Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of common and new methods to determine martensite start temperature using a dilatometer

ABSTRACT This paper analyses the start of the martensitic transformation in 4140 steel from the point of view of six definitions, and discusses in detail the implications based on the better understanding of progression of the transformation. The application of two relatively new techniques (cooling curve analysis-CCA and dilation curve analysis-DCA) is among the methods studied. These new techniques allow for a more rigorous quantification of microstructural constituents at each step of the transformation. Experiments consisted of dilatometric analysis of 12 samples of 4140 steel with prior austenite grain sizes from 16 to 44 µm that were rapidly quenched in the dilatometer to form martensite. The results indicate that DCA and CCA are superior to traditional methods used to determine the martensite start temperature. The practical choice of 10% martensite fraction in CCA and DCA yielded Ms values statistically undistinguishable from ASTM A1033 or the tangent method. The practical choice of 1% martensite fraction in CCA and DCA yielded Ms values comparable to the offset method. The important implication of this finding is that Ms values determined with empirical methods should not be confused with the temperature of first appearance of martensite; instead, they correspond to martensite fractions of the order of 10%.
Bottom‐Up Preparation of Uniform Ultrathin Rhenium Disulfide Nanosheets for Image‐Guided Photothermal Radiotherapy

Facile preparation of multifunctional theranostic nanoplatforms with well‐controlled morphology and sizes remains an attractive in the area of nanomedicine. Here, a new kind of 2D transition metal dichalcogenide, rhenium disulfide (ReS2) nanosheets, with uniform sizes, strong near‐infrared (NIR) light, and strong X‐ray attenuation, is successfully synthesized. After surface modification with poly(ethylene glycol) (PEG), the synthesized ReS2‐PEG nanosheets are stable in various physiological solutions. In addition to their contrasts in photoacoustic imaging and X‐ray computed tomography imaging because of their strong NIR light and X‐ray absorptions, respectively, such ReS2‐PEG nanosheets can also be tracked under nuclear imaging after chelator‐free labeling with radioisotope ions, 99mTc4+. Efficient tumor accumulation of ReS2‐PEG nanosheets is then observed after intravenous injection into tumor‐bearing mice under triple‐modal imaging. The combined in vivo photothermal radiotherapy is further conducted, achieving a remarkable synergistic tumor destruction effect. Finally, no obvious toxicity of ReS2‐PEG nanosheets is observed from the treated mice within 30 d. This work suggests that such ultrathin ReS2 nanosheets with well‐controlled morphology and uniform sizes may be a promising type of multifunctional theranostic agent for remotely triggered cancer combination therapy.
Effect of Apple Extracts on NF-κB Activation in Human Umbilical Vein Endothelial Cells

The mechanisms by which foods, such as fruit, are able to reduce the risk of chronic disease are still unclear. Several fruit products, including apples and apple juice, that are flavonoid-rich are reported to increase antioxidant levels in human subjects. This is supported by the finding from our previous studies that the chronic consumption of apple juice by human subjects reduced ex vivo low-density lipoprotein (LDL) oxidation; we hypothesized that this was due to the flavonoid in the apple juice, which, as we reported earlier, reduced in vitro LDL oxidation. To further explore whether the mixture of flavonoids and other phytochemicals in apples are biologically relevant antioxidants, we tested the effects of this flavonoid-rich apple extract (AE) on oxidant-related pathways in a model of the endothelium: human umbilical vascular endothelial cells (HUVECs). The effects of AE on oxidant-responsive (i.e., tumor necrosis factor [TNF]-α-induced) nuclear factor (NF)-κB signaling in cell culture were assessed in transfected HUVECs by using a construct that expressed luciferase under the control of NF-κB. Incubation of HUVEC for 24 hrs with up to 10 mM (as gallic acid equivalents) of AE demonstrated no cytotoxicity, as determined by lactate dehydrogenase release, caspase 3 activation, and apoptosis marker–based FACS analysis. AE after a 24-hr incubation period at either 200 or 2000 nM showed a complex pattern of decreased basal and TNF-α-stimulated NF-κB signaling (63% maximal decrease) as assessed by luciferase activity in the transfected HUVECs, as well as by reduced levels of IκBα protein phosphorylation detected by Western blot analysis. We suggest that AE downregulates NF-κB signaling and that this is indicative of an antioxidant effect of the flavonoids present in AE.
Graph-based Dequantization of Block-Compressed Piecewise Smooth Images

Block-based image or video coding standards (e.g. JPEG) compress an image lossily by quantizing transform coefficients of non-overlapping pixel blocks. If the chosen quantization parameters (QP) are large, then hard decoding of a compressed image-using indexed quantization bin centers as reconstructed transform coefficients-can lead to unpleasant blocking artifacts. Leveraging on recent advances in graph signal processing (GSP), we propose a dequantization scheme specifically for piecewise smooth (PWS) images: images with sharp object boundaries and smooth interior surfaces. We first mathematically define a PWS image as a low-frequency signal with respect to an inter-pixel similarity graph with edges of weights 1 or 0. Using quantization bin boundaries as constraints, we then jointly optimize the desired graph-signal and the similarity graph in a unified framework. A generalization to consider generalized piecewise smooth (GPWS) images-where sharp object boundaries are replaced by transition regions-is also proposed. Experimental results show that our proposed scheme outperforms a state-of-the-art dequantization method by 1 dB on average in PSNR.
Semantic role labelling with similarity-based generalization using EM-based clustering

We describe a system for semantic role assignment built as part of the Senseval III task, based on an off-the-shelf parser and Maxent and Memory-Based learners. We focus on generalisation using several similarity measures to increase the amount of training data available and on the use of EM-based clustering to improve role assignment. Our final score is Precision=73.6%, Recall=59.4% (F=65.7).
Design Guidelines to Balance the Flow Distribution in Complex Profile Extrusion Dies

Abstract In this work a novel methodology to balance the flow distribution in complex extrusion dies is proposed. For this purpose, the profile cross section geometry is divided into simpler geometries (L and T shaped profiles), which are balanced with a surrogate model obtained by a detailed numerical study. The numerical simulations are performed considering the non-isothermal flow of Bird-Carreau inelastic fluids, and the numerical computations are performed with a solver implemented in OpenFOAM computational library. The proposed methodology is assessed with some case studies.
Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model

Changes in oscillatory brain activity are strongly correlated with performance in cognitive tasks and modulations in specific frequency bands are associated with working memory tasks. Mesoscale network models allow the study of oscillations as an emergent feature of neuronal activity. Here we extend a previously developed attractor network model, shown to faithfully reproduce single-cell activity during retention and memory recall, with synaptic augmentation. This enables the network to function as a multi-item working memory by cyclic reactivation of up to six items. The reactivation happens at theta frequency, consistently with recent experimental findings, with increasing theta power for each additional item loaded in the network's memory. Furthermore, each memory reactivation is associated with gamma oscillations. Thus, single-cell spike trains as well as gamma oscillations in local groups are nested in the theta cycle. The network also exhibits an idling rhythm in the alpha/beta band associated with a noncoding global attractor. Put together, the resulting effect is increasing theta and gamma power and decreasing alpha/beta power with growing working memory load, rendering the network mechanisms involved a plausible explanation for this often reported behavior.
Nonlinear event‐related responses in fMRI

This paper presents an approach to characterizing evoked hemodynamic responses in fMRI based on nonlinear system identification, in particular the use of Volterra series. The approach employed enables one to estimate Volterra kernels that describe the relationship between stimulus presentation and the hemodynamic responses that ensue. Volterra series are essentially high‐order extensions of linear convolution or “smoothing. ” These kernels, therefore, represent a nonlinear characterization of the hemodynamic response function that can model the responses to stimuli in different contexts (in this work, different rates of word presentation) and interactions among stimuli. The nonlinear components of the responses were shown to be statistically significant, and the kernel estimates were validated using an independent event‐related fMRI experiment. One important manifestation of these nonlinear effects is a modulation of stimulus‐specific responses by preceding stimuli that are proximate in time. This means that responses at high‐stimulus presentation rates saturate and, in some instances, show an inverted U behavior. This behavior appears to be specific to BOLD effects (as distinct from evoked changes in cerebral blood flow) and may represent a hemodynamic “refractoriness. ” The aim of this paper is to describe the theory and techniques upon which these conclusions were based and to discuss the implications for experimental design and analysis.
Analysis and Modeling of Radio Wave Propagation

The concept of a wave is something for which it is very difficult to find a clear definition in the literature. Before proceeding, however, it is important that we have a good understanding of what we mean by a wave. In this regard, it is instructive to start with the surface water wave, a phenomenon that gives us one of the best practical illustrations of wave phenomena in general. Water waves are something that most of us have experienced and that exhibit many of the important features of waves and their propagation. As children, we have nearly all generated waves by throwing stones into a pond. Before the stone lands, the surface of the pond (the propagation medium) is calm. After impact, however, there is a ripple that travels radially outward from the point of impact. The ripple forms a circular band of disturbance that expands at a finite speed. Within the band the ripple maintains its shape but with amplitude that reduces as the radius of the band increases. As the ripple travels outward, it might encounter a floating object and then cause it to bob up and down. This motion can be used to extract energy from the wave, energy that was originally supplied by the stone’s impact (the wave source). Further, the vertical motion of the object provides a means of detecting the passage of a wave. Water waves illustrate several important features that are common to all wave phenomena. First, the wave can transport energy from one point (the source) to another (the detector), the energy being transported at a finite speed. Second, after the passage of the wave, the medium returns to its undisturbed state. This last point leads on to another important property of wave phenomena, the ability to make arbitrarily shaped waves. Instead of causing the wave by casting a single stone, we could simply drive the water up and down in an arbitrary fashion (by a sequence of impacts of varying force). The ripples would now constitute a record of the driving sequence and these would then be reproduced some distance away in a detector (our floating object bobbing up and down in sympathy with the wave as it passes). In this way, we could transfer information
Hierarchical Volume Visualization of Brain Anatomy

Scientific data-sets often come with an inherent hierarchical structure such as functional substructures within organs. In this work we propose a new visualization approach for volume data which is augmented by the explicit representation of hierarchically structured data. The volumetric structures are organized in an interactive hierarchy view. Seamless zooming between data visualization, with volume rendering, and map viewing, for orientation and navigation within the hierarchy, facilitates deeper insight on multiple levels. The map shows all structures, organized in multiple hierarchy levels. Focusing on a selected node allows a visual analysis of a substructure as well as identifying its location in the hierarchy. The visual style of the node in focus, its parent and child nodes are automatically adapted during interaction to emphasize the embedding in the hierarchy. The hierarchy view is linked to a traditional tree view. The value of this new visualization approach is demonstrated on segmented MRI brain data consisting of hundreds of cortical and sub-cortical structures.
Identification of differentially expressed genes between lung adenocarcinoma and lung squamous cell carcinoma by gene expression profiling

The present study aimed to identify the differentially expressed genes (DEGs) between lung adenocarcinoma and normal lung tissues, and between lung squamous cell carcinoma and normal lung tissues, with the purpose of identifying potential biomarkers for the treatment of lung cancer. The gene expression profile (GSE6044) was downloaded from the Gene Expression Omnibus database, which included data from 10 lung adenocarcinoma samples, 10 lung squamous cell carcinoma samples, and five matched normal lung tissue samples. After data processing, DEGs were identified using the Student's t-test adjusted via the Benjamini-Hochberg method. Subsequently, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery, and a global network was constructed. A total of 95 upregulated and 241 downregulated DEGs were detected in lung adenocarcinoma samples, and 204 upregulated and 285 downregulated DEGs were detected in lung squamous cell carcinoma samples, as compared with the normal lung tissue samples. The DEGs in the lung squamous cell carcinoma group were enriched in the following three pathways: Hsa04110, Cell cycle; hsa03030, DNA replication; and hsa03430, mismatch repair. However, the DEGs in the lung adenocarcinoma group were not significantly enriched in any specific pathway. Subsequently, a global network of lung cancer was constructed, which consisted of 341 genes and 1,569 edges, of which the top five genes were HSP90AA1, BCL2, CDK2, KIT and HDAC2. The expression trends of the above genes were different in lung adenocarcinoma and lung squamous cell carcinoma when compared with normal tissues. Therefore, these genes were suggested to be crucial genes for differentiating lung adenocarcinoma and lung squamous cell carcinoma.
Improved particle filtering for state and parameter estimation- CSTR model

This paper addresses the problem of states and parameters estimation for a continuously stirred tank reactor using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Particle Filter (PF), and the developed improved particle filter (IPF). Unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The proposal sampling distribution is obtained by minimizing the Kullback-Leibler divergence (KLD) distance. The simulation results show that the new improved particle filter superiors to the standard particle filter. In addition, IPF can still provide both convergence as well as accuracy related advantages over other estimation methods.
Heart rate variability and respiratory sinus arrhythmia assessment of affective states by bivariate autoregressive spectral analysis

The study of emotions elicited by human-computer interactions is a promising field that could lead to the identification of specific patterns of affective states. We present a heart rate variability (HRV) assessment of the autonomic nervous system (ANS) response and respiratory sinus arrhythmia during PC-mediated stimuli by means of standard and multivariate autoregressive spectral methods. 35 healthy volunteers were exposed to computer-mediated tasks during data collection. The stimuli were designed to elicit: relaxation (R), engagement (E) and stress (S); half of the subjects were exposed to E before S (RES) while the other to S before E (RSE). HRV measures clearly separate the ANS response among R, S and E. Less significant differences are found between E and S in RSE, suggesting that S stimuli may cause a lasting response affecting the E period. Results from the bivariate analysis indicate a disruption of the cardio-respiratory coupling during non-relax conditions.
Adaptive weighted real-time compressive tracking

Many tracking methods often suffer from the drift problems caused by appearance change. Therefore developing a robust online tracker is still a challenging test. Recently, a simple yet effective and efficient tracking algorithm has been proposed by compressive tracking (CT) paradigm to alleviate the drift to some degree. The CT tracker introduced an appearance model based on features extracted from the multi-scale image feature space in the compressed domain. However, the CT tracker may detect the positive sample that is less important because it does not discriminatively consider the sample importance in its learning procedure. In this study, the authors integrate the sample importance into the CT tracker online learning procedure. They also add an efficient feature select method which can choose the most discriminative power weak classifier and employ the co-training criterion into CT tracker to improve the tracking performance. Experiments show that the proposed tracker demonstrates the superior performance in robustness and efficiency than other state-of-the-art trackers.
A Study on the Power Consumption of H.264/AVC-Based Video Sensor Network

There is an increasing interest in using video sensor networks (VSNs) as an alternative to existing video monitoring/surveillance applications. Due to the limited amount of energy resources available in VSNs, power consumption efficiency is one of the most important design challenges in VSNs. Video encoding contributes to a significant portion of the overall power consumption at the VSN nodes. In this regard, the encoding parameter settings used at each node determine the coding complexity and bitrate of the video. This, in turn, determines the encoding and transmission power consumption of the node and the VSN overall. Therefore, in order to calculate the nodes' power consumption, we need to be able to estimate the coding complexity and bitrate of the video. In this paper, we modeled the coding complexity and bitrate of the H.264/AVC encoder, based on the encoding parameter settings used. We also propose a method to reduce the model estimation error for videos whose content changes within a specified period of time. We have conducted our experiments using a large video dataset captured from real-life applications in the analysis. Using the proposed model, we show how to estimate the VSN power consumption for a given topology.
The Effects of Finish Rolling Temperature and Niobium Microalloying on the Microstructure and Properties of a Direct Quenched High-Strength Steel

This paper comprehends the effects of finish rolling temperature (FRT) and Nb-microalloying on the microstructural evolu-tion and resultant properties of a low carbon direct quenched steel in the yield strength category of ≥ 900 MPa. Results indicate that a decrease in FRT close to A r3 temperature significantly influenced the microstructure following phase transformation, especially at the subsurface (~50-400 μ m) of the rolled strip. On decreasing the FRT, the subsurface microstructure revealed a fine mixture of ferrite and bainite obviously as a result of strain-induced transformation, whereas the structure at the centreline remained essentially martensitic. Further, Nb-microalloying promoted the formation of ferrite and bainite even at higher FRTs, thus influencing the mechanical properties. The microstructures of the hot-rolled strips were further corroborated with the aid of CCT diagrams.
Identifying Generalized Reed-Muller Codewords by Quantum Queries

We provide an exact quantum query algorithm that identifies uncorrupted codewords from a degree-d generalized Reed-Muller code of length qn over the finite field of size q. When d is constant, the algorithm needs 𝒪(nd-1) quantum queries, which is optimal. Classically, Ω(nd) queries are necessary to accomplish this task, even with constant probability of error admitted. Our work extends a result by Montanaro about learning multilinear polynomials.
Mode I Interlaminar Fracture Toughness of Natural Fiber Stitched Flax/Epoxy Composite Laminates – Experimental and Numerical Analysis

The mode I interlaminar fracture toughness of stitched and unstitched composite laminates of continuous UD flax fiber and epoxy matrix was experimentally and numerically investigated. A twistless flax fiber yarn was used for stitching purpose. The mode I interlaminar energy release rate of the unstitched and stitched laminates was experimentally determined through Double Cantilever Beam (DCB) test. Tensile test was conducted to characterize the tensile properties of the dry and resin impregnated forms of the stitch yarn. A 3D Finite Element (FE) model of DCB specimen to analyze delamination propagation of stitched flax fiber laminate was developed using cohesive element with nonlinear softening law and embedded 2-node beam element. The out-of-plane flax yarn stitching was found to generate a twofold increase in the delamination resistance of the composite laminate at a medium stitch density. The FE analysis results agreed well with the experimental results, where a good fit between the predicted and experimental R-curves were achieved.
Approximation of Conditional Density of Markov Random Field and its Application to Texture Synthesis

Markov random field (MRF) based sampling method is popular for synthesizing natural textures. The main drawback of the synthesis procedure is the large computational complexity involved. In this paper, we propose an approximation of the conditional density description for the reduction of computational complexity required in sampling texture pixels from the conditional density. Assuming, Y isin Lambda, and X isin Lambdad, we in this work studied the approximation of the conditional density function P(Y|X) as P(Y|thetast X), where thetas isin Rd, is a unit vector. We have also shown that the classical gradient based optimization method is not suitable for finding the solution of thetas. We have estimated thetas using genetic algorithm. The perceptual (visual) similarity and neighborhood similarity measures between the textures synthesized using the full conditional description and approximated description, are shown for validating the method developed.