FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

Plantar erythrodysesthesia with bullous otitis externa, toxicities from sorafenib: a case report

Lung cancer is the leading cause of cancer death worldwide and the use of novel agents, such as sorafenib has now demonstrated activity in Non Small Cell Lung Cancer. We present a case of a 77-year-old Caucasian male with advanced adenocarcinoma of the lung, who was being treated on clinical trial with single agent sorafenib. After seven weeks of treatment the patient presented to clinic with difficulty walking. Physical exam revealed acral erythema with bollous formation on bilateral soles of his feet. Otoscopic exam revealed bilateral external canal bullous lesion. The patient was diagnosed with plantar erythrodysesthesia with bullous otitis externa, a new toxicities in patients being treated with sorafenib

Chemical patterning for the highly specific and programmed assembly of nanostructures

We have developed a new chemical patterning technique based on standard lithography-based processes to assemble nanostructures on surfaces with extraordinarily high selectivity. This patterning process is used to create patterns of aminosilane molecular layers surrounded by highly inert poly (ethylene glycol) (PEG) molecules. While the aminosilane regions facilitate nanostructure assembly, the PEG coating prevents adsorption of molecules and nanostructures, thereby priming the semiconductor substrate for the highly localized and programmed assembly of nanostructures. We demonstrate the power and versatility of this manufacturing process by building multilayered structures of gold nanoparticles attached to molecules of DNA onto the aminosilane patterns, with zero nanocrystal adsorption onto the surrounding PEG regions. The highly specific surface chemistry developed here can be used in conjunction with standard microfabrication and emerging nanofabrication technology to seamlessly integrate various nanostructures with semiconductor electronics

Three-Port Bi-Directional DC–DC Converter with Solar PV System Fed BLDC Motor Drive Using FPGA

The increased need for renewable energy systems to generate power, store energy, and connect energy storage devices with applications has become a major challenge. Energy storage using batteries is most appropriate for energy sources like solar, wind, etc. A non-isolated three-port DC–DC-converter energy conversion unit is implemented feeding the brushless DCmotor drive. In this paper, a non-isolated three-port converter is designed and simulated for battery energy storage, interfaced with an output drive. Based on the requirements, the power extracted from the solar panel during the daytime is used to charge the batteries through the three-port converter. The proposed three-port converter is analyzed in terms of operating principles and power flow. An FPGA-based NI LabView PXI with SbRio interface is used to develop the suggested approach’s control hardware, and prototype model results are obtained to test the proposed three-port converter control system’s effectiveness and practicality. The overall efficiency of the converter’s output improves as a result. The success rate is 96.5 percent while charging an ESS, 98.1 percent when discharging an ESS, and 95.7 percent overall

Stroke from Delayed Embolization of Polymerized Glue Following Percutaneous Direct Injection of a Carotid Body Tumor

A 52-year-old male with right carotid body tumor underwent direct percutaneous glue (n-butylcyanoacrylate [NBCA]) embolization. Several hours later, he developed left hemiparesis from embolization of the polymerized glue cast. Migration of glue during percutaneous tumor embolization is presumed to occur only in the liquid state, which may lead to stroke or cranial nerve deficits. To the best of our knowledge, this is the first report of delayed glue embolization from a treated hypervascular tumor of the head and neck

Tracheo-innominate Artery Fistula in a Complicated Penetrating Neck Trauma: A Successfully Managed Rare Complication of Low Tracheotomy

Tracheo-innominate artery fistula is a rare complication of tracheotomy with very high mortality rate. Only a few patients survive this complication as reported in the literature. Here we report the case of a 54-year-old gentleman who presented to the emergency department with a history of penetrating neck trauma following a road traffic accident. Neck exploration and tracheotomy were done to secure the airway. After two weeks, the patient had an episode of massive stomal bleed for which he was taken to the operating room and re-explored. A tracheo-innominate artery fistula was detected, and right side aorto-carotid and right side aorto-subclavian anastomoses were done using reversed saphenous vein graft with interruption of flow. Following a successful surgery, the patient was decannulated later, and now lives a healthy normal life. Early diagnosis and immediate intervention are the key in managing this complication. Bedside management also plays a vital role

Evaluation of electrocardiographic and serum biochemical changes in arrhythmias associated with renal diseases of dogs

The present study was conducted to investigate electrocardiographical (ECG) and haemato-biochemical changes in arrhythmia associated with renal diseases in dogs. The dogs with renal affections confirmed through appropriate diagnostic methods were selected and screened for arrhythmia. The ECG and haemato-biochemical parameters of twenty dogs with arrhythmia were compared with that of the control group and ECG parameters were statistically correlated with the haemato-biochemical parameters for correlation studies. It was found that the occurrence of arrhythmia was 51.2 per cent in renal diseases. Arrhythmia was more predominant in dogs with chronic kidney disease (CKD) followed by acute kidney injury (AKI). Sinus arrhythmia followed by first-degree AV block and wandering pacemaker were the common types of arrhythmias observed. A significant increase in R-R interval and a decrease in heart rate was noticed in comparison. The haemato-biochemical analysis revealed anaemia, leukocytosis with neutrophilia, elevated blood urea nitrogen (BUN), creatinine and magnesium level. A significant positive correlation was noticed between haemoglobin, volume of packed red cells (VPRC) and red blood cell count (RBC) with T amplitude and, creatinine and BUN levels with corrected Q-T interval. A significant negative correlation was noticed between VPRC, RBC and haemoglobin with the corrected QT interval. The present study revealed ECG and haemato-biochemical parameters had a significant role in renal diseases in dogs which might help in the early diagnosis and proper management of arrhythmia associated with renal diseases

Capturing dynamical correlations using implicit neural representations

The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $\omega$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. We benchmark this approach on a Linear Spin Wave Theory (LSWT) simulator and advanced inelastic neutron scattering data from the square-lattice spin-1 antiferromagnet La$_2$NiO$_4$. We find that the model predicts the unknown parameters with excellent agreement relative to analytical fitting. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data, without the need for human-guided peak finding and fitting algorithms. This prototypical approach promises a new technology for this field to automatically detect and refine more advanced models for ordered quantum systems.Comment: 12 pages, 7 figure