Response Speed Research Articles

PEDOT:PSS-Based Wearable Flexible Temperature Sensor and Integrated Sensing Matrix for Human Body Monitoring.

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body temperature as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, and stable monitoring. In this paper, we reported a resistive temperature sensor designed as an ultrathin laminated structure with a serpentine pattern and a bioinspired adhesive layer, which was fabricated with a composite of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall carbon nanotubes/reduced graphene oxide (PEDOT:PSS/SWCNTs/rGO) and polydimethylsiloxane (PDMS). The temperature sensor exhibited a high temperature sensitivity of 0.63% °C-1, coupled with outstanding linearity of 0.98 within 25-45 °C. Furthermore, it showed fast response and recovery speeds of 4.8 and 5.8 s, respectively, between 25 and 36 °C. It also demonstrated exceptional stability when subjected to stress and bending disturbances with the maximum bending interference deviation of 0.03%. Additionally, it displayed good cyclic stability over a broad temperature range from 25 to 85 °C, and the standard deviation at 25 °C is 0.14%. A series of experiments including blowing detection, respiratory monitoring with or without a mask, and during rest or sleep were conducted to show the potential of the flexible temperature sensors in human body monitoring. Furthermore, a 4 × 4 flexible temperature sensor matrix was integrated to detect and map objects such as wrenches and blood vessels through human hand skin. The results were consistent with those of infrared measurements. The flexible temperature sensor is capable of real-time temperature monitoring and has the potential in tracking human respiration, assessing sleep quality, and mapping the temperature of various objects.

Design and inversion study of a 1.6 µm high resolution non-modulated laser heterodyne radiometer (NM-LHR)

Laser heterodyne detection boasts exceptional advantages such as high spectral resolution and high signal-to-noise ratio (SNR). It excels at capturing spectral line broadening information of upper atmospheric molecules, which presents substantial research value in the realms of greenhouse gas profile measurement and the assessment of laser propagation effects in the atmosphere. This paper delves into the investigation of the processing method for heterodyne signals, adopting a non-modulated signal processing method to construct a near-infrared non-modulated laser heterodyne radiometer. This innovative design significantly enhanced the response speed and SNR. The radiometer achieved a spectral resolution of 0.006 cm-1 and an SNR of 300. This facilitated the acquisition of vertical profile distribution and column concentration of CH4 by measuring the absorption spectrum. Comparative tests revealed compelling advantages of the non-modulated device. The modulated device collected data 6 times in 6 minutes, yielding an SNR of 58. In contrast, the non-modulated device demonstrated superior efficiency by collecting data 6000 times in 2 minutes, resulting in a remarkable SNR of 103. In the process of inversion, the influence of the solar spectrum was coupled to improve the accuracy of inversion results. The inversion results of the CH4 column concentration from the laser heterodyne radiometer were compared with those from the Fourier transform spectrometer (EM27/SUN), with average concentrations of 1.946 ppmv and 1.930 ppmv, and exhibited an overall deviation of approximately 0.8%. The non-modulated laser heterodyne radiometer provides a new reference for the rapid, accurate and high spectral resolution measurements of greenhouse gas concentration.

High‐performance visible and near‐infrared dual‐band photodetector based on anisotropic 2D NbS3 with perpendicularly reversed polarization behaviors

AbstractDual‐band photodetectors exhibit considerable advantages in target discrimination and navigation compared to single‐band devices in complex circumstances. However, it remains a major challenge to overcome the limitations of traditional devices in terms of their integration with multiple light‐absorbing layers and complicated optical components. In this study, a visible and near‐infrared (NIR) dual‐band polarimetric photodetector with a single light‐absorbing layer is constructed by utilizing the distinctive conversion of linear dichroism (LD) polarity in two‐dimensional niobium trisulfide (NbS3). The NbS3 photodetector exhibits selective detection behaviors in the visible and NIR bands, in which by switching the polarization angle of the incident light from 0° to 90°, photocurrent decreases in the visible region, and increases in the NIR region. Specifically, the degrees of linear polarization of photocurrent are 0.59 at 450 nm and −0.47 at 1300 nm, respectively. The opposite photoresponse in the visible and NIR bands of the photodetector significantly enhances the dual‐band information recognition. Therefore, clear visible and NIR dual‐band polarimetric imaging is accurately realized based on the NbS3 photodetector taking advantage of its fast response speed of 28 μs. Such anisotropic materials, with unique LD conversion features, can facilitate selective modulation between the visible and NIR spectral ranges and promote the development of next‐generation multi‐dimensional photodetection for various applications, including com/puter vision, surveillance, and biomedical imaging.image

Fabrication of SnSe nanostructures visible light photodetectors

Tin selenide (SnSe), a potential low-cost material for technological use, possesses good optical, electrical, and optoelectronic properties. In the present study, thermally evaporated SnSe thin films with vertically grown nanosheet morphology are used to fabricate photoconductor-type photodetectors for visible light detection. The as-deposited films at room temperature (RT) are annealed in a vacuum from 100 to 250 °C and are investigated by EDX, XRD, Raman, FE-SEM, and UV–visible spectroscopy. The morphology and crystallinity of films that had been annealed at 200 °C were better, with (111) plane being the preferred orientation. From the optical transmittance measurements, the absorption coefficient and optical bandgap (Eg) of the deposited films were determined. Here, photo-sensing parameters of the detectors fabricated by depositing Ag contact and tested systematically using a 532 nm laser source of varied power intensities (1–5 mW/cm2) are reported. The photodetector’s performances were evaluated, and the fabricated film annealed at 200 °C had a responsivity of 25 × 10−2 AW−1, a peak EQE of 83 %, and an elevated detectivity of 30 × 108 Jones. The response speed of the fabricated photodetector was reasonably good with τr = 3.8 s and τf = 4.4 s under 5 V bias. The research showed the potential SnSe thin films might be used in the field of detecting visible light.

Research on the Performance and Control Strategy of Electro-Hydraulic Servo System for Selective Hole Digging Tree Planter

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system’s extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the “double ten indicators” requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth.

Effect of Surface Morphology on the Response Speed of Moisture Sensors

Relative humidity sensors are used for high-humidity measurement and moisture sensor or dew point sensors are used for low-humidity measurement (<1 ppmv). We fabricated moisture sensors using α-Al2O3 films as porous dielectric materials deposited by anodic spark deposition. In this study, a variety number of small pores has been studied to investigate the response speed of moisture sensors. Three different surface morphologies have been studied using scanning electron microscopy. One sample has the maximum number of small pores, one sample has the medium number of small pores, and the last sample has the minimum number of small pores. Small pores are defined as pore size ranging from 40 nm to 200 nm. We found that the sensor made as porous material with the maximum number of small pores has the fastest response speed, and that from the minimum number of small pores has the slowest response speed either from high humidity to low humidity or from low humidity to high humidity.

Improving Access to Mental Health Care through a Stepped Care Approach: Preliminary Results from a University Students’ Sample

Introduction: Recently, university counseling services have been faced with an increasing number of students seeking psychological support. However, human resources and time constraints limit the services to respond adequately. Recently, national guidelines for Mental Health Services in Higher Education recommended the stepped care approach to increase efficiency. The present study aimed to explore the implementation of a stepped care model in a Portuguese public university, analyzing students’ sociodemographic and clinical characteristics, and presenting the flow of the stepped care approach and the students’ waiting periods in each step. Methods: An observational study was conducted in the University of Coimbra’s health services between May 2019 and June 2021. Descriptive statistics were performed to describe the sociodemographic and clinical characteristics of the sample, the flow of the stepped care approach, and students’ waiting periods. Results: We enrolled 295 students in the present study. The findings of this study revealed that over 40% had a clinical diagnosis of anxiety or depressive disorders. In terms of students’ flow within the stepped care model, 82% of students sought psychological help through self-referral. Concerning the implementation of the stepped care model, results have shown that most students wait less than a month to access each step. Discussion/Conclusions: Although preliminary, our results suggest that the stepped care model might be a useful service organization, mainly in a community in which accessibility to services and speed of response are particularly important. Most students being self-referred highlight the importance of direct contact between the university community and the health services. Additionally, anxiety and mood disorders were the most prevalent diagnoses in our sample, which also points to the relevance of a stepped care approach, since other studies have highlighted its advantages for this kind of clinical population.

Research on intelligent control system of dynamic magnetic tubular linear compressor

Abstract Addressing the cooling power control issue of linear compressors, a PID controller based on a cubic spline improved grey wolf optimization (tri-GWO) algorithm was designed. The mechanical and electromagnetic characteristics of the linear compressor were used to construct the cooling power transfer function of the linear compressor. The effects of different control algorithms on the cooling power of the linear compressor were studied in Simulink, and the response speed, settling time, and robustness of different algorithms were compared based on the simulation results. Further analysis was conducted on the impact of different algorithms in PID control. The results show that in terms of stability and robustness, the tri-GWO-PID controller has an overshoot close to 0 and quickly stabilizes at the target cooling power; in terms of tracking, the tri-GWO-PID controller exhibits the best tracking performance with the smallest cooling power deviation. The research results provide a reference for the study of cooling power control of linear compressors.

Application of model predictive control scheme for piezoelectric ceramic micro-displacement

Abstract Currently, in the domain of micro-control technology, many operations need to meet the standard of nanometer-level positioning. As a high-precision micro-displacement element, piezoelectric ceramics are widely used in the field of micro-control due to their characteristics such as no need for transmission devices, high resolution, large output force, small size, and fast response speed. However, the inherent hysteresis nonlinearity of piezoelectric ceramics can lead to decreased displacement accuracy, oscillation, and even system instability. Therefore, eliminating the influence of nonlinear characteristics and improving displacement accuracy possess significant practical importance. In this paper, a model predictive control scheme is designed, which achieves faster response speed and higher accuracy compared to traditional control methods such as Proportional Integral Derivative control. Relevant simulations and tests have been conducted. In this scheme, hysteresis nonlinearity is first modeled and then compensated through feedforward, thereby simplifying the displacement control of piezoelectric ceramics into a more manageable linear problem. Subsequently, a linear model predictive control method is adopted to control the entire system. Both simulation and experimental results have verified the effectiveness of this control method.

Investigations on full-Heusler alloys Mn2TaAl and Mn2WAl for spintronic and thermoelectric applications

Half-metallic materials are widely used as spintronic devices such as electrodes, magnetic tunnelling junction and giant magnetoresistance etc. In this work, we have systematically investigated the structural stability, Gilbert damping, electronic structure and magnetism together with exchange interactions and Curie temperatures for Mn2TaAl and Mn2WAl alloys. Initially, we estimate their structural stability and offer possible phase synthesis. Subsequently, the Gilbert damping parameters calculated by the linear response theory are used to assess their response speed as spintronic materials. Furthermore, the Mn2TaAl and Mn2WAl are predicted to be half-metallic and nearly half-metallic ferrimagnets, their total magnetic moments obey the Mt=Zt-18 rule. Accordingly, their Curie temperatures for Mn2TaAl and Mn2WAl are also evaluated by the mean-field approximation. Finally, their thermodynamic parameters within 0∼600 K and thermoelectric properties within 200∼900 K are discussed. Overall, our research for Mn2TaAl and Mn2WAl alloys might provide some valuable clues for their application in spintronic devices.

Research on the Optimization of the PID Control Method for an EOD Robotic Manipulator Using the PSO Algorithm for BP Neural Networks

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters Kp, Ki, and Kd. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators.

Complex trade-offs in a dual-target visual search task are indexed by lateralised ERP components

In everyday tasks, the choices we make incorporate complex trade-offs between conflicting factors that affect how we will achieve our goals. Previous experimental research has used dual-target visual search to determine how people flexibly adjust their behaviour and make choices that optimise their decisions. In this experiment, we leveraged a visual search task that incorporates complex trade-offs, and electroencephalography (EEG), to understand how neural mechanisms of selective attention contribute to choice behaviour in these tasks. On each trial, participants could choose to respond to the gap location on either of two possible targets. Each target was colour coded such that colour indicated which of the two had the easier gap discrimination. Orthogonally, we manipulated the set size of coloured distractors to modulate how efficiently each target could be found. As a result, optimised task performance required participants to trade-off conflicts between the ease of finding a target given the current set size, and the ease of making its associated gap discrimination. Our results confirm that participants are able to flexibly adjust their behaviour, and trade-off these two factors to maintain their response speed and accuracy. Additionally, the N2pc and SPCN components elicited by search displays could reliably predict the choice that participants would ultimately make on a given trial. These results suggest that initial attentional processes may help to determine the choice participants make, highlighting the central role that attention may play in optimising performance on complex tasks.

Revolutionizing Cloud Security: A Novel Framework for Enhanced Data Protection in Transmission and Migration

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability—core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system’s capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies.

A novel colorimetric and fluorometric dual-signal identification of crude baijiu based on La-CDs

The correct classification of strong-flavored crude baijiu affects its quality and overall standard and is crucial for the intelligent development of the baijiu industry. In this work, we developed a novel optical sensing array using lanthanum-doped carbon dots (La-CDs). Using La-CDs with three metal complex dyes—chromium black T, alizarin red, and dimethylphenol orange—we were able to detect organic acids and tannic acid (TA) in crude baijiu in a way that was both colorimetric and fluorescent for the first time. Based on the indicator displacement (IDA) principle, organic acids competitively replace the dyes' binding sites on La3+, causing the dye colors to change to varying degrees. TA quenches the fluorescence of quantum dots through an internal filtering effect. We analyzed the data using pattern recognition algorithms such as HCA, PCA, and LDA, successfully classifying and identifying 16 types of strong-flavored crude baijiu, which included 10 types of carboxylic acids and various grades. In blind tests of 32 crude baijiu samples, the colorimetric method achieved a 94 % accuracy rate, while the fluorescence method achieved 100 %. The sensor demonstrates significant advantages in response speed.

The relationship between the high-frequency performance of supercapacitors and the type of doped nitrogen in the carbon electrode

Nitrogen doping has been widely used to improve the performance of carbon electrodes in supercapacitors, particularly in terms of their high-frequency response. However, the charge storage and electrolyte ion response mechanisms of different nitrogen dopants at high frequencies are still unclear. In this study, melamine foam carbons with different configurations of surface-doped N were formed by gradient carbonization, and the effects of the configurations on the high-frequency response behavior of the supercapacitors were analyzed. Using a combination of experiments and first-principle calculations, we found that pyrrolic N, characterized by a higher adsorption energy, increases the charge storage capacity of the electrode at high frequencies. On the other hand, graphitic N, with a lower adsorption energy, increases the speed of ion response. We propose the use of adsorption energy as a practical descriptor for electrode/electrolyte design in high-frequency applications, offering a more universal approach for improving the performance of N-doped carbon materials in supercapacitors

Magnetron sputtering preparation of flexible ZnO/AlN thin-films sensors with hybrid piezoelectric effect for broad-range human motions detection

Wearable sensors with excellent comfort, flexibility and fast response are widely applied in human-machine interactions, artificial skin, motion detection and healthcare monitoring. However, preparation processes of the most of current piezoelectric sensors are relatively expensive and complicate, limiting their mass productions in the applications. This work demonstrates the superior piezoelectric performance and stability of ZnO/AlN flexible thin films prepared by magnetron sputtering for broad-range human motions detection, and first-principles calculation are applied to reveal the complicate piezoelectric effect of the hybrid system. The ZnO/AlN hybrid thin films sensor exhibits superior piezoelectric performance and excellent reliability, with presenting high outputs voltage (3.63 V), high degree of response speed (42.33 ms), water stability and robust performance upon 10000 cycles. First-principles calculations reveal that formation of the ZnO/AlN interface facilitates charge transport and improves efficiency of carrier transport, and results in reduced in band gap and enhanced electrical conductivity. By applying the sensors in human health monitoring, the underlying behaviors can be determined by output voltage of the sensor in real-time. This work offers a sensitive, simple structured and robust solution for human motion detection.

Anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism

In laser vision-based robotic welding, existing seam tracking methods are seriously affected by noise and lack effective utilization of spatio-temporal information during welding process. To address this, this paper proposes an anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism. First, a memory mechanism with welding history memory information is designed, which can better adapt to seam appearance changes and noise interference during welding. Subsequently, a laser stripe tracker based on spatio-temporal memory is constructed, it enables the current frame to adaptively search historical valid information to eliminate effects of noise and appearance changes. The experimental results show that the proposed method has an average response speed of 43 FPS and an average tracking error of 1.35 pixels for various weld seams. The actual tracking error is less than 0.6 mm. The introduction of memory mechanism improves the weld tracking accuracy by 52.1 % compared to a single tracking template.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO2 emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO2 do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

Knowledge-Data Driven Optimal Control for Nonlinear Systems and Its Application to Wastewater Treatment Process.

Optimal control is developed to guarantee nonlinear systems run in an optimum operating state. However, since the operation demands of systems are dynamically changeable, it is difficult for optimal control to obtain reliable optimal solutions to achieve satisfying operation performance. To overcome this problem, a knowledge-data driven optimal control (KDDOC) for nonlinear systems is designed in this article. First, an adaptive initialization strategy, using the knowledge from historical operation information of nonlinear systems, is employed to dynamically preset parameters of KDDOC. Then, the initial performance of KDDOC can be enhanced for nonlinear systems. Second, a knowledge guide-based global best selection mechanism is used to assist KDDOC in searching for the optimal solutions under different operation demands. Then, dynamic optimal solutions of KDDOC can be obtained to adapt to flexible changes in nonlinear systems. Third, a knowledge direct-based exploitation mechanism is presented to accelerate the solving process of KDDOC. Then, the demand response speed of KDDOC can be improved to ensure nonlinear systems with optimal operation performance in different states. Finally, the performance of KDDOC is validated on a simulation and a practical process. Several experimental results illustrate the effectiveness of the proposed optimal control for nonlinear systems.

Visible-near-infrared self-powered photodetector based on FePSe3

As a type of layered van der Waals material, transition metal phosphorus trichalcogenide FePSe3’s high carrier mobility and light absorption capability offering this p-type semiconductor promising utilization in broadband optoelectronics detection. Despite of the specific photoelectric response behavior of the FePSe3-based solid-state photodetectors, the self-powering potential of the FePSe3-based devices still remains unexplored. In this study, we harnessed the architecture of the photoelectrochemical photodetector and fabricated a FePSe3-based device to investigate its photoelectric performance under different illumination and bias conditions. It was found that the device demonstrated a visible-near-infrared self-powering detection capability ranging from 400 to 800 nm, fast response speed, high sensitivity and outstanding cyclic stability. This research signifies the promising potential of FePSe3 in the field of self-powered optoelectronics.