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In the last couple of years, massive growth has been seen in the Internet of Things (IoT). It is being adopted by all the application areas worldwide and playing a vital role in making smart environments. In IoT based paradigms, millions of sensors and intelligent devices are linked with each other and produce a massive volume of data. For data computation and analysis, we need computing resources that will able to provide the services at the edge levels. These computing resources may also be capable of dealing with bandwidth, storage, and latency related issues. The computing technologies in IoT may offer the services at the end-user levels, which will increase the system's overall performance with higher throughput in real-time. The developing rate of IoT based smart devices or sensors needs mobility with extensive geographical distribution, which is only possible by computing technologies. In this paper, we have discussed the emerging opportunities of Cloud, Fog, and Mist computing in IoT paradigms and also described the layer-wise architecture of these computing technologies. Apart from this, various characteristics and significance toward the problem-solving in the IoT paradigm have also been deeply discussed.

Mixed results on the impact of microfinance continue to create concern among the policymakers due to the opportunity cost of the resources allocated towards micro-lending. While microfinance reduces income inequality, it becomes necessary to know its effect on economic growth, as income equality and growth are negatively correlated in developing economies. We hypothesise that microfinance leads to the economic growth of the nation at the macro level, using India's state wise panel data extracted by exploiting the database of Microfinance Institutions Network and database of the Reserve Bank of India. The study uses a random effects instrumental variable model to deal with the endogeneity and state and time effect involved due to bi-causality and panel data respectively. Results obtained, suggest that micro-lending has a significant and positive impact on the economic growth of a nation, which implies that microfinance can also contribute towards positive economic growth along with its role of reducing income inequality.

In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. The objective of our research study is the adaptation of Data Science and its approaches for prediction of various diseases in early stages. In this study we review latest proposed approaches with few limitations and their possible solutions for future work. This study also shows importance of finding significant features that improves results proposed by existing methodologies. This work aimed to build classification models such as Naïve Bayes, Logistic Regression, k-Nearest neighbor, Support vector machine, Decision tree, Random Forest, Artificial neural network, Adaboost, XGBoost and Gradient boosting. The experimental study chooses group of features by means of three feature selection approaches such as Correlation-based selection, Information Gain based selection and Sequential feature selection. Various Machine learning classifiers are applied on these feature subsets and based on their performance best feature subset is selected. Finally, ensemble based Max Voting Classifier is proposed on top of three best performing models. The proposed model produces an enhanced performance label with accuracy score of 99.41%.

Background: Perforation is a rare but serious adverse event of endoscopic retrograde cholangiopancreatography (ERCP). The aim of this study was to determine the predictors of morbidity and mortality after surgical management of ERCP-related perforation (EP). Methods: The records of patients with EP requiring surgical intervention at a tertiary referral center in a 12-year period (2004–2016) were retrospectively analyzed for demography, indications for ERCP, risk factors, timing and type of surgical repair, post-operative course, hospital stay, and outcome. Multiple logistic regression was used to identify the parameters predicting survival. Results: Of 25,300 ERCPs, 380 (1.5%) had EP. Non-operative management was successful in 330 (86.8%) patients. 50 (13.2%) patients were operated for EP. Out of 50, the perforation was detected during ERCP (intra-procedure) in 32 patients (64%). In 30 patients (60%), the surgery was performed within 24 h of ERCP. Twenty patients underwent delayed surgery (after 24 h of ERCP) following the failure of initial non-operative management. The delayed surgery after an unsuccessful medical treatment had a detrimental effect on morbidity, mortality and hospital stay. Post-operative duodenal leak was the only independent predictor of 90-day mortality (p= 0.02, OR = 9.1, 95% CI 1.52–54.64). Addition of T-tube duodenostomy (TTD) to the primary repair for either type I or type II perforations increased post-operative duodenal leak (type I, p= 0.048 and type II; p= 0.001) and mortality (type I, p= 0.009 and type II, p= 0.045). Conclusion: Although EP is a rare event, it has a serious impact on morbidity and mortality. Delaying of surgery following failed non-operative management worsens the prognosis. Addition of TTD to the repair is not helpful in these patients.

AbstractPower method is one of the basic iterative method used by Brin and Page to compute the PageRank vector. However, this method may not be efficient when the hyperlink matrix becomes large or damping factor value closes to 1. In last few years, Researchers have considered several numerical methods like Gauss-Seidel, Monte-Carlo, and Arnoldi extrapolation to accelerate the convergence of PageRank computation. In this paper, we have proposed a novel iterative method to compute the PageRank vector of hyperlink matrix by using Aitken extrapolation algorithm. Also, we have analyzed convergence behavior of proposed algorithm i.e.Aitken-extrapolation Gauss Seidel. Further, we have compared Aitken-extrapolation Gauss Seidel algorithm to existing methods such as Power method, Aitken-Power, and Gauss-Seidel method experimentally and found that our method is more feasible and efficient as compared to these existing approaches.

AbstractWe evaluated the loss of heterozygosity (LOH) at 10q23.3locus of microsatellite markers; D10S198, D10S192, and D10S541 of PTENgene in 223 North Indian colorectal cancer (CRC) specimens. DNA was isolated and microsatellite-specific markers polymerase chain reaction was performed. Out of total 223 cases 102 showed LOH for at least one of the locus. In addition, thereto a significant association was found with the clinicopathologic features like grade of differentiation, clinical stage, invasion, lymph node invasion, and the clinical outcome (p < 0.05). These data argue that the given markers to check the possible LOH of PTENgene at locus 10q23.3could be considered as one of the diagnostic markers in CRC.

Internet of Things (IoT) comprises of connected devices that collaborate to perform numerous tasks individually and collectively to facilitate services to the users. Distributed denial of service attacks aim to exhaust the resources of the target by sending enormous inessential traffic from numerous sources. IoT systems, generally being a resource-constrained network, are highly vulnerable to DDoS attacks. We aim to achieve the detection of DDoS attacks by rule-based detection method using information theory metrics. Variation in entropy of traffic features beyond a threshold is an indicator of change in density of traffic to a network. In this paper, we present a performance analysis of different parameters associated with entropy variation-based detection of DDoS attacks. Three feature sets were used for studying the impact of entropy variation in detection. Further, we have analysed the three kinds of entropies used for the detection namely, Shannon entropy, generalized entropy, and ϕ-entropy. Along with that, threshold determination for entropy values have been done in two ways, i.e. [average−standarddeviation,average+standarddeviation]and [lower quartile, upper quartile]. We found that quartile-based threshold has generally given better recall and accuracy and more consistent results with respect to the window sizes. It is also found that ϕ-entropy has given overall better results as compared to the Shannon entropy and Generalized entropy. Therefore, a combination of ϕ-entropy and quartile-based threshold is found to be the best parameters to identify the DDoS attacks.

Anastomotic leak is one of the main causes of morbidity following esophageal resection for carcinoma of the esophagus and gastroesophageal junction. We compared hand sewn and stapled cervical esophagogastric anastomotic techniques in terms of postoperative complications.

ABSTRACTNeurocysticercosis (NCC) is a central nervous system (CNS) infection caused by the metacestode stage of the parasite Taenia solium. During NCC, the parasites release immunodominant glycan antigens in the CNS environment, invoking immune responses. The majority of the associated pathogenesis is attributed to the immune response against the parasites. Glycans from a number of pathogens, including helminths, act as pathogen-associated molecular pattern molecules (PAMPs), which are recognized by pattern recognition receptors (PRRs) known as C-type lectin receptors (CLRs). Using a mouse model of NCC by infection with the related parasite Mesocestoides corti, we have investigated the role of mannose receptor C type 1 (MRC1), a CLR which recognizes high-mannose-containing glycan antigens. Here we show that MRC1-/-mice exhibit increased survival times after infection compared with their wild-type (WT) counterparts. The decreased disease severity correlates with reduced levels of expression of markers implicated in NCC pathology, such as interleukin-1ß (IL-1ß), IL-6, CCL5, and matrix metalloproteinase 9 (MMP9), in addition to induction of an important repair marker, fibroblast growth factor 2 (FGF2). Furthermore, the immune cell subsets that infiltrate the brain of MRC1-/-mice are dramatically altered and characterized by reduced numbers of T cells and the accumulation of granulocytic cells with an immune phenotype resembling granulocytic myeloid-dependent suppressor cells (gMDSCs). The results suggest that MRC1 plays a critical role in myeloid plasticity, which in turn affects the adaptive immune response and immunopathogenesis during murine NCC.

Quantifying customer satisfaction in any manufacturing or service supply chain is extremely important to know the end-user preferences. The higher the customer satisfaction, the higher is the sales, which in turn leads to better supply chain performance. In the current study customer satisfaction is measured quantitatively from a dairy food supply chain perspective in India. The data collected from the customers have been subjected to a rigorous analysis giving due weightage to the sampling and reliability of the instrument used for the study. From large number of indicators, factors have been drawn by using factor analysis and customer satisfaction index has been calculated thereto. The empirical findings are expected to add value to the concept where products are highly perishable and need conditioned transportation at every stage.

Splenic artery aneurysms although rare are clinically significant in view of their propensity for spontaneous rupture and life-threatening bleeding. While portal hypertension is an important etiological factor, the majority of reported cases are secondary to cirrhosis of the liver. We report three cases of splenic artery aneurysms associated with extrahepatic portal vein obstruction and discuss their management.

Risk and uncertainty have been found to be one of the indispensable parts of any manufacturing or service supply chain. No matter how strong is the supply chain, risk and uncertainty come into the picture by disrupting the operational flow. The situation further gets complicated, if the nature of the material the supply chain deals in, is perishable requiring conditioned transportation and storage. In this context, dairy-food supply chain is not a deviation. The empirical paper describes the risks and uncertainties of a dairy-food supply chain case in India. The study involves all the stakeholders in the system to identify the potential risks for the entire chain and strategies to address the same. The data, from 1,063 sample respondents, at different levels of the supply chain, are collected and assessed to detect major operational risks for the dairy industry. In total, 15 significant risks are identified throughout the supply chain of which eight are assessed to be critical ones. Strategies to minimise the high risks are also mentioned at different levels so as to increase the efficiency of the supply chain.

This paper is a slightly modified version of the lecture recently delivered by the author.  It addresses a question, based on India’s experience, if the current practice of disaster management needs to be revisited in the context of the COVID-19 pandemic that has had an unprecedented impact. The pandemic is being widely described as a Black Swan event, an event that has outsized impact, that is harder to predict and even harder to compute it probabilities.  A contrary view is that, in many ways, it is an entirely expected event, given that all the drivers of risk were there in plain sight. In whichever manner we characterise this event, disaster risk management systems as well as public health systems in India and across the world have been challenged and stretched to the limit.While it is too early to draw final conclusions, some early lessons could be drawn. A preliminary analysis suggests five key lessons: 1) Need for more dynamic risk assessment tools, 2) No substitute for community action, 3) Risk is global, resilience is local, 4) From managing risk we need to focus on managing uncertainty, and 5) From managing risk we need to focus on building resilience. There is a need to build our robustness and resilience to risks that may not be known or may not be fully understood yet. Some authors have introduced the concept of “Antifragile: things that gain from disorder.”  This paper highlights five pillars of our efforts in this direction: 1) A further strengthened Disaster Risk Management system, particularly at the local level, 2) Resilient Infrastructure, 3) Resilient Financial System with equitable access, 4) Social protection, and 5) Sustainable Natural Resource Management.