A two-layer neural circuit controls fast forward locomotion in Drosophila.

Fast forward locomotion is critical for animal hunting and escaping behaviors. However, how the underlying neural circuit is wired at synaptic resolution to decide locomotion direction and speed remains poorly understood. Here, we identified in the ventral nerve cord (VNC) a set of ascending cholinergic neurons (AcNs) to be command neurons capable of initiating fast forward peristaltic locomotion in Drosophila larvae. Targeted manipulations revealed that AcNs are necessary and sufficient for fast forward locomotion. AcNs can activate their postsynaptic partners, A01j and A02j; both are interneurons with locomotory rhythmicity. Activated A01j neurons form a posterior-anteriorly descendent gradient in output activity along the VNC to launch forward locomotion from the tail. Activated A02j neurons exhibit quicker intersegmental transmission in activity that enables fast propagation of motor waves. Our work revealed a global neural mechanism that coordinately controls the launch direction and propagation speed of Drosophila locomotion, furthering the understanding of the strategy for locomotion control.

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization.

Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology-the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R2) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R2, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.

Fixed-Time-Convergent Sliding Mode Control with Sliding Mode Observer for PMSM Speed Regulation.

This paper focuses on the speed control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. Conventional sliding mode control (CSMC) can only converge asymptotically in the infinite domain and will cause unacceptable sliding mode chattering. To improve the performance of the PMSM speed loop in terms of response speed, tracking accuracy, and robustness, a hybrid control strategy for a fixed-time-convergent sliding mode controller (FSMC) with a fixed-time-convergent sliding mode observer (FSMO) is proposed for PMSM speed regulation using the fixed-time control theory. Firstly, the FSMC is proposed to improve the convergence speed and robustness of the speed loop, which can converge to the origin within a fixed time independent of the initial conditions. Then, the FSMO is used as a compensator to further enhance the robustness of the speed loop and attenuate sliding mode chattering. Finally, simulation and experimental results show that the proposed method can effectively improve the dynamic performance and robustness of the PMSM speed control system.

Evaluation of point-to-point speed cameras to control speeding behavior in Thailand.

INTRODUCTION: This research examines the effectiveness of point-to-point speed cameras in Thailand compared with spot speed camera enforcement, which is widely used in the country. METHOD: The evaluation includes the speed control's efficiency by observing the 85th percentile average speed of drivers on the road; the speed detection rate; conducting a comparison with spot speed camera enforcement; and a cost effectiveness analysis of this measure. RESULTS: After speed detection using point-to-point cameras were employed during which warning tickets were issued in many days of experiment, the 85th percentile average speed in the controlled area decreased by 10 km/hr, with a maximum decrease of 20 km/hr. When comparing the detection rate of the two camera models, point-to-point cameras could detect 95% of drivers' driving speed, with 45% of the detected drivers identified as speeding drivers, while spot speed cameras were only able to detect 10% to 20% of speeders. Drivers traveling through the area with point-to-point cameras were more likely to drive at a constant speed throughout the average speed detection area. PRACTICAL APPLICATIONS: The point-to-point camera system is determined to be quite cost-effective.

Unique Neural Mechanisms Underlying Speed Control of Low-Force Ballistic Contractions.

According to the speed-control hypothesis, the rate of force development (RFD) during ballistic contractions is dictated by force amplitude because time to peak force (TPF) remains constant regardless of changes in force amplitude. However, this hypothesis has not been tested at force levels below 20% of an individual's maximum voluntary contraction (MVC). Here, we examined the relationship between the RFD and force amplitude from 2 to 85% MVC and the underlying structure of muscle activity in 18 young adults. Participants exerted ballistic index finger abductions for 50 trials in each of seven randomly assigned force levels (2, 5, 15, 30, 50, 70, and 85% MVC). We quantified TPF, RFD, and various EMG burst characteristics. Contrary to the speed-control hypothesis, we found that TPF was not constant, but significantly varied from 2 to 85% MVC. Specifically, the RFD slope from 2 to 15% MVC was greater than the RFD slope from 30 to 85% MVC. Longer TPF at low force levels was associated with the variability of EMG burst duration, whereas longer TPF with higher force levels was associated with the EMG burst integral. Contrary to the speed-control hypothesis, we found that the regulation of TPF for low and high force levels was different, suggesting that neuronal variability is critical for force levels below 30% MVC and neuronal amplitude for force levels above 30% MVC. These findings present compelling new evidence highlighting the limitations of the speed-control hypothesis underscoring the need for a new theoretical framework.

Lichens as bio-monitors of polycyclic aromatic hydrocarbons: Measuring the impact of features and traffic patterns.

The role of road characteristics, including gradient and speed control devices, in influencing emission dynamics remains to be fully elucidated. Most studies have focused on fuel consumption as an indirect indicator of sector emissions instead of directly quantifying specific pollutants, like polycyclic aromatic hydrocarbons (PAHs). This research approach is often due to the complexities involved in capturing these pollutants and their subsequent analysis. Bio-monitors, such as lichens, offer an economically viable method. Their wide distribution across various habitats enables the comparison of PAH levels in diverse environments. Against this background, The present work analyses the ability of tropical lichens to indicate the effect that traffic patterns and geometric design features of roads (traffic activity, road gradient, traffic control devices, and vehicular speed) have on the emission of PAH concentration. Results showed that PAHs in lichens strongly correlated with the road gradient (Spearman correlation, p<0.005 with R=0.98). Each 1% increase in road gradient implies a rise of 24 ngPAH/gLichen in National Road. Additionally, a trend coherent of PAH concentration with the vehicle speed profile was observed on Panamericana Road. Speed control devices were associated with higher concentrations of PAHs due to acceleration and braking actions that increment fuel consumption. Finally, the results evidenced that lichens helped determine the source of aromatics and their carcinogenic potential using the diagnostic ratio of PAHs and the carcinogenic equivalence sum, respectively.

In-Wheel Motor Control System for Four-Wheel Drive Electric Vehicle Based on CR-GWO-PID Control.

In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles of electric vehicles, mathematical and simulation models for the whole vehicle are established. In order to improve the control performance of the hub motor, the traditional Grey Wolf Optimization algorithm is improved. In particular, an enhanced population initialization strategy integrating sine and cosine random distribution factors into a Kent chaotic map is proposed, the weight factor of the algorithm is improved using a sine-based non-linear decreasing strategy, and the population position is improved using the random proportional movement strategy. These strategies effectively enhance the global optimization ability, convergence speed, and optimization accuracy of the traditional Grey Wolf Optimization algorithm. On this basis, the CR-GWO-PID control algorithm is established. Then, the software and hardware of an in-wheel motor controller are designed and an in-wheel motor bench test system is built. The simulation and bench test results demonstrate the significantly improved response speed and control accuracy of the proposed in-wheel motor control system.

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model.

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

A sensorless anti-windup speed control approach to axial gap bearingless motors with nonlinear lumped mismatched disturbance observers.

In this paper, sensorless robust speed control with nonlinear lumped mismatched disturbance observers for a permanent magnet type axial gap bearingless motor (AGBM) is designed. Multistage anti-windup-based dynamic surface control combined with integral backstepping control is proposed to control the motor's axial displacement and rotor speed. The approach is against parameter uncertainties and external disturbances, improving steady-state accuracy, eliminating the derivative explosion phenomenon, no chattering problem, and reducing the magnitude of the control system when current saturation occurs. In addition, a novel nonlinear lumped mismatched disturbance observer is proposed to improve the approach under unmodeled dynamics and external disturbances. To obtain high-accuracy tracking control, the control system includes the robust controller combined with the disturbance observers and anticipatory activation of anti-windup (AW) compensation, which means the controller is more complex. Then, to design a sensorless robust speed control for the motor, the rotor position and speed observer require higher accuracy. High-gain back-EMF observer combined with an improved phase-locked loop is proposed to estimate rotor angular position and speed even when the motor speed is reversed. Overall stability of closed-loop system control, including a sensorless speed control approach for motors using back-EMF estimation combined with saturation of the currents and lumped disturbance observers, is mathematically proven. Finally, the simulation results under measurement noise show that the proposed control system are obtained the effectiveness, feasibility, and robustness.

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System.

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

Torque ripple minimization of bridgeless CUK converter-based BLDC motor using Improved Jellyfish Search Algorithm.

This paper proposed an improved jellyfish Search (ImpJS) technique for torque ripple minimization on CUK converter based BLDC motor. In this paper, crossover and mutation operator are utilized to improve searching behavior of the JS algorithm. Hence, it is named as improved jellyfish algorithm (ImpJS). At first, BLDC motor is considered along with the Cuk converter is improved through the switched inductor. Simultaneously, the execution of the BLDC motor operation includes speed and torque control strategy is also analyzed. In order to improve these two strategies, the proposed ImpJS system is introduced. The best gain parameter is tuned to upgrade the controller operation considering the objective function. Finally, the proposed technique-based BLDC motor is performed on MATLAB/Simulink platform in order to analyze the performance is compared with other existing system to determine the effectiveness. The existing techniques like particle swarm optimization (PSO), ant lion optimizer (ALO) and salp swarm algorithm (SSA). In case 1 and case 2, the MSE for proposed technique achieves the value of 0.01093 and 0.01095. In case 1 and case 2, the voltage deviation for the proposed system achieves the value of 2 and 2.

Combined strategy for tuning sensor-less brushless DC motor using SEPIC converter to reduce torque ripple.

The brushless DC motor (BLDCM) is widely used in computer numerical control (CNC) machines, aerospace applications and auto industry applications in the field of robotics. But it is still affected by the transmission torque ripple, which mostly depends on the speed and the transient line current at the transmission interval. This manuscript proposes a combined approach for tuning sensor-less brushless DC (BLDC) motors using a single-ended primary-inductor converter (SEPIC). The proposed technique is a combination of Golden Eagle Optimization (GEO) and Radial Basis Function Neural Network (RBFNN), hence it is called GEO-RPFNN. The control of speed and torque is to reduce the torque ripple in the motor. Here, the modified bridgeless single-ended primary-inductor converter is proposed to improve speed and torque control. The proposed method is used to adjust the parameters of proportional integral derivative (PID) controller and to improve the performance of PID controller. Therefore, the GEO-RBFNN technique is proposed to recover the control loop function. The proposed algorithm is explored to control the speed and torque error as BLDC motor. Nevertheless, the output of the proposed approach is subject to the input of speed and torque controllers. The proposed method is executed in MATLAB Simulink site. The performance of the proposed system is compared with existing FA and PSO methods. As per the state of comparison outcomes, the GEO-RBFNN gives better result than the existing techniques which has higher ability to conquer the related issues. The THD in stator current, power factor and torque ripple gives the value using proposed method is 1.26%, 0.9951 and 7.4.

Effect of Periodic Changes in Rotation Speed on Torsional Stress and Screw-in Force by Alternative Rotation Technique.

INTRODUCTION: This study evaluated the effect of periodic changes in rotation speed on torsional stress and screw-in force using the dedicated alternative rotation technique (ART) motion of the EQ-M (Metabiomed, Cheongju, Korea) endodontic motor. METHODS: Two ART modes of the EQ-M motor in 2 alternative techniques (ART30 and ART50) and continuous rotation were compared using ProTaper Next X2 (Dentsply Sirona, Charlotte, NC) files and simulated resin blocks (n = 12 per group). ART30 and ART50 were operated by continuous rotation of 350 rpm for 360° and then rotated at 30% increased speed from the base speed for 180° and at 50% increased speed for 180°, respectively. Before the test, the simulated resin blocks were pre-enlarged using ProTaper Gold S1 and S2 (Dentsply Sirona) and fixed on a metal stage connected to the force- and torque-measuring unit. During shaping the simulated canal in an automatic up-and-down manner, the parameters of maximum torque, sum of torque, maximum screw-in force, and maximum apical force were measured. The data were statistically analyzed using 1-way analysis of variance and the Tukey post hoc comparison test at the 95% significance level. RESULTS: The ART30 and ART50 groups showed a lower maximum torque, sum of torques, screw-in force, and apical drive force than the continuous rotation group. There was no significant difference between the ART30 and ART50 groups (P > .05). When the screw-in force increased suddenly, the torque correspondently increased. CONCLUSIONS: Under the limitations of this study, the ART mode could reduce the torsional stress and apical forces of the screw-in during instrumentation in comparison with continuous rotation.

Ecological Interface Design and Head-Up Displays: The Contact-Analog Visualization Tradeoff.

OBJECTIVE: This study investigated how the visualization of an ecological interface affects its subjective and objective usefulness. Therefore, we compared a simple 2D visualization against a contact-analog 3D visualization. BACKGROUND: Recently, head-up displays (HUDs) have become contact-analog and visualizations have been enabled to be merged with the real environment. In this regard, ecological interface design visualizing boundaries of acceptable performance might be a perfect match. Because the real-world environment already provides such boundaries (e.g., lane markings), the interface might directly use them. However, visual illusions and undesired interference with the environment might influence the overall usability. METHOD: To allow for a comparison, 49 participants tested the same ecological interface in two configurations, contact-analog (3D) and two dimensional (2D). Both visualizations were shown in the car's head-up display (HUD). RESULTS: The driving simulator experiment reveals that 3D was rated as more demanding and more disturbing, but also more innovative and appealing. However, regarding driving performance, the 3D representation decreased the accuracy of speed control by 6% while significantly increasing lane stability by 20%. CONCLUSION: We conclude that, if we want environmental boundaries guiding our behavior, the indicator for the behavior should be visualized contact-analog. If we desire artificial boundaries (e.g., speed limits) to guide behavior, the behavioral indicator should be visualized in 2D. This is less prone to optical illusions and allows for a more precise control of behavior. APPLICATION: These findings provide guidance to human factors engineers, how contact-analog visualizations might be used optimally.

Users' adaptations to the proportional speed control of a motorised walker.

PURPOSE: Speed control is commonly used to regulate the forces applied by motorised walkers (MW) and there are often situations where the speed targeted deviates from the preferred walking speed of its users, such as when encouraging higher walking speeds and due to safety consideration. This study investigates the effects of different MW's target speeds on the selected walking speeds, force applied, perceived exertion, and gait of MW users during steady-state walking. MATERIALS AND METHODS: The spatiotemporal gait parameters and perceived exertion of twenty young healthy participants were measured as they walked at a comfortable, self-selected speed using a MW as it was controlled to target forward speeds of 0.6, 0.8, 1.0, 1.2, and 1.4 m s-1 as well as when no assistive force was applied by the MW. RESULTS: On average, users would walk slower when their "No Assist" walking speed is higher than the MW's speed target and vice versa. Additionally, the force applied to the MW is proportional to the difference in speed, either faster or slower, when compared to "No Assist". CONCLUSION: The user's exertion and the energy used by the MW are both minimised when target speed is close to the preferred walking speed of the user. Additionally, these findings suggest that the speed target can be used to change the walking speed of users but only to a certain extend and at the cost of higher perceived exertion.Implications for rehabilitationThe larger the difference between the target speed of the MW and the preferred walking speed of the user, the more likely the user is to push or pull on the MW.Users would push or pull on the MW with a force proportional to the difference from their preferred walking speed even when matching the MW's target speed.Users can be encouraged to walk at higher than preferred speeds, even though this would come at the cost of higher perceived exertion.

Fast-forward scaling theory.

Speed is the key to further advances in technology. For example, quantum technologies, such as quantum computing, require fast manipulations of quantum systems in order to overcome the effect of decoherence. However, controlling the speed of quantum dynamics is often very difficult due to both the lack of a simple scaling property in the dynamics and the infinitely large parameter space to be explored. Therefore, protocols for speed control based on understanding of the dynamical properties of the system, such as non-trivial scaling property, are highly desirable. Fast-forward scaling theory (FFST) was originally developed to provide a way to accelerate, decelerate, stop and reverse the dynamics of quantum systems. FFST has been extended in order to accelerate quantum and classical adiabatic dynamics of various systems including cold atoms, internal state of molecules, spins and solid-state artificial atoms. This paper describes the basic concept of FFST and reviews the recent developments and its applications such as fast state-preparations, state protection and ion sorting. We introduce a method, called inter-trajectory travel, recently derived from FFST. We also point out the significance of deceleration in quantum technology. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.

Multimodal Bubble Microrobot Near an Air-Water Interface.

The development of multifunctional and robust swimming microrobots working at the free air-liquid interface has encountered challenge as new manipulation strategies are needed to overcome the complicated interfacial restrictions. Here, flexible but reliable mechanisms are shown that achieve a remote-control bubble microrobot with multiple working modes and high maneuverability by the assistance of a soft air-liquid interface. This bubble microrobot is developed from a hollow Janus microsphere (JM) regulated by a magnetic field, which can implement switchable working modes like pusher, gripper, anchor, and sweeper. The collapse of the microbubble and the accompanying directional jet flow play a key role for functioning in these working modes, which is analogous to a "bubble tentacle." Using a simple gamepad, the orientation and the navigation of the bubble microrobot can be easily manipulated. In particular, a speed modulation method is found for the bubble microrobot, which uses vertical magnetic field to control the orientation of the JM and the direction of the bubble-induced jet flow without changing the fuel concentration. The findings demonstrate a substantial advance of the bubble microrobot specifically working at the air-liquid interface and depict some nonintuitive mechanisms that can help develop more complicated microswimmers.

Improving the sensitivity of lateral flow immunoassay for Salmonella typhimurium detection via flow-rate regulation.

Application of the traditional immunochromatographic assay (ICGA) has been limited by its poor sensitivity. The objective of this study was to increase the sensitivity of the traditional ICGA. A dual-mode ICGA (D-M ICGA) was developed by combining a nanozyme-assisted signal-amplification strategy with a magnetic-nanoparticle-based flow-speed-control strategy. Salmonella typhimurium can be detected simultaneously based on color and magnetic signals in the detection area of the D-M ICGA strip. The calculated limits of detection of 50 cfu·mL-1 and 75 cfu·mL-1 in the color and magnetic modes, respectively, were approximately 1000 times lower than those of the traditional ICGA. The selectivity and practical applicability of the D-M ICGA were also confirmed in this study. The results prove that the D-M ICGA is an assay that could be used for Salmonella typhimurium detection and can be easily adapted to detect other pathogenic bacteria.

Data-driven model-free resilient speed control of an autonomous surface vehicle in the presence of actuator anomalies.

This paper is concerned with the resilient speed control of an autonomous surface vehicle (ASV) in the presence of actuator anomalies. A data-driven model-free resilient speed control method is presented based on available input and output data only with pulse-width-modulation inputs. Specifically, a data-driven neural predictor is designed to learn the unknown system dynamics of the speed control system of the ASV. Then, a resilient speed control law is designed based on the learned dynamics obtained from the neural network predictor, where a cost function is designed for selecting the optimal duty cycle for the motor. The stability of the data-driven neural predictor is analyzed by using input-state stability (ISS) theory. The advantage of the developed data-driven model-free resilient control method is that the optimal speed control performance can be achieved in the presence of actuator anomalies without any modeling process. Simulation results show the learning ability of the data-driven neural predictor and the effectiveness of the proposed data-driven model-free resilient speed control method for the ASV subject to actuator anomalies.

How to take speed decisions consistent with the available sight distance using an intelligent speed adaptation system.

Travelling at excessive speed increases the risk of having a road crash. Intelligent Speed Adaptation (ISA) systems might help the driver to make safe speed decisions along road sections with limited visibility. A recently developed ISA system, called V-ISA (Hazoor et al., 2021), is able to estimate the dynamic (real-time) speed limit, based on the prevailing sight conditions and stopping distance. The V-ISA operates in the following three ways: it can (i) display visual information, (ii) alert the driver with a warning sound, and/or (iii) intervene directly to modify and control vehicle speed. The effects of V-ISA on driving performance have yet to be investigated. Thus, the question of whether V-ISA modulates driving speed choice remains open. Here, we assessed the impact of V-ISA variants on driver speed choice. Thirty expert drivers experienced four simulated driving conditions, in which the three V-ISA variants together with the V-ISA off control condition were tested separately. Furthermore, drivers were asked for feedback on the acceptance and usability of the three V-ISA. Our results suggested that V-ISA was effective in mitigating the risks associated with speeding, with relatively high acceptance and perceived usability levels. The results indicate that V-ISA can have a positive impact on road safety by helping drivers to modulate their chosen driving speed.