Improved Accuracy in Estimation of Temperature for Permanent Magnet Synchronous Motor (PMSM) using Machine Learning (ML) method for Electric Vehicle Application

With the advancement of an electrical transportation system, demand for the efficient electric vehicles (EV’s) will be more. So, manufacturing industries of EV’s are using the latest technologies to design an efficient and reliable electric vehicles for the customers. Since, electric motor is the main driving component of the EVs, so our major concern is to protect the motor from various faults in the very early stage with better accuracy and with minimum error. Various types of faults which mainly oc-curs in the motors are, overheating, bearing fault, insulation breakdown, over speed, vibration, noise etc. So, in this paper Machine Learning (ML) technique is used to analyze various electrical parameters of Permanent Magnet Synchronous Motor (PMSM) taking coolant, ambient temperature, voltage, current, speed and torque as input parameters and winding temperature as output parameter. The test is performed in MATLAB software and the results found with the above method is found more improved and accurate with least error. The proposed method classifies the stator winding temperature into respective classes with 93.13% classification accuracy, sensitivity and specificity are 90.22% and 94.78% respectively

Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.Comment: This paper has been accepted for publication at ACM WWW 201

Multi-Purpose Agriculture Machine

The paper aims on the design, development and the fabrication of the vehicle which can dig the soil, sow the seeds, leveler to close the soil and pump to spray water, these whole systems of the vehicle works with the battery and solar power, the vehicle is controlled by toggle switch. In recent years the development of the autonomous vehicles in the agriculture has experienced increased interest. The advantages of these vehicles are hands-free and fast input operations. In the field of agricultural autonomous vehicle, a concept is been developed to investigate if multiple small autonomous machine could be more efficient than traditional large tractors and human forces. Keeping the above ideology in mind, a unit with the following feature is designed, Ploughing is one of the first steps in farming. During this process we till the land and make it ready for the seed sowing. By tilling we mean that a plough will be used which will have teeth’s like structure at the end and will be able to turn the top layer of soil down and vice-versa. Seed sowing comes next where the seeds need to be put in ground at regular intervals and these needs to be controlled automatically. Limiting the flow of seeds from the seeds chamber is typically doing this. soil leveler is fitted to close the seeds to the soil and to level the ground. Water pump is used to spray the water

Bronchial adenoma masquerading as pneumonia: a rare case report

A 29-year old non-smoker woman presented with breathlessness, cough with minimal expectoration and hemoptysis. Serial chest X-rays were suggestive of progressive collapse of right lung field. CECT chest revealed large endobronchial lesion in right main bronchus and obstructive collapse consolidation of right lower lobe and part of the middle lobe. Fiberoptic bronchoscopy revealed an endobronchial mass obstructing the right main bronchus. This reddish polypoid mass bleed on touch that was suggestive of bronchial adenoma and it was confirmed by histopathological examination. Patient was treated empirically with anti-tubercular drugs due to high prevalence of tuberculosis in our setting; however, the patient had clinical deterioration. Following the histopathology report, the diagnosis of bronchial adenoma was established. Bronchial adenoma should be considered in a young female patient presenting with hemoptysis and obstructive pneumonia

Plasmodium falciparum PhIL1-associated complex plays an essential role in merozoite reorientation and invasion of host erythrocytes.

The human malaria parasite, Plasmodium falciparum possesses unique gliding machinery referred to as the glideosome that powers its entry into the insect and vertebrate hosts. Several parasite proteins including Photosensitized INA-labelled protein 1 (PhIL1) have been shown to associate with glideosome machinery. Here we describe a novel PhIL1 associated protein complex that co-exists with the glideosome motor complex in the inner membrane complex of the merozoite. Using an experimental genetics approach, we characterized the role(s) of three proteins associated with PhIL1: a glideosome associated protein- PfGAPM2, an IMC structural protein- PfALV5, and an uncharacterized protein-referred here as PfPhIP (PhIL1 Interacting Protein). Parasites lacking PfPhIP or PfGAPM2 were unable to invade host RBCs. Additionally, the downregulation of PfPhIP resulted in significant defects in merozoite segmentation. Furthermore, the PfPhIP and PfGAPM2 depleted parasites showed abrogation of reorientation/gliding. However, initial attachment with host RBCs was not affected in these parasites. Together, the data presented here show that proteins of the PhIL1-associated complex play an important role in the orientation of P. falciparum merozoites following initial attachment, which is crucial for the formation of a tight junction and hence invasion of host erythrocytes

Second Primary Lung Cancer with Glottic Laryngeal Cancer as Index Tumor – A Case Report

Patients with laryngeal cancer have a high risk of developing lung cancer in the future. A patient presented with a complaint of left-sided chest pain for the last 3 months. Chest X-ray posterior-anterior view showed a homogeneous opacity in the left upper lung field with elevation of the diaphragm on the left side. CECT thorax revealed a heterogeneously enhancing soft tissue density mass lesion in the superior segment of the lingular lobe. On histopathological examination of the mass lesion, it was diagnosed as adenocarcinoma of the lung. About 10 months previously, the patient was treated for a laryngeal squamous cell carcinoma. Patients with laryngeal cancer should be routinely screened during follow-up with chest X-ray or CT scan for the early detection of lung cancer

Evaluation of retinal nerve fibre layer, optic nerve head, and macular ganglion cell analysis measurements for early glaucoma detection using spectral domain optical coherence tomography

Background: Glaucoma is the leading cause of irreversible blindness worldwide. It is very important to diagnose glaucoma in early stages so that timely management can be done. Spectral domain optical coherence tomography (SD-OCT), is a newer device which helps to diagnose glaucoma early. The aim of our study was to evaluate the RNFL, ONH, and mGCA (GCL+IPL) measurements for early glaucoma detection using spectral domain optical coherence tomography (SD-OCT).Methods: Total 30, POAG (primary open angle glaucoma) suspects were compared with 30 normal controls. The Cirrus HD-OCT optic disc cube 200 × 200 protocol was used to measure ONH, RNFL and macular parameters.Results: The average cpRNFL thickness of all quadrants was significantly lower in POAG suspects, (84.13±7.42 μm versus 103.85±8.95 μm, p<0.001). The superior GCL+IPL thickness of POAG suspects and controls was 75.75±2.60 μm and 80.05±1.74 μm, respectively, (p<0.001). The inferior GCL+IPL thickness of POAG suspects and controls was 75.98±2.59 μm and 80.00±1.79 μm, respectively, (p<0.001).Conclusions: The SD-OCT is an important device to diagnose POAG suspects, early. The GCA measurements and average RNFL (especially superior and inferior) measurements, both are equally good to discriminate between glaucoma suspects and normal controls

Mycobacteria-responsive sonic hedgehog signaling mediates programmed death-ligand 1 and prostaglandin E<SUB>2</SUB>-induced regulatory T cell expansion

CD4+CD25+FoxP3+ regulatory T cells (Tregs) are exploited by mycobacteria to subvert the protective host immune responses. The Treg expansion in the periphery requires signaling by professional antigen presenting cells and in particularly dendritic cells (DC). However, precise molecular mechanisms by which mycobacteria instruct Treg expansion via DCs are not established. Here we demonstrate that mycobacteria-responsive sonic hedgehog (SHH) signaling in human DCs leads to programmed death ligand-1 (PD-L1) expression and cyclooxygenase (COX)-2-catalyzed prostaglandin E2 (PGE2) that orchestrate mycobacterial infection-induced expansion of Tregs. While SHH-responsive transcription factor GLI1 directly arbitrated COX-2 transcription, specific microRNAs, miR-324-5p and miR-338-5p, which target PD-L1 were downregulated by SHH signaling. Further, counter-regulatory roles of SHH and NOTCH1 signaling during mycobacterial-infection of human DCs was also evident. Together, our results establish that Mycobacterium directs a fine-balance of host signaling pathways and molecular regulators in human DCs to expand Tregs that favour immune evasion of the pathogen

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages