Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow estimation using subharmonics of microbubbles

It has been observed that the subharmonic signal of the microbubbles has a significant contrast-to-tissue ratio. The flow velocity can be evaluated precisely even without wall filter for subharmonics since the vessels containing the microbubbles would be distinguishable from the surrounding tissues. However, the microbubble cannot generate the subharmonics unless the applied pressure exceeds the required onset threshold. Moreover, it will undergo cavitation if the acoustic pressure is over the cavitation threshold. While the microbubble moves through the sample volume, there are three different regions inside the excitation beam. The beam-weighted pressure is below the onset threshold, between the onset and cavitation thresholds, and over the cavitation threshold. Since the subharmonics can occur only when the beam-weighted pressure is between the onset and cavitation thresholds, the observation time for subharmonics is shorter than that for the fundamental. Our numerical results showed that the onset threshold is very close to the cavitation threshold. Moreover, some microbubbles are found to generate subharmonics and undergo cavitation simultaneously. Our mathematical analysis showed that the consecutive received signals would have very low correlation for the subharmonics. The experimental data from the Levovist/spl reg/ suspension were used to verify the theoretical predictions. It can be shown that the absolute value of the normalized autocorrelation (first lag) of Doppler signals for the subharmonics was much smaller than the fundamental and the second harmonics when the emitted frequency is 2.1 MHz and the acoustic pressure is 0.8 Mpa. In addition, it is found that the correlation value is always below 0.3 when the emitted frequency is swept from 1.5 to 2.5 MHz and the applied pressure is varied from 0.1 to 1.6 Mpa.
Persistence Augmented Graph Convolution Network for Information Popularity Prediction

In recent years, information popularity prediction of online social media plays a vital role in crisis early warning and malicious content propagation identification within public opinion management application scenarios. Existing work lacks effective mechanisms for interactive topology feature extraction among multiple correlated cascades, which contributes the propagation scale prediction especially at the early propagation stage. In this paper, we propose a Persistence augmented Graph Convolution Network framework (PT-GCN) to make popularity prediction defined as retweet number of information propagation. Persistence as the topological data analysis approach is utilized to measure and find out the salient topology structure feature. Based on it, we propose to use multi-dimensional cascade graphs to model the correlated cascades and then execute PT-GCN to form the interactive propagation features from correlated nodes in propagation and correlated cascades with persistence & contents. The performance evaluations are based on three datasets from Weibo and Twitter platform. By comparisons with the other related work, PT-GCN achieves much better efficiency in terms of MSLE and precision, which performs well for early stage propagation.
Robust control of a trilayer conducting polymer actuator

Performance of the conducting polymer actuators (CPAs) are affected by material uncertainties, operating conditions and time of operation. The same size CPAs may have different actuation capabilities, which can also degrade over the course of operation. For accurate and repeatable position tracking, the uncertainties and variations in the actuator dynamics have to be carefully addressed to achieve a desirable control performance. This paper presents a systematic approach for the identification of parametric uncertainties and designing robust H∞ control to achieve a guaranteed performance when the CPA is used for position tracking. We identify the uncertainties in actuator dynamics by performing series of experiments using two geometrically equivalent CPAs. A set of system models is obtained to determine the average actuation capability. The variations in the actuator dynamics are modeled as a parametric uncertainty. H∞ controllers are designed and the robustness of the controllers is validated by experiments on two different but same sized CPAs. The performance of the H∞ controller is also compared with a proportional-integralderivative (PID) controller. We demonstrate that the robust H∞control strategy performs repeated acceptable performances on both samples.
Coding Techniques for Backscatter Communications—A Contemporary Survey

Backscatter communication (BackCom) networks enable passive/battery-free Internet-of-Thing devices, providing reliable, massive connectivity while ensuring self-sustainability, low maintenance, and low costs. Effective channel codes and decoding algorithms are necessary to achieve these objectives. However, a comprehensive survey/review paper on such techniques for BackCom networks has not been available. This paper aims to fill this gap. Because tags have limited computational resources, traditional coding techniques may not suit them. We first describe the basics of BackCom, channel codes and their relevant design parameters, and codes for general communication networks. We address the BackCom limitations, requirements, and channel characteristics. As conventional codes may not seamlessly move to the BackCom arena, we identify the potential BackCom coding techniques and multiple access schemes. We further highlight potential approaches for addressing code implementation complexity and reliability. Finally, we discuss open issues, challenges, and potential future research directions.
How should we treat acute kidney injury caused by renal congestion?

Decreased kidney function is associated with increased risk of cardiovascular events and mortality, and heart failure (HF) is a well-known risk factor for renal dysfunction. Acute kidney injury (AKI) in patients with HF often is attributed to prerenal factors, such as renal hypoperfusion and ischemia as a result of decreased cardiac output. Another such factor is reduction of absolute or relative circulating blood volume, with the decrease in renal blood flow leading to renal hypoxia followed by a decrease in the glomerular filtration rate. However, renal congestion is increasingly being recognized as a potential cause of AKI in patients with HF. Increased central venous pressure and renal venous pressure lead to increased renal interstitial hydrostatic pressure and a reduction of the glomerular filtration rate. Both decreased kidney function and renal congestion have been shown to be important prognostic factors of HF, and adequate control of congestion is important for improving kidney function. Loop and thiazide diuretics are recommended as standard therapies to reduce volume overload. However, these agents are associated with worsening renal function even though they are effective for improving congestive symptoms. There is growing interest in tolvaptan, which can improve renal congestion by increasing excretion of free water and decreasing the required dose of loop diuretic, thereby improving kidney function. This review summarizes renal hemodynamics, the pathogenesis of AKI due to renal ischemia and renal congestion, and diagnosis and treatment options for renal congestion.
Modulation of redox homeostasis: A strategy to overcome cancer drug resistance

Cancer treatment is hampered by resistance to conventional therapeutic strategies, including chemotherapy, immunotherapy, and targeted therapy. Redox homeostasis manipulation is one of the most effective innovative treatment techniques for overcoming drug resistance. Reactive oxygen species (ROS), previously considered intracellular byproducts of aerobic metabolism, are now known to regulate multiple signaling pathways as second messengers. Cancer cells cope with elevated amounts of ROS during therapy by upregulating the antioxidant system, enabling tumor therapeutic resistance via a variety of mechanisms. In this review, we aim to shed light on redox modification and signaling pathways that may contribute to therapeutic resistance. We summarized the molecular mechanisms by which redox signaling-regulated drug resistance, including altered drug efflux, action targets and metabolism, enhanced DNA damage repair, maintained stemness, and reshaped tumor microenvironment. A comprehensive understanding of these interrelationships should improve treatment efficacy from a fundamental and clinical research point of view.
Axial Consistent Memory GAN With Interslice Consistency Loss for Low Dose Computed Tomography Image Denoising

Degradation of the diagnostic quality of Low Dose CT (LDCT) due to noise is a major bottleneck that hinders the widespread application of LDCT imaging as an alternative to standard-dose computed tomography (CT) imaging. Recent deep learning-based methods for denoising LDCT images utilizing interslice similarity of CT slices have shown promising results; however high computational cost of those methods is a significant bottleneck for practical deployment. This study introduces an alternative approach for utilizing the interslice similarity among the CT slices for LDCT denoising. First, we propose a novel memory network that remembers the aggregate information about the previous slices and uses it to denoise the contemporary slice. Next, we proposed a novel axial consistent discriminator network and a novel interslice consistency loss to assist the memory network in learning the interslice information flow. Our proposed discriminator network works both as a spatial discriminator and a volume discriminator. The proposed loss appeals to the network to generate an output consistent with the previous axial slice, consequently helping to suppress the artifacts present in the current slice. The extensive experiment on two publicly available datasets validates that our method performs favorably against the existing state-of-the-art methods.
Characterization of Beef Coming from Different European Countries through Stable Isotope (H, C, N, and S) Ratio Analysis

The need to guarantee the geographical origin of food samples has become imperative in recent years due to the increasing amount of food fraud. Stable isotope ratio analysis permits the characterization and origin control of foodstuffs, thanks to its capability to discriminate between products having different geographical origins and derived from different production systems. The Framework 6 EU-project “TRACE” generated hydrogen (2H/1H), carbon (13C/12C), nitrogen (15N/14N), and sulphur (34S/32S) isotope ratio data from 227 authentic beef samples. These samples were collected from a total of 13 sites in eight countries. The stable isotope analysis was completed by combining IRMS with a thermal conversion elemental analyzer (TC/EA) for the analysis of δ(2H) and an elemental analyzer (EA) for the determination of δ(13C), δ(15N), and δ(34S). The results show the potential of this technique to detect clustering of samples due to specific environmental conditions in the areas where the beef cattle were reared. Stable isotope measurements highlighted statistical differences between coastal and inland regions, production sites at different latitudes, regions with different geology, and different farming systems related to the diet the animals were consuming (primarily C3- or C4-based or a mixed one).
Improving Handwritten Mathematical Expression Recognition via Similar Symbol Distinguishing

Handwritten mathematical expression recognition (HMER) is an essential task in the OCR community, which consists of two sub-tasks, i.e., symbol recognition and structure parsing. Modern literature treats HMER as a LaTeX sequence predicting problem that simultaneously recognizes symbols and parses the structures of MEs. Although deep learning-based HMER methods have been achieving promising results on public benchmarks, it is admitted that the misclassification error between visually similar symbols still prevents these approaches from more generalized scenes. In this paper, we try to solve this issue from three aspects. 1) We enhanced the feature extraction progress by introducing path signature features, which incorporates local writing details and global spatial information. 2) We developed a language model that uses contextual information to correct the symbols misclassified by vision-only-based recognition models. 3) We solved the misalignment problem in existing ensemble method by designing a dynamic time warping (DTW) based algorithm. By combining the above improvements, our method achieved state-of-the-art results on three CROHME benchmarks, outperforming previous methods by a large margin.
Dislocation reduction in gallium nitride films using scandium nitride interlayers

We describe a method of reducing threading dislocation densities in 0001-oriented GaN from (5.0±0.5)×109cm−2to(3.1±0.4)×107cm−2 (for coalesced films) or to below 5×106cm−2 (for partially coalesced films) in a single step, without lithography. Lattice-matched, dislocation-blocking scandium nitride interlayers are deposited on a 500nm GaN-on-sapphire template. Dislocation-free GaN islands grown on the ScN interlayer nucleate both on the interlayer and on tiny areas of the GaN template exposed through openings in the interlayer. However, some dislocations are generated above the interlayer during subsequent island coalescence.
Prediction of S&P500 Stock Index Using ARIM and Linear Regression

This paper mainly establishes a linear model suitable for the volatility of the S&P 500 index and forecasts the S&P 500 index. Firstly, the data set is divided into the training set and test set. After testing a series of data attributes such as the smoothness of the original data series and log series, the original data series and log series of the S&P 500 index weekly data series are modeled based on the ARIMA model. The next step is to check the fit of the model and use ACF and PACF to determine the parameters of two different models to fit the original data series and the log data series, respectively. Based on two different models, the rationality of the model is confirmed by the residual white noise test and various natural maps. By establishing the model and analyzing the residual error of the model, finding out unreasonable fluctuation of the residual error of the model fitting and giving the corresponding explanation combined with the history. Finally, a fitted model to make rough forecasts for the S&P 500 from January 2020 to December 2020. Although this forecasting model cannot predict detailed fluctuations daily, it can still correctly determine whether a stock is going up or down. To sum up, the ARIMA model does not perform well in stock forecasting and it may need to be improved using other methods.
Transcranial Magnetic Stimulation in Disorders of Consciousness: An Update and Perspectives

Disorders of consciousness (DOC) is a state in which consciousness is affected by brain injuries, leading to dysfunction in vigilance, awareness, and behavior. DOC encompasses coma, vegetative state, and minimally conscious state based on neurobehavioral function. Currently, DOC is one of the most common neurological disorders with a rapidly increasing incidence worldwide. Therefore, DOC not only impacts the lives of individuals and their families but is also becoming a serious public health threat. Repetitive transcranial magnetic stimulation (rTMS) can stimulate electrical activity using a pulsed magnetic field in the brain, with great value in the treatment of chronic pain, neurological diseases, and mental illnesses. However, the clinical application of rTMS in patients with DOC is debatable. Herein, we report the recent main findings of the clinical therapeutics of rTMS for DOC, including its efficacy and possible mechanisms. In addition, we discuss the potential key parameters (timing, location, frequency, strength, and secession of rTMS applications) that affect the therapeutic efficiency of rTMS in patients with DOC. This review may help develop clinical guidelines for the therapeutic application of rTMS in DOC.
Revisiting Microbial Diversity in Hypersaline Microbial Mats from Guerrero Negro for a Better Understanding of Methanogenic Archaeal Communities

Knowledge regarding the diversity of methanogenic archaeal communities in hypersaline environments is limited because of the lack of efficient cultivation efforts as well as their low abundance and metabolic activities. In this study, we explored the microbial communities in hypersaline microbial mats. Bioinformatic analyses showed significant differences among the archaeal community structures for each studied site. Taxonomic assignment based on 16S rRNA and methyl coenzyme-M reductase (mcrA) gene sequences, as well as metagenomic analysis, corroborated the presence of Methanosarcinales. Furthermore, this study also provided evidence for the presence of Methanobacteriales, Methanomicrobiales, Methanomassiliicoccales, Candidatus Methanofastidiosales, Methanocellales, Methanococcales and Methanopyrales, although some of these were found in extremely low relative abundances. Several mcrA environmental sequences were significantly different from those previously reported and did not match with any known methanogenic archaea, suggesting the presence of specific environmental clusters of methanogenic archaea in Guerrero Negro. Based on functional inference and the detection of specific genes in the metagenome, we hypothesised that all four methanogenic pathways were able to occur in these environments. This study allowed the detection of extremely low-abundance methanogenic archaea, which were highly diverse and with unknown physiology, evidencing the presence of all methanogenic metabolic pathways rather than the sheer existence of exclusively methylotrophic methanogenic archaea in hypersaline environments.
Thermal Scale Modeling of Radiation-Conduction-Convection Systems

An investigation is made of thermal scale modeling applied to radiation-conduction-convection systems with particular emphasis on the spacecraft cabin atmosphere/cabin wall thermal interface. The thermal similitude criteria, scaling techniques and the results of an experimental investigation are presented. The "Modified Material Preservation," "Temperature Preservation," "Scaling Compromises" and "Nusselt Number Preservation' ' scale modeling techniques and their inherent limitations and problem areas are described The compromised scaling techniques of mass flux preservation and heat-transfer coefficient preservation were investigated further and show promise of giving adequate thermal similitude while preserving both gas and temperature in the scale model. The use of these compromised scaling techniques was experimentally demonstrated in tests of full scale and i scale models. It is concluded that either mass flux or heat-transfer coefficient preservation may result in adequate thermal similitude depending on the system to be modeled. Heat-transfer coefficient preservation should give good thermal similitude for manned spacecraft scale modeling applications.
The absorption properties of one-dimensional spherical photonic crystals based on plasma material

The first theoretical treatment of the electromagnetic properties of one-dimensional plasma spherical photonic crystals (1D PSPCs) in the non-magnetized state is carried out to obtain the expressions for the electric and magnetic fields. The utilization of the transfer matrix method provides a new idea for the exploration of the 1D PSPCs, based on which the expressions for the absorptance of the 1D PSPCs are derived. A structure of quasi-periodic 1D PSPCs arranged according to the Thue-Morse sequence is proposed to acquire the ultra-wide absorption bands (ABs) at large incidence angles by adding an air layer and setting the plasma frequency in a gradient. And the factors affecting the ABs are meticulously analyzed. It is figured out that the primary factors affecting ABs are topology and plasma frequency, both of which have vigoroso influences on the bandwidths and positions of ABs. However, the initial radius and the collision frequency prevailingly affect the amplitude of the absorptance. The unexceptionable geometric properties of the 1D PSPCs make them sensitive to parameter changes and can be applied in the field of absorbers and radomes, which have great application prospects and development space.
Brownsche Motoren: Wie aus Brownscher Bewegung makroskopischer Transport wird

Eine Fragestellung aus der Zellbiologie hat auch Physiker infiziert: Die Biologen wollen klaren, warum entlang einer periodischen Struktur (Mikrotubulus) gerichtete Bewegung auftritt, ohne das eine makroskopische Kraft, ein Konzentrations- oder Temperaturgradient vorliegt. Die von den Physikern zur Erklarung vorgeschlagenen Modelle sollten auch in vielen anderen Bereichen nutzlich sein, wie zum Beispiel bei der Separation unterschiedlicher Teilchen. Rauschkrafte aller Art sind dabei die wichtigsten Faktoren.
Reactive scattering of molecular beams

Abstract The rate and mechanism of chemical change has been studied for many years but until recently experimental work has been concerned with bulk systemv so that the microscopic level of action was manifest only in some average way. In contrast, methods such as X-ray scattering yield direct information on molecular structure. The technique of molecular beam scattering is now a similar tool, capable of probing the dynamics of chemical reaction at the molecular level and leading to an understanding of the process in terms of the trajectories of the atoms participating. Discussion of the reaction is thus naturally in quantum channel cross-section terms; a ohange in emphasis, from conditions in the bulk sample to the collision proper, which has stimulated theoretical work. In particular, concepts and techniques developed for use in the atomic and nuclear collision field have been extended into this area. These rather rigorous calculations will be limited to the very simplest systems but, nevertheless, will.
Ascents of size less than d in compositions

A composition of a positive integer n is a finite sequence π1π2. πm of positive integers such that π1+. +πm = n. Let d be a fixed number. We say that we have an ascent of size d or more (respectively, less than d) if πi+1 ≥ πi+d (respectively, πi < πi+1 < πi + d). Recently, Brennan and Knopfmacher determined the mean, variance and limiting distribution of the number of ascents of size d or more in the set of compositions of n. In this paper, we find an explicit formula for the multi-variable generating function for the number of compositions of n according to the number of parts, ascents of size d or more, ascents of size less than d, descents and levels. Also, we extend the results of Brennan and Knopfmacher to the case of ascents of size less than d. More precisely, we determine the mean, variance and limiting distribution of the number of ascents of size less than d in the set of compositions of n.
A differential-algebraic condition for controllability and observability of time varying linear systems

An algebraic rank condition for controllability and observability of time-varying linear systems is given. The time-varying structure matrix is expanded in generated Lie algebra, with respect to a basis. It is proved that under a differential-algebraic condition for the time-dependent coefficients, controllability and observability are equivalent to a multivariable Kalman's condition, independent of the time-dependent terms. <<ETX>>