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Background: Coronavirus disease 2019 (COVID-19) barrier box is being used by health-care workers for protection against aerosol-transmitted infection. Usually, a Macintosh laryngoscope (MC) or a video laryngoscope (VL) is used for endotracheal intubation (ETI). We aimed to determine the most suitable laryngoscope blade in terms of time to ETI, ease of ETI, and the first-pass success rate. Methods: American Society of Anesthesiologists Grade I and II patients undergoing surgery under general anesthesia were randomized into the MC and the King Vision VL groups in a 1:1 ratio. ETI was performed using either the MC (the MC group) or the King Vision VL (the VL group) with a COVID-19 barrier box. The first-pass intubation success rate, intubation time, and ease of ETI were analyzed. Results: The first-pass success rate was higher in the MC group (P = 0.43). The mean duration of ETI was 33 s and 47 s in the MC group and VL group, respectively. The difference was statistically significant between the groups (P = 0.002). The ease of ETI was comparable between the groups (P = 0.57), and the Cormack–Lehane grade was significantly different between the groups (P = 0.0025). Conclusion: ETI duration was shorter in the MC group than in the VL group. Hence, a MC can be used along with a COVID-19 barrier box by experienced operators for the prevention of aerosol spread.

Purpose of the Study: Reversible proximal tubular dysfunction associated with distal renal tubular acidosis (dRTA) mimics type 3 RTA, a condition classically associated with features of both proximal RTA (pRTA) and dRTA. Proximal tubulopathy has been reported in children with primary dRTA, but the data in adults are lacking. Study Design: In this hospital record-based retrospective study, data from 66 consecutive cases of RTA, between January 2016 to December 2018, were retrieved and analyzed. Results: Mean age of the study population was 25.3 years (range: 3 months to 73 years). Six (9.1%) of them had pRTA, 58 (87.9%) had dRTA, 1 (1.5%) had type 3 RTA, and the remaining 1 (1.5%) had type 4 RTA. Ten patients (17.2%) with dRTA and 3 patients of pRTA (50%) had underlying secondary etiologies. Data on proximal tubular dysfunction were available for 30 patients with dRTA, of whom 1 had isolated dRTA, and the rest 29 patients had accompanying completely reversible proximal tubular dysfunction. Among the 10 cases of secondary dRTA, 6 were not evaluated for proximal tubular dysfunction. Of the remaining 4, 3 had reversible form of proximal tubular abnormality. Fifty-two patients with dRTA came from a population, indigenous to the “Rarh” region of India. Conclusions: Proximal tubular dysfunction often accompanies dRTA; 75% of the children with primary dRTA, at least 29% of adults with primary dRTA, and at least 30% of adults with secondary dRTA manifest such completely reversible form of proximal tubulopathy. “Rarh' region of India probably is a hotspot for endemic dRTA.

With the rise of Big Data, there has been a significant effort in increasing compute power through GPUs, TPUs, and heterogeneous architectures. As a result, many applications are memory bound, i.e., they are bottlenecked by the movement of data from main memory to compute units. One way to address this issue is through data prefetching, which relies on accurate prediction of memory accesses. While recent deep learning models have performed well on sequence prediction problems, they are far too heavy in terms of model size and inference latency to be practical for data prefetching. Here, we propose clustering-driven compact LSTM models that can predict the next memory access with high accuracy. We introduce a novel clustering approach called Delegated model that can reliably cluster the applications. For each cluster, we train a compact meta-LSTM model that can quickly adapt to any application in the cluster. Prior LSTM-based work on access prediction has used orders of magnitude more parameters and developed one model for each application (trace). While one (specialized) model per application can result in more accuracy, it is not a scalable approach. In contrast, our models can predict for a class of applications by trading off specialization at the cost of few retraining steps at runtime, for a more generalizable compact meta-model. Our experiments on 13 benchmark applications demonstrate that clustering-driven ensemble compact meta-models can obtain accuracy close to specialized models using few batches of retraining for majority of the applications.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Introduction: Chronic total occlusions (CTO) of the superficial femoral artery (SFA) are generally managed by an endovascular retrograde contralateral “crossover” or ipsilateral antegrade common femoral artery (CFA) approach. In cases with flush occlusion or a very short SFA stump at common CFA bifurcation, antegrade recanalization frequently fails due to an inability to engage the true ostium of the SFA. The purpose of this study was to evaluate the efficacy and safety of retrograde popliteal approach for endovascular revascularization of flush SFA CTO. Materials and Methods: We retrospectively investigated 18 patients (14 males and 4 females, mean age 61 ± 14 years) with flush SFA CTO undergoing retrograde popliteal artery (PA) revascularization at two endovascular centers in North India between January 2017 and December 2018. All the patients had critical limb ischemia (CLI) and flush SFA CTO with ostial involvement. Hemostasis was achieved by the manual compression only. Study endpoints were technical success rate, puncture site complications, and limb salvage. Results: CLI with Rutherford category 5 dominated the clinical presentation (56%) with most of the patients presenting late after disease onset. Majority were Trans-Atlantic Inter-Society Consensus-C (72%) chronic SFA occlusions (mean lesion length 185 ± 52 mm). Doppler-guided popliteal puncture and sheath placement were successful in 100%. Technical success rate of retrograde revascularization was 83%. Intraluminal recanalization was possible in 56% from the popliteal access, and subintimal recanalization was done in 28%. There was no major access site-related complication or mortality in the follow-up period. The primary patency rates at 6 months and 1 year postintervention were 93% and 80%, respectively. Conclusions: Retrograde PA approach under Doppler guidance was effective in terms of technical success rate of popliteal puncture as well as retrograde revascularization of flush SFA CTO. The approach was safe with no major access site complications.

Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform training in an efficient and scalable way. We propose a novel parallel training framework. Through sampling small subgraphs as minibatches, we reduce training workload by orders of magnitude compared with state-of-the-art minibatch methods. We then parallelize the key computation steps on tightly-coupled shared memory systems. For graph sampling, we exploit parallelism within and across sampler instances, and propose an efficient data structure supporting concurrent accesses from samplers. The parallel sampler theoretically achieves near-linear speedup with respect to number of processing units. For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning. Our partitioning is a 2-approximation strategy for minimizing the communication cost compared to the optimal. We further develop a runtime scheduler to reorder the training operations and adjust the minibatch subgraphs to improve parallel performance. Finally, we generalize the above parallelization strategies to support multiple types of GNN models and graph samplers. The proposed training outperforms the state-of-the-art in scalability, efficiency and accuracy simultaneously. On a 40-core Xeon platform, we achieve 60x speedup (with AVX) in the sampling step and 20x speedup in the feature propagation step, compared to the serial implementation. Our algorithm enables fast training of deeper GNNs, as demonstrated by orders of magnitude speedup compared to the Tensorflow implementation. We open-source our code at https://github.com/GraphSAINT/GraphSAINT., 43 pages, 8 figures. arXiv admin note: text overlap with arXiv:1810.11899

Introduction: The term “chronic dermatophytosis” can be described as patients who have suffered tinea infections for more than 6 months, with or without recurrence. Since last few years we are facing an onslaught of chronic and recurrent dermatophytosis. These infections are increasingly becoming debilitating with severe symptoms like itching and increasingly showing lack of response to traditional treatment, hence it has become utmost important to assess impact of dermatophytosis today on quality of life of patients. Objectives: To determine quality of life in patients with chronic dermatophytosis using Dermatology Life Quality Index (DLQI). Materials and Methods: Adult literate patients having chronic dermatophytosis which was confirmed by KOH mount were included in the study. History, clinical features and other relevant details were included in a proforma. All patients were asked to fill DLQI questionnaire in Hindi. Controls having disease for less than six months were also included. Data thus obtained was collected and tabulated. The data was analysed using SPSS software and relevant statistical tests were used. Results: Total number of cases collected were 263, 137 controls were also included. In gender distribution, cases had 71.86% males and 28.14% female while controls had 63.5% males and 36.5% females. Mean DLQI of cases was 14.28+/-5.16 and controls was 11.56+/-3.60. DLQI distribution of cases -162(61.6%) had very large effect, 64(24.33%) had moderate effect, and 32(12.17%) had extremely large effect on DLQI. Domains of Symptoms and feelings (72.67%), work and school related activities (69%) and treatment related problems (67.67%) posed maximum impairment. Quality of life derangement increased significantly with presence of lesions on both exposed and non exposed sites and increase in body surface area. Conclusion: While superficial dermatophytosis affects quality of life (QoL) in all patients, chronic dermatophytosis has a significantly more derogatory effect on the QoL of a person with some of the patient also showing extremely large effect on QoL.

Diabetic kidney disease (DKD) occurs in approximately 20–40% of patients with type 2 diabetes mellitus. Patients with DKD have a higher risk of cardiovascular and all-cause mortality. Angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and antihyperglycemic drugs form the mainstay of DKD management and aim to restrict progression to more severe stages of DKD. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) control hyperglycemia by blocking renal glucose reabsorption in addition to preventing inflammation, thereby improving endothelial function and reducing oxidative stress; consequently, this class of prescription medicines is emerging as an important addition to the therapeutic armamentarium. The EMPA-REG OUTCOME, DECLARE TIMI 58, and CANVAS trials demonstrated the renoprotective effects of SGLT2i, such as restricting decline in glomerular filtration rate, in the progression of albuminuria, and in death due to renal causes. The renoprotection provided by SGLT2i was further confirmed in the CREDENCE study, which showed a 30% reduction in progression of chronic kidney disease, and in the DELIGHT study, which demonstrated a reduction in albuminuria with dapagliflozin compared with placebo (− 21.0%, confidence interval [CI] − 34.1 to − 5.2, p = 0.011). Furthermore, a meta-analysis demonstrated a reduced risk of dialysis, transplantation, or death due to kidney disease (relative risk 0.67; 95% CI 0.52–0.86; p = 0.0019) and a 45% risk reduction in worsening of renal function, end-stage renal disease, or renal death (hazard ratio 0.55, CI 0.48–0.64, p

Collective behavior appearing abundantly in nature at various scales, such as swarming of insects, flocking of birds or schooling of fish, has motivated an interdisciplinary research thrust towards artificial swarms thanks to its numerous advantages. Although a lot of work has been done on coming up with controllers for these multi-agent systems towards optimizing coordination and planning, the inverse problem of identifying behaviors and models from observing a swarm is still not explored enough. Efficient tools for the analysis of the complex spatial-temporal dataset an observed swarm generates, are still missing. In this paper, we propose a methodology to solve the problem of identifying the leader in a swarm governed by leader-follower control framework using only the macroscopic view of the entire swarm. A methodology that combines clustering and probability methods is used to analyze the spatial-temporal data of a swarm of robots to narrow down on a subset of the robots that includes the leader. The clustering parameters are optimized over multiple simulated behaviors. The results show that this automated system can narrow down on a subset of agents (cluster) that includes the leader of the swarm with high accuracy. Applications of the proposed methodology include automatic identification of leader and swarm dynamics in both artificial and biological swarms, which can lead to a better understanding of collective behaviors and predictions of future behaviors.

In this pandemic era, businesses are highly affected due to low availability of manpower. In this situation, IoT is making a significant contribution to managing and controlling human activities in an industrial perspective and daily life. In the era of machine to machine communication, IoT is contributing to the interaction of machine and humans in a more innovative way. This chapter focuses on providing the entrepreneurial approach to manage the IoT devices used in the different sectors like health, agriculture, home appliances, research and communication as an intelligent machine. It will also focus on the impact and support of cloud computing in the implementation of IoT in the above-mentioned areas. This chapter elaborates the key feature of cloud computing services used to overcome the limitation of IoT resources in different implementation domains. This chapter also focuses on the major contribution of cloud computing on handling the IoT and making it more intelligent to encourage innovation and research in the IoT context.

Background: Polycystic ovary syndrome (PCOS) is associated with insulin resistance and elevated risk of cardiovascular disease and diabetes. Chronic inflammation has been observed in PCOS in several studies but there is also opposing evidence and a dearth of research in Indians. Objective: To estimate chronic inflammation in PCOS and find its relationship with appropriate anthropometric and biochemical parameters. Materials and Methods: Chronic inflammation was assessed in 30 women with PCOS (Group A) and 30 healthy controls (Group B) with highly sensitive C-reactive protein (hsCRP), interleukin-6 (IL-6), tumour necrosis factor alpha (TNFα), and platelet microparticles (PMP). In group A, the relationship of chronic inflammation with insulin resistance, waist hip ratio (WHR) serum testosterone, and serum glutamate pyruvate transaminase (SGPT) were examined. Results: In group A, the hsCRP, TNFα, and PMP were significantly elevated compared to group B. However, IL-6 level was similar between the groups. In group A, PMP showed a significant positive correlation with waist-hip ratio and serum testosterone. IL-6 showed a significant positive correlation with insulin sensitivity and significant negative correlation with insulin resistance and serum glutamate pyruvate transaminase. Conclusion: PCOS is associated with chronic inflammation and PMP correlates positively with central adiposity and biochemical hyperandrogenism in women with PCOS. Key words: Polycystic ovary syndrome, Inflammation, C-reactive protein, Interleukin-6, Tumor necrosis factor, Microparticles.

Energy storage systems (ESS) are effective solutions to reduce the cost of smart grid operations due to their ability to store and supply electricity on demand. Traditionally, ESS have been used to implement a plethora of cost reduction techniques for smart grid operations such as temporal supply-demand shifting, frequency regulation, voltage regulation etc. However, the stationary nature of ESS limits their flexibility, potentially leading to low utilization and risk being a stranded asset. In this work, we explore the use of Mobile ESS in implementing such cost reduction techniques in a grid consisting of multiple microgrids. We develop an algorithmic framework to assign multiple Mobile ESS to various microgrids of the smart grid across several days. Our framework attempts to maximize the cost reduction achieved due to Mobile ESS assignment minus the routing cost. We show that our algorithm is a polynomial time $1/e-$1/e-approximation for the NP-Hard problem of optimal assignment of MESS.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

In today’s world, as the population increases so do diseases and more and more people are becoming dependent on medicines for their living. With medicine being of such great importance, it is necessary for one to take his doses on time. But older people and virus-infected people find it difficult to remember their medicine schedule. To overcome this problem, this proposed work “Smart Medicine Dispenser” stores their daily medicine schedule and notifies them on the basis of the schedule about when and which medicine to take. Using Wi-Fi, it can also notify the caretaker of the patient about whether the patient has taken his medicine or not. It comprises a box containing several small sections in which we can store medicines according to their daily dose. It uses a real-time clock (RTC) module and an alarm system to make sure that the patient takes his dose on time. The experimental results show that the proposed model gives better results in comparison with existing work.

Systematic study of conventional and femtosecond optical tweezers demonstrates an interplay of thermal and optical nonlinearity due to tight focusing. Specifically, Brownian motion of the 250nm radius trapped bead is measured using a quadrant photodiode.

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Nowadays, IoT plays a very important role in the field of the corporate sector, to make smart devices like smart watches, smart houses, and smart city and security purposes. One of the application of smart city which have smart light, smart parking system, smart garbage management system the author will propose in this research paper. Here, IoT works with technology like big data, cloud, and wireless sensors. The combination of these techniques leads to making a smart city. In this proposed model, the smart light will be used for energy consumption, smart parking to monitor the parking traffic, smart garbage management where we reduce the contaminated substances. The author proposes a real-time model that will enhance efficiency.

As HPC progresses toward exascale, writing applications that are highly efficient, portable, and support programmer productivity is becoming more challenging than ever. The growing scale, diversity, and heterogeneity in compute platforms increases the burden on software to efficiently use available distributed parallel resources. This burden has fallen on developers who, increasingly, are experts in application domains rather than traditional computer scientists and engineers. We propose CASPER-Compiler Abstractions Supporting high Performance on Extreme-scale Resources-a novel domain-specific compiler and runtime framework to enable domain scientists to achieve high performance and scalability on complex HPC systems. CASPER extends domain-specific languages with machine learning to map software tasks to distributed, heterogeneous resources, and provides a runtime framework to support a variety of adaptive optimizations in dynamic environments. This paper presents an initial design and analysis of CASPER for synthetic aperture radar and computational fluid dynamics domains.

Background Pycnodysostosis is an autosomal recessive skeletal dysplasia with easily recognizable clinical features and marked molecular heterogeneity. In this study, we explored the clinical and molecular spectrum of 25 Indian patients with pycnodysostosis from 20 families. Methods Clinical information was collected on a predesigned clinical proforma. Sanger method was employed to sequence all the exons and exon/intron boundaries of the CTSK gene. Novel variants were systematically assessed by prediction softwares and protein modelling. The pathogenicity of variant was established based on ACMG-AMP criteria. An attempt was also made to establish a genotype-phenotype correlation and devise a diagnostic scoring system based on clinical and radiological findings. Results Consanguinity and positive family history were present in 65% (13/20) and 45% (9/20) of the families respectively. Short stature and fractures were the predominant presenting complaints and was evident in 96% (24/25) and 32% (8/25) of affected individuals respectively. Gestalt facial phenotype and acro-osteolysis were present in 76% (19/25) and 82.6% (19/23) of the individuals respectively. Hepatosplenomegaly was present in 15% (3/20) of the individuals with one of them having severe anaemia. Causative sequence variations were identified in all of them. A total of 19 variants were identified from 20 families amongst which 10 were novel. Homozygous variants were identified in 90% (18/20) families. Amongst the novel variants, there was a considerable proportion (40%) of frameshift variants (4/10). No significant genotype-phenotype correlation was noted. Scoring based on clinical and radiological findings led to the proposal that a minimum of 2 scores in each category is required in addition to high bone density to diagnose pycnodysostosis with certainty. Conclusion This study delineated the genotypic and phenotypic characterisation of Indian patients with pycnodysostosis with identification of 10 novel variants. We also attempted to develop a clinically useful diagnostic scoring system which requires further validation.

While heterogeneous systems provide considerable opportunities for accelerating big data applications, the variation in processing capacities and communication latency of different resources makes it challenging to effectively map the applications on the platform. To generate an optimized mapping of the input application on a variety of heterogeneous platforms, we design a flexible annotated task interaction graph based framework which 1) allows modeling of mixed CPU and GPU architectures, and 2) identifies an efficient task-hardware mapping of the input application, given the dependencies and communication costs between tasks that constitute the applications. The annotated task interaction graph (ATIG) representation captures all the information that is necessary to execute the application and the meta-data, such as performance models for estimating runtime on a target resource and communication latencies. Our framework supports solving the problem of mapping tasks in the ATIG onto available resources by including variations of greedy algorithm and LP relaxations with rounding. We show that our framework can achieve high speedup, allowing domain experts to efficiently compile a broad set of programs to parallel and heterogeneous hardware.

Introduction Polymerase chain reaction (PCR) testing of cerebrospinal fluid (CSF) is a sensitive and specific method in diagnosing herpes simplex virus (HSV) encephalitis. However there are increasing reports of false negative HSV PCR. Case description We present a patient in the 9th decade of life with abnormal behavior and focal seizures with MRI showing a right temporal T2 hyperintense non-enhancing lesion with electrographic evidence of right lateralized periodic discharges. CSF analysis and PCR for HSV-1 and 2 yielded negative results twice, and therefore acyclovir was discontinued. Patient initially improved following correction of hyponatremia. Patient however deteriorated and imaging revealed a new right parietal lesion. Third CSF sample showed lymphocytic pleocytosis with positive HSV-1 PCR. Patient improved following antiviral treatment. Discussion Acyclovir treatment should continue in high clinical suspicion scenarios despite negative HSV PCR. We further discuss causes of PCR false negatives and challenges it poses for patient care., Neuroscience; Nervous System; Viral Disease; Neurology; Diagnostics; neurovirology, MRI, infectious disease, EEG

Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a 'kernel explosion' problem that, even after compression (pruning), imposes a high memory burden and off-chip bandwidth requirement for kernel access. This creates a performance gap between the potential acceleration offered by compression and actual FPGA implementation performance, especially for low-latency CNN inference. In this paper, we develop a principled approach to overcoming this performance gap and designing a low-latency, low-bandwidth, spectral sparse CNN accelerator on FPGAs. First, we analyze the bandwidth-storage tradeoff of sparse convolutional layers and locate communication bottlenecks. We then develop a dataflow for flexibly optimizing data reuse in different layers to minimize off-chip communication. Finally, we propose a novel scheduling algorithm to optimally schedule the on-chip memory access of multiple sparse kernels and minimize read conflicts. On a state-of-the-art FPGA platform, our design reduces data transfers by 42% with DSP utilization up to 90% and achieves inference latency of 9 ms for VGG16, compared to the baseline state-of-the-art latency of 68 ms.

Smart education is the constituent of smart cities. Smart education is the use of computers in the classroom. However, there are many other factors to improve a child’s quality of education. One part is the amount of time the child spends on a bus from traveling bus to home. Although, with currently available technologies as the Internet of Things (IOT) and Android, with these advanced technologies we will be able to track or visibility in a child’s life. This paper presents the design and implementation of an IOT based system that permits parents, schools and regulatory bodies to track the conditions of comfort and safety inside a bus in a real-time manner. A variety of reports for various activities for bus comfort and attendance are then generated from this real-time providing complete visibility of buses.

Recent aggressive scientific and technological developments all these focus on a global environmental issue considering air quality system, reveals the fact that India is facing severe health hazards. In recent reports, more than 10 cities in India are listed on top. The air quality index (AQI) in India launched in 2014 under Swachh Bharat Abhiyan monitors air pollution on 10 scales ranging from low (green) to moderate (yellow) to serious (red) through data analysis of various air contaminating matters like pm 2.5, O3, NO2, SO2, CO. The present paper develops an Internet of Things (IoT) that enabled air quality monitoring system mobile in nature analyzing real-time surrounding data measuring Carbon Monoxide, Smoke and PM level. The system can measure local area air contamination and generate analyzed data based on which it alerts the people through a buzzer device integrated into the system. The user-friendly and easy handling of the system technology is such that it can be installed in houses and in small places.

nowadays development of IoT applications with robotics is an ongoing revaluation. This paper mainly focuses on the security, remote surveillance, and monitoring of our homes done by the surveillance robots. Remote surveillance has become the most important research topic over the past decade. Through this paper we put forward a surveillance robot that can be used in domestic areas and many other places. Robots are becoming important in our day to day life activities as they reduce the human labor and probability of error. We can control robots manually or they can be automatic based on the need of people. This paper focuses on design and implementation of mobile robot for obstacle detection and avoidance in a real-time basis.

In this paper, we present an IoT-based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many techniques are in existence but nowadays more work is going on in this field because this is era of IoT and most of the organization is shifted toward IoT-based system. So in our proposed system we are using the Raspberry Pi-3 which are having quad-core processor that can be very useful and gives better result and accurate number even when the humans are very close to each other. This IoT-based model can easily be implemented in crowded areas and monitor the same. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model, it can be easily fixed on the walls of street light and in the time of darkness or in night the camera captures clear images for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.

With the rise of Big Data, there has been a significant effort in increasing compute power through GPUs, TPUs, and heterogeneous architectures. As a result, many applications are memory bound, i.e., they are bottlenecked by the movement of data from main memory to compute units. One way to address this issue is through data prefetching, which relies on accurate prediction of memory accesses. While recent deep learning models have performed well on sequence prediction problems, they are far too heavy in terms of model size and inference latency to be practical for data prefetching. Here, we propose extremely compact LSTM models that can predict the next memory access with high accuracy. Prior LSTM based work on access prediction has used orders of magnitude more parameters and developed one model for each application (trace). While one (specialized) model per application can result in more accuracy, it is not a scalable approach. In contrast, our models can predict for a class of applications by trading off specialization at the cost of few retraining steps at runtime, for a more generalizable compact meta-model. Our experiments on 13 benchmark applications demonstrate that three compact meta-models can obtain accuracy close to specialized models using few batches of retraining for majority of the applications.

To improve the reliability of content shared on social media, effective strategies for mitigating the diffusion of fake news are increasingly necessary. Traditionally, to counter false belief a competing cascade approach is used. This approach assumes that the opposite belief is already known, and thus, not applicable to newly spreading fake news. Another approach is to block nodes and links of the network to impede the flow of fake news (rumor/influence blocking). However, a more active way to battle the dissemination of fake news is to propagate the corresponding real news, since people who receive the real news in tandem with the fake news are less likely to believe in fake news. Such a setting is especially useful on a messaging platform such as WhatsApp, where the news item flows as a private message and the correction of fake news and its propagation must be performed by the users within the network as they receive it. To achieve this goal, we propose network-based recruitment for active fake news correction (RecANt) to find a set of individuals of a pre-defined size to be incentivized for actively fact-checking and passing on the real news so as to reach the maximum number of nodes in the network. These individuals should be such that they are likely to receive the fake news so that they can test its credibility, and when they propagate the corresponding real news, it reaches a large number of individuals. We prove that RecANt is NP-Hard with a monotone and submodular objective, leading to a polynomial time greedy algorithm (AFC) which provides a (1 – 1/e – ϵ)-approximation. We further optimize the runtime of AFC by developing a fast graph-pruning heuristic (RAFC) that performs as well as AFC in checking the spread of fake news while reducing the runtime significantly. Simulations on several networks demonstrate that our approach outperforms popular social network centrality measures and state-of-the-art information diffusion algorithm.

Introduction: Since the late 1980s, critical illness myopathy (CIM) and polyneuropathy (CIP) have been increasingly recognized in the intensive care unit (ICU). We explored whether these causes of ICU weakness were now more likely to lead to electrodiagnostic studies (EDX) at our institution than they were 19-20 years earlier. Methods: We reviewed 100 consecutive ICU patients who underwent EDX from 2009-2015 and compared them to a previously reported study population from 1990-1995.10 Results: Thirty-seven (39%) had CIM, CIP, or both versus 55% in the previous study (p=0.04). Thirty-four (36%) were diagnosed with “traditional” pre-ICU causes of weakness, such as motor neuron disease or Guillain-Barre syndrome, versus 29% in the earlier study (p=0.3). Discussion: CIM and CIP continue to be common disorders that lead to ICU EDX, but their proportion declined compared to 19-20 years earlier, possibly due to the perceived role and selective use of EDX in the ICU. This article is protected by copyright. All rights reserved.

In Modern days, real time system plays an important role in our modern and digital society. The success of any real time application is totally depending upon the selection of optimal scheduling algorithm. In real time application every task should have the nature of deadlines and time when they arrived, on the basis of these parameters we observe the response time of different scheduling algorithm then we select the optimal algorithm for a particular application. So in this paper our aim is to reduce the complexity of real time system researcher for selection of scheduling algorithm for a particular application. This artificial intelligence based approach is an extent the state of any real time system in the area of scheduling. This approach works in any uni-processor system. Introduction

Objective: 17-α-Hydroxylase deficiency (17-OHD) is a very rare disorder of adrenal and gonadal steroidogeneis. The patient usually presents as a phenotypic female with hypertension, hypokalemia, and delayed puberty due to hypergonadotropic hypogonadism. Karyotype may be 46,XX or 46,XY. Timely diagnosis leads to effective management of the patient and appropriate screening of family members.Methods: Case report and review of the literature.Results: A 24-year-old woman presented with primary amenorrhea and poor development of secondary sexual characters along with persistent hypokalemia and hypertension of several years duration. Examination did not show any stigma of Turner syndrome, features of any pituitary hormone deficiency, or hypothyroidism. Further evaluation revealed hypergonadotropic hypogonadism with persistent hypokalemia and refractory hypertension. The karyotype was 46,XX. Possible diagnosis of 17-OHD was considered, which was supported by low serum cortisol, high adrenocorticotropic hormone, and very high serum progesterone level and suppressed plasma renin activity. The patient had a younger sister (11 years old) whom we called for evaluation. She was found to have diffuse skin and mucosal hyperpigmentation along with hypertension. Analysis of the CYP17A1 gene of these two patients revealed a homozygous mutation, p.R362C (c.1084C>T), which has been shown to completely abolish the activity of both 17-α-hydroxylase and 17,20-lyase.Conclusion: The p.R362C (c.1084C>T) mutation led to 17-OHD in two sisters. One sister was diagnosed as a result of family screening. This is the first report of this mutation in India.Abbreviations: 17-OHD 17-α-hydroxylase deficiency; ACTH adrenocorticotropic hormone; CYP17A1 cytochrome P450 17-α-1; PRA plasma renin activity

Progressive multifocal leukoencephalopathy (PML) is a life-threatening central nervous system (CNS) disorder, most commonly described in patients infected with the human immunodeficiency virus (HIV). Limited data exist on its natural history and treatment in solid organ transplant (SOT) recipients. A complication of PML is the immune reconstitution inflammatory syndrome (IRIS), which develops after T cell reconstitution and can have severe consequences when it occurs in the CNS. While well described in HIV-infected individuals, its clinical features, diagnosis, and treatment after SOT are largely unknown. We report a case of a kidney transplant recipient who was diagnosed with PML and developed significant worsening of her symptoms upon reduction of immunosuppression. Thallium SPECT showed avid uptake suggestive of lymphoma, but the diagnosis of PML-IRIS was ultimately established by brain biopsy. She survived with nearly complete restoration of her functional status after a prolonged steroid taper.

Runtime-reconfigurable software coupled with reconfigurable hardware is highly desirable as a means towards maximizing runtime efficiency without compromising programmability. Compilers for such software systems are extremely difficult to design as they must leverage different types of hardware at runtime. To address the need for static and dynamic compiler optimization of workflows matched to dynamically reconfigurable hardware, we propose a novel design of the central component of a dynamic software compiler for software defined hardware. Our comprehensive design focuses not just on static knowledge but also on semi-supervised extraction of knowledge from program executions and developing their performance models. Specifically, our novel dynamic and extensible knowledge base 1) continuously gathers knowledge during execution of workflows 2) identifies optimal implementations of workflows on optimal (available) hardware configurations. It plays a hub role in storing information from, and providing information to other components of the compiler, as well as the human analyst. Through a rich tripartite graph representation, the knowledge base captures and learns extensive information on decomposition and mapping of code steps to kernels and mapping of kernels to available hardware configurations. The knowledge base is implemented using the $C++$ Boost Library and is capable of quickly processing offline and online queries and updates. We show that our knowledge base can answer queries in $1 ms$ regardless of the number of workflows it stores. To the best of our knowledge, this is the first design of a dynamic and extensible knowledge base to support compilation of high-level languages to leverage arbitrary reconfigurable platforms.

Violence is a phenomenon that severely impacts homeless youth who are at an increased risk of experiencing it as a result of many contributing factors such as traumatic childhood experiences, involvement in delinquent activities, and exposure to perpetrators due to street tenure. Reducing violence in this population is necessary to ensure that the individuals can safely and successfully exit homelessness and lead a long productive life. Interventions to reduce violence in this population are difficult to implement due to the complex nature of violence. However, a peer-based intervention approach would likely be a worthy approach as previous research has shown that individuals who interact with more violent individuals are more likely to be violent, suggesting a contagious nature of violence. We propose uncertain voter model to represent the complex process of diffusion of violence over a social network that captures uncertainties in links and time over which the diffusion of violence takes place. Assuming this model, we define violence minimization problem where the task is to select a predefined number of individuals for intervention so that the expected number of violent individuals in the network is minimized over a given time frame. We also extend the problem to a probabilistic setting, where the success probability of converting an individual into nonviolent is a function of the number of “units” of intervention performed on them. We provide algorithms for finding the optimal intervention strategies for both scenarios. We demonstrate that our algorithms perform significantly better than interventions based on popular centrality measures in terms of reducing violence. Finally, we use our optimal algorithm for probabilistic intervention to recruit peers in a homeless youth shelter as a pilot study. Our surveys before and after the intervention show a significant reduction in violence.

Graph sampling is an important tool to obtain small and manageable subgraphs from large real-world graphs. Prior research has shown that Induced Edge Sampling (IES) outperforms other sampling methods in terms of the quality of subgraph obtained. Even though fast sampling is crucial for several workflows, there has been little work on parallel sampling algorithms in the past. In this paper, we present parIES - a framework for parallel Induced Edge Sampling on shared-memory parallel machines. parIES, equipped with optimized load balancing and synchronization avoiding strategies, can sample both static and streaming dynamic graphs, while achieving high scalability and parallel efficiency. We develop a lightweight concurrent hash table coupled with a space-efficient dynamic graph data structure to overcome the challenges and memory constraints of sampling streaming dynamic graphs. We evaluate parIES on a 16-core (32 threads) Intel server using 7 large synthetic and real-world networks. From a static graph, parIES can sample a subgraph with > 1.4B edges in

On April 18, 2019, the Indian Central Board of Direct Taxes released a public consultation document on amending India’s rules for profit attribution to permanent establishments. This remarkable document is the first time a national government has proposed what in essence amounts to an abandonment of the arm’s length standard (ALS), the governing standard in transfer pricing for over 80 years, in favor of a formulary system. Given that the OECD has also raised the option of using formulas to allocate profits to market jurisdictions, India’s unilateral move may signal the proverbial break in the ALS dam, with the potential of leading to an abandonment of the ALS in favor of a unitary tax system.

Neurological emergencies are a significant source of morbidity and mortality in immunocompromised patients. Immunodeficiency can be attributed to congenital and acquired causes. Congenital immunodeficiencies can either be combined with involvement of both cellular and humoral immunity or restricted to impairment of either type. Common combined immunodeficiency syndromes include ataxia telangiectasia, Wiskott Aldrich syndrome, and DiGeorge syndrome whereas X linked agammaglobulinemia and common variable immunodeficiency are characterized by isolated impaired humoral immunity. As opposed to rare patients with congenital immunodeficiencies, acquired immunodeficiencies are much more common. Acquired immunodeficiency is seen in HIV patients, diabetics, and in various patients treated with immunosuppressive, cytotoxic, and antirejection medications (e.g., transplant recipients, patients with autoimmune disorders, and cancer). These people are at increased risk of various neurologic emergencies including opportunistic CNS infections, posterior reversible encephalopathy syndrome (PRES), immune reconstitution inflammatory syndrome (IRIS), epilepsy, and stroke.

Osteolysis, caused by active resorption of bone matrix by osteoclasts, can be primary or can develop secondary to a variety of disease processes. An elevated level of inflammatory cytokines in the local milieu and increased blood flow secondary to infection or autonomic neuropathy stimulate the osteoclasts and cause bone loss in the diabetic foot. Charcot's neuroarthropathy and osteomyelitis are well-known foot complications of diabetes, and secondary osteolysis has largely been underappreciated and, hence, underreported. Plain radiographs, an initial component in the evaluation of the diabetic foot, may not successfully differentiate secondary osteolysis from osteomyelitis. We describe a patient with phalangeal osteolysis secondary to soft-tissue infection in whom a correct and timely diagnosis helped avoid unnecessary surgical interventions.

In this digital era, here the sum of data is generate with store has prolonged inside a less period of time. The data in this era generated high speed leads to many challenges. The size is primarily and periodically, just the measurement that bounces in the big data position. In this survey, we have attempt to give a broad explanation of big data that captures its other unique and important features. We have discussed 4V’s model. Also, the latest technologies uses in big data, like Hadoop, HDFS, MapReduce and different type of methods used by big data. Finally various benefits of using big data analysis and include some feature of cloud computing.

To facilitate deep penetration of solar energy in smart grids, we need high observability of solar generation at the edges of the grid. Current advanced metering infrastructures (AMI) only monitor the aggregated measurements from net-metered households, but disaggregated consumption and solar generation components are required for grid optimizations. We propose an unsupervised disaggregation model for disaggregating solar generation from AMI measurements without the need of training data. The model requires only AMI measurements from consumers in a region and the solar irradiance as input, and models the consumption of consumers by neighboring households without rooftop photovoltaics (PV) to perform the disaggregation. We evaluate our results on a real life dataset from Austin, Texas. We show that our model is able to disaggregate consumption and solar generation measurements with 42.24% and 31.67% less mean squared error, respectively, in comparison to a baseline technique that uses supervised learning. This shows that our model is capable of disaggregating historical data even if the dataset has no training data and only contains minimal exogenous data.

Background There is a constant search for an ideal treatment for vitiligo. Platelet-rich plasma (PRP) is a novel modality for treating stable vitiligo. Presently, literature regarding the efficacy of combining either PRP or platelet-poor plasma (PPP) with psoralen+sunlight (PUVASOL) in vitiligo does not exist. Objective This study compares the safety and efficacy of combining PUVASOL with either PRP or PPP against PUVASOL alone in patients with stable nonsegmental vitiligo. Patients and methods This prospective, open-labeled, comparative study was done in 20 patients with stable nonsegmental vitiligo, each having at least three vitiligo patches of similar shape and size. All the patients received PUVASOL as standard treatment modality. PRP or PPP was injected into two separate study patches as an adjuvant treatment with PUVASOL; however, the third patch acted as a control and was treated by PUVASOL alone. These patients were followed up for 4 weeks after the end of four treatment sessions. Results Compared with control areas (exposed to PUVASOL only), statistically significant more repigmentation was seen in areas treated with a platelet preparation (either PRP or PPP) along with PUVASOL. However, there was no significant difference between PRP and PPP arm (P=0.824) as per qualitative and quantitative assessment. Conclusion Addition of a platelet preparation (either PRP or PPP) had an additive beneficial effect on the response of PUVASOL therapy in patients with stable vitiligo at the study end point of 16 weeks. However, PRP and PPP showed comparable efficacy at the study end point in terms of repigmentation when used in conjunction with PUVASOL.

Introduction Critical hand ischemia with advancing gangrene of digits requires urgent intervention to salvage as much tissue as possible. The purpose of this study was to evaluate the efficacy of “palmar arch loop” technique for endovascular management of critical hand ischemia by establishing inline flow to the palmar arch via both radial artery and ulnar artery, in patients with failed antegrade recanalization. To the best of our knowledge, this is the first case series evaluating the efficacy of “palmar arch loop” technique, with retrograde percutaneous transluminal angioplasty of the involved radial artery and/or ulnar artery. Material and methods We retrospectively investigated 10 patients (60% female; mean age 42 ± 18 years; mean time of presentation post-acute event 24 ± 11 days) with critical hand ischemia undergoing endovascular intervention using “palmar arch loop” technique at a single center in northern India between April 2017 and March 2019. All patients were followed up at regular intervals (weekly for a month, fortnightly for 3 months, and then at 6 and 12 months) with clinical assessment and SpO2 measurement. Study end points were technical success rate, hand healing, and primary patency rate at one year. Results Causes for critical hand ischemia were iatrogenic injuries due to inadvertent intra-arterial injection in 50% ( n = 5) and thromboembolic events in 50% patients ( n = 5). Vessels involved were: both radial artery and ulnar artery along with the PA in 50%; radial artery and palmar arch in 30%; ulnar artery and palmar arch in 20%. All of them had total occlusion of the involved vessel (>2/3rd of total length) with occlusion/diffuse disease of palmar arch as well; 70% technical success rate was achieved ensuring inflow to palmar arch via both the arteries with improved flow distally to the common and proper digital arteries. Retrograde percutaneous transluminal angioplasty of radial artery in 50% ( n = 5) and ulnar artery in 20% ( n = 2) was done successfully by looping the guidewire across the palmar arch; 90% showed subjective improvement in pain with healing of the lesions and/or formation of clear line of demarcation with reversal of pregangrenous changes proximally. Out of the eight patients with gangrene of fingers, three underwent minor amputation of the gangrenous digits and five underwent auto-amputation of the gangrenous tissue with complete healing of the stump. Primary patency rate was 85.7% at one year. There was no access site-related complication or mortality in the follow-up period. Conclusions Endovascular management of critical hand ischemia by “palmar arch loop” technique is an efficient technique to deal with occluded forearm vessels, particularly when antegrade recanalization fails. This technique, with good technical success and patency rates, is potentially a unique tool in the endovascular armamentarium for salvaging hand.

Interventions to reduce violence among homeless youth are difficult to implement due to the complex nature of violence. However, a peer-based intervention approach would likely be a worthy approach as it has been shown that individuals who interact with more violent individuals are more likely to be violent, suggesting a contagious nature of violence. We propose Uncertain Voter Model to represent the complex process of diffusion of violence over a social network, that captures uncertainties in links and time over which the diffusion of violence takes place. Assuming this model, we define Violence Minimization problem where the task is to select a predefined number of individuals for intervention so that the expected number of violent individuals in the network is minimized over a given time-frame. We extend the problem to a probabilistic setting, where the success probability of converting an individual into non-violent is a function of the number of "units" of intervention performed on them. We provide algorithms for finding the optimal intervention strategies for both scenarios. We demonstrate that our algorithms perform significantly better than interventions based on popular centrality measures in terms of reducing violence.

Background: Postoperative nausea and vomiting (PONV) was termed “the big little problem” nearly a quarter century ago in an editorial .The past decade has witnessed the introduction of several significant innovations to combat PONV, but it still remains as big a problem as before because newer choices and confusions over standardization added side by side . The aim of this study is to compare the efficacy of palonosetron with that of granisetron and ondansetron in the treatment and prevention of postoperative nausea and vomiting after laparoscopic cholecystectomy. Methods: This randomized prospective comparative clinical study was carried out in the NIMS Medical College & Hospital, NIMS University, Jaipur, during the period of January 2013 to December 2013 amongst female patients of ASA grade I and II (ASA = American Society of anesthesiology) , scheduled for laparoscopic cholecystectomy under general anesthesia. A total number of 90 patients were selected and randomized into three groups of 30 patients each. Group-I was given inj. palonosetron (0.25 mg), Group-II was given inj. granisetron (1 mg) and Group–III was given inj. ondansetron (8 mg). One way analysis of variance (ANOVA) and Pearson’s Chi-square test were done as the tests of significance whenever applicable to compare the mean of different groups. Results: The incidence of nausea was 10% in Palonosetron-group while it was found to be 60% in Ondansetron-group. The incidence of vomiting was 6.7% in group-I, 26.6% in group-II & 53.3% in group-III respectively. The difference was statistically significant between group I vs group II (p

The case study relates to firearm suicide by a security personal and such incidents are very common among armed forces Incident of firearm cases are often reported to forensic science laboratory for reconstruction to establish the crime There are various reasons contributed for such incidents among security forces It is observed either head or chest is the most common site for self inflicted firearm wound in almost all suicide cases In one case a security personal was on sentry duty with Indian Small Arms System INSAS rifle and committed suicide by his service firearm The deceased was lying in a pool of blood along with firearm and one fired cartridge case near the body The autopsy surgeon reported having entry wound on the back side situated in occipital bone cm above cervical vertebra and exit wound over the forehead of the deceased The observation was controversial since the presence of entry wound was at uncommon place showing absence of singeing blackening burning tattooing and devoid of impression of the muzzle In addition it was also difficult for the shooter to fire from back side Moreover such type of incident has never been reported in literature in suicide cases by firearm Further bullet wound study along with spot analysis could confirm the entry wound to be on forehead and exit on the back The mystery of origin of entry exit wound could be established by wound ballistics study

Introduction: Glycated hemoglobin (HbA1c) can be altered in different conditions. We hypothesize that HbA1c levels may change due to altered thyroid status, possibly due to changes in red blood cell (RBC) turnover. Objectives: The objective of this study was to determine the effects of altered thyroid status on HbA1c levels in individuals without diabetes, with overt hyper- and hypo-thyroidism, and if present, whether such changes in HbA1c are reversed after achieving euthyroid state. Methods: Euglycemic individuals with overt hypo- or hyper-thyroidism were selected. Age- and sex-matched controls were recruited. Baseline HbA1c and reticulocyte counts (for estimation of RBC turnover) were estimated in all the patients and compared. Thereafter, stable euthyroidism was achieved in a randomly selected subgroup and HbA1c and reticulocyte count was reassessed. HbA1c values and reticulocyte counts were compared with baseline in both the groups. Results: Hb A1c in patients initially selected was found to be significantly higher in hypothyroid group. HbA1c values in hyperthyroid patients were not significantly different from controls. HbA1c reduction and rise in reticulocyte count were significant in hypothyroid group following treatment without significant change in glucose level. Hb A1c did not change significantly following treatment in hyperthyroid group. The reticulocyte count, however, decreased significantly. Conclusion: Baseline HbA1c levels were found to be significantly higher in hypothyroid patients, which reduced significantly after achievement of euthyroidism without any change in glucose levels. Significant baseline or posttreatment change was not observed in hyperthyroid patients. Our study suggests that we should be cautious while interpreting HbA1c data in patients with hypothyroidism.