Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between intraventricular mechanical dyssynchrony and left ventricular systolic and diastolic performance: An in vivo experimental study

Left ventricular mechanical dyssynchrony (LVMD) refers to the nonuniformity in mechanical contraction and relaxation timing in different ventricular segments. We aimed to determine the relationship between LVMD and LV performance, as assessed by ventriculo‐arterial coupling (VAC), LV mechanical efficiency (LVeff), left ventricular ejection fraction (LVEF), and diastolic function during sequential experimental changes in loading and contractile conditions. Thirteen Yorkshire pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/reinfusion and fluid bolus), and contractility (esmolol/dobutamine). LV pressure–volume data were obtained with a conductance catheter. Segmental mechanical dyssynchrony was assessed by global, systolic, and diastolic dyssynchrony (DYS) and internal flow fraction (IFF). Late systolic LVMD was related to an impaired VAC, LVeff, and LVEF, whereas diastolic LVMD was associated with delayed LV relaxation (logistic tau), decreased LV peak filling rate, and increased atrial contribution to LV filling. The hemodynamic factors related to LVMD were contractility, afterload, and heart rate. However, the relationship between these factors differed throughout the cardiac cycle. LVMD plays a significant role in LV systolic and diastolic performance and is associated with hemodynamic factors and intraventricular conduction.
Intra‐ischemic hypothermia cardioprotection involves modulation of PTEN/Akt/ERK signaling and fatty acid oxidation

Therapeutic hypothermia (TH) provides cardioprotection from ischemia/reperfusion (I/R) injury. However, it remains unknown how TH regulates metabolic recovery. We tested the hypothesis that TH modulates PTEN, Akt, and ERK1/2, and improves metabolic recovery through mitigation of fatty acid oxidation and taurine release. Left ventricular function was monitored continuously in isolated rat hearts subjected to 20 min of global, no‐flow ischemia. Moderate cooling (30°C) was applied at the start of ischemia and hearts were rewarmed after 10 min of reperfusion. The effect of TH on protein phosphorylation and expression at 0 and 30 min of reperfusion was investigated by western blot analysis. Post‐ischemic cardiac metabolism was investigated by 13C‐NMR. TH enhanced recovery of cardiac function, reduced taurine release, and enhanced PTEN phosphorylation and expression. Phosphorylation of Akt and ERK1/2 was increased at the end of ischemia but decreased at the end of reperfusion. On NMR analysis, TH‐treated hearts displayed decreased fatty acid oxidation. Direct cardioprotection by moderate intra‐ischemic TH is associated with decreased fatty acid oxidation, reduced taurine release, enhanced PTEN phosphorylation and expression, and enhanced activation of both Akt and ERK1/2 prior to reperfusion.
Multiple genetic lineages of anadromous migratory Mekong catfish Pangasius krempfi revealed by mtDNA control region and cytochrome b

Abstract Population genetic structure of migratory fishes can reflect ecological and evolutionary processes. Pangasius krempfi is a critically important anadromous catfish in the Mekong River, and its migration pathways and genetic structure have attracted much interest. To investigate, we quantified the genetic diversity of this species using the control region (D‐loop) and Cytochrome b (Cytb) of the mitochondrial genome. Fish were sampled (n = 91) along the Mekong tributaries from upstream to estuaries and coastal areas in the Mekong Delta and compared to three samples from Pakse (Laos). The D‐loop haplotype (0.941 ± 0.014) and nucleotide diversity (0.0083 ± 0.0005) were high in all populations, but that of Cytb was low (0.331 ± 0.059 and 0.00063 ± 0.00011, respectively). No genetic difference was detected between populations, indicating strong gene flow and confirming a long migration distance for this species. Pangasius krempfi was not genetically structured according to geographical populations but was delineated into three haplogroups, suggesting multiple genetic lineages. The presence of haplogroups in each sampling location implies that migration downstream is random but parallel when the fish enter two river tributaries bifurcating from the main Mekong River. Individuals can also migrate along the coast, far from the estuaries, suggesting a longer migration path than previously reported, which is crucial for maintaining diverse genetic origin and migration pathways for P. krempfi.
Theory of single scattering by random distributed scatterers

We show that the Fraunhofer diffraction approximation can never be used in the theory of scattering by distributed scatterer (such as turbulence) in the far zone of antenna. We consider the single scattering of waves in a medium containing weak refractive index inhomogeneities using a Fresnel diffraction approximation. We do not introduce scattering volume but consider a transmitting antenna with a Gaussian distribution of current and a receiving antenna with a Gaussian distribution of attenuation across the aperture. The results obtained are valid both in the near and far zones of the antenna. The known contradiction between the applicability of the Fraunhofer diffraction approach and the size of the scattering volume formed by intersection of directivity diagrams is resolved. Similar to the far zone approach, the scattering intensity obtained is proportional to the Bragg component of the inhomogeneities that are averaged in Fourier space using some specific weighting function, depending on antenna size and pulse duration. The concept of a scattering cross section cannot be used for this problem and another more flexible normalization is suggested.
Thermal Analysis of a Blade-Twist Actuator System

Using a lumped-mass heat transfer model, predictions are made of the performance of a blade-twist actuator system that employs thermoelectric modules as both a heat source and a heat pump to effect changes in aerodynamic surfaces via shape memory alloy triggered by changes in temperature. Basic cases are presented and the ambient limitations of such a system are predicted using the model. Furthermore, means of optimizing the performance of the blade twist are investigated, as well as characterizations of how the system would perform under various startup conditions. Additional recommendations for further optimizing the system for performance under a wide variety of ambient and operating conditions are made based on simulation results.
Radiation distribution sensing with normal optical fiber

The purpose of this study is to develop a radiation distribution monitor using a normal plastic optical fiber. The monitor has a long operating length and can obtain continuous radiation distributions. A principle of;the position sensing is based on a time-of-flight technique. The monitor is sensitive to beta rays or charged particles, gamma rays, and fast neutrons. The spatial resolutions for beta rays (/sup 90/Sr-/sup 90/Y), gamma rays (/sup 137/Cs) and D-T neutrons are 30 cm, 37 cm and 13 cm, respectively. The detection efficiencies for the beta rays, the gamma rays and D-T neutrons are 0.11%, 1.6/spl times/10/sup -5/% and 1.2/spl times/10/sup -4/%, respectively. The effective attenuation length of the detection efficiency is 18 m. In this paper, we describe the basic characteristics of this monitor.
Stress-Strength Reliability of a Two-Parameter Bathtub-shaped Lifetime Distribution Based on Progressively Censored Samples

Based on progressively Type-II censored samples, this article deals with inference for the stress-strength reliability R = P(Y < X) when X and Y are two independent two-parameter bathtub-shape lifetime distributions with different scale parameters, but having the same shape parameter. Different methods for estimating the reliability are applied. The maximum likelihood estimate of R is derived. Also, its asymptotic distribution is used to construct an asymptotic confidence interval for R. Assuming that the shape parameter is known, the maximum likelihood estimator of R is obtained. Based on the exact distribution of the maximum likelihood estimator of R an exact confidence interval of that has been obtained. The uniformly minimum variance unbiased estimator are calculated for R. Bayes estimate of R and the associated credible interval are also got under the assumption of independent gamma priors. Monte Carlo simulations are performed to compare the performances of the proposed estimators. One data analysis has been performed for illustrative purpose. Finally, we will generalize this distribution to the proportional hazard family with two parameters and derive various estimators in this family.
Fluid-Structure Interaction via an Adaptive Finite Element Immersed Boundary Method

The immersed boundary (IB) method is a mathematical formulation for fluid-structure interaction problems, where immersed incompressible visco-elastic bodies or boundaries interact with an incompressible fluid. The original numerical scheme associated to the IB method requires a smoothed approximation of the Dirac delta distribution to link the moving Lagrangian domain with the fixed Eulerian one. We present an adaptive version of the finite element immersed boundary method, where the Dirac delta distribution is treated in a variational way, eliminating the need to approximate it. [ DOI : 10.1685 / CSC06022] About DOI
Agamermis sp. (Nematoda: Mermithidae) parasitizing Armadillidium vulgare (Crustacea: Isopoda) in Argentina

Abstract The Mermithidae is a family of nematodes parasitic in many kinds of insects, spiders, leeches, crustaceans and other invertebrates throughout the world. While conducting an assay with entomopathogenic nematodes, we found Armadillidium vulgare (Crustacea: Isopoda) individuals to be infected with Agamermis sp. , marking the fourth known discovery of a mermithid infection in the order Isopoda. In this work, we contribute with an 18S rDNA sequence of the isolated nematode and the morphological and morphometrical characterization of the juveniles.
Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.
Detection of Faults in Microservices using Petri Nets

Despite higher adoption of Microservices worldwide, there is a lack of work on possible fault detection during early phases such as at requirements and design phase. We propose a novel way to detect faults using model checking CTL specifications and a SMT solver. Requirements of a system involving microservices are mapped to Netflix’s Conductor specification which is later converted into Petri nets for further analysis. Traversal analysis on the converted Petri nets not only allows us to detect a faulty specification but to correct it and adopt a better design. We tried our methodology on the TrainTicket benchmark system and we are able to detect seven faults from the benchmark application using our approach.
Detect Insider Attacks in Industrial Cyber-Physical Systems Using Multi-Physical Features Based Fingerprinting

ICPS software and hardware suffer from low update frequency, making it easier for insiders to bypass external defenses and launch concealed destructive attacks. To address these concerns, we design a device fingerprinting method based on multi-physical features, augmenting current intrusion detection techniques in the ICPS environment. In this paper, we use the sorting system as an example, demonstrating that the proposed device fingerprinting technology has generality in the intrusion detection of ICPS control flow. Specifically, we first formalize the physical model of the sorting system to analyze the critical device features. Then we extract these physical features from the sensor data collected in a physical testbed. Utilizing featurized data, we train a classifier that generates fingerprints in real-time in the production environment. Moreover, we develop a differential detection model based on device fingerprints to discover stealthy insider attacks efficiently. We evaluate the proposed method in a real-world testbed. Experiment results show that the detecting performance of classifiers approaches 100% when the the number of component types is small.
Charge transport in semiconductors with degeneracy effects

It has been a common procedure to derive a model for charge transport in degenerate semiconductor material by incorporating a Fermi–Dirac distribution into the classical drift–diffusion model. In this work a Boltzmann equation with a non-linear collision term is considered. A new fluid dynamical model is derived by considering small perturbations of the thermal equilibrium. The analysis contains an existence and uniqueness proof for the Boltzmann equation, a justification of the perturbation argument and a study of initial boundary value problems for the new fluid dynamical model.
MCSH, a Lock with the Standard Interface

The MCS lock of Mellor-Crummey and Scott (1991), 23 pages. is a very efficient first-come first-served mutual-exclusion algorithm that uses the atomic hardware primitives fetch-and-store and compare-and-swap. However, it has the disadvantage that the calling thread must provide a pointer to an allocated record. This additional parameter violates the standard locking interface, which has only the lock as a parameter. Hence, it is impossible to switch to MCS without editing and recompiling an application that uses locks. This article provides a variation of MCS with the standard interface, which remains FCFS, called MCSH. One key ingredient is to stack allocate the necessary record in the acquire procedure of the lock, so its life-time only spans the delay to enter a critical section. A second key ingredient is communicating the allocated record between the acquire and release procedures through the lock to maintain the standard locking interface. Both of these practices are known to practitioners, but our solution combines them in a unique way. Furthermore, when these practices are used in prior papers, their correctness is often argued informally. The correctness of MCSH is verified rigorously with the proof assistant PVS, and experiments are run to compare its performance with MCS and similar locks.
A decision surface-based taxonomy of detection statistics

Current and past literature on the topic of detection statistics - in particular those used in hyperspectral target detection - can be intimidating for newcomers, especially given the huge number of detection tests described in the literature. Detection tests for hyperspectral measurements, such as those generated by dispersive or Fourier transform spectrometers used in remote sensing of atmospheric contaminants, are of paramount importance if any level of analysis automation is to be achieved. The detection statistics used in hyperspectral target detection are generally borrowed and adapted from other fields such as radar signal processing or acoustics. Consequently, although remarkable efforts have been made to clarify and categorize the vast number of available detection tests, understanding their differences, similarities, limits and other intricacies is still an exacting journey. Reasons for this state of affairs include heterogeneous nomenclature and mathematical notation, probably due to the multiple origins of hyperspectral target detection formalisms. Attempts at sorting out detection statistics using ambiguously defined properties may also cause more harm than good. Ultimately, a detection statistic is entirely characterized by its decision boundary. Thus, we propose to catalogue detection statistics according to the shape of their decision surfaces, which greatly simplifies this taxonomy exercise. We make a distinction between the topology resulting from the mathematical formulation of the statistic and mere parameters that adjust the boundary's precise shape, position and orientation. Using this simple approach, similarities between various common detection statistics are found, limit cases are reduced to simpler statistics, and a general understanding of the available detection tests and their properties becomes much easier to achieve.
Technical review of supervised machine learning studies and potential implementation to identify herbal plant dataset

Abstract The use of technology in everyday life is unavoidable, considering that technological advancement occurs very quickly. The current era is also known as industry 4.0. In the industry 4.0 era, there is a convergence between the industrial world and information technology. The use of modern machines in the industry makes it possible for business actors to digitize their production facilities and open up new business opportunities. One of the developments in information technology that is being widely used in its implementation is machine learning (ML) technology and its branches such as computer vision and image recognition. In this work, we propose a customized convolutional neural network-based ML model to perform image classification technique for Indonesian herb image dataset, along with the detailed review and discussion of the methods and results. In this work, we use the transfer learning method to adopt the opensource pre-trained model, namely, Xception, developed by Google.
Mining the evolution of networks using Local-Cross-Communities-Paradigm

Mining how network evolves is a crucial topic in extracting underlying information from networks. Among all the mechanisms, Common Neighbor (CN) and Preferential Attachment (PA) are basic and efficient. Recently, a new framework named Local-Community-Paradigm (LCP) provides a self-organized mechanism about the evolution of networks. Via using community mechanism instead, we propose a variant named Local-Cross-Communities-Paradigm (LCCP). We compare the four mechanisms and test them on link prediction problems. Empirical analysis on twelve real networks show that LCCP performs better than CN, PA and LCP.
Electromagnetic fields in the presence of an infinite dielectric wedge

Electromagnetic fields excited by a line source in the presence of an infinite dielectric wedge with refractive index N are determined by application of the Kontorovich–Lebedev transform. Singular integral equations for spectral functions are solved by perturbation procedure, and the solution is obtained in the form of a Neumann series in powers of . The devised numerical scheme permits evaluation of the higher-order terms and, thus, extends the perturbation solution to values of N not necessarily close to unity. Asymptotic approximations for the near and far fields inside and outside the dielectric wedge are derived. The combination of the Neumann-type expansion of the transform functions with the representation of the field as a Bessel function series extends solutions derived with the Kontorovich–Lebedev method to the case of real-valued wavenumbers and arbitrarily positioned source and observer. Numerical results showing the influence of wedges with various values of dielectric and magnetic constants on the directivity of a line source are presented and verified through finite-difference frequency-domain simulations.
Averaging and control of nonlinear systems

This dissertation investigates three principal areas regarding the dynamics and control of nonlinear systems: averaging theory, controllability of mechanical systems, and control of underactuated nonlinear systems. The most effective stabilizing controllers for underactuated nonlinear systems are time-periodic, which leads to the study of averaging theory for understanding the nonlinear effect generated by resonant oscillatory inputs. The research on averaging theory generalizes averaging theory to arbitrary order by synthesizing series expansion methods for nonlinear time-varying vector fields and their flows with nonlinear Floquet theory. It is shown that classical averaging theory is the application of perturbation methods in conjunction with nonlinear Floquet theory. Many known properties and consequences of averaging theory are placed within a single framework. The generalized averaging theory is merged with controllability analysis of underactuated nonlinear systems to derive exponentially stabilizing controllers. Although small-time local controllability (STLC) is easily demonstrated for driftless systems via the Lie algebra rank condition, STLC for systems with drift is more complicated. Furthermore, there exists a variety of techniques and canonical forms for determining STLC. This thesis exploits notions of geometric homogeneity to show that STLC results for a large class of mechanical systems with drift can be recovered by considering a class of nonlinear dynamical systems satisfying certain homogeneity conditions. These theorems generalize the controllability results for simple mechanical control systems found in Lewis and Murray [85]. Most nonlinear controllability results for classes of mechanical systems may be obtained using these methods. The stabilizing controllers derived using the generalized averaging theory and STLC analysis can be used to stabilize both systems with and without drift. Furthermore, they result in a set of tunable gains and oscillatory parameters for modification and improvement of the feedback strategy. The procedure can not only derive known controllers from the literature, but can also be used to improve them. Examples demonstrate the diversity of controllers constructed using the generalized averaging theory. This dissertation concludes with a chapter devoted to biomimetic and biomechanical locomotive control systems that have been stabilized using the generalized averaging theory and the controller construction procedure. The locomotive control systems roll, wriggle, swim, and walk, demonstrating the universal nature of the control strategy proposed.
Atomic functions and wavelet matrix transform approach for efficient solution of electromagnetic integral equations in the boundary value problems of millimeter wave diffraction

Using Atomic Functions (AF) and wavelet matrix transform produces highly sparse matrices, which can be solved efficiently. It has been found that using wavelet matrix transform, based on atomic function, one can obtain higher compression rate and better accuracy of approximate solutions than using spline wavelet transform and Daubechies' orthonormal wavelet transform. A numerical example has been developed for the boundary value problems of millimeter wave diffraction on a perfectly conducting circular cylinder to show the validity and effectiveness of the method.