Control of the Longitudinal Compression and Transverse Focus of Ultrafast Electron Beam for Detecting the Transient Evolution of Materials

Ultrafast detection is an effective method to reveal the transient evolution mechanism of materials. Compared with ultra-fast X-ray diffraction (XRD), the ultra-fast electron beam is increasingly adopted because the larger scattering cross-section is less harmful to the sample. The keV single-shot ultra-fast electron imaging system has been widely used with its compact structure and easy integration. To achieve both the single pulse imaging and the ultra-high temporal resolution, magnetic lenses are typically used for transverse focus to increase signal strength, while radio frequency (RF) cavities are generally utilized for longitudinal compression to improve temporal resolution. However, the detection signal is relatively weak due to the Coulomb force between electrons. Moreover, the effect of RF compression on the transverse focus is usually ignored. We established a particle tracking model to simulate the electron pulse propagation based on the 1-D fluid equation and the 2-D mean-field equation. Under considering the relativity effect and Coulomb force, the impact of RF compression on the transverse focus was studied by solving the fifth-order Rung–Kutta equation. The results show that the RF cavity is not only a key component of longitudinal compression but also affects the transverse focusing. While the effect of transverse focus on longitudinal duration is negligible. By adjusting the position and compression strength of the RF cavity, the beam spot radius can be reduced from 100 μm to 30 μm under the simulation conditions in this paper. When the number of single pulse electrons remains constant, the electrons density incident on the sample could be increased from 3.18×1012 m−2 to 3.54×1013 m−2, which is 11 times the original. The larger the electron density incident on the sample, the greater the signal intensity, which is more conducive to detecting the transient evolution of the material.

Modeling and Analysis of the Impact of Big Data on the Development of Education Network

The arrival of the big data era not only provides corresponding technical support for the development of Educational Networking but also promotes the acceleration of Educational Networking. Therefore, this paper puts forward the research on the impact of big data on the development of education network and constructs a Bayesian knowledge tracking model to collect and analyze the behavior data of teachers and learners in network education. The experimental results show that big data technology provides greater development space for Education Networking. Its market scale has reached 502.47 billion yuan in 2021, and there is a trend of continuous growth. At the same time, the increase in the number of users also makes its teaching content richer and teaching methods more diversified and personalized. And, through the analysis of relevant data, learners and teachers can more comprehensively and truly understand their own level, achieve the purpose of accurate assistance to learners and teachers, and help learners and teachers find their own problems and make targeted adjustments. In addition, the campus intelligent management system based on big data technology can achieve the purpose of multipurpose and information management.

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

Stopping Macroplastic and Microplastic Pollution at Source by Installing Novel Technologies in River Estuaries and Waste Water Treatment Plants: The CLAIM Project

Marine pollution from debris is a major issue nowadays, since every year large amounts of litter enter into the sea. Under the Horizon 2020 framework and within the Cleaning Litter by developing and Applying Innovative Methods in European Seas (CLAIM) project, innovative devices were designed, developed, tested and applied in laboratory and in the field. These consisted of a system named CLEAN TRASH for the prevention of macrolitter in river estuaries before entering the Sea and a filtering system for microplastics (MPs), to be placed at waste water treatment plants (WWTP). Laboratory experiments showed that macrolitters were blocked by 90% by the CLEAN TRASH system, while during the sea testing period at the Kifissos river estuary, a significant source of terrestrial based litter for the Saronikos Gulf, a total amount of 1,175 kg of litter was collected in 38 days before entering the sea, of which the 708 kg (60%) were plastic debris of various sizes and another 164 kg (14%) of styrofoam parts. The lab scale prototype of the filtering system for MPs had an efficiency of about 95%. The upscaled device was tested at the Megara WWTP and was able to withhold a significant amount of MPs. The theoretical contribution of such devices toward the reduction of plastic pollution in the Saronikos Gulf area and the Natura conservation areas therein, was also studied with the use of a 3-D coupled Hydrodynamic-Lagrangian litter tracking model. In all experiments performed, the installation of the above devices for a period of 2 years, resulted in a microplastics reduction by about 87% and a macroplastics reduction ranging from 13 to 43%, depending on the sources.

Trajectory Tracking Control of Intelligent Electric Vehicles Based on the Adaptive Spiral Sliding Mode

Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. The vehicle dynamics model and trajectory tracking model are established. In order to tackle the chattering problem in the traditional sliding mode controller, an Adaptive Spiral Sliding Mode controller is designed by taking the derivative of the controller as the upper controller, which is intended to reduce the heading deviation and lateral deviation in the trajectory tracking process whilst ensuring the stability of the vehicle itself. In the lower controller, a four-wheel tire force optimal distribution method is designed. According to the requirements of the upper controller, combined with the yaw stability of the vehicle, the directional control distribution of the four-wheel tire force is realized. A joint simulation model was built based on CarSim and Simulink, and simulation experiments were performed. The results show that the proposed control strategy can effectively control the heading deviation and lateral deviation in the vehicle trajectory tracking while ensuring the lateral stability of the vehicle.

Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware

Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.

A Numerical Analysis on Coral Larval Networks across Reef Areas on the Northwest Coast of Okinawa Main Island, Japan

Coral bleaching has recently occurred extensively over the world’s oceans, primarily due to high water temperatures. Mesophotic corals that inhabit at depths of approximately 30–150 m are expected to survive during bleaching events and to reseed shallow water corals afterward. In particular, in Okinawa, Japan, mesophotic coral ecosystems (MCEs) have been reported to serve as a refuge to preserve genotypic diversities of bleaching-sensitive corals. Connectivity of larval populations between different habitats is a key element that determines the area to be conserved for desirable coral ecosystems. Coral larvae generally behave passively to the surrounding currents and are transported by the advective and dispersive effects of ambient ocean currents. Thus, numerical ocean circulation models enable us to quantify connectivity with detailed spatiotemporal network structures. Our aim in this study is to quantify the short-distance and vertical connectivity of coral larvae in reef areas on the northwest coast of Okinawa Main Island. For the reason that both short-distance and vertical larval transport are influenced by complex nearshore topography, a very high-resolution 3-D circulation model is required. Therefore, we developed a quadruple nested high-resolution synoptic ocean model at a lateral spatial resolution of 50 m, coupled with an offline 3-D Lagrangian particle-tracking model. After validation of the developed model, short-distance horizontal coral connectivity across reef areas on the northwest coast was successfully evaluated. Furthermore, a series of Lagrangian particle release experiments were conducted to identify the vertical coral migration and 3-D connectivity required for the preservation of MCEs. The model revealed that coral larvae released from the semi-enclosed areas tended to remain near the source area, whereas they were diffused and dispersed gradually with time. The mesophotic corals were dispersed vertically to the deeper zone below the mixed layer, while upward transport occurred to induce the mesophotic corals to emerge near the surface, under the influence of the surface mixed layer. The model results solidly indicated significant connectivity between MCEs and shallow coral ecosystems.

Medium-Term Oceanic Transport of River-Derived Microplastics in the South China Sea Analyzed with a Particle Tracking Model

Plastic waste is currently one of the biggest global environmental issues. To gain the comprehensive understanding of oceanic microplastic contamination as a key global environmental problem, Lagrangian particle tracking experiments were conducted to evaluate the transport of microplastics (MPs) derived from the four major rivers that have been known to discharge large amounts of plastic waste into the South China Sea (SCS). We carried out two types of experiments using a pre-computed 3D current climatological oceanic model: (1) 2D tracking of MP particles placed at the surface to represent positively buoyant (light) MPs, and (2) fully 3D tracking of neutrally buoyant MP particles that are passively transported by ambient current. The seasonally varying monsoons in the SCS were found to provoke strong seasonal variability in the river-derived MP transport. It was found that a large number of MPs, especially from south China, are transported to the East China Sea in the seasons when the southwesterly monsoon prevails. Moreover, the difference in the density of MPs substantially affects their oceanic transport patterns. The buoyant MPs accumulated near the surface tend to be transported toward nearshore areas by wind-driven Ekman currents.