Search

Automatic High Beam controller is a device made due to the rising number of accidents at night because of LED headlamps temporarily affecting the vision of the driver on the opposite lane. According to AAA, LED lights do a much better job of illuminating the roadway, particularly in the low-beam application versus a halogen reflector at 150 feet more during testing. But, still, that brings the max vehicle speed to about 52 miles an hour, which is still falling short of highway speeds. So, the users are suggested to drive with high beam on if there is no oncoming traffic within a reasonable distance and the roadway is improperly lit. But in practice most people drive with the high beam settings in places with or without traffic great from the user's standpoint. However, even on low beam, they have such a high intensity that on 2-lane roads, especially when the oncoming driver of a car or small pickup approaches the LED equipped truck at night, in rain, fog, or snow the oncoming driver is blinded at a level equal to or greater than if the LED equipped vehicle were using conventional halogen high beams. This device is made without distance or ambient lighting being deciding factors and entirely based on user’s discretion. The transmitter model is fitted with a button that sends a signal to the receiver model using nrf24L01 modules and AT Mega 8 microcontroller (programmed using Arduino UNO). The pressing of button causes the state of the button to be set to HIGH. This signals the receiving circuit to set the LED pin to LOW. Therefore, the High Beam is set to Low Beam for 5 seconds when the impulse is received. Due to the use of H4 C6 headlamps which are rated for 36W with a minimum power requirement of 12W an adapter was connected to convert 220V AC supply to 12V, 3A DC supply for the LED and the rest of the circuit.

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

BACKGROUND: Smokers manifest varied phenotypes of pulmonary impairment. RESEARCH QUESTION: Which pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers? STUDY DESIGN AND METHODS: We analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV(1) to FVC ratio, < 0.70). RESULTS: Pulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV(1) and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts. INTERPRETATION: Lung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV(1) and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

Introduction: Detrusor underactivity (DUA) is a highly prevalent and poorly understood disease in urology practice. Conservative treatment in the form of clean intermittent catheterization (CIC) and pharmacotherapy are in use for DUA without good results. Endoscopic prostate surgery was initially considered ineffective in such patients. But a recent series of retrospective studies showed promising results of surgery in DUA. Therefore, we conducted this prospective study to evaluate success of endoscopic prostatic surgery in a patient with DUA. Materials and methods: This is prospective observational study of 50 patients, satisfying the inclusion criteria, at Ruby Hall Clinic, Pune, conducted between March 2017 and March 2019. Inclusion criteria are that the patients are male, aged > 50 years; urodynamically diagnosed with DUA; a bladder contractility index < 90 with associated bladder outlet obstruction index (BOOI) between 20–40 (equivocal) or > 40 (obstruction), who presented with lower urinary tract symptoms and poor flow ( Qmax < 10 ml/s in a voided volume of >150 ml. Patients were excluded from the study if they had DUA due to neurological causes or spinal trauma, an acontractile detrusor, and were < 50 years. Results: The follow-up period for each patient was 12 months. The mean international prostate specific score reduced from 24.82 ± 2.74 preoperatively to 4.4 ± 1.85 postoperatively. Mean quality of life score reduced from 4.06 ± 0.68 to 0.82 ± 0.62. Mean Qmax increased from 6.26 ±1.46 ml/s to 12.22 ±1.6 ml/s. Mean post-void residual urine volume reduced form 241.9 ±74.9 to 77.3 ±20.75. All of these were statistically significant with a p value < 0.00001. Conclusion: Transurethral resection of the prostate (monopolar/laser) to reduce BOO should be considered as an alternative, viable treatment option in men with DUA. Preoperative counselling and postoperative follow-up are crucial in the management of such patients. Level of evidence

Background: Evaluation of peritoneal membrane permeability in patients on continuous ambulatory peritoneal dialysis (CAPD) is crucial in prescribing treatment regimens. This study evaluated peritoneal membrane characteristics in patients on CAPD using standard peritoneal equilibration test (PET) and fast PET. Methods: A prospective observational longitudinal study included patients on CAPD with no symptoms of peritonitis for at least 4 weeks before the PET. Both, standard and fast PET were performed using 2.5% glucose-containing dialysate. The dialysate and plasma (D/P) creatinine ratios at each time point (i.e., 0 h, 2nd h, and 4th h) in standard and at 4th hour only in fast PET were determined. Patients were classified according to D/P creatinine value as high, high-average, low-average, low transporter. The follow-up period was 6 months and changes in membrane characteristics were compared again to revalidate the efficacy of fast PET. Results: A total of 50 patients between 41 and 70 years of age were enrolled. The majority had diabetic nephropathy (40%) and chronic glomerulonephritis (28%). Based on transport type, a significant positive correlation was observed between the D/P creatinine ratio of baseline standard PET I and fast PET I (r = 0.992, P ≤ 0.05) and standard PET II and fast PET II (r = 0.969, P ≤ 0.05) done after 6 months. The results of the PET and transport category after 6 months were similar in 82% cases determined by fast PET and 98% cases determined by the standard pet. There was significant agreement between both the methods of PET (K value = 0.872, P < 0.001). A significant (P ≤ 0.001) correlation was observed between standard PET I and standard PET II transport status. Conclusion: Fast PET is a good alternative for assessing peritoneal membrane characteristics especially in the setting of less availability of resources and is a less cumbersome procedure as compared to standard PET.

Introduction: Multidrug resistance pathogens have been found to be an increasing cause of neonatal sepsis in developing country like India. The present study was conducted to study the multidrug resistant pattern in bacterial isolates of neonatal septicaemia in Neonatal intensive care unit (NICU) of a tertiary referral hospital, Northeastern part of U.P., India. Material and Methods: It was a prospective study in which blood culture sample was taken from 450 neonates who had clinical features of sepsis or had presence of risk factors (one major–prolonged rupture of membranes/evidence of chorioamnionitis, intrapartum fever, foul smelling liquor or two minor–febrile illness of mother 2 weeks prior to delivery, meconium stained liquor amnii, more than 3 vaginal examinations during labour, low birth weight or prematurity, prolonged labour, low Apgar score) over a period one year. Results: A total of 102 (22.7%) suspects were found to be positive for sepsis. In present study, among positive cases of neonatal sepsis, the prevalence of early onset was around three forth as compared to late onset sepsis. Among all gram positive and gram negative isolates Klebseilla pneumoniae was found to be maximum followed by Grp B streptococcus. Out of total isolates only five isolates were MDR, whereas 4 MRSA and 17 ESBL. Conclusion: Presence of Multidrug resistant isolates as well an increasing rate of ESBL producers among neonates noted in this study is alarming. Thus regular monitoring of susceptibility profile of bacterial pathogen is the need of the hour to treat and curtail the increasing trend of neonatal septicemia. Keywords: Neonatal sepsis, Antibiotic susceptibility, Klebseilla pneumoniae Grp B streptococcus.

India has been termed as Diabetic hub of the globe. Along with other complications, one of the underestimated complication is cognitive decline and memory loss associated with poor diabetes control. In the Indian context, structural imaging like MRI-Magnetic Resonance imaging is more commonly used in Neurological disorders such as stroke, head injury, Functional imaging of the human brain has been underutilized in the Indian scenario. One such technique is PET CT which has been typically used as a cancer biomarker in India. By virtue of current case study, we propose i) association between poor control of Diabetes and poor cognition ii) role of PET CT brain in differential diagnosis of Dementia. We present two case reports providing strong findings for utilization of PET CT brain in Dementia protocols.

The rise of COVID-19 pandemic is alarmingly exponential in nature, and there is no certain predictability in the patterns of its future growth. This research aims at providing suitable insights into the current spread of the novel coronavirus (COVID 19) and conveys detailed analysis into current statistical growth rates of the infection frequency for diverse cases. We also provide schematic visualizations contrast and compare trends between various countries and India. We inspect closely and differentiate between the curves for the mortality rates globally. Also projected categorically most affected states of India.Finally, this research proposes methodologies for design and implementation of deep learning models for the predictive modelling of the various cases around the world and delivers predictions till March 2021 for confirmed and death cases.

Introduction: With the advancement in neonatal care over the last two decades, neonatal mortality is decreased but simultaneously there is an increase in the adverse outcomes including neurodevelopmental abnormalities in the high-risk neonates. Purpose: To evaluate the association between cranial ultrasound findings and the clinical neurodevelopmental outcome among high-risk neonates at 12 months (corrected gestational age in preterm infants) and to assess the various perinatal risk factors associated with neurodevelopmental outcome. Material and Methods: 123 high-risk neonates admitted in NICU were enrolled. Cranial ultrasound was performed and morphology was noted. The neurodevelopmental assessment was done using DDST Test II at 12 months age and these neonates were labeled as normal or abnormal based on the findings. Association between the two non-parametric variables was seen using the Pearson Chi-square test. Results: There was a statistically significant association between clinical neurodevelopmental outcome and gestational age at birth; birth weight; Apgar score 0.05). The sensitivity of CUS was 89.2%, specificity 47.3%, positive predictive value 89.2%, and negative predictive value 47.3%. Conclusions: The neurodevelopmental outcome in high-risk neonates was associated with gestational age at birth; birth weight; Apgar score at 5 minutes; neonatal seizures and abnormal cranial ultrasound findings. The current study recommend the use of cranial ultrasound for the prediction of neurodevelopmental abnormalities especially in neonates with above risk factors.

Background Accidental awareness during general anaesthesia (AAGA) is a complex and rare outcome to investigate in surgical patient populations, particularly obstetric patients. We report the protocol of the Direct Reporting of Awareness in Maternity patients (DREAMY) study, illustrating how the research was designed to address practical and methodological challenges for investigating AAGA in an obstetric cohort. Methods This is the trial protocol of a prospective, multicentre cohort study of patients undergoing obstetric surgery under general anaesthesia. Accidental awareness during general anaesthesia will be detected using three repetitions of standardised direct questioning over 30 days, with responses indicating memories during general anaesthesia verified using structured interviews. Reports will be adjudicated, then classified, in accordance with pre-defined and pre-validated structures, including the Michigan Awareness Classification tool. Quantitative data will be collected on general anaesthesia conduct for all participants. This descriptive study is being conducted in England and aims to recruit a minimum of 2015 patients. Results The DREAMY study was prospectively registered (ClinicalTrials.gov Identifier: NCT03100396) and ethical approval granted. Participant recruitment began in May 2017 and one year follow up concluded in August 2019. Publication of the results is anticipated in 2020. Conclusions The DREAMY study will provide data on incidence, experience and implications of AAGA for obstetric patients, using a robust methodology that will reliably detect and translate subjective AAGA reports into objective outcomes. In addition, the study is expected to improve vigilance for AAGA in participating hospitals and encourage adoption of recommendations for support of patients experiencing AAGA.

To determine the risk associated with combined therapy of oral hypoglycemic agents and insulin in the development of cognitive decline secondary to hypoglycemia. The study comprised of two arms, in arm 1 (n=30) diabetic patients on OHA and insulin combined therapy were observed for cognitive derangement based on Mini Cog score (Pearson’s correlation r was calculated between the two)and the patients were also ruled out for hypoglycemia episodes. Similarly, in arm 2(n=30), only OHA therapy patients were evaluated on the same parameters using Pearson’s correlation. Mean Mini Cog scores were found to be 1.79 in patients on combinational therapy of OHA and insulin. In the case of patients only on OHA for diabetes control mean Mini Cog, the score was 2.0. Pearson’s coefficient r 1 between OHA plus insulin duration of therapy in years with Mini Cog score was -0.5831 at a statistically significant p value of 0.0007. While on the other hand, patients only on OHA therapy (in years)were not significantly correlated with Mini Cog scores.(r 2= - 0.0149 ; p value =0.93771).In the arm 1 (OHA + Insulin(n=30)), the average number of hypoglycemia episodes were found to be 2.633 as compared to the OHA only group in which mean hypoglycemia episodes were 0.866. There was a significant difference in the hypoglycemia episodes, as confirmed by the student t-test (4.06368) at a p value = 0.000147. Based on the study findings, combined Treatment with Oral Hypoglycemic agents and insulin in longer run may lead to cognitive derangement secondary to hypoglycemia.*Significance level

We evaluated the correlation between HbA1C and Cognitive derangement of type 2 Diabetics in the current prevalence study.HbA1C as a Biomarker to predict Cognitive Derangement in Type 2 Diabetic Subjects based on the correlation findings in a prevalence study in urban Indian population. We further investigated the role of poor Diabetes control versus hypoglycemia episodes as independent predictors of cognitive derangement in 240 type 2 Diabetics.In pilot phase ,comparison between Diabetics(n=30)and non-Diabetic subjects(n=30)for Cognitive derangement using cognitive batteries: GPCOG, AI tool and MIS was done. PET CT brain findings of one of Diabetic subjects were also observed. Further study was conducted on 240 diabetic subjects using batteries validated in the pilot phase. Pilot study provided statistically significant results of correlation between HbA1C levels versus GPCOG Score, Pearson's Correlation r1= -0.448*, for AI Score Pearson’s Correlation r2= -0.196*and for MIS score Pearson’s Correlation r3= -0.472*. The prevalence ratio for both groups was calculated as 5.8125, which implies Diabetics had > 5 fold risk of deranged cognition as compared to the non-Diabetic group. Using independent T-test, mean GPCOG score in the Hypoglycemia group(n=162) and without hypoglycemia group(n=78) were found to be 3.02 and 4.63 respectively. Similarly, based on the independent T-test, the mean AI score in the Hypoglycemia group(n=162) and without hypoglycemia group(n=78) were found to be 2.068 and 2.564, respectively. PET CT brain findings were suggestive of reduced FDG uptake indicating Alzheimer’s type of cognitive derangement.HbA1C can be linked to poor cognitive derangement in case of poor diabetes control. Hence HbA1C can be a useful biomarker to predict cognitive derangement in type 2 Diabetics.*Significance level

There is a common belief among the laity and even physicians that sun exposure is a useful source of vitamin D. However, despite the fact that sun exposure occurs almost throughout the year in India, vitamin D deficiency is widely prevalent. Although several authors have reported on the duration of sun exposure required to synthesize adequate amounts of vitamin D in the human body, they have not followed a standard and uniform protocol for measurement of sun exposure and vitamin D synthesis. For these and many other reasons, the results are difficult to interpret. The American Academy of Pediatrics (AAP) has clearly stated that infants should be protected from the sun as much as possible and vitamin D requirements should be met through diet and fortified foods rather than deliberate sun exposure. However, this recommendation is frequently ignored in clinical practice. This review aims to summarize the available literature on benefits and harm of unprotected sun exposure in infants and children with a focus on skin phototype IV to VI. Dermatologists and pediatricians in India should counsel parents about the need for sun protection, especially in fair-skinned infants and children.

The novel coronavirus disease 2019 (COVID-19) has been postulated to be associated with hypercoagulability, leading to thromboembolism in major blood vessels. There are also increasing reports of invasive fungal infections in COVID-19 patients. We report a unique case of mucormycosis associated with renal artery thrombosis leading to renal infarction and nephrectomy in a COVID-19 patient. This is the first such reported case to our knowledge.

Background: The impact of left ventricular ejection fraction (LVEF) changes after sudden cardiac arrest (SCA) on implantable defibrillator (ICD) utilization and long-term survival is not known. We therefore evaluated the influence of LVEF on these parameters in SCA survivors. Methods: Data were collected on consecutive SCA survivors who had ≥1 echocardiogram after SCA and who survived to hospital discharge (n = 655). The median time from baseline to first follow-up echocardiogram was 162 days. LVEF ≥50% was defined as normal. Patients were classified into 4 groups according to baseline (LVEFb) and follow-up (LVEFf) myocardial function: normal LVEFb and LVEFf (group1, n = 261); reduced LVEFb and normal LVEFf (group 2, n = 104); normal LVEFb but reduced LVEFf (group 3, n = 41); and reduced LVEFb and LVEFf (group 4, n = 249). All-cause mortality and time to ICD implantation were examined in all groups. Results: Over a median follow up of 4.3 years, death occurred in 279 (42%) of patients. Compared with patients in group 1, patients with any reduced LVEF at any time (groups 2–4) had significantly higher mortality, even after adjusting for unbalanced covariates (HR = 1.44, 95.0% CI 1.05–1.95, p = 0.022). ICDs were most commonly implanted in patients with persistently reduced LVEF (group 4: HR = 1.72, 95% CI = 1.26–2.35, p = 0.001). Conclusion: We demonstrate that, in survivors of SCA, a reduced LVEF at or after the index event is associated with higher mortality but that patients with persistently reduced LVEF were most likely to receive ICD therapy. These findings have implications on the management of SCA survivors. Keywords: Sudden cardiac arrest, Mortality, Myocardial function, Left ventricular ejection fraction, Implantable cardioveter-defibrillator

Pulmonary complications including pulmonary hypertension (PH) and interstitial lung disease (ILD) are leading causes of mortality in systemic sclerosis/scleroderma (SSc) [1]. PH associated with SSc (SSc-PH) has been attributed to World Health Organization (WHO) group 1 disease, pulmonary arterial hypertension (SSc-PAH); WHO group 2 due to left ventricular systolic or diastolic dysfunction (SSc-PVH); or WHO group 3 related to interstitial lung disease (SSc-PH-ILD) [2, 3]. SSc-PH is found in 10–12% of SSc patients [4]. Of the 71 out of 237 patients in the PHAROS (Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma) cohort who had PH, 69% had SSc-PAH, 10% had SSc-PVH and 21% had SSc-PH-ILD [1]. SSc-PAH has poor prognosis and less favourable response to pulmonary vasodilator therapy compared to idiopathic pulmonary arterial hypertension (IPAH) [5, 6]. SSc-PH-ILD is a strong predictor of death compared to SSc-PAH or ILD alone [7]., There is an unexpectedly high incidence of PVOD in patients with SSc-PH-ILD. Presence of PVOD may be an unrecognised contributor to the dismal prognosis of these patients. Early transplant referral should be considered for those with SSc-PH-ILD. http://ow.ly/vPvc30neJZV

Most working professionals as well as school and college students require very less physical activity and they are mostly seated during most part of the day. Maintaining a good posture while sitting is a step towards avoiding several lifestyle diseases that arise due to decreased physical activity. In this work, posture detection by placing multiple accelerometers on the back of user is presented. Novel use of one-class classification for detecting poor posture is proposed. One-class support vector machine and isolation forest are employed for posture detection. The classifiers are provided unlabeled data containing mostly samples of good posture. During testing, the poor postures, which we be different postures other than that considered as the good posture, are predicted as outliers. The performance of the models is compared quantitatively. The performance of one-class classifiers is also compared with commonly used unsupervised learning algorithm, k-mean clustering. Results are presented for tuning of the considered models and to study the effect of sensor placement. The isolation forest provides the highest accuracy of 99.3% and F1-score of 0.99.

Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C) and macrophage activation syndrome (MAS) have been speculated as three distinct phenotypes of hyperinflammation seen in children during coronavirus disease (COVID-19) pandemic. KD has been reported in association with dengue, scrub typhus and leptospirosis. COVID-19 and dengue coinfection has also been described. However, MIS-C with concomitant infection has rarely been reported. We report an adolescent girl with clinical and laboratory parameters of MIS-C resembling KD with positive serology for scrub typhus at presentation. Clinical manifestations resolved and laboratory parameters improved with IVIG, azithromycin and corticosteroids. However, she developed fever recurrence with thrombocytopenia, elevated inflammatory markers, hypofibrinogenemia and hypertriglyceridemia which were consistent with MAS. With the emergence of MIS-C and increase in the number of such cases being reported throughout world, physicians should be aware of different phenotypes of hyperinflammation associated with COVID-19 and the possibility of coexistence of MIS-C with other infections. LAY SUMMARY Clinical and laboratory parameters of multisystem inflammatory syndrome in children (MIS-C) mimic Kawasaki disease (KD). KD has been described in association with dengue, scrub typhus and leptospirosis. However, MIS-C with concomitant infection has rarely been reported in literature. A 14-year-old-girl presented with fever and rash with history of redness of eyes, lips and tongue. Investigations showed anemia, lymphopenia, thrombocytosis with elevated erythrocyte sedimentation rate, C-reactive protein, pro-brain natriuretic peptide, Interleukin-6, ferritin and d-dimer. Scrub typhus immunoglobulin M was positive. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) level was also elevated. A diagnosis of MIS-C with concomitant scrub typhus was proffered. Child received azithromycin, intravenous immunoglobulin and methylprednisolone. After an afebrile period of 2.5 days, child developed unremitting fever and rash. Repeat investigations showed anemia, worsening lymphopenia, thrombocytopenia, transaminitis, hypertriglyceridemia, hyperferritinemia and hypofibrinogenemia which were consistent with a diagnosis of macrophage activation syndrome (MAS). KD, MIS-C and MAS represent three distinct phenotypes of hyperinflammation seen in children during coronavirus disease pandemic. Several tropical infections may mimic or coexist with MIS-C which can be a diagnostic challenge for the treating physician. Identification of coexistence or differentiation between the two conditions is important in countries with high incidence of tropical infections to guide appropriate investigations and treatment.

The fundamental design issue faced by future 5G wireless communication is to solve the growing demand of faster reliable communication with limited spectrum and lower power. MIMO diversity addresses the challenge of multipath fading. The system efficiency reduces due to limited resources and the impairments of the wireless channels like noise and ISI that must be compensated. In this work, the performance characterization of the MIMO environment is presented. BER comparative analysis is done under Rayleigh fading channel using BPSK modulation in comparison with AWGN performance. The model is implemented using MATLAB and Python programming language. Characterization of Eb/No for optimal signal-to-noise ratio and hence optimize BER and compare BER performances for BPSK (Binary phase shift keying) in MIMO (Multiple input multiple output) diversity design for 5G infrastructure.

The concept of home automation has been making rapid advancements in technology and features ever since it was introduced to the world for the first time. Automation has been serving as a wide domain for research. In the present scenario, the world is shifting towards automation, for it gives better efficiency and more features. Same is with home automation. A smart home can be termed as an automated system, which enables the user to have access over all its devices, and provides a user-friendly interface as well. For connectivity amongst devices, keeping an all-time active internet connection might not be pocket friendly for all, however, if connectivity can be ensured amongst devices without the presence of internet or any external network, it will make the system a lot more secure and cheaper, making the devices accessible to the user (for a specified range) and providing a user-friendly interface, to empower the user to connect to the device in a friendly manner. Apart from that, it will also prove to be a blessing, as it provides simple topology connection which will indeed help in easy operation and diagnosis of faults (if any). After the implementation of such ideology, it can be concluded that besides being pocket friendly to user, it can also lead to the development of revolutionary changes in the concept of automation, for same efficiency and the majority of features are being provided to the user.

The problem of predicting links has gained much attention in recent years due to its vast application in various domains such as sociology, network analysis, information science, etc. Many methods have been proposed for link prediction such as RA, AA, CCLP, etc. These methods required hand-crafted structural features to calculate the similarity scores between a pair of nodes in a network. Some methods use local structural information while others use global information of a graph. These methods do not tell which properties are better than others. With an in-depth analysis of these methods, we understand that one way to overcome this problem is to consider network structure and node attribute information to capture the discriminative features for link prediction tasks. We proposed a deep learning Autoencoder based Link Prediction (ALP) architecture for the latent representation of a graph, unified with non-negative matrix factorization to automatically determine the underlying roles in a network, after that assigning a mixed-membership of these roles to each node in the network. The idea is to transfer these roles as a feature vector for the link prediction task in the network. Further, cosine similarity is applied after getting the required features to compute the pairwise similarity score between the nodes. We present the performance of the algorithm on the real-world datasets, where it gives the competitive result compared to other algorithms.

Meta-Learning, the ability of learning to learn, helps to train a model to learn very quickly on a variety of learning tasks; adapting to any new environment with a minimal number of examples allows us to speed up the performance and training of the model. It solves the traditional machine learning paradigm problem, where it needed a vast dataset to learn any task to train the model from scratch. Much work has already been done on meta-learning in various learning environments, including reinforcement learning, regression task, classification task with image, and other datasets, but it is yet to be explored with the time-series domain. In this work, we aimed to understand the effectiveness of meta-learning algorithms in time series classification task with multivariate time-series datasets. We present the algorithm’s performance on the time series archive, where the result shows that using meta-learning algorithms leads to faster convergence with fewer iteration over the non-meta-learning equivalent.

Cryptocurrencies are becoming a major moneymaker because of their high availability and abundance of easy investment platforms. In this paper, we have attempted to predict bitcoin value by taking into consideration various features that may affect its price. The amount of cryptocurrency in circulation, the volume of cryptocurrency exchanged in a day and the demand for cryptocurrency are a few of the factors that influence its cost. The forecasting is done using different time series analysis techniques like moving average, ARIMA and machine learning algorithms including SVM, linear regression, LSTM and GRU. Our goal is to compare all these models based on their observed accuracy. The dataset has been recorded daily over the course of three years.

Image and video-based content has become ever present in a variety of domains like news, entertainment and education. Users typically discover and engage with content via search and recommendation systems. It is also important to serve high quality data to users by filtering out irrelevant or harmful content. Thus, there is an increasing need to leverage the rich information in image and video content in order to power systems for search and recommendation. At the same time, the effectiveness and efficiency of these systems has been accelerated by the availability of large-scale labeled datasets and sophisticated deep learning-based models. This tutorial is aimed at providing an overview of image and video understanding, and their practical applications in the industry. We focus on deep learning-based state of the art techniques for image and video understanding. This includes tasks like image classification and segmentation, image-based content retrieval and video classification. We also focus on applications of these technologies to large-scale recommendation and low quality content detection systems. We present concrete examples from various LinkedIn production systems, and also discuss associated practical challenges.

There have been recent reports of children presenting with severe multi-system hyperinflammatory syndrome resembling Kawasaki disease (KD) during current COVID-19 pandemic. Exact pathophysiology is unknown, however, most of the children have multi-organ dysfunction and respiratory system involvement is less common compared to adults. These patients have certain characteristic laboratory parameters different from those seen in children with KD. However, only limited literature is available at present for identification and management of such patients. We report a young girl who presented with fever, rash and other manifestations mimicking classic KD and fulfilling the case definitions for pediatric multi-system inflammatory syndrome. She had lymphopenia, thrombocytopenia and hyponatremia in the absence of macrophage activation syndrome, similar to that seen in patients reported from UK and Italy. Clinical manifestations resolved and laboratory parameters improved with intravenous immunoglobulin and corticosteroids. Early recognition is important to administer immunomodulatory therapy which may be life saving for these patients.Several cases of a severe multi-system inflammatory syndrome have been reported in children during the current COVID-19 pandemic. Clinical manifestations may resemble Kawasaki disease (KD) which is the most common childhood vasculitis. A 7-year-old-girl presented with fever, rash and abdominal pain. Examination showed maculopapular rash over lower limbs, back, right ear, trunk and abdomen; erythema and swelling over bilateral upper eyelids; conjunctival injection; reddened lips and erythema over palms and soles. She had lymphopenia, thrombocytopenia, with elevated erythrocyte sedimentation rate, C-reactive protein (CRP), pro-brain natriuretic peptide and interleukin-6 (IL-6). Real-time polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 was negative. Serology, however, could not be performed due to unavailability. Macrophage activation syndrome was ruled out with normal ferritin and triglyceride with raised fibrinogen level. Echocardiography showed normal coronary diameters. Child received meropenem, intravenous immunoglobulin, aspirin and methylprednisolone. She improved with resolution of fever, decrease in CRP, increase in platelet and lymphocyte count. Index child had features similar to those reported from UK and Italy: features of KD, abdominal pain, lymphopenia, thrombocytopenia, elevated IL-6 and myocardial dysfunction with no significant respiratory involvement. Pediatricians should be aware of such uncommon presentation in children to initiate early treatment with immunomodulatory therapy.

Today we have enormous amount of data available in every sector, with the advent of technology available, it is possible to provide solutions to many problems. In this paper we are going to provide solutions to the problems related to healthcare data management using Machine Learning and Blockchain. Extracting only the relevant information from the data is possible with the use of Machine Learning. This is done using trained algorithms. Once this data is stored, the next problem is Data sharing and its reliability. This is where Blockchain comes into picture. The consensus in Blockchain technology makes sure that data is legitimate and transactions are secure. Blockchain technology can potentially change health care management for the better by placing patient at the epicentre of the healthcare system and increasing the privacy and interoperability of health data. This paper focuses primarily on solving healthcare data management problems by using Blockchain technology and including some indispensable features using Machine Learning.

Cryptography is a scientific art which deals with the methods for changing over messages, data and information in some dynamically and haphazardly planned language of characters which are mixed up and not understandable for people or notwithstanding for a machine. While doing this, it is guaranteed that authorized frameworks and humans can recuperate the original message by validating their authentication. There are different calculations and strategies are proposed for cryptography purposes. This paper focuses on examination of two individual cryptographic calculation RSA and Blowfish, how they work and what will be the impact on security and speed if these two are converged into one hybrid calculation with appropriate modification. This paper presents the comparative analysis in terms of encryption and decryption time for individual as well as hybrid algorithm.

Background Smokers are highly susceptible to lung and cardiovascular disease that can reduce their survival. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is a protein in the circulation that may suppress vascular and pulmonary inflammation. Therefore, we hypothesized that diminished circulating TRAIL levels would be associated with poor survival in smokers with lung and cardiovascular disease. Methods Serum TRAIL level was measured by immunoassay in 474 smokers. Coronary atherosclerosis was assessed by coronary artery calcium scoring along with emphysema, lung function, and survival. Results The 474 smokers were 65.7 ± 6.3 years old and 52.2% male with 55.3 ± 31.5 pack-years of tobacco-exposure. 83 of them died during 3588.2 person-years of follow up. At baseline, lower TRAIL level was associated with more coronary artery calcium (OR = 1.2 per SD, 95%CI 1.1–1.5, p = 0.02), and with history of myocardial infarction (OR = 2.3 per SD, 95%CI 1.2–4.5, p = 0.02), angina (OR = 1.6 per SD, 95%CI 1.1–2.6, p = 0.03), and angioplasty (OR = 1.8 per SD, 95%CI 1.0–3.1, p = 0.04) in models adjusted for cardiovascular risk-factors, FEV1, and emphysema. Also, lower TRAIL level was associated with emphysema severity independent of demographics and tobacco exposure (β = 0.11 sq. root units, 95% CI 0.01–0.22, p = 0.03). Further, TRAIL level was lowest in smokers with comorbid emphysema and coronary artery calcification rather than either condition alone. Finally, lower TRAIL level was independently associated with increased mortality in smokers particularly in those with comorbid emphysema and coronary artery calcification (HR = 1.38, 95% CI 1.01–1.90). Conclusions TRAIL level is reduced in smokers with comorbid emphysema and coronary artery disease, and is associated with reduced survival.

The aim of this thesis is to address capabilities in the prediction of compressive strength of concrete to affect quality control in construction. To comprehend this, a compressive strength predicting model using the principles of fuzzy logic set theory had been employed. The model put into use ‘fuzzy logic’ as a tool to predict the compressive strength of concrete on a given day. Data collected from previous researches and laboratory work had been put into use in the model construction and testing. The input variables of water/binder ratio, cement content, water content, and fly ash percentage and the output variable of 28-day cement compressive strength were fuzzified by the use of triangular membership functions and Gaussian membership functions which were deployed for the fuzzy subsets. The prediction of the 28-day cement strength data by the developed fuzzy model proved to be quite satisfactory. The training and testing of 4 different models were done. The Minimum average percentage error levels in the fuzzy model were seen to be as low as (3%) in the case of Model 3. A comparative study of the different models (all 3 Triangular and 1 Gaussian) had been done. The results indicated that the application of the fuzzy logic algorithm was quite satisfactory when a triangular membership function with decreased subset range was used. The outputs of the Triangular and Gaussian models were almost similar.

Rosai Dorfman disease (RDD) commonly presents as bilateral cervical lymphadenopathy in children and young adults. We herein report a young girl with unilateral inguinal lymphadenopathy as a rare presentation of RDD. Keywords: Child, Rosai Dorfman disease, Unilateral lymphadenopathy

Fungal sensitisation in adults is associated with severe asthma but prevalence and clinical significance of fungal sensitisation remains unclear in paediatric population. The aim of this study was to study the association of fungal sensitisation with disease severity in children with persistent asthma. One hundred children with persistent asthma in age group 7-15 years, symptom duration >2 years and forced expiratory volume in first second >50% of expected were enrolled. Skin prick test (SPT) to 8 fungal antigens and total serum immunoglobulin E (IgE) were done. Fungal sensitisation was described as positive SPT (wheel diameter more than 3 mm larger than the negative control) to any of the fungal antigens and total serum IgE >200 ng/mL. Seventeen patients showed evidence of fungal sensitisation, of which, 6 demonstrated sensitisation to multiple fungi. 17.6% patients with fungal sensitisation had severe asthma as compared to 2.4% patients without fungal sensitisation (P value .032). Significant increase in family history of allergic comorbidities was noted among patients with fungal sensitisation (47.1% vs 21.7%, P value .03). The most common implicated organism in fungal sensitised patients was Aspergillus flavus (47.1%). The results of this study, a first among Indian children with asthma, suggest that children with fungal sensitisation have more severe asthma as compared to children without fungal sensitisation.

Objectives Kawasaki disease (KD) is a multisystemic vasculitis with predominant mucocutaneous manifestations. Pulmonary involvement in KD is distinctly uncommon and is not commonly recognized. We describe our experience of managing children with KD wherein the initial presentation was predominantly pulmonary. Methods Six hundred and two children have been diagnosed with KD during the period January 1993 to May 2017 in the Allergy Immunology Unit, Advanced Pediatrics Centre, Postgraduate Institute of Medical Education and Research, Chandigarh. Data were collected from inpatient records in Allergy Immunology Unit and follow-up files in the Pediatric Rheumatology Clinic. Results Of 602 children, 11 (1.83%) had a predominant pulmonary presentation of KD. Mean age at diagnosis of KD was 2.5 years. Fever, cough and respiratory distress were the presenting complaints in all patients. First sign of KD was noted at a mean duration of 14.5 days from the onset of symptoms. Periungual desquamation was the most common clinical sign (72.7%). Persistent fever in spite of antimicrobials, thrombocytosis, and elevated erythrocyte sedimentation rate and C-reactive protein levels pointed toward a diagnosis of KD in our patients. Parenchymal consolidation was evident on chest X-ray in all patients, pleural effusion in six, empyema in three, and pneumothorax in two patients. Coronary artery abnormalities were evident in three patients. Intravenous immunoglobulin was given after a mean period of 22.4 days of onset of fever. Conclusions The diagnosis of KD is often delayed in children who have a predominantly pulmonary presentation. This can have adverse clinical consequences.

Background Cytomegalovirus (CMV) results in significant morbidity and mortality in Human Immunodeficiency Virus (HIV)-infected individuals. There is paucity of literature on paediatric CMV disease, especially from developing countries. Methods A retrospective review of records of all HIV-infected children with evidence of CMV disease was done. Results A total of 15 children were found to have CMV disease (retinitis in all, pneumonia in two and invasive gastrointestinal disease in one). Median CD4+ T cell count and percentage at diagnosis of CMV disease was 64.5 cells/µl and 3.6%, respectively. Intravenous ganciclovir was used in patients with active CMV disease. Of the 15 children, three died while two were lost to follow-up. Symptomatic patients had poor visual outcome and almost all children who were diagnosed on active screening attained normal vision. Conclusion Retinitis is the most common CMV disease in HIV-infected children. Early detection by active screening and initiation of systemic ganciclovir reduces the morbidity.

Malignant large artery stroke is associated with high mortality of 70% to 80% with best medical management. Decompressive craniectomy (DC) is a highly effective tool in reducing mortality. Convincing evidence has accumulated from several randomized trials, in addition to multiple retrospective studies, that demonstrate not only survival benefit but also improved functional outcome with DC in appropriately selected patients. This article explores in detail the evidence for DC, nuances regarding patient selection, and applicability of DC for supratentorial intracerebral hemorrhage and posterior fossa ischemic and hemorrhagic stroke.

Using Interpretive Structural Modeling (ISM) methodology this study explored the nonstatistical factors that might affect a cargo airline. ISM allows a better understanding of the mutual influence among different attributes and the consequences, which helps the decision maker make more informed decisions. The study used ISM methodology to summarize and identify the relationships among attributes that influences the competitive advantage of a cargo airline, operational factors that might influence the firm's processes, and characteristics that might affect the overall financial stability of the airline. Relationships among attributes were derived and structured into a hierarchy in order to identify subsystems of interdependent elements with corresponding driving power and dependency.

Amyloidosis is a heterogeneous group of disorders with amyloid material deposit in the extracellular compartment of the body as fibrillary protein which may be generalized or localized. Primary amyloidosis of urinary bladder is rare and large lesion measuring more than 8x8cm2 in size is not known. Majority, clinically presents with painless haematuria and irritative voiding symptoms which may confused with diagnosis of bladder carcinoma. Definitive diagnosis is made on histopathogical examination of tissue removed through cystoscopy or biopsy and after special staining. It diagnosis should alert physician to arrange for referral or appropriate investigations. Medical ttreatment using colchicine or dimethyl sulphoxide (DMSO) has been reported in the literature with good results. However, the majority of cases are still managed by transurethral resections of lesion. To our knowledge, this is first case of giant amyloidosis of urinary bladder managed by segmental cystectomy.

In recent times, deep neural networks are being used in a wide variety of applications such as autonomous vehicles, medical imaging and surveillance. While they are becoming increasingly powerful, it is possible to disrupt their task by crafting adversarial inputs. These inputs are essentially perturbations added to the original inputs so that the application using the network, such as an object recognizer, is unable to classify the object in the image. Crafting such inputs to disrupt such a recognition task is termed an adversarial attack. Here, we implement two disruption strategies, Fast Gradient Sign Method (FGSM) and generating perturbations using a generator network. While FGSM requires access to the gradient calculated by the classifier with respect to the input image, the generator trains simultaneously with the classifier network to learn how to craft perturbations. Once the generator network is trained with a particular classifier (say, VGG16), it can disrupt other classifier networks in a black-box fashion as well. Using the same dataset, in this case CIFAR-10, it is possible to adversarially train the classifier to make it more robust to perturbed images. This involves training the classifier on the CIFAR-10 images with both the original images and the ones perturbed by the generator. In experiments, the attack using the generator achieves higher disruption accuracies than FGSM on very deep networks.