Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial-to-Mesenchymal Transition and Inflammation Play Key Roles in Cyclophilin A–Induced Pulmonary Arterial Hypertension

Supplemental Digital Content is available in the text. Oxidative stress and inflammation play key roles in development of pulmonary arterial hypertension (PAH). We previously reported that an endothelial cell (EC)-specific cyclophilin A overexpression mouse developed many characteristics of PAH. In other models of cardiovascular disease, cyclophilin A stimulates smooth muscle proliferation and vascular inflammation, but mechanisms responsible for PAH have not been defined. In particular, the contribution of endothelial-to-mesenchymal transition in cyclophilin A-mediated PAH has not been studied. We identified increased levels of cyclophilin A in endothelial and neointimal cells of pulmonary arteries in patients with PAH and animal pulmonary hypertension models. In the EC-specific cyclophilin A overexpression mouse that exhibited features characteristic of PAH, lineage tracing showed high level expression of mesenchymal markers in pulmonary ECs. A significant number of mesenchymal cells in media and perivascular regions of pulmonary arterioles and alveoli were derived from ECs. Pulmonary ECs isolated from these mice showed phenotypic changes characteristic of endothelial-to-mesenchymal transition in culture. Cultured pulmonary ECs stimulated with extracellular cyclophilin A and acetylated cyclophilin A demonstrated functional changes associated with endothelial-to-mesenchymal transition such as increased cytokine release, migration, proliferation, and mitochondrial dysfunction. Acetylated cyclophilin A stimulated greater increases for most features of endothelial-to-mesenchymal transition. In conclusion, extracellular cyclophilin A (especially acetylated form) contributes to PAH by mechanisms involving increased endothelial-to-mesenchymal transition, cytokine release, EC migration, proliferation, and mitochondrial dysfunction; strengthening the basis for studying cyclophilin A inhibition as a therapy for PAH.
An ADS-B Anti-counterfeiting System Based on TDOA

The ADS-B signal is not encrypted in any form, and traditional receivers cannot verify the authenticity of the ADS-B signal, which poses a potential risk to aviation safety. Considering the insecurity of ADS-B, this paper proposes a four-station passive multilateration ADS-B anti-counterfeiting system based on TDOA. A reference station is used to synchronize the clock of each station, and the Chan Algorithm is used for solving TDOA equations. We have built the system and tested it in the real-world with several flights near Beijing Capital International Airport. The system can track the plane route in real time and compare it with the positions claimed by ADS-B messages to achieve the purpose of ADS-B anti-counterfeiting and pseudo signals localization.
Research on Target Detection and Segmentation in Forward Looking Multi-beam Sonar Images

Due to the effect of complex environment and other interference, sonar images are always filled up with contamination and noise. For that reason, accurate and fast detection and segmentation of targets in sonar images are vital for underwater target recognition. In this paper an algorithm based on depth-first search is proposed for target detection and segmentation in forward looking multi-beam sonar images. The algorithm is made up with one-dimensional peak search and two-dimensional depth-first search segmentation. It can rapidly detect and segment the parts of targets in sonar images. The algorithm is tested with data from an actual multi-beam sonar. It is also compared with other segmentation method such as Otsu, C-means and Markov Random Fields. The result shows that the algorithm can extract the target with high efficiency while automatically avoiding reverberation and noise, making it suitable for real-time process of sonar images with isolated targets.
Peripapillary Retinal and Choroidal Perfusion in Nonarteritic Ischemic Optic Neuropathy Using Optical Coherence Tomography Angiography.

SIGNIFICANCE Nonarteritic ischemic optic neuropathy (NAION) has been linked with vascular insufficiency, although the pathophysiology remains elusive. Optical coherence tomography angiography (OCTA) is a promising technology that noninvasively evaluates optic disc perfusion and that may help to characterize peripapillary vascular changes in NAION. PURPOSE This study aimed to evaluate peripapillary vascularity in NAION eyes and to compare it with fellow unaffected eyes and healthy control eyes using OCTA. METHODS In this cross-sectional study, OCTA of the optic nerve head was obtained in 10 nonacute unilateral NAION and 12 healthy age-matched controls using ZEISS Angioplex. Quantitative analysis of peripapillary retinal and choroidal vascularity of NAION eyes was done using the instrument's inbuilt algorithm and ImageJ software and compared with fellow and control eyes. RESULTS Mean total peripapillary superficial retinal vessel and perfusion density as calculated by the instrument was significantly reduced in NAION eyes compared with fellow eyes (13.93 ± 4.27 mm/0.36 ± 0.07 for NAION eyes; 17.77 ± 1.26 mm/0.43 ± 0.08 for fellow eyes; P = .01/P = .05). Using the ImageJ software technique, the mean superficial retinal perfusion was found to be significantly reduced in NAION eyes (0.17 ± 0.07) compared with fellow eyes (0.25 ± 0.06; P < .01) and control eyes (0.25 ± 0.04; P < .01). At the level of choriocapillaris, it was not significantly affected in NAION eyes (0.37 ± 0.13) versus fellow (0.34 ± 0.14; P = .1) and control eyes (0.31 ± 0.34; P = .83). Analysis with the two techniques yielded differing results: the ImageJ analysis technique found a 32% reduction in superficial retinal perfusion in NAION eyes, whereas the instrument's inbuilt algorithm found a 16% reduction compared with fellow and control eyes (P ≤.01). CONCLUSIONS Peripapillary vascularity can be estimated both at the retinal and choroidal levels using ImageJ software to analyze OCTA images. Retinal peripapillary vascularity is compromised in NAION eyes, but vascularity is not significantly affected at the choroidal level.
Simulation of Rectangular GAA NWMBCFET for sub 35nm using TCAD

In this paper, a cylindrical GAA NWFET (Gate All around Nano wire Field Effect Transistor) is compared with a rectangular GAA NWMBCFET (Gate All around Nano Wire Multi Bridge Channel Field Effect Transistor) for sub 35nm devices using TCAD (Technology Computer Aided Design) simulation tool. In a rectangular GAA NWMBCFET a total of 6 thin channels instead of one channel are created with equal distance in vertical and horizontal stacking. The device performance using coupled Drift-diffusion (DD) method and Shockley-Read-Hall Recombination method (SRH) has been numerically evaluated. Comparisons have been performed between cylindrical GAA NWFET and rectangular GAA NWFET, as well as between rectangular GAA NWFET and rectangular GAA NWMBCFET using TCAD numerical simulation calibrations to evaluate the transfer and output characteristics. Due to the presence of Ultra-Thin Body (UTB) in the multi bridge channel the current drive of the device is increased. The reduction in influence of short channel effects has been addressed by the GAA structure.
Single-Stage Bidirectional Triple Active Bridge AC-DC converter for Single Phase Grid Applications

The design of a Single-Stage Three Port AC-DC converter is analyzed in this article. The structure analyzed is the Triple Active Bridge (TAB) single phase AC-DC converter which can be used for charging of Plug in Hybrid Electric Vehicles or to control the power flow in a micro-grid composed by a renewable source, a battery pack and the single phase grid. The analysis of the converter is focused in the modulation and the high frequency (HF) transformer sizing. The three winding transformer allows safety and voltage matching between the three sources. The sources are connected to the HF transformer with three active bridges which are modulated with a fixed frequency. To compensate the time-variant AC grid voltage in the HF transformer, the grid voltage is modulated with a 50% duty ratio and, the renewable DC source and the battery are modulated with a low frequency sinusoidal time-variant duty ratio. The turn ratio relationship of the HF transformer is chosen to assure inner mode modulation. Bidirectional power flow is controlled by phase shift modulation. This modulation strategy allows soft switching in all active bridges leading to higher efficiency of the converter. The simulation of a 1-KW topology confirms the advantages of the proposed methodology.
Fabrication of Vertically Stacked Nanosheet Junctionless Field-Effect Transistors and Applications for the CMOS and CFET Inverters

In this study, conventional CMOS and complementary field-effect transistor (CFET) inverters based on a vertically stacked-nanosheet (NS) structure were fabricated. The NS below 8-nm channel layer thickness ( ${T}_{{\text {Si}}}$ ) was obtained by dry etching and wet etching processes. The channel thickness is controlled by dry etching, and the channel width was shrunk down by wet etching. Compared to single nanowire field-effect transistors (NSFETs), stacked NSFETs exhibit higher ON-current performance. For the CMOS inverter, the voltage transfer characteristics (VTCs) could be matched much better by adjusting the channel widths and layers for N-channel MOSFET (NFET) and P-channel MOSFET (PFET), respectively. For the CFET inverter, layout areas could be reduced and requires less number of lithographic and ion implantation steps contrary to the CMOS inverter. However, we observe that the VTCs of the CFET inverters still show asymmetric behavior due to the difficulties of adjustment in NS layers and systematic behavior of threshold voltages for NFETs/PFETs. This work experimentally demonstrates the CMOS and CFET inverters on the vertically stacked NS structure, which is promising for system-on-panel (SoP) and 3-D-ICs applications.
Analyse de la variabilité de la fréquence cardiaque : méthodes, limites et exemples cliniques

Heart rate variability (HRV) is an analysis of milliseconds variations in intervals between heartbeats and has become an increasingly used tool for clinical investigation of fatigue, especially in athletes. Eliciting an indirect index of the autonomic nervous system regulation on the heart rate, HRV correlates with different fatigue states and appears to be a powerful biomarker in their monitoring. This article presents the tools to familiarize with this method while detailing good practices for use and interpretation. A method allowing characterization of different fatigue states is also presented for a clinical use with a systemic approach.
On the Fairness of Distributed Ledger Technologies for the Internet of Things

Distributed networks have been widely studied in literature. However, the blockchain paradigm has inspired to revisit some of the results under a different point of view. In this paper, we analyze the "classic" spam protection problem applied to the IOTA Tangle, a distributed ledger technology which addresses Bitcoin’s (monetary and energy) efficiency issues through the absence of mining pools. However, the lack of miners makes the network vulnerable to denial of service attacks. We propose an anti spam mechanism based on the solution of a cryptographic puzzle: When a node wants to generate a new transaction, it dynamically adapts the difficulty of the puzzle depending on its target throughput and on its reputation score. Specifically, the adaptive difficulty property guarantees that any node, even with low hashing power, can achieve similar throughput for a given reputation. In the paper, we prove this claim both analytically and through simulations, and we show that fairness between low- and high-power nodes is indeed reached.
Theoretical Analysis for Extended Target Recovery Using RSFRs

Recent years, sparse recovery that exploits the sparsity of the target scene has been introduced for range-velocity estimation in the Randomized Stepped Frequency Radar (RSFR). Due to the synthesized high range resolution in RSFRs, a target usually occupies a series of range cells and is referred to as an extended target. This clustering characteristic as well as the sparsity of the target scene appear as the block-sparsity property. By leveraging the block-sparsity property, which better characterizes the priori knowledge of the extended target than the conventional sparsity property, block sparse recovery promises to outperform the conventional sparse recovery. This paper studies the RSFR range-velocity estimation using block sparse recovery, and theoretically analyzes the recovery condition. The simulation results demonstrate the superiority of the block sparse recovery over conventional sparse recovery in range-velocity reconstruction with RSFRs.
AgglutiFiT: Efficient Low-Resource Agglutinative Language Model Fine-Tuning

Text classification tends to be difficult when data are inadequate considering the amount of manually labeled text corpora. For low-resource agglutinative languages including Uyghur, Kazakh, and Kyrgyz (UKK languages), in which words are manufactured via stems concatenated with several suffixes and stems are used as the representation of text content, this feature allows infinite derivatives vocabulary that leads to high uncertainty of writing forms and huge redundant features. There are major challenges of low-resource agglutinative text classification the lack of labeled data in a target domain and morphologic diversity of derivations in language structures. It is an effective solution which fine-tuning a pre-trained language model to provide meaningful and favorable-to-use feature extractors for downstream text classification tasks. To this end, we propose a low-resource agglutinative language model fine-tuning $AgglutiFiT$ , specifically, we build a low-noise fine-tuning dataset by morphological analysis and stem extraction, then fine-tune the cross-lingual pre-training model on this dataset. Moreover, we propose an attention-based fine-tuning strategy that better selects relevant semantic and syntactic information from the pre-trained language model and uses those features on downstream text classification tasks. We evaluate our methods on nine Uyghur, Kazakh, and Kyrgyz classification datasets, where they have significantly better performance compared with several strong baselines.
Reinforcement Learning-Based Path Generation Using Sequential Pattern Reduction and Self-Directed Curriculum Learning

Recent advancements in robots and deep learning have led to active research in human-robot interaction. However, non-physical interaction using visual devices such as laser pointers has gained less attention than physical interaction using complex robots such as humanoids. Such vision-based interaction has high potential for use in recent human-robot collaboration environments such as assembly guidance, even with a minimum amount of configuration. In this paper, we introduce a simple robotic laser pointer device that follows an arbitrary planar path and is designed to be a visual instructional aid. We also propose an image-based automatic path generation method using reinforcement learning and a sequential pattern reduction technique. However, such vision-based human-robot interaction is generally performed in a dynamic environment, and it can frequently be necessary to calibrate the devices more than once. In this paper, we avoid the need for this re-calibration process through episodic randomization learning and improved learning efficiency. In particular, contrary to previous approaches, the agent controls the curriculum difficulty in a self-directed manner to determine the optimal curriculum. To our knowledge, this is the first study of curriculum learning that incorporates an explicit learning environment control signal initiated by the agent itself. Through quantitative and qualitative analyses, we show that the proposed self-directed curriculum learning method outperforms ordinary episodic randomization and curriculum learning. We hope that the proposed method can be extended to a general reinforcement learning framework.
An Analytical Framework to Control the Synchronization on Networks

In this paper, we provpose an analytical framework to control the synchronization on different structures of networks. The algebraic connectivity of the Laplacian matrix is used to replace the second Lyapunov exponent in the Master Stability Function. The spectral analysis is introduced to determine the ability of the network to support the synchronization. The relation between the average path length and the Laplacian spectrum is discussed to investigate the method to adjust the links of the networks towards faster synchronization. The opinion dynamics is used as a typical kind of synchronization for the experimental study.
Adaptive Attitude Takeover Control for Spacecraft with Input Quantization and Saturation

The attitude takeover control problem of multi-cellular satellite spacecraft with quantized drive torque and input saturation is studied. In order to reduce the communication burden between the control cell satellite and the actuator cell satellite, hysteresis quantizer is used for the control torque signal. An adaptive inversion control scheme is proposed to solve the attitude takeover control problem. The auxiliary system and Nussbaum function are introduced in the control scheme to extend the attitude dynamic system to the state of the augmented system under the influence of input saturation and adjust the dynamic gain of the internal controller. Simulation results verify the effectiveness of the control scheme.
Phase-rotated Spectral Correlation Detector Based Sensing for Wideband Spectrum at Low SNRs

Wideband spectrum sensing plays a key role in cognitive radio. Detecting wideband signals accurately and efficiently at low SNR regimes has recently attracted much interest from the research community. In this paper, two novel wideband sensing methods are presented, sequential sensing using phase-rotated spectral correlation detector (PSCD) and binary sensing using PSCD, which take advantage of PSCD's detection performance at low SNRs. They are explained in detail with diagrams and pseudo codes. Detection performance of the proposed methods in terms of detection probability and false alarm probability are evaluated in additive white Gaussian noise (AWGN) environment. The time complexity of the methods is also analyzed. Simulated results indicate that our methods outperform the energy detector at low SNRs.
4H-SiC Trench MOSFET with low on-resistance at high temperature

High breakdown voltage, low on-resistance at high temperature, and low DS leakage current were realized by a short-channel (0.34μ m) MOSFET provided with a deep P layer (ground potential) formed under trenches spaced at an interval of 2.4 μ m by self-alignment. The BVdss of this MOSFET is 1080 V, the RonS is 1.19 mΩcm2 at 25°C and 2.04 mΩcm2 at 175°C, the Vth is 4.0 V, and the Idss is 50 nA at 900 V (□ 9.6 mm). By clarifying the effect of implantation defects on leakage current, this structure was achieved by a simple process that adopts low temperature implantation for all implantation steps.
TOPOGRAFIA DO CONE MEDULAR NA ARIRANHA (Pteronura brasiliensis Zimmermann, 1780)

Aiming to offer the anatomical bases for epidural anesthesia, particularly relative to the topographical anatomy of the medullar cone, three specimens of adult giant otter (Pteronura brasiliensis), two male and one female, after natural death, were dissected. Those animals were donated from Laboratory of aquatic mammals (INPA – National Institute for Amazonia Research). After the dissection were observed: a) in two animals (66.6%), one male and one female, the medullar cone apex is close to fourth lumbar vertebra (L4) and, in other one animal (33.3%), male, between the L3 and L4 vertebrae; b) the medullar cone length is 5.50cm in alls animals to this study, and your base is close to L3 (66.6%) or between the L2 and L3 vertebrae (33.3%); c) the lumbar enlargement (Intumescentia lumbalis), that continuous to the base of the medullar cone, were noted between the T12 and L2 vertebrae. KEY WORDS: Anatomy, medullar cone, Pteronura brasiliensis.
Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

With the development of lean manufacturing and economic globalization, supply networks increasingly become complex and large-scale, within which thousands of firms inter-depend with each other. Due to these increasing inter-dependencies, disruption of a quite few critical suppliers, namely bottleneck suppliers, can induce high loss to a supply network and even make the whole network dysfunction. Identification of bottleneck suppliers is significantly important for supply network risk management. Thus, in this article, a method based on a memetic algorithm with local neighborhood search (MALNS) is proposed to identify bottleneck suppliers in a two-stage supply network. Firstly, a model based on multipartite network is designed to describe the product supply-demand relations between multiple manufacturers and suppliers, which considers the different roles of manufacturer and supplier and differentiates the products that suppliers supply. To assess the loss caused by supplier disruptions, two performance metrics of supply networks, average product availability rate and manufacturer functioning rate, are presented. Then, a MALNS-based method is proposed to identify bottleneck suppliers, i.e., suppliers whose disruption will decrease both performance metrics most greatly. Finally, a case study based on a real automobile supply network is presented to validate the applicability and effectiveness of the proposed method.
An Object Detection and Classification Method using Radar and Camera Data Fusion

Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.
Real-time Event Detection and Tracking in Microblog via Text Chain and Sentiment time series

Microblog becomes one of the most popular media for sharing first-hand information. As more close to real event, detecting and tracking event in microblogs is a research hotspot. To alleviate feature sparsity problem, traditional event detection and tracking methods exploit comments, replies, and reposts to enrich the information of posts. However, ignoring text relationship only leads to more irrelevant information. Aiming at this issue, a novel real-time event detection and tracking method for microblog stream is proposed. For each microblog from the real-time stream, similarity against historical event set is first evaluated. Then, known event microblogs are partitioned by the proposed method sentiment time series, clustered based on word relation graph, and compare with the previous graph to track burst keywords. Meanwhile, text chain is introduced to detect events for unknown event microblogs. Text chain is a unique relationship of microblogs, in which comments, replies, and reposts are used to form a word relation graph of the corresponding post. Posts and attached graphs are clustered to detect new events. Experiments are performed on real-time microblog datasets to evaluate the effectiveness of methods. Results show that the proposed method gains better F-measure than similar methods.