Rapid Speed Research Articles

SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system.

A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.

Motion-induced phase shift for dynamic structured light measurement.

Structured light 3D shape measurement is extensively utilized in semiconductor inspection, smart manufacturing, and biomedical imaging due to its rapid measurement speed, high precision, and versatile applicability to different objects. However, the traditional implementations of this method often require that the object remains static while recording the phase-shifting structured light images, which limits the adaptability of dynamic measurement. Here, we propose a dynamic 3D shape measurement using structured light based on a motion-induced phase shift (MIPS). As the object moves, the surface features distort the fringe pattern, resulting in a phase-shifting effect. By employing the MIPS method, we can determine the phase even in the situations where the knowledge of phase-shifting conditions is not accurate. This enables the acquisition of the 3D topography of the object surface with a high level of precision. Experimental results demonstrate that the MIPS method can accurately measure the 3D shape of objects moving as fast as 100 mm/s, with a relative discrepancy of less than 0.23%.

Approaching the adiabatic infimum of topological pumps on thin-film lithium niobate waveguides.

Topological pumps reveal topological insights of adiabatic mode evolution, in which adiabaticity plays a crucial role. However, the adiabatic infimum, i.e., the possible shortest evolution duration of an adiabatic topological pumping process, has remained elusive. In this study, we report the approach of adiabatic infimum in topological pumps in photonic waveguides based on the lithium niobate-on-insulator (LNOI) platform. Besides the most common way to achieve adiabaticity by slowing down the evolution of the system, the adiabaticity of a topological pump is also contingent on an effective Berry connection between instant eigenstates, which can be controlled by the selection of specific pump loops. Specifically, we develop a strategy to identify a pump loop with a minimized effective Berry connection, corresponding to the adiabatic infimum of a topological pump. As such, it is experimentally demonstrated that topological pumps can happen even with rapid evolution speed, at which the conventional pump would completely fail. The adiabatic infimum accelerates the topological pumps from the constraints of slow evolution and facilitates the design of compact topological devices.

Bamboo (Dendrocalamus Asper) as an Eco-Friendly Sustainable Material: Optimising Mechanical Properties and Enhancing Load-Bearing Capacity for Environmental Architectural Design

This study will assess the utilisation of Bamboo (a Thai vernacular material) in the construction industry. Bamboo is a sustainable and environmentally friendly material which provides a suitable alternative to traditional construction materials. This research will examine its mechanical strength properties as determined by a review of the existing literature and an examination of the bearing area variations of bamboo nodes. The research data and ensuing experiments facilitated the simulation of the load-bearing capacities of bamboo columnar and beam structures via optimisation. Additionally, this study examined the limitations of bamboo-based structures. The assimilation of mechanical property data and bamboo strength was imperative for the production of load-bearing simulations for bamboo columns and beams within ANSYS software. In many countries, bamboo is a commonly employed construction material because of the many benefits it provides such as its rapid growth speed. The compressive strength tests conducted in this study revealed that the middle nodes could withstand up to 64.9 kN, which was the highest value amongs the three samples tested. These findings will contribute to ongoing optimisation and research regarding the damage mechanisms affecting column and beam structures under load. Notably, this damage was prevalent at the junctures of column and beam interfaces. The intention is to conduct additional research that will enhance the current understanding and ensure the sustainability of bamboo as an architectural building material.

A 2DEG‐Based GaN‐on‐Si Terahertz Modulator with Multi‐Mode Switchable Control

AbstractAs terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors.

Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast.

Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m3 vs. -1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of -7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.

A high-capacity and self-healable electrochromic system based on I0/CuI conversion reaction

Constructing electrochromic devices (ECDs) requires high-performance electrochromic electrodes with not only high color contrasts, but also large charge storage capacities. In this paper, a I0/CuI conversion-type electrochromic system is developed, achieving reversible and profound color switching between colorless transparent and dark brown, while delivering a large charge storage capacity of 246.7 mA h g−1. To improve switching durability, a multifunctional P10 (Polyquaternium-10) binder is introduced into the electrode, which can effectively confine the polyiodide intermediates and suppress the shuttle effect. At the same time, the CuI/Cu redox couple is further activated by controlling the applied voltage. The generated Cu from this couple can spontaneously react with residual I0 species and facilitate the conversion from I0 to CuI, and therefore in-situ rejuvenate the degraded electrochromic performance. Thanks to these rational designs, this electrochromic electrode achieves rapid response speeds (tcoloring: 14 s, tbleaching: 5 s), high optical modulation amplitude (73.4 % at 500 nm), decant coloration efficiency (57.0 cm2 C−1) and impressive cycle stability (without performance degradation after 150 cycles). This work provides a novel electrochromic system for the construction of high-performance electrochromic devices.

Automated production of batched unclonable micro-patterns anti-counterfeiting labels with strong robustness and rapid recognition speed

Anti-counterfeiting technologies are indeed crucial for information security and protecting product authenticity. Traditional anti-counterfeiting methods have their limitations due to their clonable nature. Exploring new technologies, particularly those based on pixel-level textures, is a promising avenue to address the clonable issue due to high encoding capacity. However, research in this field is still in its early stages. This work introduces a new fluorescent anti-counterfeiting label technology with four key characteristics: efficient laser etching, high-throughput fabrication and segmentation, robustness aided by data augmentation, and an exceptionally high recognition speed. Specifically, the etching achieves a speed of 1,200 labels/3s, the high-throughput procedures yield a rate of 2,400 labels/4 mins, and the total count is about 5.2 × 104 labels. The number of labels is further augmented to about 5.2 × 106 by implementing arbitrary rotation and brightness variation to enhance the robustness in the recognition procedure. We divide these labels into 44 categories based on differences in patterns. Utilizing machine learning methods, we have achieved a total recognition (including extraction and search process) time per label averaging 421.96 ms without classification, and 40.13 ms with classification. Specifically, the search process with classification is nearly fiftieth times shorter than the non-classification method, reaching 8.52 ms in average. The overall recognition time is much faster than previous works, and achieves an accuracy of over 98.7%. This work significantly increases the practical significance of pixel-level anti-counterfeiting labels.

Dynamic interface printing

Additive manufacturing is an expanding multidisciplinary field encompassing applications including medical devices1, aerospace components2, microfabrication strategies3,4 and artificial organs5. Among additive manufacturing approaches, light-based printing technologies, including two-photon polymerization6, projection micro stereolithography7,8 and volumetric printing9–14, have garnered significant attention due to their speed, resolution or potential applications for biofabrication. Here we introduce dynamic interface printing, a new 3D printing approach that leverages an acoustically modulated, constrained air–liquid boundary to rapidly generate centimetre-scale 3D structures within tens of seconds. Unlike volumetric approaches, this process eliminates the need for intricate feedback systems, specialized chemistry or complex optics while maintaining rapid printing speeds. We demonstrate the versatility of this technique across a broad array of materials and intricate geometries, including those that would be impossible to print with conventional layer-by-layer methods. In doing so, we demonstrate the rapid fabrication of complex structures in situ, overprinting, structural parallelization and biofabrication utility. Moreover, we show that the formation of surface waves at the air–liquid boundary enables enhanced mass transport, improves material flexibility and permits 3D particle patterning. We, therefore, anticipate that this approach will be invaluable for applications where high-resolution, scalable throughput and biocompatible printing is required.

White matter integrity and motor function: a link between cerebral myelination and longitudinal changes in gait speed in aging.

Gait speed is a robust health biomarker in older adults, correlating with the risk of physical and cognitive impairments, including dementia. Myelination plays a crucial role in neurotransmission and consequently affects various functions, yet the connection between myelination and motor functions such as gait speed is not well understood. Understanding this link could offer insights into diagnosing and treating neurodegenerative diseases that impair mobility. This study analyzed 437 longitudinal observations from 138 cognitively unimpaired adults, aged 22 to 94years, to investigate the relationship between myelin content and changes in gait speed over an average of 6.42years. Myelin content was quantified using a novel multicomponent magnetic resonance relaxometry method, and both usual and rapid gait speeds (UGS, RGS) were measured following standard protocols. Adjusting for covariates, we found a significant fixed effect of myelin content on UGS and RGS. Longitudinally, lower myelin content was linked to a greater decline in UGS, particularly in brain regions associated with motor planning. These results suggest that changes in UGS may serve as a reliable marker of neurodegeneration, particularly in cognitively unimpaired adults. Interestingly, the relationship between myelin content and changes in RGS was only observed in a limited number of brain regions, although the reason for such local susceptibility remains unknown. These findings enhance our understanding of the critical role of myelination in gait performance in unimpaired adults and provide evidence of the interconnection between myelin content and motor functionimpairment.

Research on the Impact of Social Media Platform Brand Promotion: Take TikTok Platform Online Selling as an Example

Nowadays, it has been discovered that more and more online selling platforms are developing. More and more people tend to do shopping online, including Taobao, Tmall, and Pinduoduo. However, live-streaming e-commerce is also stepping forward at a rapid speed. More and more online shopping platforms are appearing. This article will mainly focus on the impact of the TikTok platform’s brand promotion. The research methods used in the research are the literature review method and the case analysis method. By digging deep into a specific example of a certain selling product on the Tiktok platform’s live streaming channel, the article jumps to the conclusion that the Tiktok platform utilizes consumers’ desire for convenience and inner mind to simplify the consumption process to attract the consumers and promote their brand quality. After completing the research, it has been discovered that most social media platforms utilize consumers’ inner activity to attract users to increase their brand influence.

Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method’s effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method’s potential for broader application in tunnel blasting assessments.

Dot-array porous medium model for windscreen and its simulation accuracy analysis

Porous medium models have long been prevalent numerical computation tools. Although they exhibit swift computational speed, their accuracy in simulating windscreen perforation structures is challenged. This paper introduces the innovative dot-array porous medium (DAPM) model, which accurately portrays the perforation structure and material characteristics of a windscreen by establishing virtual holes on the porous medium. Not only does it simplify modeling by eliminating complex perforation processes, but it also adeptly simulates the flow behavior of the windscreen. The comprehensive comparison between the DAPM model and the physical mesh model, traditional porous medium model, as well as wind tunnel test results, demonstrates that the DAPM model not only possesses rapid computational speed but also delivers outstanding precision in results. In terms of velocity distribution, vortex distribution, and flow intensity in the flow field, the model indicates a high level of accuracy, clearly exceeding that of the porous medium model. Moreover, the DAPM model showcases high versatility and adjustability in practical applications. By adjusting dimension parameters, it demonstrates the capability to precisely simulate any windscreen with holes arranged in a matrix pattern. This research provides an efficient and reliable tool for the numerical simulation of windscreens, with broad application prospects.

Electrode-Dependent and Tunable Sub-to-Super-Linear Responsivity in Mott Material-Enabled Near-Infrared Photodetectors for Advanced Near-Sensor Image Processing.

Brain-like intelligence is ushering humanity into an era of the Internet of Perceptions (IoP), where the vast amounts of data generated by numerous sensing nodes pose significant challenges to transmission bandwidth and computing hardware. A recently proposed near-sensor computing architecture offers an effective solution to reduce data processing delays and energy consumption. However, a pressing need remains for innovative hardware with multifunctional near-sensor image processing capabilities. In this work, Mott material (vanadium dioxide)-based photothermoelectric near-infrared photodetectors are developed that exhibit electrode-dependent and tunable super-linear photoresponse (exponent α > 33) with ultralow modulation bias. These devices demonstrate an opto-thermo-electro-coupled phase transition, resulting in a large photocurrent on/off ratio (>105), high responsivity (≈500 A W-1), and well detectivity (≈3.9× 1012 Jones), all while maintaining rapid response speeds (τr = 2 µs and τd = 5 µs) under the bias of 1V. This electrode-dependent super-linear response is found to arise from the electron doping effect determined by the polarity of the Seebeck coefficient. Furthermore, the work showcases intensity-selective near-sensor processing and night vision pattern reorganization, even with noisy inputs. This work paves the way for developing near-sensor devices with potential applications in medical image preprocessing, flexible electronics, and intelligent edge sensing.

5G network performance using novel MIMO mixed FSO/MMW communication systems under pointing errors effect: test analysis using image transmission

Abstract Millimeter waves (MMW) enable high data rates over short distances; free-space optical (FSO) provides high-capacity optical wireless transmissions; and multiple-input multiple-output (MIMO) maximizes antenna utilization. These are the three leading technologies that work harmoniously to deliver 5G’s superior performance. This collaborative effort results in 5G’s ability to offer high connection capacities, low latency, and rapid speeds, creating new opportunities for industrial and internet of things applications. In this paper, the proposed system combines FSO links and MMW radio frequency (RF) links to enhance the performance of mixed FSO/RF systems. FSO experiences turbulence and pointing errors (PE), while MMW undergoes fading. Using both FSO and MMW provides diversity against channel fading. Furthermore, both the FSO and MMW links utilize MIMO technology to combat fading through spatial diversity. The FSO link is modeled with a Gamma-Gamma Turbulence model and PE. The MMW link is modeled with Rayleigh fading. By combining these two types of links with MIMO, the system can achieve improved performance compared to using either FSO or MMW alone. The dual nature of the system provides robustness against different types of channels fading effects. Overall, the goal is to enhance performance by capitalizing on the complementary channel characteristics of optical and RF links and exploiting MIMO’s spatial diversity benefits. In the present work, closed-form formulas accounting for the influence of PE are generated for the bit error rate (BER). This article also explores the MIMO mixed FSO/MMW system with multiple modulation techniques under various turbulence situations. The suggested system is ultimately verified by transmitting images with suitable fixed BER.

High-speed Ta2O5-based threshold switching memristor for LIF neurons

Due to their high similarity to biological ion channels, low power consumption, small footprint, and the fact that they do not require reset circuits, threshold switching memristors have been intensively studied for simulating neurons in neuromorphic chips. Switching speed is one of the key challenges which limit the application of threshold switching memristors in chips. In this study, Ta2O5 threshold switching memristors with high switching speeds were prepared by doping with silver. The results show that 14 wt. % Ag doped Ta2O5 threshold switching memristors exhibit excellent bi-directional threshold switching performance, featuring fast switching speeds (<20 ns, <18 ns), low leakage currents (<10 pA), and high switching ratio (>107). According to the field nucleation theory, the rapid switching speed can be attributed to the low nucleation energy (0.26 eV) of silver within the Ta2O5 matrix, which is achieved by incorporating 14 wt. % Ag during the doping process. Based on Pspice, a LIF (leaky integrate-and-fire) neuron based on the silver nanoparticles doped Ta2O5 threshold switching memristors is built, and its firing function has been simulated. The results show that the LIF neuron with a short switching time is able to excite pulse spiking with high frequencies. These results demonstrated that the silver nanoparticles doped Ta2O5-based threshold switching memristors hold significant potential for constructing high-speed artificial neural networks.

Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability.

Broadband self-powered MoS2/PdSe2/WSe2 PSN heterojunction photodetector for high-performance optical imaging and communication

Two-dimensional (2D) semi-metal transition metal dichalcogenides (TMDs) have drawn significant attention for their distinctive physical properties. However, the inherent high dark current of these materials and the single structure of detectors hinder the further development of photodetectors with high performance. Here, we construct a PSN (p-type semiconductor/semi-metal/n-type semiconductor) architecture by sandwiching 2D semi-metal between two semiconductor layers. In this architecture, the top and bottom layers generate an internal built-in electric field, while the middle layer serves as an absorption layer for low-energy photons and facilitates the dissociation of photo-generated carriers. As a result, the heterojunction device demonstrates a wide spectrum optical response from visible to infrared light (405 nm to 1550 nm) without requiring an external voltage. Working in self-powered mode at room temperature, the device achieves a responsivity of 0.56 A/W, a detectivity of 5.63 × 1011 Jones, and a rapid response speed of 190/74 µs. Additionally, the device shows potential for applications in fast optical communication and multi-wavelength optical imaging. This work presents a novel approach for developing a new type of broadband, self-powered, high-performance miniaturized semi-metal-based photodetector.

BIDS: An efficient Intrusion Detection System for in-vehicle networks using a two-stage Binarised Neural Network on low-cost FPGA

Automotive networks are crucial for ensuring safety as the number of Electronic Control Units (ECUs) grows to support vehicle intelligence. The Controller Area Network (CAN) is commonly used for efficient in-vehicle communication among ECUs. However, its broadcast nature and lack of a dedicated security layer make it vulnerable to attacks. This paper proposes a novel CAN bus Intrusion Detection System (IDS), named BNN-based IDS (BIDS), which efficiently provides both unknown attack detection and known attack classification using a hierarchical two-stage Binarised Neural Network (BNN) and Generative Adversarial Network (GAN). BIDS was validated on three datasets, and its implementation achieves an average inference time of less than 0.170 ms with minimal resource utilisation on a low-cost Field Programmable Gate Array (FPGA). This rapid inference speed enables real-time inference on individual CAN messages using a sliding window technique, eliminating the need to wait for multiple accumulated CAN messages required for data preprocessing. Evaluation metrics demonstrate that our IDS achieves high accuracy in both identifying unseen attacks and categorising known attacks. Furthermore, our FPGA implementation consumes merely 2.09 W, which is a 57% reduction compared to a cutting-edge FPGA-based IDS that is capable of detecting unknown attacks using the same dataset.

Bactericidal and Antifouling Coatings with the "Killing-Repelling-Killing" Triple Function Based on Cationic Copolymers with Structure Conversion and Capsaicin Analogue Release.

The capsaicin analogue N-(4-hydroxy-3-methoxybenzyl) acrylamide (HMBA) was linked with polylauryl methacrylate-b-poly(2-(N,N-dimethylamino)ethyl methacrylate) (PLMA-b-PDMAEMA) via a quaternization reaction with 4-(acrylamidomethyl)-2-methoxyphenyl 2-chloroacetate (AAMPCA). The amphiphilic copolymers were capable of transforming its structure in response to the solvent change from aprotic to protic, which was verified by the 1H NMR spectrum. The resulting cationic copolymers underwent a hydrolysis process in water, yielding zwitterionic groups on surfaces. Meanwhile, the bactericidal reagent HMBA was released. It was proved that the hydrolysis rate of the copolymers accelerated with higher temperature, higher pH value, and higher hydrophilic block units. And the controllable, sustainable release of HMBA was achieved with copolymer-mediated hydrolysis. Protein-repellent and bactericidal tests on the surface of the coating proved that antifouling and bactericidal performances of the coating correlated to the structure conversion abilities of the corresponding copolymer. The dynamic monitoring of Escherichia coli adhesion in 3 h evidenced the antifouling and bactericidal process of copolymers with different block ratios and concentrations. The coating incorporated with 3% PLMA120-b-(PDMAEMA-AAMPCA)120 in polylactic acid base materials showed an adhesion ratio of E. coli less than 1% within 1 h, and the survival ratio of the adhered bacteria is <1%, suggesting its rapid speed and high efficiency in "bacterial repelling and killing". Also, the PLMA120-b-(PDMAEMA-AAMPCA)120 copolymer demonstrated enhanced bactericidal ability compared with the mixture of cationic poly(laruyl methacrylate)120-b-poly(carboxybetaine methacrylate ester)120 (PLMA120-b-PCBMAE120) and free HMBA. The lowest minimal inhibitory concentration was 0.078 mg/mL against Staphylococcus aureus and 0.312 mg/mL against E. coli, respectively.