Potential Field Research Articles

A Novel Strategy for Hypersonic Vehicle With Complex Distributed No-Fly Zone Constraints

Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems.

Path Planning of Oil Spill Recovery System With Double USVs Based on Artificial Potential Field Method

Path Planning of Oil Spill Recovery System With Double USVs Based on Artificial Potential Field Method

A longitudinal electrophysiological and behavior dataset for PD rat in response to deep brain stimulation

Here we presented an electrophysiological dataset collected from layer V of the primary motor cortex (M1) and the corresponding behavior dataset from normal and hemi-parkinson rats over 5 consecutive weeks. The electrophysiological dataset was constituted by the raw wideband signal, neuronal spikes, and local field potential (LFP) signal. The open-field test was done and recorded to evaluate the behavior variation of rats among the entire experimental cycle. We conducted technical validation of this dataset through sorting the spike data to form action potential waveforms and analyzing the spectral power of LFP data, then based on these findings a closed-loop DBS protocol was developed by the oscillation activity response of M1 LFP signal. Additionally, this protocol was applied to the hemi-parkinson rat for five consecutive days while simultaneously recording the electrophysiological data. This dataset is currently the only publicly available dataset that includes longitudinal closed-loop DBS recordings, which can be utilized to investigate variations of neuronal activity within the M1 following long-term closed-loop DBS, and explore additional reliable biomarkers.

Gravity and Magnetic Joint Imaging Based on Gramian Constraints

Gravity and magnetic imaging produce numerical images proportional to the density and magnetization source distribution through the upward continuation of potential field data. However, the inherent ambiguity and non-uniqueness in gravity or magnetic imaging restrict the reliability of the imaging model. We develop a joint iterative imaging framework for gravity and magnetic data based on Gramian constraints. The imaging fields of gravity and magnetic field are initially calculated by the depth from extreme points method. With the recovered magnetization and density distribution, the gradient directions of the Gramian function for these model parameters are calculated and utilized to regularize the joint imaging directions for gravity and magnetic fields. The introduction of Gramian constraints achieves mutual complementarity of the gravity and magnetic data and generates density and magnetization models with more distinct boundaries and improved structural coherence. The proposed approach is tested by two synthetic examples and applied to field data from the Galinge iron deposit in Qinghai, China. The real case results are verified by information from drillholes and physical properties measurements of ore and rock samples. Joint imaging provides an alternative to joint inversion with improved resolution and reliability of imaging models compared to separate imaging through the complementarity of gravity and magnetic data.

UAV formation control using enhanced behavior mechanism and artificial potential field

Inspired by formation flight of pigeon flock, this paper proposes a enhanced method of autonomous formation control of multiple Unmanned Aerial Vehicles (UAVs) that can maintain high symmetry based on pigeon flock behavior mechanism. Addressing the instability of formation in the original method, the follow improvements have been made. Firstly, improve leadership of top three UAVs, Secondly, modify artificial potential field strategies for top two followers. Finally, through a series of simulation experiments, it is verified that the UAVs can form the expected formation under the autonomous formation control, and can maintain the formation under the complex motion of leader UAV.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

Fault structure and hydrocarbon prospects of the Palawan Basin on the southeastern margin of the South China Sea based on gravity, magnetic, and seismic data

To study the structural features and hydrocarbon prospects of the Palawan Basin in the South China Sea (SCS), the authors collect and collate the existing gravity and magnetic data and obtain edge recognition information from the potential fields. Combined with the seismic profile data, this paper analyzes the features of the gravity and magnetic anomalies and the edge recognition information of the potential fields, determines the fault system, and delineates favorable areas for oil and gas exploration in the Palawan Basin. The results indicate that four main groups of faults with northeast, northwest, near east–west, and near south–north trends developed in the Palawan Basin and adjacent areas in the SCS. The northeast-trending fault is the regional fault, whereas the northwest-trending fault is the main fault. The northwest-trending fault often terminated at the northeast-trending fault, indicating that the northwest-trending fault is formed subsequently. This investigation has characterized two different types of exploration favorable areas (type I and type II) based on the characteristics observed. The most notable characteristic of these exploration favorable areas is that they are located in the high-value zones of the local anomaly of the Bouguer gravity anomaly and their development is obviously controlled by the faults. The amplitude of the gravity anomalies is higher and the gradient of the gravity anomalies is steeper, and there are oil and gas wells and fields distributed in the type I favorable areas for exploration. Compared with the type I favorable areas, the amplitude of gravity anomalies is relatively small, and the gradient of the gravity anomalies is relatively gentle, corresponding to the type II favorable areas.

Adenosine mediates the amelioration of social novelty deficits during rhythmic light treatment of 16p11.2 deletion female mice.

Non-invasive brain stimulation therapy for autism spectrum disorder (ASD) has shown beneficial effects. Recently, we and others demonstrated that visual sensory stimulation using rhythmic 40 Hz light flicker effectively improved cognitive deficits in mouse models of Alzheimer's disease and stroke. However, whether rhythmic visual 40 Hz light flicker stimulation can ameliorate behavioral deficits in ASD remains unknown. Here, we show that 16p11.2 deletion female mice exhibit a strong social novelty deficit, which was ameliorated by treatment with a long-term 40 Hz light stimulation. The elevated power of local-field potential (LFP) in the prefrontal cortex (PFC) of 16p11.2 deletion female mice was also effectively reduced by 40 Hz light treatment. Importantly, the 40 Hz light flicker reversed the excessive excitatory neurotransmission of PFC pyramidal neurons without altering the firing rate and the number of resident PFC neurons. Mechanistically, 40 Hz light flicker evoked adenosine release in the PFC to modulate excessive excitatory neurotransmission of 16p11.2 deletion female mice. Elevated adenosine functioned through its cognate A1 receptor (A1R) to suppress excessive excitatory neurotransmission and to alleviate social novelty deficits. Indeed, either blocking the A1R using a specific antagonist DPCPX or knocking down the A1R in the PFC using a shRNA completely ablated the beneficial effects of 40 Hz light flicker. Thus, this study identified adenosine as a novel neurochemical mediator for ameliorating social novelty deficit by reducing excitatory neurotransmission during 40 Hz light flicker treatment. The 40 Hz light stimulation warrants further development as a non-invasive ASD therapeutics.

A Dielectric MXene-Induced Self-Built Electric Field in Polymer Electrolyte Triggering Fast Lithium-Ion Transport and High-Voltage Cycling Stability.

Quasi-solid polymer electrolyte (QPE) lithium (Li)-metal battery holds significant promise in the application of high-energy-density batteries, yet it suffers from low ionic conductivity and poor oxidation stability. Herein, a novel self-built electric field (SBEF) strategy is proposed to enhance Li+ transportation and accelerate the degradation dynamics of carbon-fluorine bond cleavage in LiTFSI by optimizing the termination of MXene. Among them, the SBEF induced by dielectric Nb4C3F2 MXene effectively constructs highly conductive LiF-enriched SEI and CEI stable interfaces, moreover, enhances the electrochemical performance of the QPE. The related Li-ion transfer mechanism and dual-reinforced stable interface are thoroughly investigated using ab initio molecular dynamics, COMSOL, XPS depth profiling, and ToF-SIMS. This comprehensive approach results in a high conductivity of 1.34 mS cm-1, leading to a small polarization of approximately 25 mV for Li//Li symmetric cell after 6000 h. Furthermore, it enables a prolonged cycle life at a high voltage of up to 4.6 V. Overall, this work not only broadens the application of MXene for QPE but also inspires the great potential of the self-built electric field in QPE-based high-voltage batteries.

Trust region framework-based design of sub-6 GHz m-MIMO antenna and evaluation of SAR

PurposeCurrently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes.Design/methodology/approachThis paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF).FindingsAmong the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis.Originality/valueThe comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.

Fast finite-time formation control with obstacle avoidance of second-order multi-agent systems with input saturation

This paper proposes a leader–follower formation control protocol using fast finite-time (FFT) theory, based on second-order nonlinear multi-agent systems (MASs) with input saturation constraints. The artificial potential field method is addressed to implement the formation control with obstacle avoidance of the MASs. An adaptive FFT strategy is constructed that all the agents follow required formation performance. Neural networks are considered to approximate uncertain functions, which improved convergence and ensuring safety of distributed formation control. Finally, the validity of the theoretical approach is demonstrated by FFT stability theory validated by simulation examples.

The Impact of CSI SEEE Carbon Neutral Index Launched on Order Aggressiveness

In the context of carbon peaking and carbon neutrality goals, in order to clarify the investment direction for investors, China Securities Index Co., Ltd. (CSI) has collaborated with the Shanghai Environmental Energy Exchange to develop the CSI SEEE Carbon Neutral Index (CSCNI), which has also played a leading role in the subsequent preparation of the Green Finance Index. The launch of this index has sparked research interest among scholars in stimulating investor order aggressiveness. This study employs event study methodology to examine the impact of the CSCNI launch on order aggressiveness. The sample companies are categorized into two groups: deep low-carbon and high-carbon reduction, with a focus on studying buy and sale order aggressiveness. The results indicate that the launch of CSCNI has mobilized order aggressiveness but has led to a negative stock price effect as investors anticipate an increase in environmental costs for the sample companies. Furthermore, we reveal that the long-term growth potential of the deep low-carbon field is more promising compared to the high-carbon reduction sector, making stocks in the deep low-carbon field more attractive. The launch of CSCNI has shown contrasting effects on the buy and sale order aggressiveness of investors, with the impact of the index announcement being more significant on the sample companies. This research provides valuable insights for evaluating the impact of green finance indices and contributes to the understanding of internal mechanisms. It provides an important reference for financial regulators to evaluate the development of the current green index. At the same time, it expands the domestic research on order aggressiveness, which studies the action mechanism of the stock price effect of the green stock index from the perspective of order aggressiveness.

Geochemical and geochronologic evidence for a contiguous northeastern Wyoming Province

The extent and nature of the Wyoming Province, an Archean craton in southwestern Laurentia, are poorly understood due to limited exposure between spatially isolated basement-cored uplifts. This lack of exposure has led to debate about whether the northeastern Wyoming Province is underlain by contiguous Archean crust or Proterozoic rocks and suture zone associated with the Trans-Hudson orogeny. To assess these models we analyzed samples recovered from drill cores in the buried northeastern Wyoming Province that straddle the proposed Proterozoic suture. Whole-rock geochemical and Nd-Pb-O isotopic data suggest the rocks formed by partial melting of > 3.5 Ga hydrated mafic to tonalitic sources similar to elsewhere in the northern Wyoming Province. Zircon U-Pb dates record Mesoarchean magmatism and metamorphism (3.0–2.8 Ga). Published whole-rock Rb-Sr, hornblende and biotite K-Ar, and apatite fission track dates suggest these rocks have not been heated above 300 °C since 2.5–2.1 Ga. Geophysical potential field data are consistent across northeastern Wyoming contrasting with major discontinuities associated with documented Proterozoic orogens on all other margins of the Wyoming Province. Hence, geochemical, isotopic, and geochronologic data along with geophysical imaging can be most simply interpreted in terms of continuous Archean crust in the northeastern Wyoming Province. A geophysically-imaged reflector east of the Bighorn Mountains may juxtapose Archean terranes with similar ages and sources, similar to the boundary between the Montana metasedimentary terrane and Beartooth-Bighorn magmatic zone in the northwestern Wyoming Province. This work emphasizes the value of integrating geologic and geophysical constraints to constrain Archean provinces and their tectonic evolution.

Navigation Techniques and Algorithms for Mobile Robots and Automated Guided Vehicles

Mobile robots are self-contained devices that can navigate and move around their surroundings. They observe their environment and create maps using different sensors such as lidar, Global Positioning System (GPS), and cameras. Automated Guided Vehicles (AGVs) are vehicles that can move independently utilizing different approaches such as underground cables, laser scanners, or GPS systems. This research investigates several navigation methodologies and algorithms used in AGVs and mobile robots. It focusses on the advantages of integrating lidar technology with other sensors as well as how it is used in local navigation techniques. We look at two motion planning techniques for obstacle avoidance: the Artificial Potential Field (APF) approach and the Vector Field Histogram (VFH) algorithm. Two effective techniques for finding the best routes are discussed: the A* algorithm and Dijkstra's algorithm. The various AGV types and their navigation systems (such as wired, guide, laser, vision-based, and gyro-based) are also examined in the study. Neural networks and fuzzy logic are investigated as AGV control techniques, with line following and obstacle avoidance as examples of their use. The study emphasizes how crucial precise and dependable navigation systems are to the effective and secure operation of mobile robots and AGVs.

Gut Microbiota and Immune System in Necrotizing Enterocolitis and Related Sepsis

A severe condition of sepsis can be a complication of necrotizing enterocolitis (NEC), which can occur in premature infants and becomes a medical challenge in the neonatal intensive care unit (NICU). It is a multifactorial intestinal disease (can affect both the small and large intestine) that can lead to ischemia of the intestinal tissues that evolves into acute organ necrosis. One of these factors is that different types of nutrition can influence the onset or the progression of the disease. Cow-milk-based infant formulas have been shown to cause it in premature infants more frequently than human milk. Recently, nutrition has been shown to be beneficial after surgery. Several issues still under study, such as the pathogenesis and the insufficient and often difficult therapeutic approach, as well as the lack of a common and effective prevention strategy, make this disease an enigma in daily clinical practice. Recent studies outlined the emerging role of the host immune system and resident gut microbiota, showing their close connection in NEC pathophysiology. In its initial stages, broad-spectrum antibiotics, bowel rest, and breastfeeding are currently used, as well as probiotics to help the development of the intestinal microbiota and its eubiosis. This paper aims to present the current knowledge and potential fields of research in NEC pathophysiology and therapeutic assessment.

Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson’s disease

BackgroundSleep fragmentation is a persistent problem throughout the course of Parkinson’s disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear.MethodWe recorded subthalamic nucleus (STN) local field...

An asymmetric orifice-based active micromixer in the microfluidic chip with 3D microelectrode

The fluid mixing in microscale is central to the development of flow patterns in microfluidic chips for particles separation and biological analysis. Yet, as the size of the fluid decreases, the mixing becomes challenging to change this state of flow. In this work, a novel active micromixer for enhancing the mixing performance in the micromixer with asymmetric orifices is presented. To induce the electric-driven micromixing, an AC electric field is employed to the inserted 3D microelectrodes to produce the local electric field through the asymmetric orifices located on the opposite channel walls, which is perpendicular to the main microchannel. Then the mixing experiments were conducted in a microfluidic chip comprising microelectrode with asymmetric orifices, specifically exploiting the mixing efficiency varying with the asymmetric orifice pairs, applied electric potential and frequency of the AC electric field, and flow rate. The bending degree of the mixing interface was calculated and found increasing as a function of applied electric voltage at a given AC frequency, thus enabling effective mixing crossing the microelectrode's area. The predictions are confirmed by optical microscopy experiments and the micromixing can be significantly improved as they experience longer-exposed electrical forces. The integration of multiple asymmetric orifices leads a larger zone to increase mixing efficiency and makes it a promising method for rapid and stable fluid mixing in microscale.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

A collision-avoidance decision-making scheme based on artificial potential fields and event-triggered control

In this paper, a collision-avoidance decision-making scheme is proposed based on improved artificial potential field (APF) and event-triggered nonlinear model predictive control. A calculation model is used to calculate the collision avoidance dynamic parameters between ships. In accordance with relevant rules of the International Regulations for Preventing Collisions at Sea (COLREGs), abovementioned collision avoidance parameters are used as the main index and applied to construct the repulsive potential field. Then, an anti-collision decision-making scheme is developed by incorporating event-triggered mechanism and improved APF method into the nonlinear model predictive control algorithm. Based on improved repulsive potential field, heading of the ship and orientation of the target point, the event-triggered conditions are set to save considerable computational resources. Following this approach, optimal collision avoidance actions in line with COLREGs is realised, and the adverse effects of ignoring the manoeuvrability characteristics of ships in the process of preventing collisions are eliminated. Finally, simulation examples verify the effectiveness and rationality of the proposed scheme for multi-ship encounter scenarios.

Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.