Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Investigation of Hybrid DC–DC Non-Isolated and Non-Inverting Nx Interleaved Multilevel Boost Converter (Nx-IMBC) for High Voltage Step-Up Applications: Hardware Implementation

In significant cases, the generated voltage needs to be step-up with high conversion ratio by using the DC-DC converter as per the requirement of the load. The drawbacks of traditional boost converter are it required high rating semiconductor devices and have high input current ripple, low efficiency, and reverse recovery voltage of the diodes. Recently, the family of Multilevel Boost Converter suggested and suitable configuration to overcome the above drawbacks. In this article, hybrid DC-DC non-isolated and non-inverting Nx Interleaved Multilevel Boost Converter (Nx-IMBC) is analyzed in Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) with boundary condition and investigated in detail. The Nx-IMBC circuit combined the features of traditional Interleaved Boost Converter (IBC) and Nx Multilevel Boost Converter (Nx-MBC). The modes of operation, design of Nx-IMBC and the effect of the internal resistance of components are presented. The comparison study with various recent DC-DC converters is presented. The experimental and simulation results are presented with or without perturbation in input voltage, output power and output reference voltage which validates the design, feasibility, and working of the converter.
RLSK: A Job Scheduler for Federated Kubernetes Clusters based on Reinforcement Learning

Job scheduling in cluster is often considered as a difficult online decision-making problem, and its solution depends largely on the understanding of the workload and environment. People usually first propose a simple heuristic scheduling algorithm, and then perform repeated and tedious manual tests and adjustments based on the characteristics of the workload to gradually improve the algorithm. In this work, focusing on multi-cluster environments, load balancing and efficient scheduling, we present RLSK, a deep reinforcement learning based job scheduler for scheduling independent batch jobs among multiple federated cloud computing clusters adaptively. By directly specifying high-level scheduling targets, RLSK interacts with the system environment and automatically learns scheduling strategies from experience without any prior knowledge assumed over the underlying multi-cluster environment and human instructions, which avoids people’s tedious testing and tuning work. We implement our scheduler based on Kubernetes, and conduct simulations to evaluate the performance of our design. The results show that, RLSK can outperform traditional scheduling algorithms.
2D CNN-Based Slices-to-Volume Superresolution Reconstruction

In the case of slice-to-volume reconstruction, the usual method is to head up the slices according to their relative positions. However, it is common that the space between two scanned slices is more than the interval of the pixels on each slice, leading to the result that the reconstructed volume is anisotropic. To obtain isotropic reconstruction, traditional methods attempt to interpolate missing slices based on interpolation algorithms. However, the resolutions of the reconstructed volume are still different in different directions since the slices are highly related. Moreover, this may lead to misplacement among the slices in the nonscanning directions of the volume. As such, we intend to overcome the above two obstacles on the basis of the 2D CNN superresolution strategy. In the proposed method, the superresolution is conducted for the nonscanned slices. Moreover, the samples for training the CNN are specifically designed, which have tomographic features similar to those of the nonscanned slices. Additionally, a multichannel and dense connected CNN is borrowed to perform slice superresolution considering that each slice is actually related to its neighbors. Finally, we conduct slice-to-volume reconstruction after performing superresolution for slices. Experiments in the case of single-channel and multichannel CNNs are conducted to perform validation. The proposed method is robust and can obtain high accuracy.
Evolving a Weighted Bayesian Network for Consequence Assessment of Terrorist Attack

Over the past decade, terrorism risk has become a prominent consideration in protecting the well-being of individuals and organizations. Consequence assessment of terrorist attack has become a research hotspot in security science. Aiming at the multi-source, interactional and uncertain factors in terrorism events, we introduce Information Flow (IF) to propose an improved weighted Bayesian Network (BN) with causality-based weights. Based on the weighted BN and Rand Forest (RF), we design a data-driven and expert knowledge-based consequence assessment model of terrorist attack. Firstly, RF is applied to filter effective evaluation indicators objectively. Then, IF is adopted to calculate weights of indicators and the weighted BN is built by structure learning and parameter learning. Finally, assessment experiments are conducted with terrorist attack events recorded in Global Terrorism Database. The results show that our proposed model overcomes the shortcomings of traditional quantitative risk assessment methods in verifiability and flexibility, and can reliably achieve quantitative assessment of terrorist attack risk under multi-source and uncertain information conditions.
Transendothelial Migration of Monocytes in Rat Aorta: Distribution of F-actin, α-Catenin, LFA-1, and PECAM-1

To determine changes in the distribution of cell adhesion molecules during diapedesis of monocytes in situ, we labeled aortic whole mounts from hypercholesterolemic rats with Texas red-phalloidin and antibodies to LFA-1, PECAM-1, or α-catenin, and analyzed them by laser scanning confocal microscopy. Monocytes transmigrated through circular openings (transmigration passages) formed by pseudopodia that penetrated between adjacent en-dothelial cells. Transmigrating monocytes remained spherical above the endothelium, while spreading beneath it. The transmigration passage was lined by F-actin and partially by α-catenin, suggesting cadherin-mediated heterotypic interactions. LFA-1 was present in clusters at the monocyte cell surface throughout diapedesis, but was concentrated at the margin of the transmigration passage. PECAM-1 was enriched in the endothelial contact regions where the monocytes transmigrated. PECAM-1 was barely detectable in monocytes before and after diapedesis, but appeared during diapedesis .
A Novel EEG Correlation Coefficient Feature Extraction Approach Based on Demixing EEG Channel Pairs for Cognitive Task Classification

This paper presents a novel feature extraction method for electroencephalogram (EEG)-based cognitive task classification based on the correlation coefficients of EEG channel pairs by introducing preprocessing of the EEG signals. The preprocessing attempts to optimally demix each pair of EEG channels using a two-dimensional rotation matrix in order to mitigate the interference between channel pairs and, consequently, to enhance the resulting correlation coefficient features for cognitive task classification. For the optimization, the following criteria are proposed with an optimal rotation angle approximated for each criterion: <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>) maximum inter-class correlation coefficient distance (ICCD); <inline-formula> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula>) minimum within-class correlation coefficient distance (WCCD); and <inline-formula> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula>) maximum Fisher ratio (FR), which is the ratio of ICCD to WCCD. Performance evaluation based on the cognitive task dataset, dataset IV and Ib in BCI competition II, and Keirn and Aunon’s dataset, shows that ICCD optimization with the ‘above the mean’ and 1.5 interquartile range (IQR) feature selection method yields the best classification performance in comparison with other existing cognitive task classification methods.
Physiology and biochemistry of long-term fasting in birds

Various bird species regularly fast in connection with breeding, migration, or drastic climatic conditions. The metabolic response of penguins and domestic geese to fasting has been studied in detail. These large birds, in contrast to small species, do not become torpid when they are fasting. Nevertheless, they reduce their rate of energy expenditure by decreasing both resting metabolic rate and locomotor activity. From changes in the loss of body mass, the fast of penguins and geese has been divided into three phases: I, the loss of body mass decreases; II, it remains at a minimum level; and III, it increases. These phases reflect metabolic adjustments. Phase I is a rapid phase of adaptation, marked by a decrease in protein catabolism and mobilization of lipids. Phase II is a phase of economy, during which more than 90% of the energy expenditure derives from lipids, while protein catabolism remains at a minimum level. In phase III there still are lipid reserves and this phase is reversible; it is, howeve.
A Multi-level Grey Performance Evaluation Model for Robot Operating System

the paper presents a multi-level grey evaluation method for ROS performance testing and evaluation. Typical performance indicators of ROS are selected from application layer, system layer and kernel layer. The hierarchical grey system evaluation method based on test data is used for the fuzzy evaluation, and then, the grey system evaluation method is proposed to calculate the weight assignment of each layer set. Due to the rationalized weight assignment of each layer set of ROS, the result deviation caused by experience range is reduced, which has certain guiding significance on the performance evaluation for embedded real-time operating system.
TRANSMISSION IN SQUID GIANT SYNAPSES

Synaptic transmission was studied in giant synapses of the stellate ganglion of the squid. When bathed in air-saturated sea water, the synapses deteriorate in 10 to 20 min.; if the sea water is saturated with 100 per cent oxygen, they function steadily for up to 12 hours. Optimal results probably require a medium with lower magnesium and higher calcium than the sea water used. Of eighteen compounds known to affect other synapses (Table I), none had stimulatory effects when applied to the preparation, but ten produced synaptic depression in concentrations of 10–3 gm. per ml. or higher. The only exception was procaine, which blocked at 6 x 10–5 gm. per ml. Intracellular recording with microelectrodes near the synapse showed that the block was associated with a slower rise of the excitatory post-synaptic potential, without a change in the depolarization required to initiate the spike. Procaine was exceptional in also increasing the depolarization at which the spike occurred.
Comparison Between Analytic and Finite Element Method for Double Star Permanent Magnet Synchronous Machine Under IGBT Short-Circuit

This paper presents a magneto-thermal study for two kind of windings for a double-star permanent magnet synchronous machine (DSPMSM) in both healthy and faulty cases after the reconfiguration of the power supply due to an IGBT short-circuit fault. And for each winding the result shows the influence and the limit of the shift angle between two stars on the performance of DSPMSM. The machine operating cycle being of few seconds, the total losses increase in the faulty case impact strongly the machine winding temperature. The machine total losses and the winding temperature are determined thanks to finite element computation. Finally, a machine design is proposed which guarantees a fault tolerant operation with respect to the winding insulation temperature limit.
On rainfall type classification to improve rain attenuation prediction

In this contribution the one year period of the rain rate measurement in the Czech Republic was analyzed. The measurement was located at the experimental site of the Institute of Atmospheric Physics in the town of Hradec Králové. The measurement was performed by a tipping-bucket rain gauge of high resolution of 0.03 mm of rain amount per one tip and by 2D-videodistrometer from Joaneum Research. This study is focused on rain type classification from which the more precise rain attenuation on microwave link can be predicted. Beside description of simple methods using cheap rain amount and rain rate measurement three methods of sophisticated classification (Tokay-Short, Testud and Waldvogel methods) are described and compared using one year Czech distrometer data.
Analytical Design of a Hybrid-Excited Wound Field Synchronous Machine for the Improvement of Torque Characteristics

This paper proposed a novel hybrid-excited wound field synchronous machine (HE-WFSM) to efficiently use the field torque and reluctance torque. Meanwhile, an analytical method is utilized to achieve the torque-angle consistency principle, which allows the field torque and the reluctance torque to reach their maximum values at the same phase current angle. First, a novel HE-WFSM with two different rotor excitations is proposed. Second, the PMs and the field windings in the rotor are designed by an analytical method based on the magnetic equivalent circuit (MEC) to produce the same magnetic flux in the air gap to obtain the torque-angle consistency. Third, one practical HE-WFSM applied for a small power fan is designed by the analytical method as an example. Finally, all the performances of the proposed HE-WFSM are predicted by the finite element method (FEM), thereby verifying the improved torque characteristics.
Command Generation of a High Degree of Freedom Robot Manipulator on a Moving Platform

For general robot arm applications, the base of the manipulator is equipped on a fixed platform. However, the demand for high mobility robot control is increasing dramatically especially for intelligent factory automation. The robotics are usually equipped on certain moving platforms such as multi-axis motion stages or autonomous ground vehicles. Therefore, controlling of the end-effector (EE) position on a moving platform will be challenging. To realize such the high mobility control applications, it usually involves complicated kinematics computations. To deal with this issue, an innovative method is proposed to provide a general solution of joint space command generation. The EE position control is formulated as an optimization problem and an iterative algorithm is present for finding the local optimal solution. For a given target point and a pose of the moving platform, the sub-optimal joint angle command positions are generated. Comparing with the traditional method, such as inverse kinematics, the proposed algorithm avoids complex kinematics derivation. Finally, the developed method is further extended to higher degree of freedom (HDOF) robot manipulators to verify the feasibility for robot control on a moving platform.
Water relations of winter wheat. 5. The root system and osmotic adjustment in relation to crop evaporation

Summary Shoot and root growth and associated leaf and soil water potential relations were compared in three consecutive crops of winter wheat grown in the same field. Despite a profuse root system the crop grown in the second drought year (1976) failed to dry the soil as throughly as the crops in 1975 and 1977. Measurements of plant water potential showed that the restricted utilization of soil water reserves by this crop was associated with failure to make any significant osmotic adjustment, leading to premature loss of leaf turgor and stomatal closure. The implications of these results for models to estimate actual crop evaporation from values of potential evaporation are discussed.
An Evaluation of Inanimate and Virtual Reality Training for Psychomotor Skill Development in Robot-Assisted Minimally Invasive Surgery

Robot-assisted minimally invasive surgery (RAMIS) is gaining widespread adoption in many surgical specialties, despite the lack of a standardized training curriculum. Current training approaches rely heavily on virtual reality simulators, in particular for basic psychomotor and visuomotor skill development. It is not clear, however, whether training in virtual reality is equivalent to inanimate model training. In this manuscript, we seek to compare virtual reality training to inanimate model training, with regard to skill learning and skill transfer. Using a custom-developed needle-driving training task with inanimate and virtual analogs, we investigated the extent to which N=18 participants improved their skill on a given platform post-training, and transferred that skill to the opposite platform. Results indicate that the two approaches are not equivalent, with more salient skill transfer after inanimate training than virtual training. These findings support the claim that training with real physical models is the gold standard, and suggest more inanimate model training be incorporated into training curricula for early psychomotor skill development.
Life History Responses to Resource Variation in a Sessile Predator, The Ciliate Protozoan Tokophrya Lemnarum Stein

To identify the responses of a predator to resource variation, age-specific fecundity and survivorship were measured at several food levels for the sessile, predatory ciliate protozoan Tokophrya lemnarum Stein. The major life history characteristics identified were: (1) a direct rela- tionship exists between net reproductive rate and food level, (2) age-specific fecundity closely follows food intake rate, (3) each embryo is endowed with a nutrient supply upon birth, and (4) an inverse relationship exists between net reproductive rate-food level and life span. Totally starved individuals had the longest mean life spans. Increased survival time at low food levels probably results from low metabolic rate between meals and a slowed accumulation of toxic waste products. Fecundity is directly related to prey number and prey size. It is postulated that this set of adaptations acts to decrease variability of population size in a predator which cannot control its hunting rate and in which prey density must vary greatly during the lifetime of an individual.
Design of Motion Capture System Based on Virtual Reality Scene

In recent years, due to the continuous expansion of the power industry, the acceleration of high-tech update and the rapid improvement of automation level, making the operation and maintenance of the power system increasingly complicated. In order to ensure the operation safety of electric power operators, it is necessary to improve their safety awareness, professional knowledge, operation level and strain capacity. By simulating the operating environment of the power industry, operators conduct safe simulation training and emergency drills, which is very important for the safe and stable operation of the power system. This paper proposes a motion capture system based on HTC Vive, which mainly describes the application of virtual reality technology and motion capture technology in security emergency drills to realize the synchronization of human motion in reality and the motion of human model in virtual reality scene, so as to achieve a more realistic of electric power operation scene. The design of this system aims to solve the problems of low efficiency, high cost, limited movement and high environmental requirements of motion capture technology in the current virtual reality scene.
Activation in individuals with vision loss

Vision loss and blindness are significant causes of disability. Patient activation has been previously unstudied in individuals with vision loss. Among our 146 participants, visual acuities for 38.3 percent were better than 20/70, 43.2 percent had acuities between 20/70 and 20/400, and 12.3 percent had acuities of <20/400. Participants with lower Patient Activation Measure scores missed more clinic visits (p = 0.017); those participants with caregivers also had lower PAM scores (p = 0.002). Targeting interventions to address patient activation in patients with vision loss may increase patient involvement in their care, increase treatment protocol adherence, and improve outcomes.
A Real-time Multi-lane Detection Approach Using Features Geometric Uncertainty and Stereo Vision

Mobile robots is getting popular with the public areas, such as airport, train station, etc. Among the technical methods for mobile robotic system, lane detection is an essential part, because lanes provide information for the robot system to keep current lane or switch to another lane space. However, existing lane detection algorithms are based on the assumption that the lane markings have good quality, but sometimes the lane markings are non-continuous and poor. In this paper, we propose a real-time multi-lane detection algorithm that is robust to the challenging lane condition. For basic lane detection part, we extract the lane features from images, and based on the features geometric uncertainty algorithm we can detect the lanes, guardrails, other vehicles in real-time. What’ s more, the accuracy can be improved with the other vehicles direction information and guardrail depth information from stereo vision when the lane features are non-continuous or poor. Next, to prevent the false detection, an improved RANSAC (random sample consensus) algorithm is introduced in the algorithm. Finally, the proposed algorithm is evaluated on our dataset with different lane conditions. Experimental results show good robustness on variation of the lane conditions.
Voltage Disturbance Compensator based Control Strategy for Three-phase Voltage Source Converter

For three-phase voltage source pulse width modulation (PWM) converter, there is existing distortion in the grid currents and exhibiting fluctuation in the DC bus voltage under inner-loop voltage disturbance. In this paper, a robust control strategy based on voltage disturbance compensator (VDC) is introduced into the double closed-loop control structure to suppress the current distortion and voltage fluctuation. The design and compensation mechanism of the VDC is presented. The stability of the system is analyzed by investigating the real and imaginary part of the closed-loop poles. Simulation and experiments are carried out to validate the proposed control strategy. The results indicate that the three-phase voltage source PWM converter shows a strong robustness under external disturbance when the proposed VDC is applied. Compared with the traditional PI control strategy, it is found that grid currents are highly sinusoidal and DC bus voltage has small fluctuation with the proposed control strategy.