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Survivors of paediatric intracranial tumours are at increased risk of psychosocial, neuro-developmental, and functional impairment. This study aimed to evaluate long-term health-related quality-of-life (HRQOL) outcomes in patients with benign paediatric brain tumours treated curatively with surgical resection alone.This was a cross-sectional study of patients with benign paediatric intracranial tumours managed with surgery alone between 2000 and 2015. Eligible patients with a minimum of 5-years follow-up after surgery were identified. Validated health-related quality of life (HRQOL) questionnaires were administered: SF-36, QLQ-BN20, QLQ-C30 and PedsQL™.Twenty-three patients participated (median age at surgery 13 years; range 1-18; 12 male). The most common diagnosis was pilocytic astrocytoma (n = 15). Median time from surgery to participation was 11 years(range 6-19). Fourteen patients achieved A-level qualifications and two obtained an undergraduate degree. Twelve patients were employed, eight were studying and three were unemployed or volunteering. HRQOL outcomes demonstrated significant limitation from social functioning (p = 0.03) and cognitive functioning (p = 0.023) compared to the general population. Patients also experienced higher rates of loss of appetite (p = 0.009) and nausea and vomiting (p = 0.031). Ten patients were under transitional teenager and young-adult (TYA) clinic follow-up. TYA patients achieved higher levels of education (p = 0.014), were more likely to hold a driver's license (p = 0.041) compared to patients not followed-up through these services.Childhood brain-tumour survivors have a greater risk of developing psychological, neuro-cognitive and physical impairment. Early comprehensive assessment, specialist healthcare and TYA services are vital to support these patients.

Covid-19 has impacted the lives of people in various aspects positive as well as negative. It has an impact on different areas of life, including the economy, industries, global markets, agriculture, human health, etc. The virus caused disruptions to daily life and restrictions on activities in many communities during its multiple outbreaks. Hope for a potential end to the pandemic emerged with the stepping up of vaccination campaigns. However, the continual appearance of mutant strains and vaccine hesitancy has been problems both nationally and internationally. People's normal activities, routines, and livelihoods have been disrupted by self-isolation and quarantine, potentially leading to an increase in loneliness, anxiety, depression, insomnia, excessive alcohol and drug use, and self-harm or suicidal activity. The psychological aspects of the post-covid-19 era are the effect of covid-19 on CNS, and physiological and psychological diseases caused due to covid-19. The article explains Post Trauma Stress Disorder as a psychological scar, Social evils arising due to covid-19, and predictive reasons for the increase in mental illness Keywords- Covid-19, Pandemic, Mental Health, Post Trauma Stress Disorder, Depression, Anxiety

A quick deployment and high-efficiency helium surveillance balloon which can be used as a mobile surveillance systemto monitor and locate trespassers near borders and high-security facilities are presented in the paper. The paperprovides the description of a motorized helium-filled balloon that is remotely controlled and provides a real-timevideo of any site that needs surveillance. The paper also provides the conceptual design, fabrication, and, calculationof the payload connected to of the helium balloon tracker. The payload consists of the control and monitoringsystem which has a camera and sensors and streams this data to the user over the internet which can be used forpatrolling and monitoring infiltration

Surgical resection of meningioma leaves residual solid tumour in over 25% of patients. Selection for further treatment and follow-up strategy may benefit from knowledge of volumetric growth and factors associated with re-growth. The aim of this review was to evaluate volumetric growth and variables associated with growth in patients that underwent incomplete resection of a meningioma without the use of adjuvant radiotherapy. A systematic review was conducted in accordance with the PRISMA statement and registered a priori with PROSPERO (registration number: CRD42020177052). Six databases were searched up to May 2020. Full text articles analysing volumetric growth rates in at least 10 patients who had residual meningioma after surgery were assessed. Four single-centre, retrospective studies totalling 238 patients were included, of which 99% of meningioma were WHO grade 1. The absolute tumour growth rate ranged from 0.09 to 4.94 cm3 per year. The relative growth rate ranged from 5.11 to 14.18% per year. Varying methods of volumetric assessment and definitions of growth impeded pooled analysis. Pre-operative and residual tumour volume, and hyperintensity on T2 weighted MRI were identified as variables associated with residual meningioma growth, however this was inconsistent across studies. Risk of bias was high in all studies. Radiological regrowth occurred in 42–67% of cases. Our review identified that volumetric growth of residual meningioma is scarcely reported. Sufficiently powered studies are required to delineate volumetric growth and prognostic factors to stratify management.

The proposed study reports the essential parameters required for the battery charging schemes deployed for Electric Vehicle (EV) applications. Due to efficient power delivery, cost‐effectiveness, and environmental acclimation, EVs have emerged as a suitable alternative to the Internal Combustion (IC)‐based engines. However, prominent challenges for leveraging the EVs are the suitable availability of battery charging infrastructure for high energy/power density battery packs and efficient charging topologies. Despite the challenges, EVs are gradually being implemented across the globe to avoid oil dependency, which currently has a 5%–7% decline rate of post‐peak production. The vast deployment of EVs as private and commercial vehicles has created a major challenge for the grids in maintaining the power quality and peak load demand. This study, therefore, reviews the various battery charging schemes (battery charger) and their impact when used in EV and Hybrid EV applications. The available constituents of the battery chargers such as ac‐dc/dc‐dc converter topologies, modulations, and control techniques are illustrated in detail. The comprehensive study classifies the charging topologies depending upon the power and charging level. Some appropriate battery charging converter topologies that are suitable for domestic, industrial, and commercial applications like EVs are suggested in the study. In addition, a decision‐making inference is developed through a flow chart that decides on the suitable selection of the converter topology based on the required applications. Furthermore, the charging infrastructures along with the converters' design standards are also discussed concisely, which adds a significant contribution to the review article.

Commercial trucks are not always transporting goods at full capacity, which means that the box-shape trailer is partially occupied or even completely empty on occasions. In this work, we explore foldable trailer design to improve truck efficiency and sustainability. To achieve the folding feature we explore active compliant hinges in origami-inspired design. Active origami engineering relies on two-dimensional smart hinges able to interact in a three-dimensional design without external forces. The truck trailer is envisioned to be able to fold itself into an aerodynamic wedge shape when the trailer is (partially) empty. This design can be made lighter and more environmentally friendly than the original trailer design and the use of such a foldable trailer can reduce fuel consumption. Smart material layered hinges made from dielectric elastomers actuate the plastic faces of the construction to achieve the folding and deploying of the adaptable trailer design. Initial studies of geometry, materials, and actuation power as well as the potential for energy savings are presented. The drag coefficient is reduced by 33.77% when folded into an aerodynamic shape which leads to an annual decrease in fuel consumption of 6.32% for the average truck that drives around empty 20% of the time and 15.8% for one way transport trucks that drive around empty 50% of the time.

The gas hydrate deposits of the Krishna-Godavari (KG) basin in India have a huge potential of fulfilling the energy needs of the country for several years to come. We present a detailed thermodynamic and kinetic study of methane hydrate formation and dissociation in the presence of an actual Krishna-Godavari (KG) basin marine sediment sample at varying concentrations, i.e., 10, 20, and 35 wt %, in an aqueous solution. This study revealed the possibility of dual induction during methane hydrate formation in the presence of pure water and 10 wt % KG basin sediment solution. As the sediment concentration increased, the inhibition effect during the methane hydrate formation became more pronounced. The heat of dissociation values were found to be decreasing with the increase in sediment concentration. The kinetic experiments depicted the stochastic nature during the hydrate formation. The methane gas consumption and the rate of methane hydrate formation were found to be high in the case of pure water, followed by 10, 20, and 35 wt % sediment solutions. However, the initial rate of methane hydrate formation among the sediment solutions was observed to be high in the case of the 20 wt % sediment solution. The water-to-hydrate (W-H) conversion and gas-to-hydrate (G-H) conversion were found to be reducing with an increase in sediment concentration due to the confining effect of the water–gas system. During kinetic dissociation, the stable temperature range before the hydrates actually started dissociating was found to be increasing with sediment concentration, and the maximum temperature up to which the hydrates remained stable was ∼6 °C. The rate of hydrate dissociation was high in the case of the pure water system and slightly decreased in the 10 wt % sediment solution; however, it was found to be almost similar in 20 and 35 wt % sediment solutions. This study will certainly add value toward a better understanding of the KG basin reservoir for future exploration of methane gas from the hydrate deposits.

International audience; Advancements in machine learning have sparked significant interest in designing mechanical metamaterials, i.e., materials that derive their properties from their inherent microstructure rather than just their constituent material. We propose a data-driven exploration of the design space of growth-based cellular metamaterials based on star-shaped distances. These two-dimensional metamaterials are based on periodically-repeating unit cells consisting of material and void patterns with non-trivial geometries. Machine learning models exploiting large datasets are then employed to inverse design growth-based metamaterials for tailored anisotropic stiffness. Firstly, a forward model is created to bypass the growth and homogenization process and accurately predict the mechanical properties given a finite set of design parameters. Secondly, an inverse model is used to invert the structure–property maps and enable the accurate prediction of designs for a given anisotropic stiffness query. We successfully demonstrate the frameworks’ generalization capabilities by inverse designing for stiffness properties chosen from outside the domain of the design space.

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full-field displacement and net force data; ii. a very wide material model library — in our case, a very large number of terms in the Prony series; iii. the linear momentum balance constraint; iv. the sparsity constraint. The devised strategy comprises two stages. Stage 1 relies on sparse regression; it enforces momentum balance on the data and exploits sparsity-promoting regularization to drastically reduce the number of terms in the Prony series and identify the material parameters. Stage 2 relies on k-means clustering; starting from the reduced set of terms from stage 1, it further reduces their number by grouping together Maxwell elements with very close relaxation times and summing the corresponding moduli. Automated procedures are proposed for the choice of the regularization parameter in stage 1 and of the number of clusters in stage 2. The overall strategy is demonstrated on artificial numerical data, both without and with the addition of noise, and shown to efficiently and accurately identify a linear viscoelastic model with five relaxation times across four orders of magnitude, out of a library with several hundreds of terms spanning relaxation times across seven orders of magnitude. ISSN:0167-6636 ISSN:1872-7743

Developments in the gas turbine technology have caused widespread usage of the Turbomachines for power generation. With increase in the power demand and a drop in the availability of fuel, usage of turbines with higher efficiencies has become imperative. This is only possible with an increase in the turbine inlet temperature (TIT) of the gas. However, the higher limit of TIT is governed by the metallurgical boundary conditions set by the material used to manufacture the turbine blades. Hence, turbine blade cooling helps in drastically controlling the blade temperature of the turbine and allows a higher turbine inlet temperature. The blade could be cooled from the leading edge, from the entire surface of the blade or from the trailing edge. The various methods of blade cooling from leading edge and its comparative study were reviewed and summarized along with their advantages and disadvantages.

We report a 33-year-female patient of hypertension detected at the age of 19 year. Her blood pressure was not well controlled. The case was investigated for secondary hypertension. CT Thoracic and Abdominal aorta Angiogram of the present case showing the narrowing in the thoracic aorta extending at the level of T8 to T10 vertebral body level for the length of 7.5cm. Reconstructed CT of the case showed multiple tortuous collaterals between the branches of internal mammary artery and external iliac artery and between axillary and intercostal artery. Relevant history was reviewed and discussed.

Selecting the optimal set of features to determine sentiment in online textual content is imperative for superior classification results. Optimal feature selection is computationally hard task and fosters the need for devising novel techniques to improve the classifier performance. In this work, the binary adaptation of cuckoo search (nature inspired, meta-heuristic algorithm) known as the Binary Cuckoo Search is proposed for the optimum feature selection for a sentiment analysis of textual online content. The baseline supervised learning techniques such as SVM, etc., have been firstly implemented with the traditional tf-idf model and then with the novel feature optimization model. Benchmark Kaggle dataset, which includes a collection of tweets is considered to report the results. The results are assessed on the basis of performance accuracy. Empirical analysis validates that the proposed implementation of a binary cuckoo search for feature selection optimization in a sentiment analysis task outperforms the elementary supervised algorithms based on the conventional tf-idf score.

As the world is engulfed with COVID-19 pandemic and the glimpse of vaccine is still a distant dream, taking precautions and maintaining the norms suggested by WHO will keep us safe. With this, we present in this paper a solution that would help travelers induce confidence in traveling while keeping in mind the guidelines that must be followed. The solution focuses on an end to end service that will not only help the travelers to make informed and safe decisions but also allow the hospitality industry to monetize from this application. This paper is focused on a detailed analysis of the solution that is being presented to tackle the problems faced by various industries and their fear of resuming the work. A software-based approach is taken for providing a simple and engaging user experience to the user along with an AI approach to detect and predict the COVID trend in various cities. Along with this, a system that detects if people are wearing a mask or not will also be verified by thealgorithm.

We propose an approach for data-driven automated discovery of material laws, which we call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we apply it here to the discovery of plasticity models, including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening laws. The approach is unsupervised, i.e., it requires no stress data but only full-field displacement and global force data; it delivers interpretable models, i.e., models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalog of candidate functions; it is one-shot, i.e., discovery only needs one experiment. The material model library is constructed by expanding the yield function with a Fourier series, whereas isotropic and kinematic hardening is introduced by assuming a yield function dependency on internal history variables that evolve with the plastic deformation. For selecting the most relevant Fourier modes and identifying the hardening behavior, EUCLID employs physics knowledge, i.e., the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the domain. Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected. Through virtual experiments, we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity., npj Computational Materials, 8, ISSN:2057-3960

The modern cardinal rebellion led robots to become common in many areas such as military, medicine, agriculture, various industry etc. for surveillance or improve the quality. Robots are specially used for safety and security of human being. This enhances the quality of human life. Surveillance can also save life of the people or protect them from serious injuries. Intentional crime can be caught with the help of surveillance. This project propose Specially Programmable Intelligent Bot (SPiBot) robot is connected to the local network and takes commands from a computer on network. This can be done using SSH or Anydesk. The raspberry pi streams the feed from the camera to the local network using a command which starts the stream using the VLC media player’s in built network streaming functionality. The user can open the stream on any computer on the local network. The video is denoised using Ridgelet Transform

Vehicular traffic is endlessly increasing everywhere within the world and may cause terrible traffic jams at intersections. Traffic congestion and tidal flow are major facts that cause delays to emergency vehicles. Fire brigade officials, ambulances, and police officers often get delayed due to such congestion and traffic. With the use of the right technology, like Artificial Intelligence and real-time monitoring of the traffic, such a predicament can be moderated thereby saving the lives of the needy. The solution presented allows emergency Vehicle (EV) drivers to select the route of commute on the mobile application. Modules on that route selected are activated for the adaptive traffic control system. The driver’s GPS is dynamically updated on the cloud and fetched by the Raspberry Pi module. As the vehicle enters a given radius, the module checks for other EVs in the vicinity for priority assignment. The module starts traffic density detection and changes the traffic light states to clear the traffic if the density is above a certain threshold. After the EV comes closer to the signal, the light turns green irrespective of the traffic density. The camera cross validates if the EV has passed. Once it does, traffic signals switch to regular operation. This will allow the emergency vehicles to reach the destination on time and save the lives of those in need without delay. The entire solution is robust and reliable and can manage traffic efficiently keeping in mind the imperativeness of the emergency vehicles.

Background Patients with distal femur fractures are associated with mortality rates comparable to neck of femur fractures. Identifying high-risk patients is crucial in terms of orthogeriatric input, pre-operative medical optimisation and risk stratification for anaesthetics. The Nottingham Hip Fracture Score (NHFS) is a validated predictor of 30-day mortality in neck of femur fracture patients. In this study, we aim to investigate and evaluate the suitability of the NHFS in predicting 30-day as well as one-year mortality of patients who have sustained distal femur fractures. Methods Patients admitted to a level 1 major trauma centre with distal femur fractures were retrospectively reviewed between June 2012 and October 2017. NHFSs were recorded using parameters immediately pre-operatively. Results Ninety-one patients were included for analysis with a mean follow-up of 32 months. The mean age was 69, 56 (61%) patients were female, 10 (11%) were open fractures and 32 (35%) were peri-prosthetic fractures with 85% of patients being surgically managed. Forty-one patients were found to have an NHFS >4. Overall mortality at 30 days was 7.7% and at 1 year was 21%. Patients with an NHFS of ≤4 had a lower mortality rate at 30 days of 6% compared with those with >4 at 9.8% (p=0.422). On Kaplan-Meier plotting and log-rank test, patients with an NHFS of >4 were associated with a higher mortality rate at 1 year at 36.6% compared to patients with an NHFS of ≤4 at 8% (p=0.001). Conclusion NHFS is a promising tool not only in neck of femur fractures but also distal femur fractures in risk-stratifying patients for pre-operative optimisation as well as a predictor of mortality.

Spider’s webs are elegant examples of natural composites that can absorb outof- plane impact energy to capture prey. Different spiders have different methods and structure of webs, and these variations in topologies have a significant effect on the prey catching abilities of the web. Taking inspiration from the spiders, metamaterials that have architectured topology can be fabricated according to end applications such as energy absorbers or impact tolerant materials. In this investigation, we theoretically examined impact loading on various orb-spider webs modeled with metamaterial architecture using materials that show size-dependent behavior. Using the size-dependent properties of nano-reinforced polymer-derived ceramics (PDCs), various metamaterial topologies were evaluated for out-of-plane impact due using ANSYS Ls-Dyna. The material properties capture the size dependency of the ceramics where smaller elements have higher strength due to reduced flaw intensity; the mechanical strength of these elements does not follow the conventional Griffith Theory. In this study, spider web geometries fabricated with PDCs with varying size elements were examined.

Microstructural patterns emerge ubiquitously during phase transformations, deformation twinning, or crystal plasticity. Challenges are the prediction of such microstructural patterns and the resulting effective material behavior. Mathematically, the experimentally observed patterns are energy‐minimizing sequences produced by an underlying non‐(quasi)convex strain energy. Therefore, identifying the microstructure and effective response is linked to finding the quasiconvex, relaxed energy. Due to its nonlocal nature, quasiconvexification has traditionally been limited to (semi‐)analytical techniques or has been dealt with by numerical techniques such as the finite element method (FEM). Both have been restricted to primarily simple material models. We here contrast three numerical techniques—FEM, a Fourier‐based spectral formulation, and a meshless maximum‐entropy (max‐ent) method. We demonstrate their performance by minimizing the energy of a representative volume element for both hyperelasticity and finite‐strain phase transformations. Unlike FEM, which fails to converge in most scenarios, the Fourier‐based spectral formulation (FFT) scheme captures microstructures of intriguingly high resolution, whereas max‐ent is superior at approximating the relaxed energy. None of the methods are capable of accurately predicting both microstructures and relaxed energy; yet, both FFT and max‐ent show significant advantages over FEM. Numerical errors are explained by the energy associated with microstructural interfaces in the numerical techniques compared here.

The Krishna Godavari offshore basin to the east of India is a proven reserve, rich in natural gas hydrate. The impact of sediment characteristics on the hydrate formation has already been well established in open literature. In the present study, we have attempted to investigate the mineralogy of sediments collected from KG basin by elemental analysis (energy dispersive spectroscopy) and x-ray diffraction. Various physicochemical characteristics of the clayey sediments such as TDS, salinity, pH, conductivity and resistivity were analysed at various concentrations and compared together. Also, this paper highlights the rheological behavior of the sediment samples with concentrations of 20, 35 and 50 wt% at different experimental temperatures (278.15 K, 283.15 K and 288.15 K). Viscosity measurements were performed for a wide range of shear rates for all concentrations and comparative studies have been conducted on their exhibited behavior. The viscosity of sediment sample were found to be varying from 76.3 to 0.003 Pa s depending on the sediment concentration, temperature and shear rate. In addition, viscoelastic measurements were carried out at various angular frequencies for all the sediment samples. The work aims to characterise the sediments of KG basin and analyse the rheological behavior of sediment solution which has not yet been reported in open literature. This will provide vital information for possible methane recovery from KG basin hydrate reservoir by manipulating the host sediment behavior.

Information on the high pressure rheological properties of methane hydrate sediment slurry is an important precursor to understand their impact on the mechanical behaviour of host sediments during natural gas production from hydrate reservoirs. However, rheological analysis of gas hydrate slurries in the presence of natural sediments containing complex minerals and salts is not yet available in the open literature. The multiphase nature of hydrate systems is another challenge when it comes to the high pressure rheology study. Since, the conventional Couette geometry does not ensure proper mixing of solid, liquid and gas phases during hydrate formation and rheological measurements in a multiphase system, in this work, it has been modified to enhance the mixing capabilities. In this work, the rheological experiments on methane hydrate formation in sediment sample collected from Krishna Godavari Basin of offshore India have been performed. Rheological investigations were carried out using three different sed...

AIMS To investigate the association between perioperative peripheral blood inflammatory markers and seizures in patients who have undergone meningioma resection. METHOD A single neurosurgery tertiary centre blood bank database was screened to extract pre-operative and post-operative white cell count (WCC), neutrophils, lymphocytes, monocytes, platelets and neutrophil-lymphocyte ratio (NLR) and derived NLR (dNLR). All patients who underwent resection of meningioma from 2012 to 2020 were eligible. Patients were excluded if they had an inflammatory condition, peri-operative infection, medical illness or operative complication. RESULTS 30 patients suffered pre-operative seizures only, 16 experienced de novo post-operative seizures within 1 year and 42 patients did not experience seizures throughout their treatment timeline. In those who had never had a seizure, there was a significant increase in WCC post-operatively (15.2 vs. 10.6 × 109/L (95% CI 2.1-8.2), p=0.02). Patients who suffered de novo seizures had a significantly higher post-operative day 1 to 4 WCC than those with no seizures (21.1 vs. 14.8 × 109/L (2.1-12.6), p=0.48). However, this difference of WCC was poorly predictive of de novo seizures at one year (AUC 0.61). There was no difference in pre-operative inflammatory markers between pre-operative seizure patients, de novo seizure or never seizure patients. CONCLUSION There is a significantly higher post-operative systemic inflammatory response in patients who suffered de novoseizures after meningioma resection. Peripheral blood markers have the potential to predict seizures in patients with meningioma.

AIMS Survivors of paediatric intracranial malignancies are at increased risk of psychosocial, neuro-developmental and functional impairment, important measures of patients’ well-being. This study aimed to evaluate long-term quality of life outcomes (QOL) in patients with benign paediatric brain tumours treated curatively with surgical resection alone. METHOD Cross-sectional cohort study of benign paediatric intracranial tumours managed with surgery alone between 2000-2015. Validated QOL questionnaires were administered: SF-36, QLQ-BN20, QLQ-C30 and PedsQLTM. RESULTS Twenty-three patients participated (median age at surgery 13 years; range 1-18), twelve were male. The most common diagnosis was pilocytic astrocytoma (n=15). Median time from surgery to participation was 11 years (range 6-19). Fourteen patients achieved A-level qualifications and two obtained an undergraduate degree. Twelve patients were employed, eight were studying and three were unemployed or volunteering. Twelve patients were currently driving. QOL outcomes demonstrated significant limitation from social functioning (p=0.03) and cognitive functioning (p=0.023) compared to the general population norms. Patients also experienced increased loss of appetite (p=0.009) and symptoms of nausea and vomiting (p=0.031). Ten patients were under transitional teenager and young-adult (TYA) clinic follow-up. TYA patients achieved higher levels of education (p=0.014), were more likely to hold a driver’s license (p=0.041) and had improved physical functioning (p=0.005) compared to patients not transitioned through these services. CONCLUSION Childhood brain-tumour survivors are particularly vulnerable and at greater risk of developing psychological, neuro-cognitive, socialisation and physical development challenges. Early identification, comprehensive assessment and specialist TYA cancer healthcare input are vital to support these patients and improve their quality of life.

Coronavirus disease (COVID-19) is a contagious [1] disease caused by becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [2]. Numerous nations have acquainted social distancing measures to slow down the spread of the COVID-19 pandemic as people can spread the virus before they know they are sick. The solution focuses on a web-based solution to alert the residents and outsiders in case of any non-compliance of rules. In this work, an AI-powered solution is developed that leverages Machine learning based algorithm to guarantee that individuals are keeping up a safe distance from one another. A software-based approach is taken for providing a simple and engaging user experience to the user that will help the society and other authorities track and analyses the implications of such rules. The web application will be used to alert the users in case of any breaking of the law. Along with this, a system that detects if people are wearing a mask or not will also be verified by the algorithm. The model built can be deployed in the existing CCTV cameras to monitor each and every place of gathering without the need for any additional hardware systems.

Introduction The clinical and radiological presentation of PCNSL is diverse. Biopsy is required prior to commencing oncological therapy. Steroids administration or inconclusive histopathology may delay diagnosis. This study aimed to assess outcomes of patients managed under clinical oncology and haematology, and investigate factors related to survival. Method Retrospective cohort study (2004-2019) of 104 patients. Univariate analysis, using Kaplan Meier curves and multivariate Cox regression analysis were performed. Results Median WHO performance status was 1. 12 patients (11.5%) required repeat biopsy. 40 cases were managed under oncology, 25 males (median age 67 years; IQR 60-71). Median time from imaging to diagnosis (TTD) was 18 days (range 8-227). Median time from diagnosis to treatment (TTT) was 19 days (range 3-63). Median overall survival (OS) was 5 months (95% CI 0-15.6). 61 cases were managed under haematology, 32 males (median age 65 years; IQR 54-71). Median TTD was 25 days (range 10-233). Median TTT was 14 days (range 2-96). Median OS was 5 months (95% CI 2.1-7.9). No significant prognostic indicators of OS were identified on multivariate analysis. Conclusions PCNSL carries a poor prognosis regardless of treatment team. The wide range in time to treatment suggests that the management pathway needs streamlining to improve outcomes.

In a molecular dynamics simulation study of several oligosaccharides comprising of the very basic building block of carbohydrate, the α- or β-d glucopyranose units, linked by any one of the 1-3/1-4 or 1-6 glycosidic linkages, we compare and contrast their structural and dynamical properties. Results indicate that the litheness of the oligosaccharide chain is noticeably controlled by the composition, anomeric nature and glycosidic linkage type of the units. In mixed β 1-4/1-3 d-glucopyranosides, as those found in oats and barley, the ratio of the β 1-4 and β 1-3 linked residues is crucial in determining the structural and dynamical attributes. Principal component analysis (PCA) using the internal coordinates of torsion angles subtended by glycosidic oxygen atoms and subsequent K-means clustering of the dynamical space spanned by PC1 to PC2 point to the dynamical and structural disparity in the various types of oligosaccharides studied. The properties simulated in this work are meant to provide a systematic yet comparative understanding of the importance of linkage and anomericity on the oligosaccharide chain properties and are in line with some experimental structural attributes.

In the era of Information Warfare, the malicious actors try to penetrate the network devices and delete their traces. In order to detect such network intrusion we need to do continuously monitor logs of each and every network connected devices. SIEM solution is used for centralized collection, consolidation and correlation for analyzing logs in real time. NIDS and NIPS inspect each & every packet travelling through the network, and generate alerts using pre-defined signatures or heuristics. This paper discusses an approach to design and implement, a solution by integrating the different IDS/ IPS like Snort, Suricata, and Bro with ArcSight SIEM solution in order to provide enhanced Information Security in an environment by reducing false positives/negatives and achieving better alert confidence. We show that the alerts generated on different NIDS for same types of attacks are different and this when integrated in the ArcSight SIEM solution gives a much higher degree of confidence of detection than each of the solution used individually.

Protections of converters are a challenging task to avoid the damage of the converter and minimize operational efficiency. In this paper, a new dc short-circuit (SC) protection approach with boost converter is implemented to protect the dc bus, load, and converter’s component from the SC fault current. If a SC fault occurs in the dc bus or load side, the proposed protection scheme isolates the power supply from the converter and reconfigures the converter for damping the SC current. The SC current circulates through the damping resistance thus, damps gradually to zero. The significant key point of the proposed scheme infers that the detection of a short-circuit fault is done by the voltage control loop. Other key points are low component count and fast isolation. The fast isolation is achieved even in case of lower SC resistance. The presented fault protection scheme integrated with the converter is useful in its application at load or source side in the dc microgrids. The detailed analysis, working principle, and simulation results are presented in this paper. The proposed protection scheme is verified using the SPICE model of the circuit components on the “LTspice” simulator using Modified Trapezoidal Integration solver.

Objective: To understand the current evidence and guidelines behind the appropriate management of cardiac tumours. Methods: A comprehensive electronic literature search has been performed in major databases - PubMed, Embase, Scopus, Ovid, and Google Scholar. All articles that discussed all different forms of cardiac tumours, their clinical presentation, diagnosis, and management methods have been critically appraised in this narrative review. Results: All relevant studies have been summarized in appropriate sections within our review. Cardiac tumours are rare but can be catastrophic and life-threatening if not identified and managed on timely manner. Utilization of all the available imaging methods can be of equivocal importance, relevant to each cardiac tumour. Surgical excision is the ultimate treatment method, however histopathological results can guide the adjunct treatment. Conclusion: Early detection of cardiac tumours has significant effect on planning the method of intervention. Technological advancements and increased availability of imaging modalities have enabled earlier and more accurate detection of these tumours. Novel medical therapies, recommendations for screening, and operative techniques have all contributed to overall improving knowledge of these tumours and ultimately patient outcomes.

This paper proposes a novel switch fault-tolerant operation for switched-mode power conversion. The switch fault-tolerant scheme is implemented on the boost converter operates with a redundant switch. The redundant/secondary switch is driven by a delayed version of primary switching pulses. Both the switches provide charging paths to the inductor when individually turned-on. Thus, the inductor experiences doubled operating frequency than individual switches. Since the operating switching frequency is shared by two switches the control scheme allows the switches to share equal currents in healthy conditions. If one of the switches gets failed, the closed-loop control reconfigures the switching pulse width such that the healthy switch could provide the required power. The key features of the proposed schemes are low component count, continuous operation of the redundant switch, and less circuit complexity. For evidence of the proposed control scheme, MATLAB/Simulink results are presented. The simulation results of 600 W Boost Converter show promising operation in faulty conditions.

An azimuth tree-based network algorithm is proposed on a uniform random distribution of scattered points which is actually graph-based. In the proposed azimuth on a tree-based network, first tree is established through the algorithm for finding the weight function of the tree would constrain the number of points to be considered during azimuth routing, thereby limiting the search space, reducing time complexities, and inducing further optimizations. The idea behind our paper would be applying the azimuth algorithm on a selected number of points, which would be achieved through the efficient tree-based routing protocol. So, firstly, we would apply our tree-based protocol on the uniform random distribution of points. The limited number of points output by this algorithm would be fed as input to azimuth routing. And in the protocol proposed by us, the aggregation of data in this tree-based network helps in reducing the network load and the energy consumption. And this research work mainly revolves around the criteria to be taken into consideration for balancing factors like these and construct a tree-based network which is better in terms of both lifetime of the network and the successful routing of protocols. With the help of simulation implemented using C++ and then constructing its corresponding graph, we have shown how considering all the factors possible has improved the performance of tree-based network. And finally, a comparative analysis is done where our proposed model is compared to the already existing traditional routing protocols namely AODV, DSDV, LEACH, Azimuth-based algorithm.

This paper presents an application of optical character recognition(OCR) which can extract information from images of bills and receipts; store it as machine-processable text; in an organized manner for ease of access. It can do this efficiently even in the presence of watermarks on the bills or any shadows in the images of the bills. In developing this application, OpenCV has been used for the processing of the images and the Tesseract OCR engine has been used for optical character recognition and text extraction. The image is first processed using OpenCV for the removal of any shadows or watermarks present in it. For longer invoices, by employing the image bifurcation process, the data can be easily extracted which was not possible earlier. Furthermore, the processed image is passed on to the Tesseract OCR engine for the retrieving of text present in it. The text is then searched for important information, such as the total amount spent and the date on the receipt, using string processing.

We demonstrate a benign and straightforward method to modify the chitosan (CH) by carbamoylation. The free amines on CH are converted into carbamyl functionalities by reacting with potassium cyanate (KCNO). One wt% CH solution, when reacted with KCNO ⩾ 0.1 M, leads to the sol–gel transition of CH through the hydrogen bonding to form carbamoylated chitosan (CCH) hydrogel. Gelation time of CCH decreases with an increase in the KCNO concentration and an interconnected porous network is formed as observed under SEM. Rheological studies show that while one wt% CH solution is a viscous liquid, the CCH hydrogel with 0.5 M KCNO has a storage modulus (G') of 104 Pa. The CCH hydrogel is proved to be non-cytotoxic and promotes the attachment and growth of the small lung cancer model A549, and the neuroblastoma SH-SY5Y cell lines. CCH hydrogel also promotes the differentiation of SH-SY5Y cells into neuronal cells, as supported by immunostaining and thus demonstrating its utility as a versatile scaffold for three-dimensional cell-culture systems.

The application of DC microgrid is increasing due to the elevation of renewable energy (especially solar energy) practice. Increasing reliance on the DC microgrid seeks more reliable or faultless operation for impeccable control over the DC grids. The concept of reliability enhancement is at its verge since none of the electronic devices can be operated forever. Therefore, the idea of fault tolerance in the presence of redundancy has emerged. This paper proposes the review on fault phenomenon and reliability of the power electronics converters from various aspects such as performance, application, efficiency, etc. Different fault-tolerant schemes reported in the literature have been critically reviewed, compared, and explained in detail to have insight into the prominent solutions. This paper aims a bridge between the inference of reliable and fault-tolerant ability of power electronic converters for DC microgrid`s application. The ability to diagnose and intimate such faults in real-time has also been discussed in addition to various fault-tolerant converter topologies.

Natural gas hydrate is a potential source of methane which needs to be extracted from under the sea bed. For the economic recovery of methane from natural gas hydrates, production approaches such as depressurization, thermal stimulation, and inhibitor injection are being investigated. However, studies involving hydrate-bearing clayey sediments and recovery of methane from such reservoirs are rare. This work investigates in detail the potency of hydrate dissociation methods such as depressurization by constant rate gas release, thermal stimulation and the combination of two for energy recovery from hydrate bearing clayey sediments underlying a free gas zone. Pure water and two different mud samples containing 3 and 5 wt% of bentonite were used for methane hydrate formation and dissociation studies. Thermodynamic study of methane hydrate in the presence of bentonite clay was also conducted for the above two concentrations. No considerable effect of clay on the inhibition or promotion of methane hydrate formation was observed. Initially, methane hydrate formation has been investigated using pure water, 3 and 5 wt% bentonite mud at an initial hydrate formation pressure of 8 MPa and at a temperature of 278.15 K. Subsequently, methane hydrate dissociation experiments were carried out using depressurization, thermal stimulation and their combination. The effect of the rate of gas release on hydrate dissociation by depressurization was investigated using two different rates of 10 mL/min and 20 mL/min. Thermal stimulation experiments were carried out for ΔT = 15 K at the rate of 7.5 K/hr and the results on methane recovery were recorded. The detailed investigation shows that the combination of the two methods is more efficient for methane production than the standalone method in clayey hydrate reservoir. This study provides important insights into the hydrate production methodology from clayey hydrate reservoirs.