Miracle-dz/UniFAR-FTUs huggingface dataset:
Outcomes after ischemic stroke caused by intracranial atherosclerosis vs dissection

Objective To compare the outcomes between patients with nontraumatic intracranial arterial dissection (ICAD) and intracranial atherosclerotic stenosis (ICAS) using high-resolution MRI (HR-MRI). Methods We conducted a prospective study using HR-MRI in patients with acute symptomatic cerebrovascular disease due to intracranial occlusive disease and no dissection on luminal images. Patients were followed-up for 27.9 ± 19.3 months. We compared the functional outcome, recurrence, and changes in vascular status between patients with ICAD (dissection and no plaque on HR-MRI) and ICAS (atherosclerosis plaque on HR-MRI). Results We included 312 patients (mean age, 59.0 ± 14.2 years; men, 58.3%), of whom 113 had ICAD and 199 had ICAS. The functional outcome (as measured by modified Rankin Scale score) on the 90th day after symptom onset was not different between the groups, after adjusted for other factors (p = 0.095). However, recurrent ischemic cerebrovascular disease on the relevant vascular territory was lower in the ICAD group (7 patients, 6.2%) than in the ICAS group (37 patients, 18.6%). ICAD was a significant independent determinant of disease recurrence (hazard ratio, 0.43; 95% confidence interval, 0.19–0.98). Improvement in vascular stenosis on follow-up vascular studies was more frequently observed in ICAD (50.7%) than in ICAS (11.6%). ICAD was an independent determinant of vascular improvement (odds ratio, 7.94; 95% confidence interval, 3.32–19.01). Conclusion Considering the high prevalence of ICAD in the patients with presumed ICAS and the differential outcomes between ICAD and ICAS, HR-MRI may be a useful diagnostic tool in this population.
Kinetic approach to superconductivity hidden behind a competing order

Nonvolatile and reversible switching of superconductivity has been demonstrated with electric pulses. Exploration for superconductivity is one of the research frontiers in condensed matter physics. In strongly correlated electron systems, the emergence of superconductivity is often inhibited by the formation of a thermodynamically more stable magnetic/charge order. Thus, to develop the superconductivity as the thermodynamically most stable state, the free-energy balance between the superconductivity and the competing order has been controlled mainly by changing thermodynamic parameters, such as the physical/chemical pressure and carrier density. However, such a thermodynamic approach may not be the only way to materialize the superconductivity. We present a new kinetic approach to avoiding the competing order and thereby inducing persistent superconductivity. In the transition-metal dichalcogenide IrTe2 as an example, by using current pulse–based rapid cooling of up to ~107 K s−1, we successfully kinetically avoid a first-order phase transition to a competing charge order and uncover metastable superconductivity hidden behind. Because the electronic states at low temperatures depend on the history of thermal quenching, electric pulse applications enable nonvolatile and reversible switching of the metastable superconductivity, a unique advantage of the kinetic approach. Thus, our findings provide a new approach to developing and manipulating superconductivity beyond the framework of thermodynamics.
Mechanistic basis for the evolution of chalcone synthase catalytic cysteine reactivity in land plants

Flavonoids are important polyphenolic natural products, ubiquitous in land plants, that play diverse functions in plants' survival in their ecological niches, including UV protection, pigmentation for attracting pollinators, symbiotic nitrogen fixation, and defense against herbivores. Chalcone synthase (CHS) catalyzes the first committed step in plant flavonoid biosynthesis and is highly conserved in all land plants. In several previously reported crystal structures of CHSs from flowering plants, the catalytic cysteine is oxidized to sulfinic acid, indicating enhanced nucleophilicity in this residue associated with its increased susceptibility to oxidation. In this study, we report a set of new crystal structures of CHSs representing all five major lineages of land plants (bryophytes, lycophytes, monilophytes, gymnosperms, and angiosperms), spanning 500 million years of evolution. We reveal that the structures of CHS from a lycophyte and a moss species preserve the catalytic cysteine in a reduced state, in contrast to the cysteine sulfinic acid seen in all euphyllophyte CHS structures. In vivo complementation, in vitro biochemical and mutagenesis analyses, and molecular dynamics simulations identified a set of residues that differ between basal-plant and euphyllophyte CHSs and modulate catalytic cysteine reactivity. We propose that the CHS active-site environment has evolved in euphyllophytes to further enhance the nucleophilicity of the catalytic cysteine since the divergence of euphyllophytes from other vascular plant lineages 400 million years ago. These changes in CHS could have contributed to the diversification of flavonoid biosynthesis in euphyllophytes, which in turn contributed to their dominance in terrestrial ecosystems.
A Safety Protection Control Method for Aero-Engines Based on the Switched Equilibrium Manifold Expansion Model

In this paper, we investigate the problem of safety protection control for aero-engines. Without loss of generality, we consider one safety boundary which is the over-temperature. The outlet temperature of the high-pressure turbine, T35, is considered as the protected variable. Firstly, based on the switched equilibrium manifold expansion (SEME) model, the output, namely the protected temperature is modeled as a switched function. The switching law is on the basis of a proposed performance index which measures the accuracy of the expression for the temperature. Furthermore, based on the proposed output model, we construct an ideal temperature dynamic to design the temperature protection controller for any subsystem of the SEME model. The switching temperature protection controller can avoid the over-temperature for aero-engines effectively. Then we give a condition in order to guarantee the stability and safety simultaneously when the temperature protection controller takes over the aero-engine from the speed regulation controller. Finally, the proposed method is applied to a simulation of a two-spool turbofan engine model to verify the effectiveness.
Hybrid Particle Swarm Algorithm Applied to Flexible Job-Shop Problem

In the globalized world, with highly competitive markets, companies are looking for ways to reduce costs in a sustainable manner, optimizing their production lines to increase their economic advantages. Thus, several studies appeared with the objective of modeling the productive sectors, among which it is possible to highlight the Flexible Job-Shop. This model aims to efficiently organize the distribution of tasks to be processed in a set of available machines so that the complete execution of these tasks takes the shortest possible time considering several productive constraints. The resolution of this model involves complex combinatorial calculations, which allow the development of computational tools for this purpose, supporting the decision-making process. Therefore, this work presents a hybrid computational proposal based on Particle Swarm Optimization and Simulated Annealing algorithms to use the intrinsic advantages of these approaches to scheduling industrial productions. The results show that the proposed hybrid algorithm efficiently solves the production scheduling problem in a partially flexible scenario, overcoming the minimization of the production completeness time present in some benchmarks found in the literature for this class of problems.
Graphene-like Monolayer Yttrium Nitride: A Moderate Semiconductor and Pronounced Electronic Insensitivity to Strain

It is extremely important to explore two-dimensional materials with excellent properties due to their potential applications in future electronic devices. In the present study, a 2D hexagonal YN (h-YN) is predicted based on theoretical calculations. By assessing the phonon spectrum, ab initio molecule dynamics and elastic constants, the h-YN monolayer is proved to own satisfying thermal, dynamic and mechanical stability. Distinguishing from the most reported 2D transition metal mononitrides which exhibit metallic, monolayer h-YN presents a semiconducting characteristic with a indirect bandgap of 1.144 eV. In particular, h-YN presents nusually insensitive responses of electronic structures to tensile or compressive strain due to the valence orbital hybridization. Moderate bandgap together with insensitive electronic responses to strain endow h-YN a promising candidate in future nanoscale electronic devices in high-strain conditions.
A 0.12–0.4 V, Versatile 3-Transistor CMOS Voltage Reference for Ultra-Low Power Systems

In this paper, we propose an ultra-low power compact 3-transistor voltage reference capable of operating at ultra-low supply voltages. The proposed circuit is based on the self-cascode MOSFET (SCM), which provides a reference voltage proportional to the threshold voltage (<inline-formula> <tex-math notation="LaTeX">$V_{T}$ </tex-math></inline-formula>) difference of the two NMOS transistors that compose it. Reverse short-channel and narrow-width effects are explored to obtain such <inline-formula> <tex-math notation="LaTeX">$V_{T}$ </tex-math></inline-formula> difference while using the same type of transistor. Ultra-low power operation and low line sensitivity is achieved by biasing the SCM with a zero-<inline-formula> <tex-math notation="LaTeX">$V_{T}$ </tex-math></inline-formula> (native) transistor, also leading to an area efficient design. To show its versatility, three versions of the proposed circuit were fabricated in a standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. Measurement performed over five samples showed an average temperature coefficient of 150–1500 ppm/°C. Minimum supply voltages of 0.12–0.4 V was observed while providing reference voltages around tens of mV. The proposed circuits consume 0.33–50 pW at room temperature and minimum supply voltage. The occupied area for any version is less than 0.0012 mm<sup>2</sup>.
Ontologies in the Automation Problem for Situational Modelling

The automation problem is considered for development of situational systems to model dynamic hierarchical spatial complexes (DHSC) under the guidance of a set of ontologies. The novelty of the proposed approach consists in automated semantic search for particular models of DHSC components in a specialized sector of the Internet, their correct automatic connection to the general model of DHSC with analysis of completeness and consistency for the obtained model. Compared to prototypes, the developed model allows detailing the link control up to each material or information signal transferred among DHSC elements. This raises description adequacy of links among object components and decidability control at branching links among model elements. The successful completion of the introduced procedures of correctness analysis assures possibility to automatically generate correct job specifications for simulation performance. The given paper mostly presents matters of the model consistency checks.
Global Stabilization of the Discrete Linear System with State Saturation and Parameter Uncertainties

This paper is concerned with the stabilization of a class of uncertain discrete linear system with state saturation. By introducing two invertible matrices and a free matrix, we obtain new results to guarantee the stability of the systems. Based on this stability criterion, the state feedback control law synthesis problem is also studied. Furthermore, these results can be formulated in the terms of bilinear matrix inequalities. A numerical example illustrates the effectiveness of the proposed method.
A Novel Integral Terminal Sliding Mode Control of Yaw Stability for Steer-by-Wire Vehicles

In this paper, a novel integral terminal sliding mode control scheme is developed, to enhance the yaw stability of a steer-by-wire (SbW) vehicle. A yaw rate controller using integral terminal sliding mode control (ITSMC) strategy is designed based on the tracking error between the actual yaw rate and the desired yaw rate. Because the required sideslip angle information is difficult to measured directly for practical applications, a sliding mode observer(SMO) based on a two-wheel bicycle model is proposed to estimate the sideslip angle accurately. The uniqueness of the proposed control is that not only are the high tracking precision and strong robustness of the SbW vehicles against system nonlinearities and disturbances achieved, but also the fast error convergence can be ensured. Simulation results based on Matlab and CarSim are demonstrated to verify the excellent yaw tracking and anti-interference performance of the proposed controller.
From Best Effort to Deterministic Packet Delivery for Wireless Industrial IoT Networks

Wireless industrial networks require reliable and deterministic communication. Determinism implies that there must be a guarantee that each data packet will be delivered within a bounded delay. Moreover, it must ensure that the potential congestion or interference will not impact the predictable properties of the network. In 2016, IEEE 802.15. 4 time-slotted channel hopping (TSCH) emerged as an alternative medium access control to the industrial standards such as WirelessHART and ISA100.11a. However, TSCH is based on traditional collision detection and retransmission, and cannot guarantee reliable delivery within a given time. This paper proposes LeapFrog Collaboration (LFC) to provide deterministic and reliable communication over a routing protocol (RPL) based network. LFC is a novel multipath routing algorithm that takes advantage of route diversity by duplicating the data flow onto an alternate path. Simulations and analytical results demonstrate that LFC significantly outperforms the single-path retransmission-based approach of RPL $+$ TSCH and the state-of-the-art LinkPeek solution.
On the Equivalence Between AR Family Time Series Models and Fuzzy Models in Signal Processing

In the paper an advanced analysis of the relationships between statistical Autoregressive (AR) type models and fuzzy models have been presented. The examined family of AR type models includes Autoregressive models of order p, AR(p), Threshold AR (TAR) as well as Smooth Transition Autoregressive (STAR) models. On the other hand, fuzzy models representing different approach, characteristic for Computational Intelligence technics, have been tested for time series analysis and forecasting. The data have been taken from financial market. The research can enrich knowledge which is useful for experts using both approaches to modelling.
A Green Self-Adaptive Approach for Online Map Matching

GPS trajectory data plays a critical role for the intelligent transportation. Due to many factors like measure error, sampling error and battery power-saving requirement, the directly obtained trajectories will inaccurately align to the digital map. This requires an online map matching algorithm with high precision, low latency, and energy consumption saving. However, the existing approaches need to make some trade off among these criterions. Hence, based on the hidden Markov model, this paper proposes an adaptive online map matching algorithm to improve the performances in these perspectives at the same time: 1) the probabilistic method integrating the geometric information and topological information is developed to improve the accuracy; 2) the adaptive sampling frequency method is proposed to reduce the energy consumption; and 3) the adaptive sliding window method is presented to reduce the output delay. The experiments demonstrate that our approaches can not only improve the matching precision, but also reduce the latency and energy consumption simultaneously.
Nuclei Detection in Histopathological Images with Deep Learning and Heuristic Optimization

Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H&E) stained histopathology images of colorectal adenocarcinoma.
High-Frequency Modeling of Common-Mode Chokes Based on the Identification of Frequency-Varying Parameters

To overcome the disadvantage of impedance measurement which is influenced by parasitic parameters and non-ideal characters of conductor lines as well as cannot perform the frequency-varying parameters. This paper presents a high-frequency modeling method of common-mode chokes based on frequency-varying parameters. A high-frequency lumped parameter model was built, which included winding inductances, leakage inductances, parasitic capacitances between and in the windings, equivalent resistances of magnetic cores and copper losses. Using traditional least-square method, the forgetting factor and rectangular window was introduced to estimate the frequency-varying parameters. The mean square convergence of this method is proved. The modeling method proposed in this paper is proven accurately by comparing the experimental result with simulation result between 150 kHz–30 MHz. The restraining effects are best at the resonance frequency of the common-mode choke. In addition, an experiment is conducted to verify the conclusion by restraining the electromagnetic interference produced by a switching circuit.
A 0.7/1.1-dB Ultra-Low Noise Dual-Band LNA Based on SISL Platform

This paper presents an ultra-low noise concurrent 2.45-/5.25-GHz dual-band low-noise amplifier (LNA) based on a substrate integrated suspended line (SISL) platform. This is the first LNA design in the SISL platform. To achieve optimal noise and power gain at both bands, the transition part of this SISL LNA is modeled and optimized to work with the matching networks consisting of $LC$ components and transmission lines. The SISL platform has the merits of high performance, low loss, and low cost with the standard print circuit board process. With the proposed technique, the dedicated LNA demonstrates the advantages of self-packaging, compact size, ultra-low noise figure of 0.7/1.1 dB, and rather high power gain of 28.4/28.8 dB at 2.45/5.25 GHz, respectively.
Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared

Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions of the electromagnetic spectrum. While these observations provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In this study we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT) to bridge this gap from highly resolved measurements in the shortwave infrared (SWIR). These new measurements of near surface columnar water vapour are free of topographic artefacts and are interpreted as a proxy for bulk PBL water vapour. Validation (over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between −2% to 5%. Analysis on broad latitudinal bands show biases between −3% and 2% moving from high latitudes to the equatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a new GOSAT PBL water vapour Climate Data Record (CDR).
Cluster Survival Model of Concept Drift in Load Profile Data

An accurate scenario of customer’s power consumption patterns is a worthwhile asset for electricity provider. This paper proposes a cluster survival model of concept drift in load profile data. The cluster survival model of concept drift retrieves the dynamic behaviors of the clusters over time. We formulate a new data stream clustering algorithm, I-niceStream, which identifies the number of clusters and initial cluster centers automatically for producing the clustering results. We derive a modified Kullback–Leibler divergence for computing the concept drift scores from the clustering results. The concept drift scores are used to estimate the related clusters and the clustering patterns. The survival model categorizes the clustering patterns into sustaining, fading, and emerging types. Experiments were conducted on both synthetic datasets and real-world load profile dataset collected from different factories at Guangdong province in China. Experimental results show that the cluster survival model is able to identify the clustering patterns effectively from load profile data stream. The I-niceStream algorithm significantly outperformed three state-of-the-art algorithms in clustering accuracy on synthetic stream datasets.
A New Approach for Estimation of Heart Beat Rates from Speech Recordings

Today, people are able to have information about their mental state, behavior, and health status in some issues from the features of the voices. The study involves calculating the heart rates of people using nonlinear equations with the help of the features of sound recordings. The model proposed for the study consists of the four inputs of the difference equation parameters which change with constant and variable sound features. When the experimental studies were examined, it was observed that the heart rate could be predicted with an accuracy of 89.76% by using 10s sound recordings. With the proposed equation, it is observed that the heart beat rate is related to the speech features, can be calculated these features with minimal error rate and also the nonlinear equation is presented in the literature.
Graph Regularized Weighted Low-Rank Representation for Image Clustering

Low-rank representation have been widely used in computer vision processing. In this paper, we propose a new models of graph regularized low-rank representation that introduce the regularization term of graph which can describe the similarity between samples. In order to further enhance the low rank of the matrix, we propose a graph regularization weighted low-rank representation model which introduce weighted nuclear norm. The purpose of this paper is to study the influence of the low-rank representation of the regularization term of graph on the image clustering effect. The dataset used in this experiment is USPS (United States Postal Service) handwritten digits data set. The clustering algorithm is a k-means algorithm. Two types of experiments were done for each model: clustering of two classes of numbers and clustering of three classes of numbers. Similarly, this paper also compares the clustering effects of these two models. Finally, the experimental results show that the graph regularized can enhance the clustering effect of the image obviously.