Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of Sub-Sample Unit Root Tests to Detect Changes in Persistence

We investigate the behaviour of rolling and recursive augmented Dickey-Fuller (ADF) tests against processes which display changes in persistence. We show that the power of the tests depend crucially on the window width and warm up parameter for the rolling and recursive procedures respectively, on whether forward or reverse recursive sequences of tests are computed, and on the persistence change process generating the data. To ameliorate these dependencies we extend the available critical values for these tests, and propose a number of new sub-sample unit root tests for which finite sample and asymptotic critical values are also provided. An empirical illustration on OECD real output data is also provided. Copyright 2005 Blackwell Publishing Ltd.
The English-Chinese Translation of Scale from the Perspective of Translator Behavior Criticism

With the development of high-quality assessment tools in the medical field, the awareness of copyright in the pharmaceutical industry, the increasing clinical demand for medical scales, and the gradual introduction of foreign authorized scales in China, domestic pharmaceutical companies have purchased the required genuine medical scales from online platforms such as ePROVIDE, EuroQol and Cardiff University Medical Resources. Translator behavior criticism is an evaluative theoretical tool simply used for postevaluation, a dynamic evaluation model that provides a comprehensive and objective guide to translation criticism to ensure the reliability and validity of the scales. In this paper, 20 medical scales are collected. Combined with “in-translation” and “out-of-translation” scales and guided by translation examples in the framework of “truth-seeking-utility-attaining” evaluation, the model will be discussed in detailed translation problems to solve the reliability and validity of the scales, and to provide strategies for improving the quality of the scales.
Kinematic models for non‐coaxial granular materials. Part I: theory

The purpose of this paper is to present a physically based plasticity model for non‐coaxial granular materials. The model, which we shall call the double slip and rotation rate model (DSR2 model), is a pair of kinematic equations governing the velocity field. The model is based on a discrete micro‐analysis of the kinematics of particles in contact, and is formulated by introducing a quantity called the averaged micro‐pure rotation rate (APR) into the unified plasticity model which was proposed by one of the authors. Our macro–micro mechanical analysis shows that the APR is a non‐linear function of, among other quantities, the macro‐rotation rate of the major principal axis of stress taken in the opposite sense. The requirement of energy dissipation used in the double‐sliding free‐rotating model appears to be unduly restrictive as a constitutive assumption in continuum models. In the DSR2 model the APR tensor and the spin tensor are directly linked with non‐coaxiality of the stress and deformation rate tensors. We also propose a simplified plasticity model based on the DSR2 model for a class of dilatant materials, and analyse its material stability. Copyright © 2005 John Wiley & Sons, Ltd.
Verbal memory formation across PET-based Braak stages of tau accumulation in Alzheimer’s disease

Abstract A classical early sign of typical Alzheimer’s disease is memory decline, which has been linked to the aggregation of tau in the medial temporal lobe. Verbal delayed free recall and recognition tests have consistently probed useful to detect early memory decline, and there is substantial debate on how performance, particularly in recognition tests, is differentially affected through health and disease in older adults. Using in vivo PET-Braak staging, we investigated delayed recall and recognition memory dysfunction across the Alzheimer’s disease spectrum. Our cross-sectional study included 144 cognitively unimpaired elderly, 39 amyloid-β+ individuals with mild cognitive impairment and 29 amyloid-β+ Alzheimer’s disease patients from the Translational Biomarkers in Aging and Dementia cohort, who underwent [18F]MK6240 tau and [18F]AZD4694 amyloid PET imaging, structural MRI and memory assessments. We applied non-parametric comparisons, correlation analyses, regression models and voxel-wise analyses. In comparison with PET-Braak Stage 0, we found that reduced, but not clinically significant, delayed recall starts at PET-Braak Stage II (adjusted P < 0.0015), and that recognition (adjusted P = 0.011) displayed a significant decline starting at PET-Braak Stage IV. While performance in both delayed recall and recognition related to tau in nearly the same cortical areas, further analyses showed that delayed recall rendered stronger associations in areas of early tau accumulation, whereas recognition displayed stronger correlations in mostly posterior neocortical regions. Our results support the notion that delayed recall and recognition deficits are predominantly associated with tau load in allocortical and neocortical areas, respectively. Overall, delayed recall seems to be more dependent on the integrity of anterior medial temporal lobe structures, while recognition appears to be more affected by tau accumulation in cortices beyond medial temporal regions.
Cholecystokinin Receptor Antagonist Induces Pancreatic Stellate Cell Plasticity Rendering the Tumor Microenvironment Less Oncogenic

Simple Summary The tumor microenvironment of pancreatic cancer consists of dense fibrotic stroma, which, to a certain extent, accounts for the relative drug resistance of this malignancy. Activated pancreatic stellate cells (PSC) and myofibroblasts are responsible for this fibrosis, and strategies to target the fibrosis or render the stellate cells quiescent have been investigated in order to improve therapies for pancreatic cancer. Many have studied TGF-β and other pathways involved in fibrosis. In this work, we study the role of a novel cholecystokinin receptor signaling pathway in pancreatic cancer fibrosis. Treatment with a CCK receptor antagonist, proglumide, induced plasticity of activated mouse and human fibroblasts to revert the cells to a quiescent state with decreased migration, proliferation, and production of collagenous proteins of the tumor microenvironment. Interruption of the CCK-B receptor pathway provides a novel strategy to alter the extracellular matrix in pancreatic cancer by changing PSCs from an activated state to quiescence. Abstract CCK receptors are expressed on pancreatic cancer epithelial cells, and blockade with receptor antagonists decreases tumor growth. Activated pancreatic stellate cells or myofibroblasts have also been described to express CCK receptors, but the contribution of this novel pathway in fibrosis of the pancreatic cancer microenvironment has not been studied. We examined the effects of the nonselective CCK receptor antagonist proglumide on the activation, proliferation, collagen deposition, differential expression of genes, and migration in both murine and human PSCs. CCK receptor expression was examined using western blot analysis. Collagen production using activated PSCs was analyzed by mass spectroscopy and western blot. Migration of activated PSCs was prevented in vitro by proglumide and the CCK-B receptor antagonist, L365,260, but not by the CCK-A receptor antagonist L365,718. Proglumide effectively decreased the expression of extracellular matrix-associated genes and collagen-associated proteins in both mouse and human PSCs. Components of fibrosis, including hydroxyproline and proline levels, were significantly reduced in PSC treated with proglumide compared to controls. CCK peptide stimulated mouse and human PSC proliferation, and this effect was blocked by proglumide. These investigations demonstrate that targeting the CCK-B receptor signaling pathway with proglumide may alter the plasticity of PSC, rendering them more quiescent and leading to a decrease in fibrosis in the pancreatic cancer microenvironment.
Quantifying Single and Dual Channel Live Imaging Data: Kymograph Analysis of Organelle Motility in Neurons

Live imaging is commonly used to study dynamic processes in cells. Many labs carrying out live imaging in neurons use kymographs as a tool. Kymographs display time-dependent microscope data (time-lapsed images) in two-dimensional representations showing position vs. time. Extraction of quantitative data from kymographs, often done manually, is time-consuming and not standardized across labs. We describe here our recent methodology for quantitatively analyzing single color kymographs. We discuss the challenges and solutions of reliably extracting quantifiable data from single-channel kymographs. When acquiring in two fluorescent channels, the challenge becomes analyzing two objects that may co-traffic together. One must carefully examine the kymographs from both channels and decide which tracks are the same or try to identify the coincident tracks from an overlay of the two channels. This process is laborious and time consuming. The difficulty in finding an available tool for such analysis has led us to create a program to do so, called KymoMerge. KymoMerge semi-automates the process of identifying co-located tracks in multi-channel kymographs and produces a co-localized output kymograph that can be analyzed further. We describe our analysis, caveats, and challenges of two-color imaging using KymoMerge.
Bayesian Learning, Shutdown and Convergence

This article investigates a partial equilibrium production model with dynamic information aggregation. Firms apply Bayesian learning to estimate the unknown model parameter. In the baseline setting, where prices and quantities are supported by the real line and the noise term is Gaussian, convergence of the limited information to the full information setting is obtained. Imposing a non-negativity constraint on quantities destroys the convergence results obtained in the baseline model. With the constraints rms learning an unknown demand intercept parameter exit with strictly positive probability, even when the true value of this parameter would induce production in the full information setting. Parts of the model can be rescued by assuming bounded support for the stochastic noise term. Although, shut-down can be excluded with bounded support when the forecasts of the agents satisfy relatively mild regularity conditions, Bayesian learning and convergence need not take place in general.
Design of Time-Encoded Spiking Neural Networks in 7-nm CMOS Technology

In biologically inspired spiking neural networks (SNNs) neurons communicate by short pulses, called spikes. SNNs have the potential to be more power efficient than artificial neural networks (ANNs), thanks to the fewer computational steps required by the spike transmission and processing, as compared to the multiply-and-accumulate (MAC) operations with wide bit-vectors usually adopted in ANNs. We present the design of two types of SNNs with integrate-and-fire dynamics and single-spike per neuron operation, where neural communication is based on synchronous time-to-first-spike (sTTFS) and time-to-first-spike (TTFS) encoding schemes. In the considered time-encoded SNNs, the information is carried by the timing of the spikes with respect to a reference time. In 7nm CMOS technology both designs are synthesized as VHDL-based random-logic-macros (RLMs) and compared to an equivalent ANN design in terms of power consumption, latency and silicon area, using the Iris data set for inference. A cost function expressed as a product of energy consumption and silicon area is introduced to compare the three network designs. With respect to this cost function, it turns out that the SNN-TTFS implemented for the considered classification task outperforms the ANN used as baseline model.
Ghrelin Modulates Differential Expression of Genes Relevant to Immune Activities and Antimicrobial Peptides in Primary Head Kidney Cells of Rainbow Trout (Oncorhynchus mykiss)

Simple Summary Ghrelin is a peptide hormone that regulates food intake and energy balance, but also acts as a cytokine in many species to modulate various immune activities. Multiple ghrelin isoforms were discovered in rainbow trout, but the roles of most isoforms are undefined. This study examined the modulation of immune genes by two major ghrelin variants; the originally identified isoform and its three amino acids truncated isoform. After treating primary cells from rainbow trout head kidney with the two ghrelin analogs, the expression patterns of selected immune-related genes were profiled over time by reverse transcriptase-coupled qPCR. The results suggest that the two ghrelin isoforms exerted divergent modulatory effects on various genes at different time points. These results provide further insight into the direct actions of ghrelin as a critical component of the rainbow trout immune system. Abstract Ghrelin is a peptide hormone/cytokine that regulates metabolic processes and plays essential roles in the immune system. To evaluate the immunomodulatory actions of ghrelin isoforms in rainbow trout (RT), an in vitro model was utilized with primary cells isolated from fish head kidney (HKD). These RT-HKD cells were treated with synthetic rainbow trout ghrelin and its truncated isoform, desVRQ-ghrelin, over time (0, 2, 4, and 24 h). Reverse transcriptase-coupled qPCR was used to measure the differential expression patterns of genes relevant to various immune processes and genes of antimicrobial peptides. Ghrelin isoform treatments resulted in functional perturbations that displayed overlapping and divergent patterns of gene expression. The differing actions between the two ghrelin isoforms on various assessed genes, and at differing time points, suggested that the two analogs may activate unique pathways, thereby eliciting distinct responses in fish immunity.
Esterification of oleic acid to biodiesel using biowaste‐based solid acid catalyst under microwave irradiation

A novel carbon‐based solid catalyst was synthesized from waste biomass of watermelon peel (Citrullus lanatus) by one pot carbonization and functionalization with H2SO4 for biodiesel production. Oleic acid was utilized as a test substrate in this study given that it is a key component for majority of feedstocks used for biodiesel production. Synthesized catalyst was extensively characterized by X‐ray diffraction, Fourier transform infrared, thermogravimetric analysis, X‐ray photoelectron spectroscopy, scanning electron microscopy–EDX and Brunauer–Emmett Teller analysis. The catalyst revealed aromatic structure, hydrophilic OH and COOH groups as well as a high density of SO3H active centres with a total acidity of 3.6 mmol g−1. Using the optimized reaction condition (MeOH: oleic acid molar ratio 20:1, catalyst loading 8 wt.%, temperature 100°C and reaction time of 60 min) under microwave irradiation (50 W, 100 psi) a conversion as high as 94.36 ± 0.3% of oleic acid to biodiesel was reported. Activation energy of the esterification was found to be 38.16 kJ mol−1. Catalyst recovery was probed to over five reaction cycles, with a modest loss in activity (81.73 ± 0.4% conversion in the fifth cycle) resulting from limited active site leaching.
Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method

Rail surface defect inspection is of particular importance in modern railways. Accurate and efficient surface defect detection approaches support optimized maintenance. This enables the safe operation of the railway network. However, the scale and harsh working environments of the railway still pose challenges to existing manual and vision-based inspection methods. Inspired by recent advances in laser measurement and deep learning in computer vision, this article proposes a laser-based 3-D pixel-level rail surface defect detection method that combines high-precision laser measurement data with the concept of deep semantic segmentation. In the proposed method, the rail surface is first measured in 3-D using a low-cost 2-D laser triangulation sensor. Then, a new deep semantic segmentation network is introduced. The network is composed of a fully convolutional segmentation module and two symmetric mapping modules, which can take 3-D laser measurement data as input and output 3-D pixel-level defect detection results in an end-to-end manner. The modular design of the network allows the use of various segmentation modules for different applications or scenarios. Experiments on a 3-D rail dataset demonstrate the feasibility of the proposed method with a pixel-level detection accuracy measured by mean intersection over union (mIoU) of up to 87.9%. The 3-D output provides not only location and boundary information but also the 3-D characterization of defects, giving an essential reference for further defect management and repair tasks.
Topotactically induced oxygen vacancy order in nickelate single crystals

The strong structure-property coupling in rare-earth nickelates has spurred the realization of new quantum phases in rapid succession. Recently, topotactic transformations have provided a new platform for the controlled creation of oxygen vacancies and, therewith, for the exploitation of such coupling in nickelates. Here, we report the emergence of oxygen vacancy ordering in Pr$_{0.92}$Ca$_{0.08}$NiO$_{2.75}$ single crystals obtained via a topotactic reduction of the perovskite phase Pr$_{0.92}$Ca$_{0.08}$NiO$_{3}$, using CaH$_2$ as the reducing agent. We unveil a brownmillerite-like ordering pattern of the vacancies by high-resolution scanning transmission electron microscopy, with Ni ions in alternating square-pyramidal and octahedral coordination along the pseudocubic [100] direction. Furthermore, we find that the crystal structure acquires a high level of internal strain, where wavelike modulations of polyhedral tilts and rotations accommodate the large distortions around the vacancy sites. Our results suggest that atomic-resolution electron microscopy is a powerful method to locally resolve unconventional crystal structures that result from the topotactic transformation of complex oxide materials.
A new three stage implicit Runge-Kutta type method with error estimation for first order ordinary differential equations

This paper presents a new three stage implicit Runge-Kutta type method for solving initial value problems of first order ordinary differential equations (ODEs). Collocation approach is used to derive a method of Continuous coefficient system and when evaluated at special Gaussian points gives three different discrete schemes. These schemes were further reformulated to three stage Runge-Kutta type method and when tested with numerical experiments, it demonstrates highly efficient, stable and low implementation cost when compared with existing methods. This scheme can handled both Stiff and non Stiff ODEs.
Recherches linguistiques de Vincennes 29 - 2000 - p. 73-100

A BSTRACT This article examines the expression of number features in relation to (but as distinct from) person features in ASL. We consider parallelisms in the spatial instantiation of agreement features in the nominal and verbal domains, which may be seen as natural consequences of the existence, in both domains, of functional projections of person, number, and aspect.
A Hybrid Magnetic Current Sensor With a Multiplexed Ripple-Reduction Loop

This article presents a hybrid magnetic current sensor for galvanically isolated measurements. It consists of a CMOS chip that senses the magnetic field generated by current flowing through a lead-frame-based current rail. Hall plates and coils are used to sense low-frequency (dc to 10 kHz) and high-frequency (10 kHz to 5 MHz) magnetic fields, respectively. With the help of on- chip calibration coils, the biasing current of the Hall plates is trimmed to match the sensitivity of the Hall and coil signal paths. The sensitivity drift of the coil path with temperature is compensated by using temperature-dependent gain-setting resistors, while the drift of the Hall path is compensated by biasing the Hall plates with a proportional- to-absolute-temperature (PTAT) current. The resulting sensitivity drift is less than 9% from–40 °C to 80 °C. The offset of the Hall plates is reduced by the current spinning technique, and the resulting ripple is suppressed by a multiplexed ripple-reduction loop (MMRL). Fabricated in a standard 0.18- $\mu \text{m}$ CMOS process, the current sensor occupies 4.6 mm2 and draws 7.8 mA from a 1.8-V supply. It achieves a gain variation of only ±2% in a 5-MHz BW. It also achieves high energy efficiency, with an figure of merit (FoM) of 1.6 fW/Hz.
Fluid flow over and through a regular bundle of rigid fibres

The interaction between a fluid flow and a transversely isotropic porous medium is described. A homogenized model is used to treat the flow field in the porous region, and different interface conditions, needed to match solutions at the boundary between the pure fluid and the porous regions, are evaluated. Two problems in different flow regimes (laminar and turbulent) are considered to validate the system, which includes inertia in the leading-order equations for the permeability tensor through a Oseen approximation. The components of the permeability, which characterize microscopically the porous medium and determine the flow field at the macroscopic scale, are reasonably well estimated by the theory, both in the laminar and the turbulent case. This is demonstrated by comparing the model’s results to both experimental measurements and direct numerical simulations of the Navier–Stokes equations which resolve the flow also through the pores of the medium.
A Secure Scheme Based on a Hybrid of Classical-Quantum Communications Protocols for Managing Classical Blockchains

Blockchain technology affords data integrity protection and building trust mechanisms in transactions for distributed networks, and, therefore, is seen as a promising revolutionary information technology. At the same time, the ongoing breakthrough in quantum computation technology contributes toward large-scale quantum computers, which might attack classic cryptography, seriously threatening the classic cryptography security currently employed in the blockchain. As a better alternative, a quantum blockchain has high expectations of being immune to quantum computing attacks perpetrated by quantum adversaries. Although several works have been presented, the problems of impracticality and inefficiency in quantum blockchain systems remain prominent and need to be addressed. First, this paper develops a quantum-secure blockchain (QSB) scheme by introducing a consensus mechanism—quantum proof of authority (QPoA) and an identity-based quantum signature (IQS)—wherein QPoA is used for new block generation and IQS is used for transaction signing and verification. Second, QPoA is developed by adopting a quantum voting protocol to achieve secure and efficient decentralization for the blockchain system, and a quantum random number generator (QRNG) is deployed for randomized leader node election to protect the blockchain system from centralized attacks like distributed denial of service (DDoS). Compared to previous work, our scheme is more practical and efficient without sacrificing security, greatly contributing to better addressing the challenges in the quantum era. Extensive security analysis demonstrates that our scheme provides better protection against quantum computing attacks than classic blockchains. Overall, our scheme presents a feasible solution for blockchain systems against quantum computing attacks through a quantum strategy, contributing toward quantum-secured blockchain in the quantum era.