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The long-term outcomes of patients with congenital and childhood complete atrioventricular block (CCAVB/CAVB) after pacemaker implantation are unclear.We performed a meta-analysis of all the studies of CCAVB. A systematic search of PubMed and CENTRAL databases from January 1, 1967 to January 31, 2020 was performed. The quality of studies included was critically appraised using the Newcastle-Ottawa scale, and outcome data were analyzed using the restricted maximum likelihood function.Twenty-nine studies were eligible for analysis, with a total of 1553 patients. The all-cause-mortality was 5.7% (95% confidence interval [CI]: 2.5%-9.9%), while pacing-induced cardiomyopathy (PICM) was seen in 3.8% (95% CI: 1.2-7.2). Diagnosis at birth (effect size [ES] [95%CI]: -2.23 [-0.36 to -0.10]; p .001), presence of congenital heart disease (ES [95%CI]: -0.67 [0.41-0.93]; p .001), younger age at pacemaker implantation (ES [95%CI]: -0.01 [-0.02 to -0.001]; p = .02), and duration of pacing (ES [95%CI]: -0.03 [-0.05 to -0.003]; p = .03), were associated with an higher mortality on binominal logistic regression. None of the parameters were significant on multivariate analysis.Pooled proportional mortality in patients with CCAVB and CAVB is 5.7% with an infrequent incidence of PICM (3.8%) in the paced patients with AVB suggesting that pacing in these patients is an effective management strategy with a low incidence of long-term side effects. Registry and randomized data can throw additional light regarding the natural history and appropriate management strategy in these patients.

The use of intracardiac echocardiography (ICE) is beneficial during the ablation of atrial fibrillation (AF). Evidence is conflicting regarding the clinical impact of using ICE on arrhythmia recurrence and mortality.Patients undergoing catheter ablation of AF during 2010-2017 were identified using the International Classification of Diseases-9th and 10th Revision-Clinical Modification (ICD-9-CM and ICD-10-CM) from the Nationwide Readmissions Database. Propensity matching was used to generate a control group. Patient demographics, Charlson comorbidity indexes, time from discharge to readmission, and the reason of readmission were extracted.From 2010 to 2017, 51 129 patients were included in the analysis out of which ICE was used in 8005 (15.7%) patients. The in-hospital mortality at readmission was significantly higher in the patients without ICE use (2.9% vs. 1.7%, p = .02). The length of stay (LOS) at readmission was significantly higher in non-ICE arm (median [interquartile range, IQR]: 3 [2-6] vs. 2 [3-5] days, p .0001) with similar healthcare-associated cost (HAC) in both the groups (median [IQR]: US$7507.3 [4057.8-15 474.2] vs. 7339.4 [4024.8-15 191.6], p = .43). Freedom from readmission was 12% higher (hazard ratio [HR] [95% confidence interval, CI]: 0.88 [0.83-0.94], p .0001) with the use of ICE at 90-day follow-up, which was driven by 24% reduction in heart failure (HF) at follow-up (HR [95% CI]: 0.76 [0.60-0.96], p = .02).ICE use during AF ablation procedure reduces readmissions at 90 days by 12%, driven by a 24% decrease in HF-related admissions. The non-ICE arm showed a significantly higher LOS which offsets marginally higher HAC in the ICE arm.

For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions. In the present work, we propose a highly efficient deep-learning surrogate framework that is able to accurately predict the response of bodies undergoing large deformations in real-time. The surrogate model has a convolutional neural network architecture, called U-Net, which is trained with force–displacement data obtained with the finite element method. We propose deterministic and probabilistic versions of the framework. The probabilistic framework utilizes the Variational Bayes Inference approach and is able to capture all the uncertainties present in the data as well as in the deep-learning model. Based on several benchmark examples, we show the predictive capabilities of the framework and discuss its possible limitations.

A 60-year-old male with recurrent episodes of supraventricular tachycardia on regular medications had another episode. Was taken up for electrophysiological study and radiofrequency ablation (EPS and RFA) under 3-D electro-anatomical mapping (EAM). The challenges in the case were need for isoprenaline infusion for sustenance of tachycardia and relatively wider area of ablation requirement for success.

This work has been done as a part of scientific deliverable 4.1 of ITN Rainbow.(https://rainbow.ku.dk). A. In first partprogram is written for predicting the position of brain tumour in brain surgery. In the surgery, as surgeonmakes incision and opens the skull, the brain tumour relocates under new boundary conditions. Predicting the position of tumor under different incision sizes is invaluable data for surgeons, which is done by using gaussian process regressor meta-model. B. Second part shows the potential ofdeep learning approach to solve non-linear deformation behaviour of a cantilever beam inreal time constraints. It is shown that deep learning model is able to predict the solution in a very less time compared to finite element solution time and yet maintaining the reasonable accuracy. &nbsp

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types., KDD 2020

Aim: Distal radius fracture is common fracture in daily orthopaedic practice accounting for 1/6th of all the fractures. Conventionally the fractures were treated with closed reduction and immobilization with casts. Even though union of these fractures occurs, they have a very high incidence of going in for malunion and wrist joint instability especially with comminution & intra articular extension cases. Moreover a change in trend to younger age groups as a result of RTA’s (road traffic accidents) & trauma is leading to more complicated fractures with intraarticular extension and comminution. Also the importance of anatomical alignment and reconstruction of radiocarpal and radioulnar ulnar joints has been emphasized in recent trends. Hence the management of distal radius fractures changed from universal use of cast immobilisation to operative interventions. Aim of this study is to observe the effect of external fixation for unstable distal radius fractures in rural set up. Materials and methods: Prospective analysis of 30 patients with unstable distal radius fractures, who were operated at MIMS hospital, nellimarla village, vizianagaram district between may 2016 to feb 2017 treated with external fixator. Patient position- supine with arm table support. For external fixation of the wrist we used the 4 mm (small) external fixator system. For pin insertion into the second metacarpal, the distal pin is inserted proximal to the transition of the metacarpal head into the shaft and the more proximal pin is inserted distal to transition of the shaft into the metacarpal base.The proximal two pins are inserted proximal to the muscle bellies of abductor pollicis longus (APL) and extensor pollicis brevis (EPB). Proximal to these muscles, the radial shaft is palpated through the skin between the bellies of the extensor digitorum communis (EDC) and extensor carpi radialis longus/brevis (ECRL/ ECRB) over a distance of 3-4 cm at which the proximal pins inserted into the radial shaft with 4-5cms apart. Longitudinal traction is applied on the thumb and index finger or the distal partial frame to reduce the fracture. T clamps are fixed to bridging rods, first proximally followed by tightening of distal fragment T clamps to maintain the traction and reduction, there by healing of fracture by the principle of ligamentotaxis. Results: Average Mean hospital stay is 4days, there are 13males and 17 females between age group 28-60yrs, 27 cases out of 30 had functional and radiological union by 12weeks (range 6-18 weeks), 2 had pin track infection which were treated successfully with iv antibiotics. 1 patient had deep bone infection and pin loosening which was managed with external fixator removal, long course antibiotics and casing. We followed standard Sarmiento radiological scoring system, Gartland and Werley Score (demerit system) demerit point scoring system, DASH scores, Green & O Brien grade scoring systems to evaluate the end results. Conclusion: External fixation of unstable distal radius fractures can be considered as effective treatment in elderly populations of Indian rural set up as it is simple, cost effective, short duration procedure with good results.

Twenty-five low-lying isomers of C9H2 lying within 2 eV (∼46.12 kcal mol−1) have been theoretically investigated using high-level quantum chemical calculations. Among them, the linear triplet isomer (1), the cyclopropenylidene derivatives (2 and 3) and the cumulene carbene isomer (11) have already been detected in the laboratory. The present theoretical study proposes fourteen cyclic structures and four open-chain structures that have not been investigated before. On the basis of relative energies obtained from coupled-cluster calculations, the current theoretical study reveals that six isomers (4 and 6–10), which energetically lie below 11, and fourteen isomers (12–25), which energetically lie above 11, are remaining elusive in the laboratory to date. Considering the astronomical importance of cyclopropenylidene and cumulene carbenes, the current quantum chemical data may be of relevance to molecular spectroscopists in identifying new C9H2 isomers in the laboratory. Consequently, such studies may benefit radioastronomers in confirming or neglecting C9H2 isomers in the interstellar medium.

Ability to predict the future performance of a storage system is critical for its efficient management. A modern storage system is a very complex combination of hardware and software elements and inferring its state from those of its individual components is practically impossible. Moreover, the state of the system undergoes continuous changes due to diverse and evolving workloads, frequent configuration changes and natural degradation in components. This paper presents an approach to predict the future response time of a storage system by applying a combination of machine learning techniques on the data of its internal parameters. To the best of our knowledge, this is the first attempt to quantify the response time of a storage system directly from the time-series of its internal parameters. We present a systematic experimental setup for collecting the system data and for measuring its response time while loading the system with different standard workloads. Another novel contribution of the paper is a combination of time-series forecasting model followed by a regression model to predict the response time repeatedly in real-time. The said model is shown to be able to repeatedly predict fairly accurate 15-minutes-ahead response time values using the system data of the past 20-minutes' window. It is shown that the validation calculations typically complete within ~30 seconds on an average using a computer of moderate configurations making the presented model applicable for practically reasonable prediction horizons. The design of the model is such that it can be adaptively tuned online at regular intervals as more system data is observed.

A new approach to the problem of optimal control of linear dynamic systems is proposed that makes use of a method of input and state parametrization to transform the original problem into a problem of the Calculus of Variations. In contrast to the standard approaches for this class of problems, two salient features of the new approach are that no Lagrange multiplier functions need to be invoked and that the class of inputs can be restricted to the - relatively small - class of continuous functions, even for problems with fixed end-states. The resulting necessary conditions of optimality, i.e., the Euler Lagrange equation and the boundary conditions for the transformed problem, are proved to be equivalent to those resulting from the standard method of First Variations. In case of quadratic cost functionals, the new approach provides a simpler alternative to the well known, but equally difficult, Riccati differential equation approach and results in a simple dynamic state-feedback implementation of the optimal control. (C) 2011 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

In real-time optimization, enforcing the constraints that need to be active is important for optimality. In fact, it has been established in the context of parametric variations that, if these constraints are not satisfied, the optimality loss would be O($\eta^2$) – denoting the magnitude of the parametric variations. In contrast, the loss of optimality upon enforcing the correct set of active constraints would be O($\eta^2$). However, no result is available when the set of active constraints changes due to parametric variations, which forms the subject of this paper. Herein it is shown that, if the optimal solution is unique for each , keeping only the strictly active constraints of the nominal solution active will lead to O($\eta^2$) loss in optimality, even when the remaining active constraints of the perturbed system are different from that of the nominal system. This, in turn, means that, in any input adaptation scheme for real-time optimization, identifying changes in active constraints is not important as long as it is possible to enforce the strictly active constraints of the nominal solution to remain active.

Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

The paper proposes a new simplex-based direct search algorithm for box-constrained nonlinear programming (NLP) problems. As is typical of such methods, the optimal solution is obtained by iteratively transforming a multi-dimensional simplex and no gradient information is calculated. In contrast to unconstrained optimization problems, where the decision about way of transforming an iterate simplex is based solely on the consideration of optimality, in the proposed algorithm, both optimality and constraint satisfaction affect the iterations. First novel feature of the proposed algorithm is a fast way of checking if a planned iteration of current simplex violates any of the bounds and, if yes, a systematic way of choosing an alternative without compromising on the optimality considerations. The proposed procedure, in fact, guarantees that each iterate simplex lies entirely within the feasible region. Second novel feature of the algorithm is the use of so-called free and bounded variables that allow for different dimensions of simplices in different iterations. This feature helps convergence of the algorithm when one or more of the decision variables lie on bounds. The performance of the proposed algorithm is demonstrated on some well known test problems.

This paper deals with input adaptation in dynamic processes in order to guarantee feasible and optimal operation despite the presence of uncertainty. The proposed adaptation consists in using the nominal optimal inputs and adding appropriately designed input variation functions. For optimal control problems having both terminal and mixed control-state path constraints, two orthogonal sets of directions can be distinguished in the space of input variation functions: the so-called sensitivity-seeking directions, along which a small variation will not affect the respective active constraints, and the complementary constraint-seeking directions, along which a variation will affect the respective constraints. It is shown that the sensitivity-seeking directions satisfy certain linear integral equations. Two selective input adaptation strategies are then defined, namely, adaptation in the sensitivity- and constraint-seeking directions. This paper proves the important result that, for small parametric perturbations, the cost variation resulting from adaptation in the sensitivity-seeking directions (over no input adaptation) is typically smaller than that due to adaptation in the constraint-seeking directions.

This paper is concerned with input adaptation in dynamic processes in order to guarantee feasible and optimal operation despite the presence of uncertainty. For optimal control problems having terminal constraints, two sets of directions can be distinguished in the input function space: the so-called sensitivity-seeking directions, along which a small input variation does not affect the terminal constraints, and the complementary constraint-seeking directions, along which a variation does affect the terminal constraints. Two selective input adaptation scenarios are thus possible, namely, adaptation along each set of input directions. This paper proves the important result that the cost variation due to the adaptation along the sensitivity-seeking directions is typically smaller than that due to the adaptation along the constraint-seeking directions.

In the context of dynamic optimization, plant variations necessitate adaptation of the input profiles in order to guarantee both feasible and optimal operation. For those problems having path constraints, two sets of directions can be distinguished in the input space at each time instant: the so-called sensitivity-seeking directions, along which a small input variation does not affect the active path constraints; the complementary constraint-seeking directions, along which a variation affects the path constraints. Hence, three selective input adaptations are possible, namely, adaptation along each set of input directions and adaptation of the switching times be- tween arcs. This paper considers parametric variations around a nominal optimal solution and quantifies the influence of these variations on each type of input adaptation.