Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating satellite‐ and reanalysis‐based precipitation products over the Qinghai‐Tibetan Plateau in the perspective of a new error‐index system

The necessity of evaluating satellite‐ and reanalysis‐based precipitation products as optional proxies of precipitation observations is widely understood. Four typical satellite‐ or reanalysis‐based precipitation products from TRMM, CPC, CMFD and ERA5‐Land datasets were evaluated at temporal and spatial scales using a new error‐index system method across the Qinghai‐Tibetan Plateau (QTP) from 1979 to 2018. The error‐index system consisted of 10 indices, which were classified into two categories, that is, the comprehensive error indices and error component indices. A local error decomposition scheme suitable for the QTP was built according to the simulation test. Results showed the datasets except ERA5‐Land performed better at monthly scale than that at daily scale generally, though significant differences were found among the products. Particularly, CMFD achieved the highest accuracy in estimating monthly and daily precipitation, followed by CPC, while TRMM performed similarly to ERA5‐Land. It was estimated that over one fifth of the precipitation were falsely recorded in the four products. Approximately over 15% of daily precipitation were overestimated or underestimated with an accumulated extent reaching up to 60 mm per month in summer. Spatial results indicated that large deviations were found in the southeast QTP, which were derived from a significant underestimation. Besides, a large proportion of false and missing estimation of precipitation events were witnessed in the northeast river basins. Through the comparison, CMFD and CPC had reduced all error components except HNB in both magnitude and the ratio. And the datasets tended to overestimate the slight precipitation and underestimate the heavy rainfall. And the products did not eliminate the large deviations in the southeast and north QTP. The assessment results reported here will provide some valuable feedbacks for the improvement of these products.
Growth habits and defects in ZnO nanowires grown on GaN/sapphire substrates

Growth habits and defects in epitaxial ZnO nanowires grown from Au catalyst on (00.1) GaN/sapphire substrate using the vapor-liquid-solid (VLS) technique were studied using electron microscopy and x-ray diffraction. The results revealed presence of both horizontal (crawling-like) and vertical nanowires having similar orientation relationship to the substrate (00.1)ZnO‖(00.1)GaN, [11.0]ZnO‖[11.0]GaN. The crawling-like growth precedes the vertical growth, and the coalescence and overgrowth of the crawling nanowires produce a highly defective layer which separates the substrate and vertical nanorods. Transmission electron microscopy revealed a high density of planar defects in this interfacial layer. A significant density of stacking faults residing on the (0001) planes was also observed in the shorter vertical nanorods. The crawling nanowires are under residual compressive strain, whereas the vertical nanorods grow strain-free.
Two-phase flow structure of sheet cavitation

An experimental study of flow within sheet cavities is performed in a cavitation tunnel equipped with a Venturi-type test section. The flow is investigated by means of a double optical probe allowing void fraction, velocity, and chord length of the vapor structures to be measured. Laser velocimetery, wall pressure measurements, and visualization techniques are also used to characterize the liquid flow around the cavity. The consistency of the experimental results was checked though mass and momentum balances. The effects of Reynolds and cavitation numbers are analyzed. Special attention is given to the dynamic behavior of the flow, and to the vapor flow rate within the cavities. The measurements show a complex two-phase flow characterized by the presence of an extended reversed flow occurring along the solid surface and a regular decrease in void fraction along the cavity. The phase transitions seem to be mainly restricted by the dynamic of the bubbles and thermodynamic effects.
Digital IT Innovation to Improve Supply Chain Resilience: A Systematic Literature Review

The advance of digital technologies such as big data, cloud computing, and artificial intelligence ushers in the digital era for modern societies. Digital IT innovation plays an increasingly important role in helping supply chains recover from disruptions due to disastrous events like the COVID-19 outbreak. Nevertheless, there is a lack of systematic literature review on the phenomenon. As such an attempt, this paper explores the role of digital technology innovation in enhancing supply chain resilience and answers this question through a literature review and summarizes six dimensions of supply chain resilience, which provides some theoretical guidance for subsequent studies.
RL2NdgsNet: Reinforcement learning based efficient classifier for mediastinal lymph nodes malignancy detection in CT images

Diagnosis of lymph nodes (LNs) in benign and malignant categories significantly impact the treatment planning of cancer. Invasive methods like biopsy are complex and painful procedures for cancer detection. Radiological modalities (like X-ray, USG, CT, and MRI) based medical imaging approaches are non-invasive and painless procedures for the cancer detection. Deep learning (DL) based architectures significantly improved the performance of medical imaging related tasks like organ detection and disease diagnosis. This paper introduces the reinforcement learning (RL) based DL network named – RL2NdgsNet (reinforcement learning based lymph nodes diagnosis network), for diagnosis of mediastinal lymph nodes (MLNs) in benign and malignant categories. Furthermore, we have designed custom DQN policy for the proposed RL2NdgsNet and also experimented with various state-of-the-art activation functions and exploration fraction. The proposed RL2NdgsNet achieves best results of sensitivity = 98.03%, specificity = 98.35%, accuracy = 98.2%, and AUC = 98.19%.
ON THE DISTRIBUTION OF THE FIRST PRODUCT MOMENT-COEFFICIENT, IN SAMPLES DRAWN FROM AN INDEFINITELY LARGE NORMAL POPULATION

LET the origin be taken at the mean of the bivariate population and let that population be defined by -,, 0-2 and p. Let the sample be defined by mln, M2, 4-1y 2 and r. Then it is known that the distribution surface of in1, m2 is independent of that for E' 2 and r, so that we need not consider in1,, M2 further. The surface for 14 2 and r is given by* it 1 12 2r'p,1 12 1220 = z~e (1 -p2) y12 61P 2 + 22 n-2 1n-2 (2 -4). (i)
To Evaluate and Analyze the Performance of Anomaly Detection in Cloud of Things

The Internet of Things (IoT) has become a household name in our day-to-day life. The IoT concept offers several opportunities to improve human life as well as save the time, but it also has the potential to increase the vulnerability of personal information once it is collected. In terms of security and privacy, data collected in IoT is typically offloaded to the cloud, which leaves data vulnerable to a variety of attacks. The integration of IoT and cloud can be considered as cloud of things (CoT). In this research, we look for abnormalities in security measures that were once extensively used, such as wired or wireless networks. The huge number of sources that generate these data, as well as the connectivity and traffic patterns in CoT. This research focuses on evaluating the performance of anomaly detection in the CoT using three primary machine learning algorithms: KNN, CNN, and Naive Bayes. The evaluation of the anomalies on the BotIoT dataset which has been created using the simulation of IoT devices and routers. The best accuracy is achieved by CNN is 99.94% with 99.5% F1-score. Our evaluation demonstrates the promising results in terms of RoC curve and confusion matrix.
Enhancement of Contrast Ratio by Using Ferroelectric Nanoparticles in the Alignment Layer of Liquid Crystal Display

We demonstrate the effect of the ferroelectric nanoparticles dispersed in the alignment layer of a twisted nematic (TN) liquid crystal (LC) panel. In comparison to a conventional TN LC panel with a conventional polymide layer, the TN LC panel with an alignment layer doped with ferroelectric nanoparticles shows a much higher contrast ratio. The effects of barium titanate nanoparticles at several different concentrations have been studied, among which 2.5 and 5 wt % barium titanate nanoparticles mixtures show better performances. The surface texture of the alignment layers has also been studied by atomic force microscopy (AFM).
Sim-to-Real Transfer of Image-Based Autonomous Guidewire Navigation Trained by Deep Deterministic Policy Gradient with Behavior Cloning for Fast Learning

Percutaneous coronary intervention (PCI) is a frequently used surgical treatment for cardiovascular disease, one of the leading cause of death in the world. In traditional PCI, a doctor navigates a thin guidewire in a patient's vessel toward a target location by looking into live X-ray angiogram images of the patient. Recently, researchers are using reinforcement learning to automate this guidewire navigation process without attaching any sensor to the guidewire tip. These researchers use a real vessel phantom to train their behavior policy using reinforcement learning. Training a reinforcement learning algorithm on a real setup can give a good guidewire control on that setup, but it is under question whether the trained algorithm can be applied to other vessel structures. We can make various vessel phantoms and train the algorithm on the setups, but it can be really time and money consuming. In this paper, we devise a method for sim-to-real transfer of a guidewire navigation trained by reinforcement learning using only images. We pretrain our behavior policy using data collected by running an expert algorithm in the virtual environment. Then, we train the behavior policy by deep deterministic policy gradient (DDPG) in a virtual environment. With behavior cloning, our method learns to successfully navigate a guidewire in much shorter time than training DDPG from scratch without behavior cloning. After done with the training, we transfer the behavior policy trained in the virtual environment to the guidewire navigation in a real vessel phantom. Our trained behavior policy navigates the guidewire to destinations successfully in all test episodes and navigates faster than the expert algorithm. Experiment video is available at: https: //youtu.be/HCEbIhZsXqw
Crescent Fracture of the Pelvis: A Case Report

Crescent fractures are a rare type of pelvic injury. They result from a lateral compression force and are rotationally unstable. The following is a case of a young male who suffered a crescent fracture on the right side of the pelvis and was treated for the same. These fractures, being rare and complex, need to be managed in accordance with other injuries sustained by the patient and also need fixation for a better functional outcome.
Analytical Electro-Thermal Model for Multianode Schottky Diodes With Improved Temperature-Dependent Parameters Extraction Method

An analytical electro-thermal model for multianode Schottky diodes is proposed in this work. By characterizing the self-heating effects, the temperature-dependent parameters including series resistance, ideality factor, and saturation current could be accurately extracted. By means of self-consistent mapping between thermal and electrical domain, the proposed model could eliminate the errors in conventional method, which makes the assumption that each anode operates at the same temperature even at high-junction current levels. This approach is capable of determining highly accurate real-time parameters for diode under different dissipated powers, which is critical for circuit reliability analysis and high-power multiplier design. The proposed electro-thermal model has been applied to analyze a novel four-port balanced tripler, operating up to 220 GHz, with the comparison to two conventional models. With an input power of 350 mW, the circuit efficiency simulated based on this extraction method only features a difference of ~1.1% at 219-GHz measured results.
RF LNA with Simultaneous Noise-Cancellation and Distortion-Cancellation for Wireless RF Systems

In this paper, the design of an RF LNA with simultaneous noise-cancellation and distortion-cancellation is presented. The proposed LNA simulation results show a S21 gain of 15.4 dB and total noise Figure cancellation of 0.45dB with a circuit noise Figure NF of 1.6 dB and S12 reverse isolation of 38.2 dB at frequency 2.4 GHz. The simulation results show a 12 dBc of IM3 distortion-cancellation and 6 dB IIP3 improvement with power consumption of 9.5 mA. Proposed RF LNA simultaneous cancellation of noise and distortion improves circuit noise Figure and linearity and in turn improves the overall wireless RF system dynamic range.
VMVG-Loc: Visual Localization for Autonomous Driving using Vector Map and Voxel Grid Map

This study proposes a visual localization method using a vector map and voxel grid map with a stereo camera. The two maps provide different modality advantages and are integrated using a particle filter. In contrast to other vector map-based methods, our method does not use road markings because creating and maintaining vector maps that include high-accuracy road markings is laborious. Furthermore, it limits the regions where they are available. This method uses only lane center-lines from vector maps, which are easier to create than road markings. The method performs ray casting and computes the reprojection error to evaluate the vehicle position for voxel grid maps. Although this makes the method environmentally sensitive, the constraints by lanes make the estimation stable. Experiments confirmed that the method could perform localization stably and accurately without failure even over long distances. In addition, an ablation study showed the benefits of combining both maps.
Cola-HRL: Continuous-Lattice Hierarchical Reinforcement Learning for Autonomous Driving

Reinforcement learning (RL) has shown promising performance in autonomous driving applications in recent years. The early end-to-end RL method is usually unexplainable and fails to generate stable actions, while the hierarchical RL (HRL) method can tackle the above issues by dividing complex problems into multiple sub-tasks. Prior HRL works either select discrete driving behaviors with continuous control commands, or generate expected goals for the low-level controller. However, they typically have strong scenario dependence or fail to generate goals with good quality. To address the above challenges, we propose a Continuous-Lattice Hierarchical RL (Cola-HRL) method for autonomous driving tasks to make high-quality decisions in various scenarios. We utilize the continuous-lattice module to generate reasonable goals, ensuring temporal and spatial reachability. Then, we train and evaluate our method under different traffic scenarios based on real-world High Definition maps. Experimental results show our method can handle multiple scenarios. In addition, our method also demonstrates better performance and driving behaviors compared to existing RL methods.
Efficiently Learning Manipulations by Selecting Structured Skill Representations

A key challenge in learning to perform manipulation tasks is selecting a suitable skill representation. While specific skill representations are often easier to learn, they are often only suitable for a narrow set of tasks. In most prior works, roboticists manually provide the robot with a suitable skill representation to use e.g. a neural network or DMPs. By contrast, we propose to allow the robot to select the most appropriate skill representation for the underlying task. Given the large space of skill representations, we utilize a single demonstration to select a small set of potential task-relevant representations. This set is then further refined using reinforcement learning to select the most suitable skill representation. Experiments in both simulation and real world show how our proposed approach leads to improved sample efficiency and enables directly learning on the real robot.
Network Sum-Rate Maximization for Network-Coded Uplink Clustered NOMA Relay Networks

In this paper, network sum-rate maximization (NSR-MAX) for network-coded uplink clustered non-orthogonal multiple-access (NOMA) relay networks is considered. In particular, the goal is to maximize network sum-rate via optimal power allocation, where the user clusters communicate with the base-station over an amplify-and-forward relay, subject to quality-of-service (QoS) constraints. Two NOMA transmission models are proposed, where in the first model, network-coding (NC) is applied at the relay only, while the second model applies NC for user clusters’ transmissions and at the relay to minimize the transmission delay. The formulated NSR-MAX problem happens to be non-convex, and hence, is computationally-intensive. Thus, a low-complexity iterative two-layer algorithm (ITLA) is devised, which decouples the formulated problem and efficiently solves its over two-layers. Specifically, the inner-layer solves the NSR-MAX problem to obtain the optimal relay transmit power, while the outer-layer determines the optimal users’ transmit powers. Numerical results are presented, which illustrate that the proposed ITLA yields near-optimal solutions for all transmission models, in comparison to the formulated NSR-MAX problem (solved via global optimization package) as well as outperforming its OMA-based counterparts.
Text Mining for Exploring UX Issues of Qualitative Think Aloud Data on EV Sound

The study aims to explore the UX issues of electric vehicle (EV) sound by mining qualitive think aloud text data. Electric vehicle, which is more eco-friendly than traditional vehicle, becomes a popular development trend in the automotive industry. The user’s preference for sound can have a great influence on EV purchases. Think aloud is widely-used method to collect users’ thoughts and needs. 40 participants join in the experiment by speaking their own opinions on EV sound while driving. Then, text mining is conducted to explore UX issues from qualitative think aloud data. The UX labels are generated according to word frequency, and ten UX labels are divided into five UX aspects. Sentiment analysis is also taken and the associated words with UX labels are generated. Finally, insights based on the five UX aspects (speed, mode, vehicle component, environment, and sound type) on EV sound have been drawn. The outcomes can suggest implications for EV sound designing.
Variable Stars in the DDO 187 Dwarf Galaxy

Twelve pulsating variable stars have been discovered in the dwarf irregular galaxy DDO 187. Two of these are Cepheids based on CCD photometry on 29 nights spread over 13 years. Three more stars are possible Cepheids. The remaining 7 stars are all long-period variables (LPVs). The definite Cepheids lead to a new distance modulus for this galaxy of m - M = 29.24 ± 0.33, corresponding to 7.0 ± 1.2 Mpc. We discuss the nature of the LPVs and show that a self-consistent interpretation of these can be made that supports the Cepheid distance.
Event-Triggered Cooperative Adaptive Neural Control for Cyber–Physical Systems With Unknown State Time Delays and Deception Attacks

In this article, an event-triggered adaptive control strategy is proposed to achieve the leaderless-following consensus for a class of cyber–physical systems under the direct topology. Because the deception attacks, state time delay, and unknown external disturbance appear simultaneously, the existing method is hard to apply directly. The difficulty of this issue lies in that the actual system states are unavailable and control efficiencies are unknown, which caused by the deception attack. So as to tackle these knotty problems, the Nussbaum gain functions are employed to replace the control gains, and the available compromised system variables are applied in the controllers. In addition, the proposed disturbance observer based on the compromised state further improves the robustness of the system. To eliminate the influence of state time delays, the appropriate Lyapunov–Krasovskii functionals are used in the backstepping design process. Moreover, the computation and communication burden is dramatically decreased than by adopting the event-triggered mechanism and less adaptive laws. The boundedness of all signals in the closed-loop system is guaranteed via the Lyapunov stability theorem. Finally, the simulation results are provided to demonstrate the availability of the proposed control strategy.
Detection of optical trapping of CdTe quantum dots by two-photon-induced luminescence

Nanometer-sized CdTe quantum dots (QDs) in ${\mathrm{D}}_{2}\mathrm{O}$ can be optically trapped by a high repetition-rate picosecond Nd:YLF laser with an input power as low as $100\phantom{\rule{0.3em}{0ex}}\mathrm{mW}$. A large two-photon absorption (TPA) cross section of ${10}^{4}--{10}^{5}\phantom{\rule{0.3em}{0ex}}\mathrm{GM}$ for CdTe QDs makes it possible to detect the trapping process with TPA induced luminescence. In ${\mathrm{D}}_{2}\mathrm{O}$, the luminescence intensity of CdTe QDs was found to increase with time, whereas an intensity decrease was clearly detected in ${\mathrm{H}}_{2}\mathrm{O}$. In addition, the optically trapped CdTe QDs were fixed on a hydrophilic glass substrate. Laser trapping of QDs may provide a basic technique for quantum information and biological applications.
Numerical Study of Cavitation in the Wake of a Hypervelocity Underwater Projectile

The focus of this study is on cavitation in the wake of a high-velocity underwater projectile. A physical model based on the Euler equations is presented in terms of two-phase mixture properties. Mathematical closure is achieved by providing equations of state for the possible thermodynamic states: compressible liquid, compressible two-phasemixture,andcompressiblepurevapor.Fortheoperatingconditionsstudiedhere,allstatesaresubcritical. Theproposedmodelissolvedusingahybridcomputationalschemedevelopedtoaccuratelyresolvepropertyproe les across discontinuities. The model is validated with several one-dimensional test cases that have known analytic solutions. For modeling the hypervelocity underwater projectile, the model is shown to compute unsteady shockwave development as well as the projectile-wake cavitation zone. The model is then used to conduct a parametric study on the affect of e ow and projectile properties on cavitation.
Optimal Multi Load Shedding for Voltage Security Control in Bulk Power System

The unexpected progressing demand in most power system in the world has led to voltage decay phenomenon. This leads the system to operate below the acceptable voltage limit. Under voltage load shedding (UVLS) has been acceptably recognized as the suitable remedial action to alleviate the voltage decay, which consequently causes voltage insecurity condition. Nevertheless, UVLS is the last option. Thus, a robust optimization technique would be the pre-requisite to optimal location and sizing to ensure a reliable and effective approach; without having to victimize the prioritized areas. This paper presents an optimal UVLS using a newly develop optimization technique termed as Integrated Chaotic Clonal Jaya Evolutionary Programming (ICCJEP). This study addresses a pre-developed voltage stability index as the indicator to voltage security improvement, which needs to be minimized. Validation under several combinations of UVLS schemes on the IEEE 30- Bus RTS indicated that the optimal UVLS can improve voltage security in power system indicated by the reduction of FVSI value as the index.