Miracle-dz/UniFAR-FTUs huggingface dataset:
Myectomy with mitral valve repair versus replacement in adult patients with hypertrophic obstructive cardiomyopathy: a systematic review and meta-analysis.

We evaluated the differences in mitral valve (MV) plasty (MVP) and MV replacement (MVR) with respect to death, postoperative MV dysfunction, reoperation rates and thromboembolic events (DFRE) in patients with hypertrophic obstructive cardiomyopathy and systolic anterior motion of the anterior mitral leaflet-mediated MV regurgitation (HOCM & MR). We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, searching PubMed, Cochrane library and ClinicalTrials.gov for studies that evaluated DFRE in adults with HOCM & MR after MVP/MVR. We used a risk of bias assessment tool for non-randomized studies, and analyses were performed using Cochrane Review Manager 5.3. 5 for I2 statistics, funnel plots and forest plot and the generic inverse variance method for hazard ratios (HRs). We developed qualitative and quantitative syntheses of 35 and 23 studies, respectively, including levels of evidence of ½/3/4/5 = 3/1/11/11/9 and ½/3/4/5 = 0/1/11/11/0, respectively, from January 1980 to August 2017. A statistically significant difference between MVP and MVR favoured MVP for the prevention of DFRE in patients with HOCM & MR, on the basis of a significant reduction of the HR for DFRE: HR = 0.68 (0.57, 0.82), I2 = 68% (P = 0.002). The findings were as follows: (i) MVP should be the first-line treatment in patients with HOCM & MR (accuracy LEVEL A) and (ii) MVR may be harmful if it is used as the first-line treatment (accuracy LEVEL A).
Enhancement of piezoelectric energy harvesting using ABH structural tailoring

The acoustic black hole (ABH) in thin-walled structures allows the reduction of phase velocity of flexural waves, producing high energy density within structural areas which offers excellent potential for vibration based energy harvesting. The broadband characteristic of the ABH effect is beneficial to increase the operational frequency range of vibration energy harvesters. In this paper, finite element models of a beam with embedded ABHs and its full coupling with piezo-transducers bonded on its surface are analyzed. The size of piezoelectric patches is designed to be relatively smaller than the wavelengths of ABH features in order to make sure that the positive and negative electric charge generated on the surface of harvesters won't be neutralized. The performances of energy harvesters in the ABH structure are investigated and compared with those of the uniform beam under steady state in numerical simulations. The results show that the ABH features are much more effective for vibration energy harvesting than conventional uniform structures.
Transient Thermal Modeling and Analysis of Railway Traction Motors

This paper presents a practical approach to model and analyze transient thermal effects in air-cooled electric traction motors. The developed thermal modeling method enables accurate estimation of temperature in motor critical parts including winding and bearing. Advantages of both numerical and analytical modeling methods are exploited with the aim of realizing accurate estimation of hot spot temperatures in traction motors while keeping the computation time within a reasonable range. Computational fluid dynamics simulations are carried out to model air flow in the motor in order to provide heat transfer boundary inputs to the developed combined finite-element (FE) and lumped parameter (LP) thermal models. The combination of the FE and LP models keeps the size of the model relatively small and enables running transient calculations reasonably fast. Also, the developed model provides the possibility to study the influence of stator and rotor duct blockages on the motor thermal performance, which is a common root of failure in traction applications during operation in dirty environments. The proposed thermal model is verified using experimental results on a traction motor equipped with temperature sensors at different running conditions and a good agreement between the estimated and measured temperatures is achieved.
An Inchworm-inspired Rigid-reinforced Soft Robot with Combined Functions of Locomotion and Manipulation

The imitation of the functions of soft animals has always been the goal of soft robotic design. However, most of the current soft terrestrial robots just imitate the basic gaits and are difficult to perform in three dimensional space. In this work, we present an inchworm-inspired soft robot that integrates the locomotion and manipulation functions together. The soft robot is able to move in the vertical plane from one pole to another or work as a soft manipulator. The two functions can switch between each other naturally. Besides, the soft robot is unfettered by gravity, which greatly expands the applications of the soft robot. The deformation and load capacities of the soft components were tested, and to enhance the stiffness of the whole soft robot, the rigid-frame reinforcement was used. The performance of the soft robot shows great potential of actual applications, such as spatial maintenance and agricultural picking. Most importantly, the combination of the locomotion and manipulation functions provides a new idea for designing more applicable soft robots.
Latent Semantic Analysis: An Approach to Understand Semantic of Text

Latent semantic analysis (LSA) is a method for analyzing a piece of text with certain mathematical computation and analyzing relationship between terms in the documents, between the documents in the corpus.Various application of intelligent information retrieval, search engines, internet news sites requires an accurate method of accessing document similarity in order to carry out classification, clustering, summarizing or search tasks. So in this paper we are studying latent semantic analysis based on single value decomposition. The aim of Latent semantic analysis is to exploit the global structure of documents. The emphasis of latent semantic analysis is to find hidden relationship in document for better understanding the relationship between terms and document in dataset. In this paper, we have conducting a study using Latent semantic analysis (LSA) to find correlation of terms in a dataset consisting of research papers of various natural language processing applications.LSA shows that single value decomposition collapse multiple terms with same semantic and can identify terms with multiple meaning and represent documents in lower dimensional conceptual space.
Analytical Layout Dependent NBTI Degradation Modeling Based on Non-Uniformly Distributed Interface Traps

Previous studies reveal that interface traps induced by negative bias temperature instability (NBTI) are non-uniformly distributed under the channel of a pMOS device. The channel edges and lightly doped drain overlapping regions degrade more rapidly than the central channel. Consequently, smaller transistors suffer more severe degradation than that with larger size. In this paper, two analytical NBTI degradation models involving layout size have been derived: one for regular and the other for small size. Besides channel length and channel width, only two fitting parameters are introduced. Moreover, the proposed models have been validated and analyzed using the experimental distribution of interface traps. The results show that our proposed models can reflect the influence of layout size on NBTI degradation.
A Framework for Vision-Based Multiple Target Finding and Action Using Multirotor UAVs

This paper presents a framework for vision-based target finding and action using a multirotor UAV system. The proposed framework detects and tracks a set of ground targets by using a vision-based position estimation technique. An internal map created using the relative locations of the adjacent targets is used to overcome the vision-based position estimation error and GPS noise and drift. The framework was implemented using the Robotic Operating System (ROS) and tested in the Software in the Loop (SITL) Simulation with the Gazebo robotics simulator. The test results demonstrate that the framework is robust to drift and errors, and able to perform the intended tasks successfully.
Beware of SMOMBIES: Verification of Users Based on Activities While Walking

Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.
Total Ionizing Dose Effects on a Highly Integrated RF Transceiver for Small Satellite Radio Applications in Low Earth Orbit

Software-Defined Radios (SDR) are commonly used in state-of-the-art radio systems and are already well established in the space industry. A SDR usually describes the signal processing of a radio application in software and is often implemented into Field Programmable Gate Arrays (FPGA) or Digital Signal Processors (DSP). Most RF front-ends are strictly specified and realized for an executed application and are thus not re-configurable. With the release of new Radio Frequency Integrated Circuit (RFIC) devices, a significant portion of RF front end specifications have become programmable and alterable. This paper presents the use case and selected radiation test results of such RFIC technologies for small satellite radio applications, primary designed for low earth orbit (LEO) missions.
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory

Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles. A representative algorithm is the Stein variational gradient descent (SVGD). We prove, under certain conditions, SVGD experiences a theoretical pitfall, {\it i.e.}, particles tend to collapse. As a remedy, we generalize POS to a stochastic setting by injecting random noise into particle updates, thus yielding particle-optimization sampling (SPOS). Notably, for the first time, we develop {\em non-asymptotic convergence theory} for the SPOS framework (related to SVGD), characterizing algorithm convergence in terms of the 1-Wasserstein distance w.r. t. \! the numbers of particles and iterations. Somewhat surprisingly, with the same number of updates (not too large) for each particle, our theory suggests adopting more particles does not necessarily lead to a better approximation of a target distribution, due to limited computational budget and numerical errors. This phenomenon is also observed in SVGD and verified via an experiment on synthetic data. Extensive experimental results verify our theory and demonstrate the effectiveness of our proposed framework.
High Voltage InGaN/GaN/AlGaN RTD Suitable for ESD Protection Applications of GaN/InGaN-based Devices and ICs Validated by Simulation Results

A novel high voltage InGaN/GaN/AlGaN RTD was proposed for ESD protection of GaN/InGaN-based devices and ICs. The proposed RTD consists of a sandwiched $\text{In}_{0.21}\text{Ga}_{0.79}\mathrm{N}/\text{GaN}/\text{In}_{0.14}\text{Ga}_{0.86}\mathrm{N}/\mathrm{A}1_{0.1}\text{Ga}_{0.9}\mathrm{N}$ DBS structure on a GaN/InGaN substrate with N-original surface. Simulation experiments indicated that the proposed RTD samples are characterized of high forward block voltages at about 7.38 V, low leakage forward currents less than $10^{-38}\mathrm{A}/\mu \mathrm{m}^{2}$ and $10^{-39}\mathrm{A}/\mu \mathrm{m}^{2}$ and high reverse current densities up to the order of $10^{-5}\mathrm{A}/\mu \mathrm{m}^{2}$ at about −2.55 V bias voltage and beyond $10^{-4}\mathrm{A}/\mu \mathrm{m}^{2}$ at about −2.8 V bias voltage respectively. Analysis on HBM mode ESD circuits indicated that they can be simplified into ideal one order RC loop. This is because that the resistance of the proposed high voltage (HV) RTD pair at on-state is ignorable relative to discharging resistance R1 whether considering its parasitic capacitance or not. As a result, the sample 1 with junction area of $120\times 120\mu \mathrm{m}^{2}$ is large enough to protect the chip from ESD damaging up to $\pm 2000$ V ESV swash in ideal 840 ns. As for sample 2, its junction area of $380\times 380\mu \mathrm{m}^{2}$ is capable of providing the same strength of ESD protection as that of sample 1. As the parasitic capacitance of sample 2 was considered through analyses of charge variations in emitter region, quantum well region and collector region with bias voltage, the results indicate that the big signal differential parasitic floating capacitance of quantum region is about -0.178/-0.174 pF/cm2, which is negligible comparing with stand ESV capacitance of HBM mode ESD protection applications. Its function is to shielding the influence of applied voltage on the potential of the quantum well. Its attenuation factor of voltage is not more than −23.6 dB.
Reinforcing IoT-Enforced Security Policies

To provide confidentiality protection and access control to large amount of data generated by the Internet of Things (IoT), one promising approach lies on the use of Attribute-Based Encryption (ABE). However, the ABE-based approach needs to cope with the resource constraints of IoT devices, by providing data encryption and access control only under small access policies. With the goal to mitigate this limitation inherent to the IoT, this paper introduces a new technique that relies on semi-trusted intermediate entities to reinforce large access policies derived from small policies enforced by IoT devices without revealing the encrypted data.
Compressive Sampling and Feature Ranking Framework for Bearing Fault Classification With Vibration Signals

Failures of rolling element bearings are amongst the main causes of machines breakdowns. To prevent such breakdowns, bearing health monitoring is performed by collecting data from rotating machines, extracting features from the collected data, and applying a classifier to classify faults. To avoid the burden of much storage requirements and processing time of a tremendously large amount of vibration data, the present paper proposes a combined compressive sampling (CS)-based on multiple measurement vector (MMV) and feature ranking framework to learn optimally fewer features from a large amount of vibration data from which bearing health conditions can be classified. The CS-based on the MMV model is the first step in this framework and provides compressively sampled signals based on compressed sampling rates. In the second step, the search for the most important features of these compressively sampled signals is performed using the feature ranking and selection techniques. For that purpose, we have investigated the following: 1) two compressible representations of vibration signals that can be used within CS framework, namely, fast Fourier transform-based coefficients and thresholded Wavelet transform-based coefficients and 2) several feature ranking and selection techniques, namely, three similarity-based techniques, fisher score, Laplacian score, Relief-F; one correlation-based technique, Pearson correlation coefficients; and one independence test technique, Chi-Square (Chi-2) to select fewer features that can sufficiently represent the original vibration signals. These selected features, in combination with three of the popular classifiers–multinomial logistic regression classifier, artificial neural networks, and support vector machines, have been evaluated for the classification of bearing faults. Results show that the proposed framework achieves high classification accuracies with a limited amount of data using various combinations of methods, which outperform recently published results.
Aphid Colonization Affects Potato Root Exudate Composition and the Hatching of a Soil Borne Pathogen

Plants suffer multiple, simultaneous biotic threats from both above and below ground. These pests and/or pathogens are commonly studied on an individual basis and the effects of above-ground pests on below-ground pathogens are poorly defined. Root exudates from potato plants (Solanum tuberosum L.) were analyzed to characterize the top-down plant-mediated interactions between a phloem-sucking herbivore (Myzus persicae) and a sedentary, endoparasitic nematode (Globodera pallida). Increasing inocula of the aphid, M. persicae, reduced the root mass of potato plants. Exudates collected from these roots induced significantly lower hatching of second-stage juveniles from G. pallida eggs over a 28-day period, than those from uninfested control plants. Inhibition of hatch was significantly positively correlated with size of aphid inoculum. Diminished hatching was partially recovered after treatment with root exudate from uninfested potato plants indicating that the effect on hatching is reversible but cannot be fully recovered. Glucose and fructose content was reduced in root exudates from aphid-infested potato plants compared to controls and these sugars were found to induce hatching of G. pallida, but not to the same degree as potato root exudates (PRE). Supplementing aphid-infested PRE with sugars did not recover the hatching potential of the treatment, suggesting that additional compounds play an important role in egg hatch. The first gene upregulated in the closely related potato cyst nematode Globodera rostochiensis post-exposure to host root exudate, Neprilysin-1, was confirmed to be upregulated in G. pallida cysts after exposure to PRE and was also upregulated by the sugar treatments. Significantly reduced upregulation of Gpa-nep-1 was observed in cysts treated with root exudates from potato plants infested with greater numbers of aphids. Our data suggest that aphid infestation of potato plants affects the composition of root exudates, with consequential effects on the hatching and gene expression of G. pallida eggs. This work shows that an above-ground pest can indirectly impact the rhizosphere and reveals secondary effects for control of an economically important below-ground pathogen.
Task Assignment Algorithms for Multicore Platforms with Process Variations

We have moved into an era where modern multicore systems must deal with several critical challenges such as increased power density and high temperatures. In addition to this, ITRS projects that heterogeneity due to manufacturing process variations would continue to rapidly increase in sub-nanometer technology nodes. Spatial heat influence or the lateral heat transfer is becoming more prevalent in manycore systems and has a significant impact on the processor power consumption. Thus it is becoming increasingly difficult to meet the application throughput requirements while adhering to the system power budget and thermal constraints. Many of the existing task mapping schemes that does not take into account the underlying variations in core frequency and leakage power consumption will result in sub-optimal solutions. In this work, we first formulate the mapping problem as an optimization problem and then present a rank-based, process variation and lateral heat transfer aware application mapping algorithm to solve it heuristically. Our extensive experimental results reveal that the proposed algorithm produces near optimal results (average 6% away from optimal) while being orders of magnitude faster (4000x faster for 225 cores). Keywords– lateral heat conduction, task mapping, process variations, technology scaling, leakage power
On the Optimal Synthesis of Sum or Difference Patterns of Centrosymmetric Arrays Under Arbitrary SideLobe Constraints

This paper deals with the optimal synthesis of sum or difference patterns radiated by an array of fixed geometry. The aim is to establish the most general class of arrays for which such synthesis can be reduced to a linear programming problem, extending and completing previous results regarding uniformly spaced (linear and planar) arrays. It is shown that such a class consists of the centrosymmetric arrays, whose element patterns are either equal or Hermitian symmetric with respect to the center of symmetry. The optimal solutions are also Hermitian symmetric and the corresponding patterns are real. Furthermore, it is shown that the above class of arrays coincides with that of the arrays capable of radiating real patterns. Some numerical examples are provided to give an idea of the geometries and the synthesis problems to which the achieved result successfully applies and of the achievable computational advantage.
Introduction of Blockchain to Mitigate The Trusted Third Party Auditing for Cloud Security: An Overview

Cloud computing has become a phenomenal core technology to scale the business with less overhead. With this new growth, the data of the owners have been in the control of service provider and this has caused the concern of data integrity. The service provider although promises the user of data integrity, there is no validation of the promise. For this purpose the user can opt for Trusted Third Party Auditor (TTPA) to conduct an audit to verify the Data integrity. The TTPA provides the data integrity report; there is a strong connection mapping with regard to the hiring of TTPA and their trust parameter. In this paper we have proposed the introduction of Blockchain technology to eliminate the role of TTPA and thereby minimizing the audit overhead to user and the service provider. With the introduction of Blockchain databases, we can achieve data integrity efficiently.
Effect of a palliative care program on trends in intensive care unit utilization and do-not-resuscitate orders during terminal hospitalizations. An interrupted time series analysis

Objective To assess the effect of the implementation of a palliative care program on do-not-resuscitate orders and intensive care unit utilization during terminal hospitalizations. Methods Data were retrospectively collected for all patients who died in a tertiary hospital in Brazil from May 2014 to September 2016. We analyzed the frequency of do-not-resuscitate orders and intensive care unit admissions among in-hospital deaths. Interrupted time series analyses were used to evaluate differences in trends of do-not-resuscitate orders and intensive care unit admissions before (17 months) and after (12 months) the implementation of a palliative care program. Results We analyzed 48,372 hospital admissions and 1,071 in-hospital deaths. Deaths were preceded by do-not-resuscitate orders in 276 (25.8%) cases and admissions to the intensive care unit occurred in 814 (76%) cases. Do-not-resuscitate orders increased from 125 (20.4%) to 151 (33%) cases in the pre-implementation and post-implementation periods, respectively (p < 0.001). Intensive care unit admissions occurred in 469 (76.5%) and 345 (75.3%) cases in the pre-implementation and post-implementation periods, respectively (p = 0.654). Interrupted time series analyses confirmed a trend of increased do-not-resuscitate order registrations, from an increase of 0.5% per month pre-implementation to an increase of 2.9% per month post-implementation (p < 0.001), and demonstrated a trend of decreased intensive care unit utilization, from an increase of 0.6% per month pre-implementation to a decrease of -0.9% per month in the post-implementation period (p = 0.001). Conclusion The implementation of a palliative care program was associated with a trend of increased registration of do-not-resuscitate orders and a trend of decreased intensive care unit utilization during terminal hospitalizations.