Development of cardiac prescreening device for rural population using ultralow-power embedded system

The invention is inspired by the desire to understand the opportunities and expectations of developing economies in terms of healthcare. The designed system is a point-of-care (POC) device that can deliver heart-care services to the rural population and bridge the rural-urban divide in healthcare delivery. The product design incorporates several innovations including the effective use of adaptive and multiresolution signal-processing techniques for acquisition, denoising, segmentation, and characterization of the heart sounds (HS) and murmurs using an ultralow-power embedded Mixed Signal Processor. The device is able to provide indicative diagnosis of cardiac conditions and classify a subject into either normal, abnormal, ischemic, or valvular abnormalities category. Preliminary results demonstrated by the prototype confirm the applicability of the device as a prescreening tool that can be used by paramedics in rural outreach programs. Feedback from medical professionals also shows that such a device is helpful in early detection of common congenital heart diseases. This letter aims to determine a framework for utilization of automated HS analysis system for community healthcare and healthcare inclusion.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Microstructure-Based Estimation of Strength and Ductility Distributions for $$\alpha +\beta $$ Titanium Alloys

Titanium alloys are processed to develop a wide range of microstructure configurations and therefore material properties. While these properties are typically measured experimentally, a framework for property prediction could greatly enhance alloy design and manufacturing. Here a microstructure-sensitive framework is presented for the prediction of strength and ductility as well as estimates of the bounds in variability for these properties. The framework explicitly considers distributions of microstructure via new approaches for instantiation of structure in synthetic samples. The parametric evaluation strategy, including the finite element simulation package FEpX, is used to create and test virtual polycrystalline samples to evaluate the variability bounds of mechanical properties in Ti-6Al-4V. Critical parameters for the property evaluation framework are provided by measurements of single crystal properties and advanced characterization of microstructure and slip system strengths in 2D and 3D. Property distributions for yield strength and ductility are presented, along with the validation and verification steps undertaken. Comparisons between strain localization and slip activity in virtual samples and in experimental grain-scale strain measurements are also discussed.

     

Capillary forces between sediment particles and an air-water interface

In the vadose zone, air-water interfaces play an important role in particle fate and transport, as particles can attach to the air-water interfaces by action of capillary forces. This attachment can either retard or enhance the movement of particles, depending on whether the air-water interfaces are stationary or mobile. Here we use three standard PTFE particles (sphere, circular cylinder, and tent) and seven natural mineral particles (basalt, granite, hematite, magnetite, mica, milky quartz, and clear quartz) to quantify the capillary forces between an air-water interface and the different particles. Capillary forces were determined experimentally using tensiometry, and theoretically assuming volume-equivalent spherical, ellipsoidal, and circular cylinder shapes. We experimentally distinguished between the maximum capillary force and the snap-off force when the air-water interface detaches from the particle. Theoretical and experimental values of capillary forces were of similar order of magnitude. The sphere gave the smallest theoretical capillary force, and the circular cylinder had the largest force due to pinning of the air-water interface. Pinning was less pronounced for natural particles when compared to the circular cylinder. Ellipsoids gave the best agreement with measured forces, suggesting that this shape can provide a reasonable estimation of capillary forces for many natural particles.     

Thermoelectric cold-chain chests for storing/transporting vaccines in remote regions

The cold chain is one of the key elements of the preventive health-care delivery system. Vaccines have to be carried long distances, stored in remote places and during this period the temperature has to be maintained within certain specified values. Realizing the needs of such requirements, the Department of Science & Technology, New Delhi (Govt. of India) assigned a project to the R&D Division of MECON, Ranchi for development of Thermoelectric Cold-Chain Chest operated by 12 V DC vehicular battery. The resulting portable thermoelectric (i.e. Peltier effect) Cold-Chain Chest (TCC) operated successfully even in an ambient environment of 45 °C, mainly for preserving and transporting life-saving medicines for urban as well as rural areas.     

Intermetallic Precipitation in Low-Density Steel

Metallurgical and Materials Transactions A - Low-density steels (LDS) represent a relatively new class of material that contains a large concentration of aluminum. In the present work, we studied...     

Identification of monofloral honey using voltammetric electronic tongue

Quality assessment of honey is often related to its floral origin which is a complex task to evaluate. Traditional technique of floral assessment is made by melissopalynological method. However, this method is quite time consuming and also often operator dependent. Thus, the fallout is a large range of error in interpretation of the result and hence there is considerable demand for instrumental methods to assess the identification of pollen in honey. In this pursuit, an electronic tongue based on cyclic voltammetry is developed to discriminate honey samples based on their floral types and is described in this paper. The technique has been investigated using platinum as the working electrode and the resultant current from the potentiostat has been considered for data analysis. The use of principal component analysis (PCA) and linear discriminate analysis (LDA) proves to be useful in clustering honey samples. Finally, classification performances are investigated using back-propagation multilayer perceptron (BP-MLP) and radial basis function (RBF) neural network models for identification of different floral origin of honey.     

Quarterwave microstrip antenna on a ferrimagnetic substrate

Experiment conducted on a microstrip quarterwave antenna structure on a typical ferrite substrate reveals reduction in size, interesting radiation characteristics and a broadband nature over a wide range of frequencies in the lower UHF region.