Miracle-dz/UniFAR-FTUs huggingface dataset:
Arterial smooth muscle cell PKD2 (TRPP1) channels regulate systemic blood pressure

Systemic blood pressure is determined, in part, by arterial smooth muscle cells (myocytes). Several Transient Receptor Potential (TRP) channels are proposed to be expressed in arterial myocytes, but it is unclear if these proteins control physiological blood pressure and contribute to hypertension in vivo. We generated the first inducible, smooth muscle-specific knockout mice for a TRP channel, namely for PKD2 (TRPP1), to investigate arterial myocyte and blood pressure regulation by this protein. Using this model, we show that intravascular pressure and α1-adrenoceptors activate PKD2 channels in arterial myocytes of different systemic organs. PKD2 channel activation in arterial myocytes leads to an inward Na+ current, membrane depolarization and vasoconstriction. Inducible, smooth muscle cell-specific PKD2 knockout lowers both physiological blood pressure and hypertension and prevents pathological arterial remodeling during hypertension. Thus, arterial myocyte PKD2 controls systemic blood pressure and targeting this TRP channel reduces high blood pressure.
Satellite Pose Estimation via Single Perspective Circle and Line

Pose estimation is vital for space robot to capture satellite. Dual poses including center position and normal direction can be obtained from a single perspective view of a radius-known circle such as docking ring of the satellite. To solve the duality and recover the roll angle, this paper presents here a new method called perspective circle and line (PCL), which is based on the perspective view of a single circle and line. First of all, the dual center positions and normal directions are recovered with the circle and the image. Then, the line is utilized to recover the roll angle of the circle analytically. By reprojecting a random point on the line to the image and verifying whether the projection lies on the image line, the false pose of the object is identified. Simulation and physical experiments show that the position and orientation of the circle can be recovered. But in practice they are affected by the accuracy of camera calibration and feature extraction. As a whole, PCL is robust to noise in pose estimation, especially in recovering the orientation.
Reducing Generation Cost by Optimum Load Scheduling in Smart Grid Considering System Loss

Reducing electricity generation cost, and peak demand, are two key requirements for smart grid. Load scheduling is an effective way to reduce the peak demand for reducing generation cost. In this paper, the problem of scheduling the loads of a substation is analyzed considering system losses - which is modeled including transmission loss and transformer loss - to minimize the electricity generation cost. An optimization algorithm is then formulated to determine the optimal schedule of the loads within 24 hours of a day. The problem is solved for various load scenarios by using optimization tools including MINOS, FilMINT, and CPLEX. The numerical results show that optimal load scheduling reduces the average generation cost by more than 34% compared to that of a conventional system. However, this reduction decreases with increasing number of load. System loss also has a significant impact on the reduction of generation cost by optimal load scheduling.
Online identification of internal impedance of Li-ion battery cell using ternary-sequence injection

The internal impedance of a battery has been shown to change as a function of state-of-charge (SOC) and state-of-health (SOH). Therefore, online impedance measurements can provide useful information for SOC- and SOH-estimation algorithms. Broadband injections techniques such as pseudo-random-binary-sequence (PRBS) are attractive alternative for replacing conventional electrochemical-impedance-spectroscopy (EIS) which is very slow for online battery impedance measurements. However, non-linearities of batteries have a negative impact on the measurement results obtained by the PRBS for which the identified system is assumed to be linear. This paper demonstrates the use of ternary-sequence excitation signal for battery impedance measurements. Appropriately designed ternary-sequence can efficiently capture the linear component of the impedance by canceling out the distortion caused by the non-linear parts. The presented method can be used for rapid impedance measurements and thus, as a valuable tool in online SOC- and SOH-estimation algorithms. Experimental measurements are shown from a Lithium-Iron-Phosphate (LiFePO4) battery cell.
A 0.032-mm2 Fully-Integrated Low-Power Phase-Locked Loop Based on Passive Dual-Path Loop Filter

This paper presents a low-power and area-efficient PLL-based frequency synthesizer. The area-saving technique is based on dual-path loop filter which involves no additional active components overhead and inductor-less ring voltage-controlled oscillator (VCO). In addition, digitally controlled duty cycle corrector (DCC) and locked detector (LD) both utilized to calibrate the duty cycle of output signals are introduced. To verify the feasibility of the proposed technique, a integer-N PLL is built in standard 180-nm CMOS technology, which can cover a wide range of frequency from 0.2 GHz to 2 GHz with average RMS jitter of 0.98 ps. It consumes 3.7 mA from 1.8-V power supply at 2 GHz output frequency and occupies an active area of only 0.032-mm2.
Detecting Intention Through Motor-Imagery-Triggered Pupil Dilations

Human–computer interaction systems that bypass manual control can be beneficial for many use cases, including users with severe motor disability. We investigated pupillometry (inferring mental activity via dilations of the pupil) as an interaction method because it is noninvasive, easy to analyse, and increasingly available for practical development. In 3 experiments we investigated the efficacy of using pupillometry to detect imaginary motor movements of the hand. In Experiment 1 we demonstrated that, on average, the pupillary response is greater when the participant is imagining a hand-grasping motion, as compared with the control condition. In Experiment 2 we investigated how imaginary hand-grasping affects the pupillary response over time. In Experiment 3 we employed a simple classifier to demonstrate single-trial detection of imagined motor events using pupillometry. Using the mean pupil diameter of a single trial, accuracy rates as high as 71.25%, were achieved. Implications for the development of a pupillometry-based switch and future directions are discussed.
An Automatic Tracking Method for Multiple Cells Based on Multi-Feature Fusion

Cells automatic tracking in microscopy image sequences is an important task in many biomedical applications, especially for the analysis of anticancer drugs. However, it is still a challenging problem due to the high density, variable shape, lack of effective feature information, and occlusion of the cells by division or fusion. In this paper, the aim is to develop a fully automatic and effective method to track hundreds of cells, and a multi-feature fusion re-tracking algorithm is proposed based on the tracking-by-detection scheme. First, a region proposal method based on faster R-CNN is presented to generate cell candidate proposals. Then, a cell tracking method is proposed by fusing the bounding box and feature vector of cell candidates based on the abovementioned results. Finally, a re-tracking algorithm is employed by integrating historical information of matching frame. A series of experiments is conducted to test and verify the validity on the datasets from ISBI Cell Tracking Challenge, and then, the proposed method is applied to the T24 dataset of bladder cancer cells from the Cancer Cell Institute, University of Cambridge. The experimental results are encouraging and show that the proposed method is competitive with other state-of-the-art methods, which means that there are probably potential applications in the field of biomedical engineering.
A Method of CNN Traffic Classification Based on Sppnet

Nowadays, CNN widely used in network traffic classification. The traditional model of CNN only can be sent with the fixed traffic dataset in network traffic classification. But for the traffic dataset in network, that model must lead to a certain degree loss of the dataset by truncated or discarded. To solve this defect, a new CNN traffic classification model based on sppnet (spatial pyramid pooling) is proposed in this paper. Based on the CNN model of the LeNet-5, in the pooling layer before the fully connected layer, the new model is replaced the max-pooling to the spatial pyramid pooling which can realize the network traffic with indefinite length dataset. Through a series of experiments, the model has achieved certain achievement, and reducing the impact of human factors on traffic classification.
Faster Querying for Database Integration and Virtualization with Distributed Semi-Joins

Data integration and virtualization is commonly used to combine data for data analytics and reporting. A major challenge is handling large data sizes (“Big Data”) as moving data across a network is extremely expensive and limits query processing. Business intelligence and data visualization software require rapid response times for users, and data virtualization is often limited for use cases involving joins across systems. The contribution of this work is a semi-join based approach to data virtualization joins that minimizes data movement and utilizes the extensive resources available in the database systems rather than performing query processing in the virtualization engine. The result is significantly less data movement which translates into faster query times and higher performance. Experimental results demonstrate that performance can be increased by an order of magnitude. A unique feature of the approach is that it does not require any special software installed above the database servers such as mediators and works directly using SQL queries.
Microwave Photonic Image-Reject Mixer Based on a Tunable Microwave Photonic Filter With High Rejection

In a heterodyne receiver, the image can interfere with the desired signal and must be eliminated at the mixing stage. This paper presents a flexible microwave photonic image-reject mixer based on a widely tunable microwave photonic filter (MPF), which is used to select the desired signal and suppress the image. The MPF is realized by using stimulated Brillouin scattering, which offers significant advantages of wideband tunability, high selectivity, and large image rejection. In this work, a theoretical model is established to describe the operation principle of the proposed mixer at first. Then, in the experiment, a 7.8-GHz radio frequency (RF) signal is successfully down-converted to an intermediate frequency signal between 0.2 and 1.5 GHz, with an image rejection of larger than 40 dB. Additionally, when the RF frequency is tuned from 1.8 to 19.8 GHz, the image rejection remains above 40 dB.
Primary clear cell adenocarcinoma of the pancreas: a case report and literature update

Primary clear cell carcinoma of pancreas is extremely rare. We present a case of a 64-year-old male with a mass in the distal body and tail of the pancreas. He underwent a distal pancreatectomy. The histopathology of tumor cells showed features with abundant clear cytoplasm and prominent cell boundaries. Immunohistochemical analysis of neoplastic cells showed reactions to antibodies against cytokeratin-7 and showed no reactions to antibodies against hepatocyte nuclear factor-1β, carbonic anhydrase 9, synaptophysin, and chromogranin A. The patient was subsequently diagnosed with a primary clear cell carcinoma of the pancreas. This is the first time we have encountered it. We report this rare case and update the current literature of this tumor.
The Construction of Large-Scale Cortical Networks for P300 From Scalp EEG

Multiple studies, which studied cortical generators and cerebral networks of P300 based on electroencephalogram (EEG) and functional magnetic resonance imaging, have investigated the possible mechanism underlying the inter-subject variability of P300 component. However, few studies have investigated the large-scale networks of P300 as well as the potential relationships between large-scale networks and P300, particularly in EEG signal. When using group independent component analysis (gICA) to investigate the large-scale EEG networks, some resting-state networks, such as the default mode network, have been observed, while the related task-state networks (TSNs) of P300 are still unknown. In this paper, we estimated the time courses of cortical voxels following EEG source localization and then adopted gICA at the group level to identify the related P300-TSNs. Thereafter, we constructed and further investigated the potential relationships between the properties of functional network connectivity (FNC) and P300 amplitude. Our findings revealed six best-fit common functional networks that participated in the generation of P300, namely, the primary visual network, visual network, central executive network, left parietal network, attention network, and sensorimotor network. Specifically, the FNC properties significantly related to the P300 amplitude, as well as some FNC connections. In addition, when using FNC properties to classify the high- and low-amplitude groups, a relatively high-classification accuracy of 87.26% for linear discriminant analysis and of 86.31% for support vector machine were achieved. These findings confirmed the validation of gICA in mining large-scale networks in cortical EEG and may help us better understand the inter-subject P300 variability.
An Algebraic Binary Decision Diagram for Analysis of Dynamic Fault Tree

Dynamic fault tree (DFT) is an extension of traditional static fault tree, in which several dynamic gates are introduced to model sequential dependency between fault events. As for the quantitative analysis of DFT, sequential binary decision diagram (SBDD) has been proposed by incorporating sequential nodes into traditional binary decision diagram (BDD). With SBDD, a DFT can be reduced into a sum of disjoint products (SDP) of basic events and sequential nodes, which can avoid the space explosion problem and exponential complexity caused by traditional Markov chain and inclusion/exclusion based solutions, respectively. However, the SBDD is not developed base on a well-defined temporal or sequential algebra. Rather, it is developed based on a precedence operator with specific and very limited number of reduction rules. The applications of SBDD is thus restricted to the DFTs whose dynamic gates consist of inputs of only basic events or some specific events. In this paper, we present an algebraic binary decision diagram (ABDD) based on the algebraic framework for DFT proposed by G. Merle et al. In addition to the existing laws for the reduction of arbitrary DFT with any structure, we introduce a set of new laws for the reduction of SDP in the ABDD. Thanks to the sound algebraic framework and complete set of laws, ABDD is applicable for the analysis of general DFT without any structure restriction. We illustrate our approach and compare the difference with SBDD by several examples.
Understanding the Apparent Non-Reliability in the Sensing Characteristics of MnO2 Self-Assembled Hierarchical Nanostructure

In this paper, the self-assembled hierarchical nanostructure of MnO<sub>2</sub>, consisting of 3-D nanoflowers and 1-D nanorods, was synthesized by employing a hydrothermal technique. The gas sensing property of the same was investigated and the apparent anomaly concerning the n-type material characteristics and p-type sensing behavior was explained using the underlying mechanistic framework. The morphological (field emission scanning electron microscopy, transmission electron microscopy), structural (X-ray diffraction), and surface compositional (X-ray photoelectron spectroscopy) characterizations were carried out in detail, to establish the formation of <inline-formula> <tex-math notation="LaTeX">$\delta$ </tex-math></inline-formula>-MnO<sub>2</sub> (nanosheets having thicknesses ~ 5–18 nm) and <inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula>-MnO<sub>2</sub> (nanorods having diameters ~ 20–24 nm) in the synthesized hierarchical nanostructure. The self-assembled MnO<sub>2</sub> nanostructure offered pulse-like gas sensing performance with very fast response time and recovery time at room temperature. A possible sensing mechanism has also been furnished considering the effect of Mn interstitials (Mn<inline-formula> <tex-math notation="LaTeX">${_{i}}$ </tex-math></inline-formula>) reduction from Mn<sup>4+</sup>/Mn<sup>3+</sup> states to Mn<sup>3+</sup>/Mn<sup>2+</sup> states (in the presence of methanol), to explain the apparent anomaly in the sensing characteristics.
Looking for Seagrass: Deep Learning for Visual Coverage Estimation

Underwater videography enables marine researchers to collect enormous amounts of seagrass image data. This collection is fast and cheap but the manual analysis of such data is slow and expensive. Therefore, we propose a machine-learning approach for the automatic seagrass coverage estimation of the sea bottom. Our contribution is the investigation of CNN features to describe patches and superpixels of seagrass. CNN features are the activations of a specific layer in a deep convolutional neural network. We also provide the first public available dataset of seagrass images that can be used as a benchmark for automatic seagrass segmentation. Our best method achieves an accuracy of 94.5% for seagrass segmentation on the provided dataset. Our code and dataset is available on GitHub: https://enviewfulda.github. io/LookingForSeagrass/
Scene-Adaptive Fusion of Visual and Motion Tracking for Vision-Guided Micromanipulation in Plant Cells

This work proposes a fusion mechanism that overcomes the traditional limitations in vision-guided micromanipulation in plant cells. Despite the recent advancement in vision-guided micromanipulation, only a handful of research addressed the intrinsic issues related to micromanipulation in plant cells. Unlike single cell manipulation, the structural complexity of plant cells makes visual tracking extremely challenging. There is therefore a need to complement the visual tracking approach with trajectory data from the manipulator. Fusion of the two sources of data is done by combining the projected trajectory data to the image domain and template tracking data using a score-based weighted averaging approach. Similarity score reflecting the confidence of a particular localization result is used as the basis of the weighted average. As the projected trajectory data of the manipulator is not at all affected by the visual disturbances such as regional occlusion, fusing estimations from two sources leads to improved tracking performance. Experimental results suggest that fusion-based tracking mechanism maintains a mean error of 2.15 pixels whereas template tracking and projected trajectory data has a mean error of 2.49 and 2.61 pixels, respectively. Path B of the square trajectory demonstrated a significant improvement with a mean error of 1.11 pixels with 50% of the tracking ROI occluded by plant specimen. Under these conditions, both template tracking and projected trajectory data show similar performances with a mean error of 2.59 and 2.58 pixels, respectively. By addressing the limitations and unmet needs in the application of plant cell bio-manipulation, we hope to bridge the gap in the development of automatic vision-guided micromanipulation in plant cells.
Meteotsunami Observation by WERA Ocean Radar Systems at the Dutch Coast

On 29 May 2017 a meteorological tsunami (meteotsunami) was generated in the North Sea due to an air pressure disruption, which reached the southwestern coastal area of the Netherlands. There are two operational WERA® ocean radar systems installed at the Dutch coast, which provide the sea current information around the entrance of the port of Rotterdam. After re-processing the acquired raw radar measurements from both systems using the original WERA tsunami detection software, specific sea current signatures generated by the moving meteotsunami wave were identified up to 40 km offshore. The results show good synchronization in tsunami detection both for radar systems and available tide gauge measurements. The detection of the tsunami-induced currents has shown a good applicability of phased-array radar technology for offshore tsunami monitoring and navigation safety. In Europe, the ocean radar systems are not yet used for tsunami alerting; although the systems can be easily upgraded by modifying the radar hardware and installing a tsunami detection software package. The presented results using the ocean radar systems show that offshore current measurements are feasible to be a useful complement to the already existing water level measurement network.
Indoor Pedestrian Dead Reckoning Calibration by Visual Tracking and Map Information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming an important tool in indoor navigation. This is mainly due to the development of affordable and portable Micro Electro-Mechanical Systems (MEMS) on smartphones and decreased requirement of additional infrastructures in indoor areas. The main drawback to this technology remains the problem of drift accumulation and the need for support from external positioning systems. Vision-aided inertial navigation is one possible solution to that problem. This solution has become more popular in indoor localization with improved satisfaction compared to individual PDR system. Previous studies have used fixed platforms and visual tracking employed feature-extraction-based methods. This paper proposes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical systems can provide only relative positioning information, this paper proposes a method to integrate digital maps with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones using iOS and Android systems, based on corresponding data collection apps respectively, in order to test the robustness of method. It also uses two different methods for calibration, by time synchronization of positions and heading calibration based on time steps. Results demonstrate that localization information collected from both operating systems can be significantly improved after integrating with visual tracking data.
Position estimation of mobile robots with internal and external sensors using uncertainty evolution technique

A wheeled mobile robot on a flat plane can estimate its current location and orientation from the cumulative wheel rotation (dead reckoning). The method, however, has the drawback that estimation errors are accumulated as the robot moves, and the accuracy of estimation decreases. To address this problem, a positioning technique using an external sensor is needed. The authors propose a more general positioning method whereby the robot can act autonomously and move along a path unlimited by positioning. The robot can modify the position estimated by dead reckoning by repeating low-level sensing asynchronously and intermittently. This method uses not only the estimated position data but also the accuracy of the dead reckoning and the external sensor effectively. <<ETX>>