Dobutamine-atropine stress echocardiography for risk stratification in patients with chronic left ventricular dysfunction

OBJECTIVE: To assess the prognostic value of sustained improvement, scar and inducible ischemia with or without viability in patients with chronic left ventricular dysfunction (LVD). BACKGROUND: Dobutamine-atropine stress echocardiography (DASE) accurately detects scar, reversible dysfunction and the extent of coronary artery disease in LVD. METHODS: Three hundred fifty consecutive patients (age 62+/-13 years, mean+/-SD, 215 men/135 women) with moderate to severe LVD (LVEF < 40%, mean 30+/-8%) underwent DASE and were followed for > or =18 months. Dobutamine-atropine stress echocardiographic findings were classified according to sustained improvement in all vascular territories, scar, inducible ischemia (worsening wall motion at peak dose only or biphasic responses) and their extent. RESULTS: Sustained improvement occurred in 83 patients (24%), scar alone in 99 (28%) and inducible ischemia in 168 (48%, with biphasic responses in 104). Ischemia was induced in all vascular territories in 26 patients. Patients with sustained improvement or scar alone were treated medically, whereas 46% (78/168) with inducible ischemia were revascularized (coronary bypass surgery, n = 67 or angioplasty, n = 11). There were 76 hard events including cardiac death in 59, nonfatal myocardial infarction in 11, and resuscitated sudden death in 6. Hard events were rare in sustained improvement (5%, 4/83), uncommon in scar (13%, 13/99) and common (p < 0.01) in medically treated patients with inducible ischemia (59%, 53/90). Cardiac deaths were especially common (p < 0.01) in patients with biphasic responses (55%, 28/51). Inducible ischemia independently predicted hard events (chi2 = 75.35, p < 0.001) along with reduced LVEF at peak dose (chi2 = 8.38, p = 0.004). Hard cardiac events were uncommon (8%, 6/78, p < 0.001) in patients with inducible ischemia who underwent early revascularization. CONCLUSIONS: Inducible ischemia during DASE was the major determinant of outcome in LVD and independent of clinical data and left ventricular function. Improved wall thickening alone and scar alone predicted good outcome. Survival of patients with inducible ischemia was better after revascularization.     

Thermal relaxation of residual stress in laser shock peened Ti–6Al–4V alloy

Laser shock peening (LSP) induced residual stresses in Ti–6Al–4V, and their thermal relaxation due to short-term exposure at elevated temperatures are investigated by an integrated modeling/simulation and experimental approach. A rate and temperature-dependent plasticity model in the form of Johnson–Cook (JC) has been employed to represent the nonlinear constitutive behavior under both LSP and thermal loads. By comparing the simulation results with experimental data, model parameters for Ti–6Al–4V are first calibrated and subsequently applied in analyzing the thermal stability of the residual stress in LSP-treated Ti–6Al–4V. The analysis shows that the magnitude of stress relaxation increases with the increase of applied temperature due to material softening. Most of stress relaxation occurs before 10 min to 20 min exposure in this study, and stress distribution becomes more uniform after thermal exposure. An analytical model based on the Zener–Wert–Avrami formula is then developed based on the simulation results. The activation enthalpy of the relaxation process for laser shock peened Ti–6Al–4V is determined to be in the range of 0.71 eV to 1.37 eV.     

Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which can affect the robustness of the model in the constraint environment. As a result, this paper proposes a sewer condition monitoring framework based on deep learning, which can effectively detect and evaluate defects in sewer pipelines with high accuracy. We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline. This study modified the original RegNet model by modifying the squeeze excitation (SE) block and adding the dropout layer and Leaky Rectified Linear Units (LeakyReLU) activation function in the Block structure of RegNet model. This study explored different deep learning methods such as RegNet, ResNet50, very deep convolutional networks (VGG), and GoogleNet to train on the sewer defect dataset. The experimental results indicate that the proposed system framework based on the modified-RegNet (RegNet+) model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models. The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.     

The Influence of Ti Doping at the Mn Site on Magnetoresistance and Thermopower Properties of Sm0.6Sr0.4MnO3

Journal of Superconductivity and Novel Magnetism - The influence of non-magnetic Ti doping at the Mn site on magnetoresistance and thermopower properties of Sm0.6Sr0.4MnO3 (Sm0.6Sr0.4Mn1-xTixO3,...     

A Vicenary Analysis of SARS-CoV-2 Genomes

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their chemical composition. Purines include adenine (A) and guanine (G), while pyrimidines include cytosine (C) and tyrosine (T). There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals (including neutralizing antibodies) and epitomes necessary for vaccine development. This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including; Shannon entropy, Hurst exponent, and the nucleotide guanine-cytosine content. The Shannon entropy is used to identify closely associated sequences. Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs. Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base. The GC-content is used to understand the stability of the DNA. The relevant genome sequences were extracted from the National Center for Biotechnology Information (NCBI) virus database. Furthermore, the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements

Spatial interpolation is one of the demanding techniques in Geographic Information Science (GISci) to generate interpolated maps in a continuous manner by using two discrete spatial and/or temporal data sets. Noise-free data (thematic layers) depicting a specific theme at varied spatial or temporal resolutions consist of connected components either in aggregated or in disaggregated forms. This short paper provides a simple framework: 1) to categorize the connected components of layered sets of two different time instants through their spatial relationships and the Hausdorff distances between the companion-connected components and 2) to generate sequential maps (interpolations) between the discrete thematic maps. Development of the median set, using Hausdorff erosion and dilation distances to interpolate between temporal frames, is demonstrated on lake geometries mapped at two different times and also on the bubonic plague epidemic spread data available for 11 consecutive years. We documented the significantly fair quality of the median sets generated for epidemic data between alternative years by visually comparing the interpolated maps with actual maps. They can be used to visualize (animate) the spatiotemporal behavior of a specific theme in a continuous sequence.     

An inexpensive unstructured platform for wireless mobile peer-to-peer networks

In this paper, we propose an unstructured platform, namely I nexpensive P eer-to- P eer S ubsystem (IPPS), for wireless mobile peer-to-peer networks. The platform addresses the constraints of expensive bandwidth of wireless medium, and limited memory and computing power of mobile devices. It uses a computationally-, memory requirement- and communication- wise inexpensive gossip protocol as the main maintenance operation, and exploits location information of the wireless nodes to minimize the number of link-level messages for communication between peers. As a result, the platform is not only lightweight by itself, but also provides a low cost framework for different peer-to-peer applications. In addition, further enhancements are introduced to enrich the platform with robustness and tolerance to failures without incurring any additional computational and memory complexity, and communication between peers. In specific, we propose schemes for a peer (1) to chose a partner for a gossip iteration, (2) to maintain the neighbors, and (3) to leave the peer-to-peer network. Simulation results are given to demonstrate the performance of the platform.

Cascaded neural network based small array synthesis with robustness to noise

A cascaded neural network approach has been presented in this paper to estimate the excitation for the desired field distribution using a radial basis function neural network (RBFNN). The article has employed an electromagnetic design example consisting of 5 × 5 and 6 × 6 planar antenna array of isotropic sources with inter element‐distance of 0.5λ to show the adaptation of the neural network model in estimating the desired output. A neural network is trained using a dataset of suitable excitation voltages and its corresponding radiation patterns, which proves to be efficient in predicting the excitation voltages required to generate the desired pattern. A set of techniques based on a cascaded neural network is adopted for pattern synthesis using magnitude and phase, magnitude only, and template‐based input data. The robustness of the method has also been tested by considering noise with different SNR levels. The results found in each case have a close fit with the desired pattern.     

Open die forging of a four sided irregular disc

The paper gives an analysis of open die forging of any irregular four sided disc. Experiments indicate a similar trend. Upper bound technique is used in this analysis with the assumption of constant frictional stress on top and bottom faces. The solution takes into account the bulging of sides during compression as it is experienced in practical forging.