Potential Field Research Articles

Implementing and Marketing an Online Social–Emotional Learning Yoga Program: A Collaborative Approach

Physical education teacher education and sport marketing courses both require their majors to apply theory into real-world experiences that simulate the professional experience upon earning their degrees. After-school programming serves as a potential field site for teacher candidates and can also provide sport marketing students with the opportunity to authentically engage in the process of marketing and promotion. This article describes a collaboration between two university courses focused on implementing and marketing a middle school social-emotional learning yoga after-school program. The yoga program was virtual due to the pandemic, and it was led by university personnel. Teacher candidates learning basic teaching behaviors in their coursework logged in and engaged with the students and the content throughout the semester. Sport marketing students completed a project focused on marketing the program to secure additional student participants, and they also developed group names and logos to engage the community beyond the program. Collaborations across disciplines can enhance community-engaged partnerships, practical experiences for college students, and youth experiences in after-school programming.

Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of two layers: a global path planning layer, and a local path planning layer. At the global path planning layer, a new method is proposed for calculating the terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision areas and a strategy for temporary target point selection are developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experimental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maximum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is shortened by 20%, and more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Bridging the Gap: Local Field Potentials Offer a Peek Into the Brain of a Person With Parkinson Disease.

Clinically available deep brain recordings in patients with Parkinson disease (PD) offer insights into disease mechanisms and create a pathway for personalized treatment strategies. This case illustrates the transformative potential of recordings of neuronal firing in the form of local field potentials (LFPs) by detailing a patient's clinical trajectory for 6 months after deep brain stimulation (DBS) surgery to treat their PD symptoms. LFPs, obtained easily in clinic with a tablet interface to measure and track brain rhythms across the disease course, enriched the patient's clinical picture. Specifically, strong beta peaks were captured at initial programming, and, as the beta peaks diminished over the course of optimizing settings, symptoms improved. These signals may also reveal insights into the neural dynamics of PD such as hypersynchrony in basal ganglia circuitry. Furthermore, the ability to record chronically may unlock new understanding of neuronal dysfunction in PD, possibly enabling future adaptive DBS. In conclusion, identification, tracking, and modulation of LFPs correlated with subjective and objective clinical improvement in the case presented. The use of neurophysiologic signals in the future may lead to therapeutic innovations for our patients with PD.

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

High-performance magnesium/sodium hybrid ion battery based on sodium vanadate oxide for reversible storage of Na+ and Mg2+

Magnesium ion batteries (MIBs) are a potential field for the energy storage of the future but are restricted by insufficient rate capability and rapid capacity degradation. Magnesium-sodium hybrid ion batteries (MSHBs) are an effective way to address these problems. Here, we report a new type of MSHBs that use layered sodium vanadate ((Na, Mn)V8O20·5H2O, Mn-NVO) cathodes coupled with an organic 3,4,9,10-perylenetetracarboxylic diimide (PTCDI) anode in Mg2+/Na+ hybrid electrolytes. During electrochemical cycling, Mg2+ and Na+ co-participate in the cathode reactions, and the introduction of Na+ promotes the structural stability of the Mn-NVO cathode, as cleared by several ex-situ characterizations. Consequently, the Mn-NVO cathode presents great specific capacity (249.9 mA h g−1 at 300 mA g−1) and cycling (1500 cycles at 1500 mA g−1) in the Mg2+/Na+ hybrid electrolytes. Besides, full battery displays long lifespan with 10,000 cycles at 1000 mA g−1. The rate performance and cycling stability of MSHBs have been improved by an economical and scalable method, and the mechanism for these improvements is discussed.

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

AbstractDriving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

Cross-strata co-occurrence of ripples with theta-frequency oscillations in the hippocampus of foraging rats.

Brain rhythms have been postulated to play central roles in animal cognition. A prominently reported dichotomy of hippocampal rhythms links theta-frequency oscillations (4-12Hz) and ripples (120-250Hz) exclusively to preparatory and consummatory behaviours, respectively. However, because of the differential power expression of these two signals across hippocampal strata, such exclusivity requires validation through analyses of simultaneous multi-strata recordings. We assessed co-occurrence of theta-frequency oscillations with ripples in multi-channel recordings of extracellular potentials across hippocampal strata from foraging rats. We detected all ripple events from an identified stratum pyramidale (SP) channel. We then defined theta epochs based on theta oscillations detected from the stratum lacunosum-moleculare (SLM) or the stratum radiatum (SR). We found ∼20% of ripple events (in SP) to co-occur with theta epochs identified from SR/SLM channels, defined here as theta ripples. Strikingly, when theta epochs were instead identified from the SP channel, such co-occurrences were significantly reduced because of a progressive reduction in theta power along the SLM-SR-SP axis. Behaviourally, we found most theta ripples to occur during immobile periods, with comparable theta power during exploratory and immobile theta epochs. Furthermore, the progressive reduction in theta power along the SLM-SR-SP axis was common to exploratory and immobile periods. Finally, we found a strong theta-phase preference of theta ripples within the fourth quadrant [3π/2-2π] of the associated theta oscillation. The prevalence of theta ripples expands the potential roles of ripple-frequency oscillations to span the continuum of encoding, retrieval and consolidation, achieved through interactions with theta oscillations. KEY POINTS: The brain manifests oscillations in recorded electrical potentials, with different frequencies of oscillation associated with distinct behavioural states. A prominently reported dichotomy assigns theta-frequency oscillations (4-12Hz) and ripples (120-250Hz) recorded in the hippocampus to be exclusively associated with preparatory and consummatory behaviours, respectively. Our multi-strata recordings from the rodent hippocampus coupled with cross-strata analyses provide direct quantitative evidence for the occurrence of ripple events nested within theta oscillations. These results highlight the need for an analysis pipeline that explicitly accounts for the specific strata where individual oscillatory power is high, in analysing simultaneously recorded data from multiple strata. Our observations open avenues for investigations involving cross-strata interactions between theta oscillations and ripples across different behavioural states.

Estimating the Ionospheric Induction Electric Field Using Ground Magnetometers

AbstractThe ionospheric convection electric field is often assumed to be a potential field. This assumption is not always valid, especially when the ionosphere changes on short time scales min. We present a technique for estimating the induction electric field using ground magnetometer measurements. The technique is demonstrated on real and simulated data for sudden increases in solar wind dynamic pressure of 1 and 10 nPa, respectively. For the real data, the ionospheric induction electric field is 0.15 0.015 mV/m, and the corresponding compressional flow is 2.5 0.3 m/s. For the simulated data, the induction electric field and compressional flow reach 3 mV/m and 50 m/s, respectively. The induction electric field can locally constitute tens of percent of the total electric field. Inclusion of the induction electric field increased the total Joule heating by 2.4%. Locally the Joule heating changed by tens of percent. This corresponds to energy dissipation that is not accounted for in existing models.

Evidence of lithosphere erosion in the Eastern Indian shield from multi-scale potential field modelling: geodynamic implications

Evidence of lithosphere erosion in the Eastern Indian shield from multi-scale potential field modelling: geodynamic implications

Psammaplin A and Its Analogs Attenuate Oxidative Stress in Neuronal Cells through Peroxisome Proliferator-Activated Receptor γ Activation.

Psammaplins are sulfur containing bromotyrosine alkaloids that have shown antitumor activity through the inhibition of class I histone deacetylases (HDACs). The cytotoxic properties of psammaplin A (1), the parent compound, are related to peroxisome proliferator-activated receptor γ (PPARγ) activation, but the mechanism of action of its analogs psammaplin K (2) and bisaprasin (3) has not been elucidated. In this study, the protective effects against oxidative stress of compounds 1-3, isolated from the sponge Aplysinella rhax, were evaluated in SH-SY5Y cells. The compounds improved cell survival, recovered glutathione (GSH) content, and reduced reactive oxygen species (ROS) release at nanomolar concentrations. Psammaplins restored mitochondrial membrane potential by blocking mitochondrial permeability transition pore opening and reducing cyclophilin D expression. This effect was mediated by the capacity of 1-3 to activate PPARγ, enhancing gene expression of the antioxidant enzymes catalase, nuclear factor E2-related factor 2 (Nrf2), and glutathione peroxidase. Finally, HDAC3 activity was reduced by 1-3 under oxidative stress conditions. This work is the first description of the neuroprotective activity of 1 at low concentrations and the mechanism of action of 2 and 3. Moreover, it links for the first time the previously described effects of 1 in HDAC3 and PPARγ signaling, opening a new research field for the therapeutic potential of this compound family.

Strange property of physical quantities of some petal shape objects

We study various physical quantities of objects with petal shapes. N-petal shapes exhibit N-fold rotational symmetry. Furthermore, they might have an additional characteristic: the equation defining their boundaries could be represented by F(N θ). We will show that physical quantities of objects with these characteristics may show strange properties. By ‘physical quantities’, we refer to aspects such as electric potential and electric field due to a charged petal-shaped plate or cylinder on the rotation axis, their mass and moment of inertia. We are going to show that for such objects, these physical observables do not depend on the number of petals, N. This intriguing result has a simple reason.

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease

BackgroundGait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15–30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. ObjectiveWe aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. MethodsWe studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. ResultsThe occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. ConclusionsWe show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Impact of far-side structures observed by Solar Orbiter on coronal and heliospheric wind simulations

Solar Orbiter is a new space observatory that provides unique capabilities to understand the heliosphere. In particular, it has made several observations of the far-side of the Sun and therefore provides unique information that can greatly improve space weather monitoring. In this study, we aim to quantify how the far-side data will affect simulations of the corona and the interplanetary medium, especially in the context of space weather forecasting. To do so, we focused on a time period with a single sunspot emerging on the far-side in February 2021. We used two different input magnetic maps for our models: one that includes the far-side active region and one that does not. We used three different coronal models typical of space weather modeling: a semi-empirical model (potential field source surface or PFSS) and two different magnetohydrodynamic models (Wind Predict and Wind Predict-AW). We compared all the models with both remote sensing and in situ observations in order to quantify the impact of the far-side active region on each solution. We find that the inclusion of the far-side active region in the various models has a small local impact due to the limited amount of flux of the sunspot (at most 8<!PCT!> of the total map flux), which leads, for example, to coronal hole changes of around 7<!PCT!> for all models. Interestingly, there is a more global impact on the magnetic structure seen in the current sheet, with clear changes for example in the coronal hole boundaries visible in extreme ultra-violet (EUV) on the western limb, which is opposite to the active region and the limb most likely to be connected to Earth. For the Wind Predict-AW model, we demonstrate that the inclusion of the far-side data improves both the structure of the streamers and the connectivity to the spacecraft. In conclusion, the inclusion of a single far-side active region may have a small local effect with respect to the total magnetic flux, but it has global effects on the magnetic structure, and thus it must be taken into account to accurately describe the Sun-Earth connection. The flattening of the heliospheric current sheet for all models reveals that it causes an increase of the source surface height, which in return affects the open and closed magnetic field line distributions.

Microglia Mitigate Neuronal Activation in a Zebrafish Model of Dravet Syndrome.

It has been known for a long time that epileptic seizures provoke brain neuroinflammation involving the activation of microglial cells. However, the role of these cells in this disease context and the consequences of their inflammatory activation on subsequent neuron network activity remain poorly understood so far. To fill this gap of knowledge and gain a better understanding of the role of microglia in the pathophysiology of epilepsy, we used an established zebrafish Dravet syndrome epilepsy model based on Scn1Lab sodium channel loss-of-function, combined with live microglia and neuronal Ca2+ imaging, local field potential (LFP) recording, and genetic microglia ablation. Data showed that microglial cells in scn1Lab-deficient larvae experiencing epileptiform seizures displayed morphological and biochemical changes characteristic of M1-like pro-inflammatory activation; i.e., reduced branching, amoeboid-like morphology, and marked increase in the number of microglia expressing pro-inflammatory cytokine Il1β. More importantly, LFP recording, Ca2+ imaging, and swimming behavior analysis showed that microglia-depleted scn1Lab-KD larvae displayed an increase in epileptiform seizure-like neuron activation when compared to that seen in scn1Lab-KD individuals with microglia. These findings strongly suggest that despite microglia activation and the synthesis of pro-inflammatory cytokines, these cells provide neuroprotective activities to epileptic neuronal networks, making these cells a promising therapeutic target in epilepsy.

Cell-type-specific representation of spatial context in the rat prefrontal cortex

The ability to represent one's own position in relation to cues, goals, or threats is crucial to successful goal-directed behavior. Using optotagging in knock-in rats expressing Cre recombinase in parvalbumin (PV) neurons (PV-Cre rats), we demonstrate cell-type-specific encoding of spatial and movement variables in the medial prefrontal cortex (mPFC) during goal-directed reward seeking. Single neurons encoded the conjunction of the animal's spatial position and the run direction, referred to as the spatial context. The spatial context was most prominently represented by the inhibitory PV interneurons. Movement toward the reward was signified by increased local field potential (LFP) oscillations in the gamma band but this LFP signature was not related to the spatial information in the neuronal firing. The results highlight how spatial information is incorporated into cognitive operations in the mPFC. The presented PV-Cre line opens the door for expanded research approaches in rats.

Environment Fusion Routing Protocol for Wireless Sensor Networks

Wireless sensor network (WSN) is usually deployed in unattended monitoring environments, with limited energy and large-scale characteristics. Therefore, the performance of WSN is susceptible to the influence of external environment and its own energy. The existing routing protocols try to optimize energy efficiency and routing reliability from the perspective of the network itself. However, they do not take into account the environmental impact from the outside, which makes them unable to properly respond to harsh environment. Therefore, a new Environment Fusion Routing Protocol (EFRP) is proposed. Cluster heads are selected in a probabilistic manner according to the ratio of the energy of each node and environmental factors. Nodes with excellent environment have a higher probability of being selected as cluster heads. Besides, the EFRP optimal path is determined according to the comprehensive potential field superimposed by the distance field, residual energy field and environmental field. The best path can be weighed among delay, energy saving and routing invulnerability. Therefore, the balance of network energy is realized and the invulnerability of the network in harsh environment is improved. The experimental results show that EFRP can significantly improve the data transmission and invulnerability of the network in harsh environments.

CL-FDAPF trajectory planner and FO-LADRC motion controller for autonomous sweeper vehicle

Aiming at keeping safe in time and addressing disturbance of uncertainty, an closed loop forward simulation filtering double-layer artificial potential field (CL-FDAPF) trajectory planner and first order linear active disturbance rejective control (FO-LADRC) motion controller are proposed for autonomous sweeper vehicle. Firstly, the double-layer artificial potential field, which consists of traditional potential cost layer and safe level layer, is adopted here to keep planning realtime, meet safe limitations and satisfy operational requirements, and the postprocessing of mean filtering and closed loop forward simulation is for vehicle dynamic constraints. Secondly, it is worth developing active disturbance rejection control strategy, which has the ability to accommodate uncertainty, since an accurate mathematical model of autonomous sweeper vehicle is unavailable as there being inevitable uncertainties in the system state observation and unavoidable environmental disturbances. Thirdly, several typical scenarios are designed in order to verify the real-time and reliability of the proposed algorithm. The results illustrate that the CL-FDAPF planner has highly real-time and stability as the peak time less than 0.045 s and mean time being about 0.02 s in 1000 cycles, and FO-LADRC controller has robust both at uncertainty of wheelbase and steering ratio, since the FO-LADRC have smaller lateral errors compared with two existing methods.

Distributed formation control for leader‐follower quadrotor unmanned aerial vehicles system based on practical sliding mode and event‐triggered mechanisms

AbstractThis article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples.

RCP‐RF: A comprehensive road‐car‐pedestrian risk management framework based on driving risk potential field

AbstractRecent years have witnessed the proliferation of traffic accidents, which led wide researches on automated vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time‐based frameworks cannot handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, a comprehensive driving risk management framework named RCP‐RF is novelly proposed based on potential field theory under connected and automated vehicles environment, where the pedestrian risk metric is combined into a unified road‐vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state‐of‐the‐art methods on real‐world dataset NGSIM and real AV platform.

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field’s reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints.