Admission Pattern of Newborns admitted in special newborn care unit: an observational from North West of India

Background: Globally, neonatal deaths constitute 44% of all deaths in less than 5 years age group .The concept of SNCU is based on the learning from the “Purulia model”. This research study was undertaken, to assess the profile of sick newborns admitted in the SNCU.Methods The investigators analyzed this data and establish the morbidity profile of newborns admitted in SNCU Hamirpur in 2019.Results: Total of 422 newborns were admitted 197 (46.7%) were females and 225 (53.3%) were males. Out of these 381 (90.3%) were inborn and 41(9.7%) were out born. In inborn 293 (76.9%) weighed more than 2.5 kg, 83 (21.8%) were low birth weight <2.5 kg to 1.5 kg and 2 (0.52%) were very low birth weight i.e < 1.5 kg to 1 kg and one newborn was extreme low birth weight i.e <1 kg. In the out born group, 23 (56.1%) weighed more than 2.5 kg and 13 (31.8%) were low birth weight <2.5 kg to 1.5 kg, 4 (9.8%) were very low birth weight < 1.5 kg to 1 kg. Jaundice was the most common neonatal condition 63%, infection 9.5%, respiratory diseases 9% and birth asphyxia 7.8%.Conclusions: The most sensitive time for morbidity and mortality development is during the newborn period. The main reasons for SNCU hospitalisation include neonatal jaundice, preterm, low birth weight, perinatal asphyxia, and infection. Neonatal Jaundice continues to be the primary reason for both inborn and outborn newborns admission to SNCU, and inborn babies make up the majority of this group

IoT-Fog-Edge-Cloud Computing Simulation Tools, A Systematic Review

The Internet of Things (IoT) perspective promises substantial advancements in sectors such as smart homes and infrastructure, smart health, smart environmental conditions, smart cities, energy, transportation and mobility, manufacturing and retail, farming, and so on. Cloud computing (CC) offers appealing computational and storage options; nevertheless, cloud-based explanations are frequently conveyed by downsides and constraints, such as energy consumption, latency, privacy, and bandwidth. To address the shortcomings related to CC, the advancements like Fog Computing (FC) and Edge Computing (EC) are introduced later on. FC is a novel and developing technology that connects the cloud to the network edges, allowing for decentrali zed computation. EC, in which processing and storage are performed nearer to where data is created, may be able to assist address these issues by satisfying particular needs such as low latency or lower energy use. This study provides a comprehensive overview and analysis of IoT-Fog-Edge-Cloud Computing simulation tools to assist researchers and developers in selecting the appropriate device for research studies while working through various scenarios and addressing current reality challenges. This study also takes a close look at various modeling tools, which are examined and contrasted to improve the future

URL ATTACKS: Classification of URLs via Analysis and Learning

Social Networks such as Twitter, Facebook play a remarkable growth in recent years. The ratio of tweets or messages in the form of URLs increases day by day. As the number of URL increases, the probability of fabrication also gets increased using their HTML content as well as by the usage of tiny URLs. It is important to classify the URLs by means of some modern techniques. Conditional redirection method is used here by which the URLs get classified and also the target page that the user needs is achieved. Learning methods also introduced to differentiate the URLs and there by the fabrication is not possible. Also the classifiers will efficiently detect the suspicious URLs using link analysis algorithm

Assessment of endothelial dysfunction in diabetes mellitus in Indian population by color Doppler

Background: Endothelial dysfunction is regarded as a systemic marker of cardiovascular disease. Brachial artery flow-mediated dilation (FMD) is a mode of evaluating endothelial function and for early diagnosis of atherosclerotic diseases. We studied whether there is a difference in vascular endothelial function between type 1 and 2 diabetes mellitus and normal population.Methods: We assessed %FMD of 50 patients with diabetes mellitus and 50 control populations without diabetes mellitus or other risk factors. SPSSS version 13 was used for statistical analysis. Students T test was used for comparison of means of the two groups.Results: The %FMD was significantly lower in patients with type1 and 2 diabetes mellitus compared to normal population.Conclusions: Diabetes mellitus is associated with endothelial dysfunction irrespective of Type 1 or Type 2 diabetes mellitus, as suggested by impairment in vascular reactivity to hyperemia in both. Monitoring FMD may help in assessment of progression of atherosclerosis

Biosorption of trivalent chromium from a model tanning solution by adapted aspergillus niger

Industrial effluents containing metallic species are responsible for environmental degradation which have been prioritised as major inorganic contaminants. Conventional methods are quite expensive resulting in need for cost-effective process for removing heavy metals from discharging effluents. The use of microbial biomass for removal of heavy metals from aqueous solutions (biosorption) is one such approach gaining increasing attention. Trivalent chromium ion present in tannery effluents has been the targeted ionic species for removal due to its exceeding limits in industrial discharges (<--0.3 ppm as per WHO). At NML, efforts were made for biosorption of trivalent chromium from tannery effluents with Cr (III) concentration in the range 1500-5000ppm. Aspergillus niger, obtained from a culture bank has been used in biosorption of trivalent chromium of tannery effluents. The fungal species grown in Czapek Dox Medium and adapted on Cr(III) ions ranging from 10-2000ppm at 2.5 pH and 35°C, was used for biosorption of chromium from a model tanning solution. A.niger was used in forms such as live, adapted and pre-treated (autoclaved, alkali-treated) for biosorption at pH 2.5 and 35°C. At Cr(IlI) conc. of 2000ppm in the aqueous solution, the adsorption efficiency followed the order: alkali treated (52%)>live(38%)>autoclaved dead mass(27%). The varying biosorption capacities may be attributed to exposed metal binding sites in alkali treated fungus causing high biosorption efficiency which also obeyed the sorption isotherm

Bio-hydrometallurgical approach in processing of low grade Indian uranium ore in Column Reactor

In order to augment the supply of uranium for electricity generation, bioleaching is being considered for exploiting a low-grade uranium ore (with 0.024% U3O8 of Turamdih Mines, Jharkhand, India). This ore contains silicate and magnetite as the main minerals and uraninite and hematite as minor minerals. At NML, efforts have been made to use Acidithiobacillus ferrooxidans (Ac.Tf) initially on bench scale in shake flask and then in column to recover uranium. In shake flasks, ~98% uranium dissolution was achieved in 30days at 1.7pH, 35oC temperature and 20% (w/v) pulp density. In a laboratory scale column containing 2.5kg ore, uranium bio-recovery of 55.48% was obtained in 30 days at 1.7pH. To scale up the process, the bio-leaching experiments carried out on 80kg ore showed uranium recovery of 69.8% as against a recovery of 55.12% in control set at 1.7 pH in 50 days. Bio-recovery of uranium has been correlated with the change in redox potential (Eh) and ferric ion concentration

AI on the Water: Applying DRL to Autonomous Vessel Navigation

Human decision-making errors cause a majority of globally reported marine accidents. As a result, automation in the marine industry has been gaining more attention in recent years. Obstacle avoidance becomes very challenging for an autonomous surface vehicle in an unknown environment. We explore the feasibility of using Deep Q-Learning (DQN), a deep reinforcement learning approach, for controlling an underactuated autonomous surface vehicle to follow a known path while avoiding collisions with static and dynamic obstacles. The ship's motion is described using a three-degree-of-freedom (3-DOF) dynamic model. The KRISO container ship (KCS) is chosen for this study because it is a benchmark hull used in several studies, and its hydrodynamic coefficients are readily available for numerical modelling. This study shows that Deep Reinforcement Learning (DRL) can achieve path following and collision avoidance successfully and can be a potential candidate that may be investigated further to achieve human-level or even better decision-making for autonomous marine vehicles.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

Relationship of serum vitamin D with hepatic fibrosis in patients with chronic hepatitis C

Background: Serum vitamin D concentration is proposed to have an important role on outcome in patients with chronic hepatitis C virus (HCV) infection. A few studies have shown an inverse association of vitamin D level with stage of fibrosis. The aim of the present study was to verify whether serum vitamin D level is an independent predictor of significant hepatic fibrosis.Methods: Seventy-two treatment naive chronic HCV subjects and 40 healthy age and sex matched controls were included in the study. A serum vitamin D level was assessed in both HCV subjects and controls, and liver biopsy was performed in all HCV subjects to assess for stage of fibrosis.Results: Serum vitamin D levels were significantly lower HCV patients in comparison to age and sex matched controls (18.04±6.92 versus 21.53±8.2, p<0.01). Most common genotype in HCV patients was genotype 3 (62.5%) and blood transfusion was the most common mode of transmission (28%) followed by intravenous drug user (IVDU) (17%). The HCV patients with vitamin D level <20 ng/ml had higher metavir score as compared to vitamin D≥20 ng/ml (1.67±0.66 versus 2.5±0.67, p<0.001). Both univariate and multivariate analysis performed using logistic regression revealed that vitamin D<20 ng/dl is a significant negative predictor of liver fibrosis (p<0.05).Conclusions: Chronic HCV patients had significantly lower vitamin D levels as compared to healthy controls. Serum vitamin D was a negative predictor of stage of fibrosis in patients with chronic hepatitis C