic periodontitis were selected and included in the study. Periodontal evaluation, including periodontal probing pocket depths and bleeding on probing, was performed on all four quadrants and at six sites per tooth using the Williams periodontal probe. The data were pooled from the anterior sextant and the posterior sextant as well as from the facial and lingual surfaces. Statistical analysis Comparisons were made between smokers and nonsmokers using the z-test (two-tailed test). Probing pocket depth categories 0-3 mm, 4-5 mm, 6-7 mm, and ≥8 mm and the proportion of sites having a pocket depth of ≥5 mm were used in the analysis. Results The mean percentage of sites that bleed upon probing was higher for nonsmokers as compared with smokers. Smokers had less shallow pockets (0-3 mm) than nonsmokers and more pockets of 4-7 mm (categories 4-5 mm, 6-7 mm). No significant differences were detected in the prevalence of pockets ≥8 mm. In the anterior, premolar, and molar regions, pockets of 6-7 mm were significantly more prevalent in smokers. The buccal and lingual sides also showed that smokers had more sites with deep probing depths ≥5 mm than nonsmokers. The data also showed that in the upper jaw, in the anterior and premolar teeth, the largest differences were found between smokers and nonsmokers. Conclusions From the results, it can be concluded that cigarette smoking results in periodontal tissue destruction in the different areas of the oral cavity, with the maximum periodontal destruction in the maxillary anterior and premolar region.

The paper presents a comparative analysis of different symmetric keying cryptographic algorithms. The algorithms under study are AES, ARC2, Blowfish, CAST and DES3, and the performance metrics for the basis of the study are Encryption time, Decryption time, Throughput, Plain text size vs Cipher text size. The study is conducted for a standalone system, where the algorithms are executed on a quad core i5 processor running Windows 10 and, Python is used as the programming language.

Though IT industry is rapidly growing with the use of recent technologies, IT assets management is semi-automated and unmanaged. Lots of research is carried on and existing asset management software have become intelligent by integrating it with AI(artificial intelligence) but as manual operation is required to manage the system it leads to poor efficiency and security for small and medium enterprise. It can be solved using self-management. IoT provides solution by enabling the design and development of Systems that can adapt themselves to meet the requirements without manual intervention. Internet of Things is the scenario where billions of devices and objects are being embedded with sensors and chips. As these devices gain the ability to communicate using the IoT, the resulting information networks deliver an exponential increase in insight about customer behavior. Organizations can use this insight to create new operating and business models, improve business processes and reduce costs, strengthen security and manage risk, and manage the change that comes with a dynamic environment. When you capitalize upon the insight-rich data that is generated by the Internet of Things, you can address customer service in a manner that has not been possible before and hence is transformative.

Testicular metastases from solid tumors is a rare entity. We describe one such case where a patient on treatment for prostate cancer presented with a right scrotal swelling. For restaging, whole-body 68-gallium prostate-specific membrane antigen positron emission tomography/computed tomography (PET/CT) was performed. Fused PET/CT images demonstrated intense uptake in the testicular lesion. Unique anatomic and functional information provided by hybrid PET/CT helped in reliably establishing the testicular mass as the site of metastases from prostate cancer, which had a major impact on patient management.

Additive Manufacturing (AM) is a novel manufacturing method where a component is fabricated by accumulating material layer-by-layer therefore facilitating customized near-net-shape components while maximizing design freedom. A metal AM technology suited for large-scale component fabrication that has been emerging in recent years and is commonly referred to as Wire and Arc AM (WAAM) uses Gas Metal Arc Welding (GMAW) technology to deposit material and large-scale robotic serial manipulator systems (reach >1 m cubed). One of the requirements for applying GMAW welding technology to AM is to identify and optimize deposition parameters in order to achieve a desired deposition quality measured in terms of geometrical, mechanical and metallurgical consistency. In this work, deposition process parameters qualitatively and quantitatively influencing the geometrical, mechanical and metallurgical consistency are identified and statistically validated. A deposition parameter combination is found that optimizes the quality of single-track wall benchmark components made from low-carbon steel. Moreover, correlations are found between accumulated heat during fabrication and the geometrical variations of the benchmark components due to bead slumping. Additionally, correlations between microstructure variations and geometrical variations are found. Finally, based on the presented analyses, in-situ temperature monitoring methods are proposed in order to achieve optimal component quality.

This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations. This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from ConceptNet to tackle the problem of commonsense inference. The input passage, question, and answer are augmented with relational knowledge from ConceptNet. Using this technique we are able to achieve an accuracy of 73.3 % on the official test data.

Electrical Overhead Traveling (EOT) cranes in manufacturing industries serve the purpose of material handling in complex working environment. Complexity involved in human machine interaction at the workplace make it hazard and incident prone. In current study, emerging data mining technique like Decision tree (DT) is adopted to explore the underlying causes involved in incidents happened in the studied plant from the year 2014–2016. Interesting results are obtained from the analysis like number of incidents happened during construction and maintenance activities and in weekend (Saturday, Sunday) are more. Managerial implications are suggested for betterment of safety management system of the studied plant.

Simultaneous 3D printing of disparate materials; metals, polymers and semiconductors with device quality interfaces and at high resolution remains challenging. Moreover, the precise placement of discrete and continuous domains to enable both device performance and electrical connectivity poses barriers to current high-speed 3D-printing approaches. Here, we report filaments with disparate materials arranged in elaborate microstructures, combined with an external adhesion promoter, to enable a wide range of topological outcomes and device-quality interfaces in 3D printed media. Filaments, structured towards light-detection, are printed into fully-connected 3D serpentine and spherical sensors capable of spatially resolving light at micron resolution across its entire centimeter-scale surface. 0-dimensional metallic microspheres generate light-emitting filaments that are printed into hierarchical 3D objects dotted with electroluminescent pixels at high device resolution of 55 µm not restricted by surface tension effects. Structured multimaterial filaments provides a path towards custom three-dimensional functional devices not realizable by existing approaches., Though additive manufacturing methods are highly attractive for the high-throughput fabrication of 3D electronic materials and devices, printing multimaterial devices remains a serious challenge. Here, the authors report optoelectronic multimaterial filaments for 3D-printed optoelectronics.

The estimation of Quality of diamond plays a very important role in finding its price and add a market value. Quality of diamond is being estimated in the market today by just looking into the diamond thought naked or with a lens, but the quality estimated in this way is less accurate and varies from person to person in which the consumers are put into confusion whether the diamond is of good quality or not. It is sometimes difficult to identify whether the diamond is real or not. If we use a computer aided system then the estimation of quality of diamond can be estimated accurately and quickly. Thus, paper aims to develop such computer aided system which will estimate the quality of diamond based on Gemological Institute of America GIA standards. Yash Jain N | T. P. Deepa | Riya Nayan Shah | Kajal Jain "Segmentation of Front Viewed Single Phase Diamond" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd12897.pdf

Designing a robust controller for nonlinear unstable system involves major constraints such as nonlinearity, disturbance and uncertainity. Therefore, a control strategy realization for such unstable systems generally explores interests among scientific community. The objective of the present study was to design a control strategy of anticipatory postural adjustments for balancing of ball and beam system. Due to the characteristics like unstable, nonlinear and double-integrating, the ball and beam system is commonly considered as a benchmark control operation for estimating several control strategies. Although a PID, i.e. proportional–integral–derivative controller, can be developed for stable system, for double-integrating unstable system, it is very less common. In order to control the ball’s position, a PID controller as non-model-based control strategy was presented in this study. To obtain the dynamics of ball and beam system, Euler–Lagrangian approach is used. The system’s prototype was built using UV sensor as feedback network; servomotor as an actuator; and Arduino microcontroller as PID controller. Simulation models such as set-point tracking and disturbance rejection analysis of ball and beam system are performed using MATLAB/Simulink in order to evaluate the performance of proposed system. The outcomes revealed that the proposed system provides increased stability and reduced percentage of error. Along with these, transient response is also improved and the system is more robust against disturbance.

This paper deals with real time development of a machine learning based portable system for determining eating habits of a human being using six point calibrated wearable MEMS tri-axial accelerometer. The rise of obesity as a global epidemic makes it immensely important to monitor food habits of a modern day person. In this proposed system, we have derived an easy to adopt algorithm based on a training based model for identifying the amount of calories consumed and burnt by a person. The proposed system consists of a wrist worn MEMS accelerometer that calculates calories burned per step which is directly sent over to the user's smart phone and a cloud based machine learning algorithm that does the prediction of health habit (i.e. healthy, unhealthy or undernutrition) based on the data obtained from the wrist worn device. In order to calculate the health habit of the user, the cloud uses logistic regression with calories burnt (from MEMS accelerometer) and calories consumed (daily manual input)to predict health habit of the user. The wrist worn device extracts calories burnt per step from the change in Y-axis acceleration data of the accelerometer in the wearable device, which after self-calibration is sent over to the user's smart phone through Wi-Fi. Thus, this cloud based food habit detection not only decreases the risk of obesity in a person but also introduces a low cost alternative device with reduced power consumption of $(< 13.5 \text{mW})$ and minimal covering size (12.56cm2) that can improve people's life.

Nowadays the smart pen either write on a digital pad or they write on the standard Anoto dot pattern paper. The digital pen is lacking to convert the written text into editable document. These pens are mostly concentrating on taking the image of the written text. Few pens are written on any type of paper and converts into document. They were able to correct the spelling mistakes of the user but the grammatical mistakes were unknown by the pen. This paper proposes an idea to correct the grammatical errors and correct the spelling mistakes by parsing and having a training data set stored in the in the database which ultimately make the pen smarter and intelligent. This pen will learn by itself as to what kind of mistake the user is doing frequently and this ultimately will lead to efficient and faster results.

Data Analytics is the process of analyzing unprocessed data to draw conclusions by studying and inspecting various patterns in the data. Several algorithms and conceptual methods are often followed to derive legit and accurate results. Efficient data handling is important for interactive visualization of data sets. Considering recent researches and analytical theories on column-oriented Database Management System, we are developing a new data engine using R and Tableau to predict airport trends. The engine uses Univariate datasets (Example, Perth Airport Passenger Movement Dataset, and Newark Airport Cargo Stats Dataset) to analyze and predict accurate trends. Data analyzing and prediction is done with the implementation of Time Series Analysis and respective ARIMA Models for respective modules. Development of modules is done using RStudio whereas Tableau is used for interactive visualization and end-user report generation. The Airport Trends Analytics Engine is an integral part of R and Tableau 10.4 and is optimized for use on desktop and server environments.