Potential Field Research Articles

Modelling the fundamental diagram of traffic flow mixed with connected vehicles based on the risk potential field

AbstractThe fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

A LOCAL PATH PLANNING ALGORITHM FOR AVOIDING OBSTACLES IN THE FRENET FRAME

This paper presents a local path planning algorithm in the coordinate system of the roadbed. The algorithm is based on varying initial trajectory points using the potential field method and ensuring the smooth resulting path in a new coordinate system. This algorithm is executed by minimizing an objective functional. The problem is solved with application to path planning for an unmanned transport platform: it is necessary to change the vehicle’s global smooth trajectory points in real time while maintaining smoothness and avoiding emerging obstacles. Compared to the Cartesian coordinate system, the new coordinate system is advantageous in terms of the execution time of the algorithm. The algorithm is implemented in Python. With a planning horizon being specified, this approach can be combined with various path-following algorithms having no obstacle avoidance methods. Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Effects of axial electric and transverse magnetic fields on a rotating electro-osmotic flow in micro-parallel plates

This paper explores the combined influence of an axial electric field and a perpendicular magnetic field imposed on rotating micro-parallel plates immersed in an electrolyte solution. A specialized computer program was developed to solve the velocity as well as the EDL potential fields using the finite difference method, employing the Debye-Hückel (DH) approximation to linearization the EDL potential. The study examines the influence of various non-dimensional parameters, including rotational speed (ω), Hartmann number (Ha), Debye-Hückel parameter (κ), and the non-dimensional parameter ‘S’, on axial, and transverse velocities, wall shear stress, and net flow rate. Results demonstrate that, both velocity components decrease with increased rotational speed and Hartmann number, while the net flow rate increases with the Debye-Hückel parameter for both rotating and non-rotating systems. The impact of these parameters on shear stress was also analyzed. Analysis of Ekmann spirals in the velocity plane revealed closed spirals at a higher rotational speed and open spirals at lower speeds, with spiral size reducing as rotational speed increases.

On the northward shift of the heliospheric current sheet at the end of solar cycle 24

ABSTRACT Since solar cycle 16, the heliospheric current sheet (HCS) has been found to be shifted southward during the late declining to minimum phase. However, this trend is broken at the end of solar cycle 24. In this paper, we analyse the shift of the HCS by using information obtained from coronal model and in situ data provided by the near-Earth OMNI data base and the Parker Solar Probe (PSP). Coronal potential field source surface modelling results show that the northward shift is established at the beginning of 2018 and remains stable for about 2 yr. Interplanetary magnetic field data obtained from and within 1 au also support the northward shift, as the southern polarity T appears more frequently than the northern polarity A between 2018 and 2020. Both model results and in situ observation obtained by PSP imply that the HCS shift is established in the corona, and then propagates into the heliosphere. The quadrupole term still has a significant influence on the formation of the HCS shift.

Neural effects of dopaminergic compounds revealed by multi-site electrophysiology and interpretable machine-learning.

Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis. We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown. Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamo-hippocampal network entails a larger share of D1-as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs. Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies.

Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement.

Stepping movement is delta (1-4 Hz) rhythmic and depends on sensory inputs. In addition to delta rhythms, beta (10-30 Hz) frequency dynamics are also prominent in the motor circuits and are coupled to neuronal delta rhythms both at the network and the cellular levels. Since beta rhythms are broadly supported by cortical and subcortical sensorimotor circuits, we explore how beta-frequency sensory stimulation influences delta-rhythmic stepping movement, and dorsal striatal circuit regulation of stepping. We delivered audiovisual stimulation at 10 Hz or 145 Hz to mice voluntarily locomoting, while simultaneously recording stepping movement, striatal cellular calcium dynamics and local field potentials (LFPs). We found that 10 Hz, but not 145 Hz stimulation prominently entrained striatal LFPs. Even though sensory stimulation at both frequencies promoted locomotion and desynchronized striatal network, only 10 Hz stimulation enhanced the delta rhythmicity of stepping movement and strengthened the coupling between stepping and striatal LFP delta and beta oscillations. These results demonstrate that higher frequency sensory stimulation can modulate lower frequency dorsal striatal neural dynamics and improve stepping rhythmicity, highlighting the translational potential of non-invasive beta-frequency sensory stimulation for improving gait.

Probabilistic analysis of geological and structural Prospects for Oil and gas Potential in Western Yakutia Using three-Dimensional Empirical Mode Decomposition of Potential Fields

Further development of modern formal approaches to extracting geo-structural information from gravimetric and magnetometric data is herein discussed. A practical example of using 3D GEMD (three-dimensional controlled empirical mode decomposition) algorithm to calculate potential fields transforms associated with the impact of deep and medium (of different ranks) geological structures on the adjacent territories of Western Yakutia was considered. Two types of reference groups were determined based on analysis of the known hydrocarbon fields in the Lena-Vilyuy and Lena-Tunguska oil and gas provinces. Statistical efficiency of the observed fields, fields’ area transforms (modified functions of empirical decomposition), and also spatially-linked combinations of the said attributes, aiming to solve the forecasting tasks, was determined. As a result of integration within the testing area, layout diagrams of deep geological and structural prerequisites for oil and gas potential, similar to group standards, were drawn up. A geological interpretation of forecasting and geophysical constructions is presented. It is shown that a number of identified areas predicted with high probabilities correlate with depressions of rift zones. Combined with thick volcanogenic-sedimentary formations, the latter forms most prospective oil and gas content pre-requisites.

A speculative turn in science and philosophy of science

AbstractThis paper describes the main features and goals of the speculative work in modern sciences that has greatly accelerated since World War II due to the exponential increase in computing power and newly available theoretical and conceptual tools. It points to the long historical strand of speculative philosophical work in symbiosis with the sciences, suggests the reasons for its unexpected neglect in contemporary professional philosophy of science, why it should be a major approach, and why such pursuit is not inevitable. Finally, the paper outlines potential topics, fields, and tools for such collaborative work and argues it is likelier to be more fruitful today than at any point in the past hundred years.

Scaling of the geomagnetic secular variation timescale

SUMMARY The ratio of the magnetic power spectrum and the secular variation spectrum measured at the Earth’s surface provides a timescale $\tau _{\rm sv}(l)$ as a function of spherical harmonic degree l. $\tau _{\rm sv}$ is often assumed to be representative of timescales related to the dynamo inside the outer core and its scaling with l is debated. To assess the validity of this surmise and to study the time variation of the geomagnetic field $\dot{\boldsymbol {B}}$ inside the outer core, we introduce a magnetic timescale spectrum $\tau (l,r)$ that is valid for all radius r above the inner core and reduces to the usual $\tau _{\rm sv}$ at and above the core–mantle boundary (CMB). We study $\tau$ in a numerical geodynamo model. At the CMB, we find that $\tau \sim l^{-1}$ is valid at both the large and small scales, in agreement with previous numerical studies on $\tau _{\rm sv}$. Just below the CMB, the scaling undergoes a sharp transition at small l. Consequently, in the interior of the outer core, $\tau$ exhibits different scaling at the large and small scales, specifically, the scaling of $\tau$ becomes shallower than $l^{-1}$ at small l. We find that this transition at the large scales stems from the fact that the horizontal components of the magnetic field evolve faster than the radial component in the interior. In contrast, the magnetic field at the CMB must match onto a potential field, hence the dynamics of the radial and horizontal magnetic fields are tied together. The upshot is $\tau _{\rm sv}$ becomes unreliable in estimating timescales inside the outer core. Another question concerning $\tau$ is whether an argument based on the frozen-flux hypothesis can be used to explain its scaling. To investigate this, we analyse the induction equation in the spectral space. We find that away from both boundaries, the magnetic diffusion term is negligible in the power spectrum of $\dot{\boldsymbol {B}}$. However, $\dot{\boldsymbol {B}}$ is controlled by the radial derivative in the induction term, thus invalidating the frozen-flux argument. Near the CMB, magnetic diffusion starts to affect $\dot{\boldsymbol {B}}$ rendering the frozen-flux hypothesis inapplicable. We also examine the effects of different velocity boundary conditions and find that the above results apply for both no-slip and stress-free conditions at the CMB.

Electro-Osmotic Flow and Mass Transfer through a Rough Microchannel with a Modulated Charged Surface.

In this paper, we investigate the electro-osmotic flow (EOF) and mass transfer of a Newtonian fluid propelled by a pressure gradient and alternating current (AC) electric field in a parallel microchannel with sinusoidal roughness and modulated charged surfaces. The two-wall roughness is described by in-phase or out-of-phase sine functions with a small amplitude δ. By employing the method of perturbation expansion, the semi-analytical solutions of the Poisson-Boltzmann (P-B) equation based on the Debye-Hückel approximation and the modified Navier-Stokes (N-S) equation are obtained. The numerical solution of the concentration equation is obtained by the finite difference method. The effects of sinusoidal roughness, modulated charged surface, and the AC electric field on the potential field, velocity field, and concentration field are discussed. Under the influence of the modulated charged surface and sinusoidal roughness, vortices are generated. The velocity oscillates due to the effect of the AC electric field. The results indicate that solute diffusion becomes enhanced when the oscillation Reynolds number is below a specific critical value, and it slows down when the oscillation Reynolds number exceeds this critical value.

Subthalamic nucleus local field potential stability in patients with Parkinson's disease

BackgroundDespite the large body of work on local field potentials (LFPs), a measure of oscillatory activity in patients with Parkinson's disease (PD), the longitudinal evolution of LFPs is less explored. ObjectiveTo determine LFP fluctuations collected in clinical settings in patients with PD and STN deep brain stimulation (DBS). MethodsTwenty-two STN-DBS patients (age: 67.6 ± 8.3 years; 9 females; disease duration: 10.3 ± 4.5 years) completed bilateral LFP recordings over three visits in the OFF-stimulation setting. Peak and band power measures were calculated from each recording. ResultsAfter bilateral LFP recordings, at least one peak was detected in 18 (81.8%), 20 (90.9%), and 22 (100%) patients at visit 1, 2, and 3, respectively. No significant differences were seen in primary peak amplitude (F = 2.91, p = 0.060) over time. Amplitude of the second largest peak (F = 5.49, p = 0.006) and low-beta (F = 6.89, p = 0.002), high-beta (F = 13.23, p < 0.001), and gamma (F = 12.71, p < 0.001) band power demonstrated a significant effect of time. Post hoc comparisons determined low-beta power (Visit 1–Visit 2: t = 3.59, p = 0.002; Visit 1–Visit 3: t = 2.61, p = 0.031), high-beta (Visit 1–Visit 2: t = 4.64, p < 0.001; Visit 1–Visit 3: t = 4.23, p < 0.001) and gamma band power (Visit 1–Visit 2: t = 4.65, p < 0.001; Visit 1–Visit 3: t = 4.00, p < 0.001) were significantly increased from visit 1 recordings to both follow-up visits. ConclusionOur results provide substantial evidence that LFP can reliably be detected across multiple real-world clinical visits in patients with STN-DBS for PD. Moreover, it provides insights on the evolution of these LFPs.

Dynamically regulated redox shuttling and nucleation of lithium polysulfides through the built-in ferroelectric field

Sulfur is highly desired for energy storage devices by virtue of its high theoretical specific capacity and natural abundance. Yet the lithium-sulfur battery becomes practically unstable due to the migration of lithium polysulfides (LiPSs), which is the major challenge for its widespread application. Here, we demonstrate that the polysulfide redox shuttling can be kinetically buffered, which relies on a rational design of ferroelectric/carbon interfaces in a silica-based host structure. In situ TEM observation shows that a robust sulfur host is constructed by spatially embedding ferroelectric BaTiO3 nanodots within both the shell and bulk of a yolk-shell microsphere. During the electrochemical cycling process, the as-prepared composite material functions as a LiPSs pocket. It facilitates the reversible LiPSs conversion, giving rise to long-term stabilization of lithium-sulfur battery. This is due to the synergetic effect of the built-in polarization electric field and abundant LiPSs nucleation sites at internal ferroelectric/carbon interfaces. Furthermore, we show that the nucleation and growth of Li2Sn can be regulated by the designed polarization electric field in the yolk-shell structure. This study further highlights the potential of physical field towards high-performance lithium-slfur batteries.

State-dependent complexity of the local field potential in the primary visual cortex.

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

Trajectories of Solitons Movement in the Potential Field of pPF1 Plasmid with Non-Zero Initial Velocity

Nonlinear conformational distortions, such as, for example, locally unwound regions of the DNA double helix, named open states, are considered in this work as solitons moving in the potential field of this molecule. It is believed that open states play an important role in the processes of transcription, replication, denaturation, as well as in the transmission of structural changes and information along the DNA molecule. This work examines the problem of the movement of kink-like solitons (kinks) in the potential field of the pPF1 plasmid, the sequence of which includes the genes of the fluorescent proteins Egfp and mCherry, separated by a small intermediate region. The results of calculations of energy profiles of the main and complementary sequences of the plasmid, as well as 2D and 3D trajectories of kinks with a non-zero initial velocity are presented. It has been shown that two types of kinks can be activated in the pPF1 plasmid, which can be considered as two types of quasiparticles that have their own energy, mass, velocity and move in the potential field of the plasmid. It was found that the lowest energy required to form these kinks is observed in the intermediate region located between the fluorescent protein genes. It was shown that the nature of the motion of kinks does not depend on the value of their initial velocity. It was shown that there are threshold values of the torsion field, upon reaching which the behavior of kinks changes sharply: from damped oscillations within the intermediate region to forward motion and penetration into neighboring regions. These values have been calculated. It turned out that for the first kink moving in the main sequence potential field, the threshold value is 4.95×10–22 J, and for the second kink – 4.20 × 10–22 J.

Determining and Mapping Biomass Energy Potential from Agricultural Residues in Syria

Syria faces a problem of restricted access to fossil fuels due to limited resources. In this paper, the potential of biomass and the total heat value produced from agricultural residues for 32 agricultural crops has been studied. Data from the Syrian Ministry of Agriculture for the year 2016 were utilized to determine the total annual potential of field and orchard agricultural residues using the residue-to-product ratio. The study also examined the distribution of regions with the highest production of agricultural waste in the country. The research found that approximately 1.93 million tons of agricultural residues were produced, with 0.698 and 1.213 million tons for field and orchard crops, respectively. The most significant agricultural residues came from olive trees, wheat plants, and orange trees, accounting for 35%, 11%, and 10%, respectively. The possible heat value from field and orchard crops was 23,972 and 44,932 Btu, respectively. This quantity provides 17.6% of Syria's energy consumption. The provinces with the highest production of agricultural residues were Aleppo, Lattakia , and Tartus, with values of 12.35, 11.8, and 8.04 PJ, respectively. According to the study, agricultural residues in Syria have the potential to be a sustainable source for biomass.

Model inductive bias enhanced deep reinforcement learning for robot navigation in crowded environments

Navigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future intelligent societies. As a pragmatic data-driven approach, deep reinforcement learning (DRL) holds promise for addressing this challenge. However, current DRL-based navigation methods have possible improvements in understanding agent interactions, feedback mechanism design, and decision foresight in dynamic environments. This paper introduces the model inductive bias enhanced deep reinforcement learning (MIBE-DRL) method, drawing inspiration from a fusion of data-driven and model-driven techniques. MIBE-DRL extensively incorporates model inductive bias into the deep reinforcement learning framework, enhancing the efficiency and safety of robot navigation. The proposed approach entails a multi-interaction network featuring three modules designed to comprehensively understand potential agent interactions in dynamic environments. The pedestrian interaction module can model interactions among humans, while the temporal and spatial interaction modules consider agent interactions in both temporal and spatial dimensions. Additionally, the paper constructs a reward system that fully accounts for the robot’s direction and position factors. This system's directional and positional reward functions are built based on artificial potential fields (APF) and navigation rules, respectively, which can provide reasoned evaluations for the robot's motion direction and position during training, enabling it to receive comprehensive feedback. Furthermore, the incorporation of Monte-Carlo tree search (MCTS) facilitates the development of a foresighted action strategy, enabling robots to execute actions with long-term planning considerations. Experimental results demonstrate that integrating model inductive bias significantly enhances the navigation performance of MIBE-DRL. Compared to state-of-the-art methods, MIBE-DRL achieves the highest success rate in crowded environments and demonstrates advantages in navigation time and maintaining a safe social distance from humans.

Impact of SOFC anode carbon deposition and hydrogen adsorption on the cell performance by molecular simulation.

This study primarily investigates the changes in carbon adsorption capacity and hydrogen adsorption capacity on the anode catalyst surface when using methane fuel and mixed gas fuel as the anode fuel for SOFC systems. To reduce the carbon adsorption capacity of the commonly used anode catalyst-nickel-based catalysts-towards hydrocarbon fuels, copper and gold are doped into the nickel-based catalysts to compare the effects on carbon and hydrogen adsorption capacities. Moreover, aside from calculating the carbon and hydrogen adsorption capacities, this project also evaluates the impact of mixed gas effects and doping effects on SOFC performance through the analysis of hydrogen diffusion coefficients and performance polarization curves. The findings reveal a noteworthy enhancement in the diffusion coefficient of syngas within the Au-doped Ni catalyst, showing an improvement of up to 45.46% at 973K. Furthermore, the electrical power generated by syngas in the Au-doped Ni catalyst at 973K demonstrates an increase of up to 12.06%. This study primarily employs DFT to calculate the carbon and hydrogen adsorption energies on methane, utilizing CASTEP for the calculations. During these calculations, the adsorption energy is determined through a three-layer surface approach, in conjunction with the Kohn-Sham equations, combining the Generalized Gradient Approximation and ultrasoft pseudopotentials for TS-search calculations. On the other hand, this project will analyze the diffusion coefficient of hydrogen on the anode catalyst using MD methods combined with the ReaxFF potential field, with GULP being utilized to complete all dynamics calculation theories. Finally, the project will analyze the performance of SOFC cells, incorporating relevant numerical equations with Matlab for numerical analysis.

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.