AVID triad: a case report

Asymmetric ventriculomegaly, interhemispheric cyst and dysgenesis of the corpus callosum (AVID) constitutes a rare imaging triad. Additional findings include subcortical and subependymal heterotopia, polymicrogyria, fused thalami, deficient falx, and hydrocephalus. The knowledge of this triad helps us to diagnose prenatally by sonography and fetal MRI. In this case report authors present MRI Imaging findings in a case of AVID syndrome in a 6year old male child presenting with history of seizures and delayed milestones

Focussing on Defence R&D: an Insight Into DRDO

Introduction The Defence Research and Defence Organisation (DRDO) is the premier R&D organisation under the Ministry of Defence (MoD). R&D organisations are normally esoteric and their management processes are considered challenging and complex.2 DRDO's research is primarily for the benefit of the three defence services who also fall under the umbrella of MoD. This creates a unique and unusual situation in which the customers (viz. the three services) are fundamentally, similarly placed departments, like the R&D organisation, under the same ministry. Many literary excerpts on the subject point towards a feeling amongst the services that DRDO does not give the users (‘user' is DRDO's term for customers) requirements, a competitively equal importance on a similar level as a private player would have given, in such a competitive environment3 . It has been felt that there is a lack of mutual understanding and appreciation of the constraints faced by the services and DRDO. This feeling is only heightened by the fact that many important DRDO projects overshoot the budget and project timelines. DOI: 10.5281/zenodo.340594

Development of a high resolution land surface dataset for the South Asian monsoon region

In this study, we report the development of a high resolution land surface dataset for the South Asian monsoon region for studies on land surface processes, and land and atmosphere coupling. The high resolu- tion land data assimilation system was used to develop the land surface dataset utilizing TRMM rainfall and ECMWF atmospheric variables as forcing parameters. The dataset was developed at a spatial resolution of 0.5° and temporal resolution of 1 h and spans a period of 6 years, i.e. 1 January 2005 to 31 December 2010. The major highlights in the development of the present dataset are higher spatial and temporal resolution of land surface parameters, use of sub-daily forcing parameters including rainfall, use of MODIS land-use data in lieu of USGS land-use data and weekly varying vegetation fraction instead of monthly vegetation climatology. A comparison of soil moisture and soil temperature with limited surface observations of the IMD suggests reasonable reliability of the land surface data. The model sensible heat flux data are compared with in situ measurements at Ranchi and MEERA reanalysis data. The sensitivity analysis shows that the land surface data are sensitive to rainfall and green vegetation cover data used as the forcing parameters. The dataset has been used to discuss the variations of land surface processes associated with active and break spells and a severe heat wave observed in 2009. The present dataset will be useful for many applications, including initializing numerical models for weather prediction. This high resolution land surface dataset is available for research on request

Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis

Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder

Potential link between compromised air quality and transmission of the novel corona virus (SARS-CoV-2) in affected areas

The emergence of a novel human corona virus disease (COVID-19) has been declared as a pandemic by the World Health Organization. One of the mechanisms of airborne transmission of the severe acute respiratory syndrome corona virus (SARS-CoV-2) amid humans is through direct ejection of droplets via sneezing, coughing and vocalizing. Nevertheless, there are ample evidences of the persistence of infectious viruses on inanimate surfaces for several hours to a few days. Through a critical review of the current literature and a preliminary analysis of the link between SARS-CoV-2 transmission and air pollution in the affected regions, we offer a perspective that polluted environment could enhance the transmission rate of such deadly viruses under moderate-to-high humidity conditions. The aqueous atmospheric aerosols offer a conducive surface for adsorption/absorption of organic molecules and viruses onto them, facilitating a pathway for higher rate of transmission under favourable environmental conditions. This mechanism partially explains the role of polluted air besides the exacerbation of chronic respiratory diseases in the rapid transmission of the virus amongst the public. Hence, it is stressed that more ambitious policies towards a cleaner environment are required globally to nip in the bud what could be the seeds of a fatal outbreak such as COVID-19

Resource-Efficient Quantum Circuits for Molecular Simulations: A Case Study of Umbrella Inversion in Ammonia

We conducted a thorough evaluation of various state-of-the-art strategies to prepare the ground state wavefunction of a system on a quantum computer, specifically within the framework of variational quantum eigensolver (VQE). Despite the advantages of VQE and its variants, the current quantum computational chemistry calculations often provide inaccurate results for larger molecules, mainly due to the polynomial growth in the depth of quantum circuits and the number of two-qubit gates, such as CNOT gates. To alleviate this problem, we aim to design efficient quantum circuits that would outperform the existing ones on the current noisy quantum devices. In this study, we designed a novel quantum circuit that reduces the required circuit depth and number of two-qubit entangling gates by about 60%, while retaining the accuracy of the ground state energies close to the chemical accuracy. Moreover, even in the presence of device noise, these novel shallower circuits yielded substantially low error rates than the existing approaches for predicting the ground state energies of molecules. By considering the umbrella inversion process in ammonia molecule as an example, we demonstrated the advantages of this new approach and estimated the energy barrier for the inversion process.Comment: 7 pages, 8 figure

The Chandrayaan-2 Large Area Soft X-ray Spectrometer (CLASS)

The CLASS experiment on Chandrayaan-2, the second Indian lunar mission, aims tomap the abundance of the major rock forming elements on the lunar surface using the technique of X-ray fluorescence during solar flare events. CLASS is a continuation of the successful C1XS [1] XRF experiment on Chandrayaan-1. CLASS is designed to provide lunar mapping of elemental abundances with a nominal spatial resolution of 25 km (FWHM) from a 200 km polar, circular orbit of Chandrayaan-2

An enhanced gradient-based optimizer for parameter estimation of various solar photovoltaic models

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms

Role of direct reduced iron fines in nitrogen removal from electric arc furnace steel

Electric arc furnace steel contains about 70-120 ppm nitrogen. There is no suitable method for nitrogen removal from electric arc furnace steel up to the level desired for good quality bars and flat rolled products (30 ppm max). The existing process based on vacuum degassing can remove only up to 20% of nitrogen in steel. In the present study DRI fines have been injected into a steel bath which can drift out nitrogen in steel through production of fine CO bubbles in-situ on reaction with residual FeO in DRI fines and C in bath. For high and medium carbon steel, nitrogen got reduced to 30 ppm and 60 ppm respectively where initial nitrogen was 150 - 200 ppm in steel. Nitrogen removal also depends upon bath depth and addition level of DRI

An appraisal of rainfall estimation over India using remote sensing and in situ measurements

167-177The most important meteorological parameter Rainfall, shows high variability in space and time, particularly over Tropics / Monsoon region. Many new observational and analysis methods to observe / analyse them by remote sensing techniques (Satellites, Doppler Weather Radars) have emerged over the decades, besides the dense network of in situ rain gauges, Automatic Weather Stations (AWS) etc on ground. The scales of observations being vastly different for in situ and remote sensing methods, large discrepancies between different techniques are inherent. These problems have been brought out through various validation studies by many groups in the country. Even on the daily all India spatial scale, basically only the peaks and troughs from satellite estimates match reasonably well with in situ data. Results of a case study during an intense and long-lasting rain event over Chennai, from DWR, with different satellite products and ground truth are presented. The importance of DWR rainfall data in significantly improving the integrated products is emphasised. A simple two-way approach to establish Z – R relationship for the DWRs in the country is also suggested. A well-coordinated integrated programme to study the inter comparability of precipitation at various spatio- temporal scales in the context of our water resources, model validation, extreme rainfall events, Climate change, etc., is called for. The desired accuracies from satellite data vis a vis IMD gridded data for different applications have been summarised