Real-time Response Research Articles

Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

As the Internet of Things, artificial intelligence, and the fourth industrial revolution advance, smart factories and machines increasingly gain intelligent features that enable the integration of more sophisticated functionalities. Approaches to achieving this intelligence involve both internal systems, such as human–machine interface (HMI), and external systems, such as big data platforms and cloud services. Although current research leans toward studying external systems, accomplishing intelligent functions through such means poses more challenges in achieving real-time responses during machining processes than using internal systems. When intellectualizing machine tools through internal HMI systems, three critical issues must be addressed. First, HMI functions are structured to depend on the HMI itself, leading to a ripple effect where a problem occurring in one HMI function impacts the entire system. Second, owing to differences in development tools and programming languages, the interconnectivity between functions developed by multiple stakeholders to be loaded onto the HMI may suffer, leading to potential inefficiencies and increased maintenance costs. Third, although various types of computer numerical control (CNC) machines need to communicate with the HMI function, the diverse communication methods and development tools used by each CNC manufacturer result in identical intelligent functions being developed separately for each CNC type. To address these challenges, this study proposes an innovative HMI platform capable of executing and developing various intelligent functions. The HMI platform and its major components are designed and implemented through component-based development (CBD). Subsequently, the performance and effectiveness of the platform are validated using quality attribute scenarios.

Research and Application of FPGA Function Verification Methods

FPGA functional verification methods include simulation and timing simulation. Simulation and testing of digital circuits through mathematical models before physical implementation can help reduce development risks. Time series simulation focuses on evaluating the time behavior of FPGA design to ensure that the signal meets the timing requirements. Hardware Description Language (HDL) plays a crucial role in FPGA design, supporting design reuse and improving efficiency. The application areas include communication systems, image processing, automotive electronics, and industrial control systems. Verification requirements include temporal consistency, real-time response, and adaptability.

Empowering Network Security: A Comprehensive Integration of Python Flask for Enhanced User Interfaces and Real-Time Responsiveness

Empowering Network Security: A Comprehensive Integration of Python Flask for Enhanced User Interfaces and Real-Time Responsiveness

Shear fracture behavior and damage constitutive model of rock joints considering the effect of pre-peak cyclic loading

The research on mechanical properties and fracture nalysed of rock joints under dynamic loading disturbance is essential for the stability analysis of rock mass engineering. In this work, a series of pre-peak cyclic shear tests considering the number of cycles and cycle amplitude are conducted for rock joints. Firstly, the shear fracture nalysed and damage evolution law of rock joints are investigated in detail. Then, based on the statistical damage theory, the novel damage constitutive model of rock joints under pre-peak cyclic shear loading is established assuming that the microscopic element damage of rock joints material obeys the Weibull distribution. Finally, the real-time response of acoustic emission and the failure mode of joint surface are nalysed. The results demonstrate that the peak and residual shear stress are positively correlated with the cyclic amplitude. The cumulative cyclic displacement grows with the increase in the number of cycles and cycle amplitude. Moreover, the developed damage model considering the effect of pre-peak cyclic loading is verified by comparing the shear test results with the theoretical values, which are in good agreement. The variation of the shear wear zone of joint surface with cyclic parameters is discussed by binarization calculations. The results of this work can provide a valuable theoretical basis for further research on the shear fracture mechanism of rock joints under dynamic loading disturbance and mitigating geological disasters.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

Low Part-Per-Trillion, Humidity Resistant Detection of Nitric Oxide Using Microtoroid Optical Resonators.

The nitric oxide radical plays pivotal roles in physiological as well as atmospheric contexts. Although the detection of dissolved nitric oxide in vivo has been widely explored, highly sensitive (i.e., low part-per-trillion level), selective, and humidity-resistant detection of gaseous nitric oxide in air remains challenging. In the field, humidity can have dramatic effects on the accuracy and selectivity of gas sensors, confounding data, and leading to overestimation of gas concentration. Highly selective and humidity-resistant gaseous NO sensors based on laser-induced graphene were recently reported, displaying a limit of detection (LOD) of 8.3 ppb. Although highly sensitive (LOD = 590 ppq) single-wall carbon nanotube NO sensors have been reported, these sensors lack selectivity and humidity resistance. In this report, we disclose a highly sensitive (LOD = 2.34 ppt), selective, and humidity-resistant nitric oxide sensor based on a whispering-gallery mode microtoroid optical resonator. Excellent analyte selectivity was enabled via novel ferrocene-containing polymeric coatings synthesized via reversible addition-fragmentation chain-transfer polymerization. Utilizing a frequency locked optical whispering evanescent resonator system, the microtoroid's real-time resonance frequency shift response to nitric oxide was tracked with subfemtometer resolution. The lowest concentration experimentally detected was 6.4 ppt, which is the lowest reported to date. Additionally, the performance of the sensor remained consistent across different humidity environments. Lastly, the impact of the chemical composition and molecular weight of the novel ferrocene-containing polymeric coatings on sensing performance was evaluated. We anticipate that our results will have impact on a wide variety of fields where NO sensing is important such as medical diagnostics through exhaled breath, determination of planetary habitability, climate change, air quality monitoring, and treating cardiovascular and neurological disorders.

Microscopic theory of current-induced skyrmion transport and its application in disordered spin textures

Introduction: Magnetic skyrmions hold great promise for realizing compact and stable memory devices that can be manipulated at very low energy costs via electronic current densities.Methods: In this work, we extend a recently introduced method to describe classical skyrmion textures coupled to dynamical itinerant electrons. In this scheme, the electron dynamics is described via nonequilibrium Green’s function (NEGF) within the generalized Kadanoff–Baym ansatz, and the classical spins are treated via the Landau–Lifshitz–Gilbert equation. Here, the framework is extended to open systems by the introduction of a non-interacting approximation to the collision integral of NEGFs. This, in turn, allows us to perform computations of the real-time response of skyrmions to electronic currents in large quantum systems coupled to electronic reservoirs, which exhibit linear scaling in the number of time steps. We use this approach to investigate how electronic spin currents and dilute spin disorder affect skyrmion transport and the skyrmion Hall drift.Results: Our results show that the skyrmion dynamics is sensitive to a specific form of the spin disorder, such that different disorder configurations lead to qualitatively different skyrmion trajectories for the same applied bias.Discussion: This sensitivity arises from the local spin dynamics around the magnetic impurities, a feature that is expected not to be well-captured by phenomenological or spin-only descriptions. At the same time, our findings illustrate the potential of engineering microscopic impurity patterns to steer skyrmion trajectories.

Artificial Intelligence Integration with Nanotechnology: A New Frontier for Sustainable and Precision Agriculture

Abstract: Addressing the challenges posed by climate change, surging population, rival demands on land for renewable fuel manufacturing, and adverse soil conditions is crucial for ensuring global food security. Achieving sustainable solutions necessitates the integration of multidisciplinary knowledge, such as materials technology and informatics. The convergence of precision agriculture with nanotechnology and artificial intelligence (AI) offers promising prospects for sustainable food production. Through real-time responsiveness to crop growth using advanced technologies, such as nanotechnology and AI, farmers can optimize resource allocation and make informed decisions. Newer opportunities for sustainable food production arise through the integration of precision agriculture, nanotechnology, and artificial intelligence. This convergence enables farmers to dynamically respond to crop growth variations using advanced techniques. By combining nanotechnology and informatics methods with existing models for nutrient cycling and crop productivity, it becomes possible to enhance critical aspects, such as precision targeting, efficient absorption, effective distribution, optimized nutrient assimilation, and long-term effects on soil microbial communities. This integration offers significant potential for improving agriculture and addressing sustainability challenges in food production. Ultimately, this synergy allows for the development of nanoscale agrochemicals that offer a balance between safety and functionality, ensuring optimal performance in agricultural systems.

Exploring the relationship between attributional style measured in virtual reality and bullying among children at familial high risk of schizophrenia or bipolar disorder compared with controls

BackgroundChildren of parents with severe mental illness report bullying more often compared with controls. We hypothesized that deviations in attributional styles may explain the increased prevalence of bullying experiences. We aimed to assess real-time responses to standardized ambiguous social situations, bullying experiences by children, their primary caregivers, and teachers, and to investigate potential associations between attributional styles and bullying. MethodThe study included 465 children aged 11–12, born to parents with schizophrenia, N =179, bipolar disorder, N = 105, or population-based controls, N = 181. Attributional style was evaluated using virtual reality environments depicting ambiguous social everyday situations. We created a tailored assessment since no suitable assessments were found. Bullying was assessed through self-reports and reports from primary caregivers and teachers. ResultsWe observed no group differences in the attributional style of the children. Reports from children, primary caregivers, and teachers revealed that compared with controls, children born to parents with schizophrenia were more likely to perceive bullying victimization, with high consistency among reports. No associations were found between bullying reports and attributional style. ConclusionsChildren of parents with schizophrenia consistently experienced more bullying, as reported by the children themselves, primary caregivers, and teachers. No differences in attributional style were found, indicating that attributional style did not explain the increased prevalence of bullying reports. While it cannot be ruled out that our virtual environments were insufficient to trigger a sense of social exclusion, the results suggest that the observed differences in reported bullying are genuine and not a result of the child's attributional style.

Perspectives of telemedicine-based services among family caregivers of patients with end-of-life cancer: a qualitative study in mainland China

BackgroundDespite being driven by a strong sense of duty and familial obligation, providing care for patients nearing the end of life poses challenges for family caregivers. Telemedicine has rapidly gained traction as a transformative approach to healthcare delivery, offering an array of benefits that could be particularly valuable in end-of-life care. However, research on the perspectives of telemedicine-based services among family caregivers of patients with end-of-life cancer is limited. Therefore, this study aims to explore the perspectives and preferences of telemedicine-based services among family caregivers of patients with end-of-life cancer and provide a framework for developing and executing a tailored telemedicine-based end-of-life care program that addresses the unique needs of family caregivers in mainland China.MethodA descriptive phenomenological approach was used. Family caregivers were selected using purposive sampling at a tertiary cancer hospital. One-on-one semi-structured interviews were conducted with the participants from November to December 2022. Colaizz’s method was used to analyze the interviews.ResultsFourteen participants participated in interviews. Three themes and ten subthemes were identified: motivation to receive telemedicine services (relief from the burden of home care; access to professional health care services), supportive care needs for telemedicine services (support for symptom management; negative emotional adjustment; death education; daily life care guidance), and functional expectations of telemedicine service platforms (ease of use; real-time online guidance and response; personalized automatic reminder; targeted matching push of health knowledge).ConclusionFamily caregivers expressed interest in telemedicine-based services and identified various care needs before receiving telemedicine services. The findings of this study can help policymakers and healthcare providers develop more effective and culturally appropriate telemedicine-based service programs that can better support family caregivers of end-of-life cancer patients.

SWOT Analysis of ChatGPT: Implications for Educational Practice and Research

Technology plays a significant role in our lives today. OpenAI, a company at the forefront of artificial intelligence since 2015, has taken remarkable steps to make artificial intelligence more accessible and beneficial for everyone. One significant achievement in its journey is the development of Chat Generative Pre-trained Transformers (ChatGPT). We used the SWOT analysis framework to outline ChatGPT’s strengths and weaknesses and to discuss its opportunities for and threats to education. The strengths include using a sophisticated natural language model to generate plausible answers, self-improving capability, and providing personalised and real-time responses. As such, ChatGPT can increase access to information, facilitate personalised and complex learning, and decrease teaching workload, thereby making key processes and tasks more efficient. The weaknesses are a lack of deep understanding, difficulty in evaluating the quality of responses, a risk of bias and discrimination, and a lack of higher-order thinking skills. Threats to education include a lack of understanding of the context, threatening academic integrity, perpetuating discrimination in education, democratising plagiarism, and declining high-order cognitive skills.

Application of graphene oxide nanosheet lactate biosensors in continuous assessment of athlete fitness

This study presents a groundbreaking approach in sports science, introducing a novel graphene oxide (GO) nanosheet-based lactate biosensor designed for the non-invasive, continuous assessment of athlete fitness. Traditional lactate monitoring techniques, typically invasive and offering only intermittent data, are significantly outperformed by this innovative method. Our biosensor uniquely combines lactate oxidase (LOx) with GO nanosheets to measure lactate levels in sweat with unprecedented accuracy and real-time response. Notably, this sensor exhibits exceptional sensitivity and a wide dynamic range, effectively capturing the complete spectrum of physiological lactate variations, from resting states to peak exertion levels. The practical implications of this research are vast, offering a revolutionary tool for enhancing athlete performance monitoring and tailoring training regimens. This biosensor stands as a testament to the potential of nanotechnology in transforming sports science, providing a non-invasive, efficient solution for precise lactate threshold measurement and opening new horizons in personalized athletic training and health monitoring.

FedNow: An Efficiency-Aware Incentive Mechanism Enables Privacy Protection and Efficient Resource Utilization in Federated Edge Learning

Federated edge learning (FEL) has recently attracted great interest due to its real-time response and energy-efficient characteristics. Most existing work focuses on designing algorithms to improve model performance, ignoring the malicious behavior and personal decision-making of self-interested edge servers. Although some efforts have been devoted to incentivizing honest edge server engagement by compensating training costs, this rarely considers resource efficiency and often assumes that edge servers provide complete information to the platform, which may lead to the risk of private attribute leakage. Hence, we aim to achieve an incentive mechanism that promotes secure and efficient model training between the platform and edge servers. However, edge servers’ multi-dimensional private attributes and training strategies make the optimization problem nonconvex, and incomplete information further increases the complexity of the analysis. In order to address these challenges and by integrating contract theory and exponential mechanism, we propose an efficiency-aware incentive mechanism, FedNow, which enables edge servers to personally determine their local training rounds while motivating their participation without giving access to their true training strategies and private attributes. Specifically, we enabld edge servers to add noise to their submitted training strategy to hide their true training rounds; then, we carefully designed an efficiency score function to select honest and efficient edge servers without disclosing their private attributes. In order to demonstrate that FedNow strictly outperforms existing schemes in terms of total costs, we theoretically derived sufficient conditions for making the total costs of FedNow lower than existing schemes and designed a greedy algorithm that uses the Monte Carlo method to find feasible near-optimal solutions in polynomial time. Our extensive experimental assessment using synthetic and real datasets shows the superiority of FedNow.

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.

Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.

Low-consumption stepper motor controller with real-time target position change responsiveness based on field programmable gate array.

In response to the demand for low resource consumption, parallel control, and real-time response to target position changes in precision measurement and manufacturing of multi-axis stepper motor controllers, this paper proposes a field programmable gate array-based method for generating trapezoidal velocity profiles and pulse generation, which can easily keep parallelism and independence during multi-axis control. By avoiding using multiplication and division, this controller not only reduces resource consumption but also enhances the pulse output frequency. To address the real-time responsiveness of the velocity profile generation algorithm to changes in the target position during the control process, the algorithm introduces a novel real-time comparative state transition logic for speed control, which makes it capable of adjusting the acceleration within a single clock cycle, enabling its application in scenarios that require higher levels of real-time performance. Finally, the designed controller is applied to a four-axis positioning system for performance validation.

The Effect of Regional Electronic Cargo Tracking Systems on Cargo Safety at the Kenya Revenue Authority

The Regional Electronic Cargo Tracking System (RECTS) was adopted by the Kenya Revenue Authority (KRA) to enhance cargo security and boost revenue collection. RECTS is aimed at addressing inefficiencies in vendor-operated Electronic Cargo Tracking Systems (ECTs) whose geo-fence routes can be manipulated, making it challenging to determine the exact location of the cargo. Their failure to send alerts on seal tampering and diversions has resulted in a substantial loss of revenue. This study sought to assess the impact of RECTS on cargo security with a focus on KRA customs operations. The study addressed the impact of RECTS cargo tracking on cargo security, the impact of RECTS cargo monitoring on cargo security, and the measurement of the impact of RECTS real-time response on cargo security at KRA. The research adopted a descriptive research design. The target population for this study was the 140 Rapid Response Units (RRU) and the Cargo Monitoring Unit (CMU) staff working at the five designated RRU stations along the Northern Corridor. Stratified simple random sampling was used to select 104 KRA staff to participate in the study. The primary data was collected by means of a self-administered questionnaire provided to the staff working at the five designated stations. Secondary data was collected from the records of the Cargo Monitoring Unit on incidents of theft, tampering and damage of cargo. The data was analysed using SPSS to compute descriptive and inferential statistics. The study established that the independent variables of cargo tracking, cargo monitoring and real-time response, significantly influence the dependent variable as they account for an 82.90 per cent variation in cargo security. The results of a multiple regression analysis established that cargo tracking has a positive and significant effect on cargo security (β1=0.873, p=0.000); that cargo monitoring has a positive and significant effect cargo security (β2=0.175, p=0.015); and that real-time response has a positive and significant effect cargo security (β3=0.222, P=0.000). The findings of the study revealed that cargo tracking enhances the security of the cargo by deterring cases of cargo diversion and by providing the exact location of the stolen, damaged or tampered cargo enabling the Rapid Response Unit to locate the cargo and resolve the situation immediately. Cargo monitoring enhances cargo security by providing real-time alerts in respect of cargo seal tampering which minimises the tampering that previously occurred during changeover of seals at border points. Additionally, real-time response enhances the security of cargo by reducing the time taken by the Rapid Response Unit to respond to and resolve cases of theft, tampering and cargo accidents during transit.

ANALYZING THE CAN PROTOCOL: VULNERABILITIES, PROTECTIVE MEASURES AND IMPROVEMENTS

The Controller Area Network (CAN) bus, originally designed for internal communication among a limited number of Electronic Control Units (ECUs) within vehicles, faces significant security challenges due to the rapid expansion of ECUs in modern automobiles. This expansion necessitates accessibility for diagnostic purposes, yet the CAN protocol lacks fundamental security features such as encryption, authentication, and integrity checks, rendering it vulnerable to various attacks including message injection, Denial of Service (DoS), and masquerading ECUs. This paper surveys existing literature and investigates potential intrusions on the CAN bus, highlighting attacks ranging from GPS spoofing to remote sensor tampering. Various solutions are discussed, including network subdivision, encryption, authentication techniques, and intrusion detection systems (IDS). Recent research proposes IDS solutions utilizing machine learning algorithms such as Random Forest, Support Vector Machine, and Deep Neural Networks (DNN), demonstrating effectiveness in detecting known attacks. However, challenges persist in identifying unknown attacks and enhancing overall system performance. Innovative approaches, such as event-triggered detection and bloom filtering techniques, show promise in mitigating specific attack vectors but may introduce overhead or limitations in real-time response. Deep learning-based IDS systems exhibit high performance but struggle with the detection of novel attacks. Furthermore, while some solutions address specific vulnerabilities, others propose more comprehensive security measures, such as preventing remote manipulation of critical vehicle functions like steering and braking. Overall, the research emphasizes the critical need for robust security measures in automotive systems to safeguard against evolving threats to vehicle safety and integrity.

A Comprehensive Review of Interactive System Architecture

This paper presents a detailed review of interactive software architecture (ISA). ISA has emerged as an important aspect in software engineering offering a framework that prioritizes user interaction, real-time responsiveness and adaptability. The paper explores various aspects crucial for the design, development, and maintenance of robust and scalable software systems. The review encompasses architectural patterns, design principles, scalability, maintainability, security, and emerging trends. The paper also examines the significance of ISA and challenges for the same. Objective: The objective of the paper is to provide a comprehensive understanding of the key considerations in interactive system architecture and how they impact the overall success of a software project. The paper also goes on to discuss the challenges and benefits of implementation of ISA Methodology: the study adopted a desktop approach by reviewing existing literature on the topic at hand so as to provide a simplified understanding of the interactive system architecture. Recommendations: at the end of the paper it provides recommendation on future developments in this field.

Contractile and Tensile Measurement of Molecular Artificial Muscles for Biohybrid Robotics.

A printable artificial muscle assembled from biomolecular motors, which we have recently developed, showed great potential in overcoming the design limitations of conventional biohybrid robots as a new bio-actuator. Characterizing its contractility for extending its applicability is important. However, conventional measurement methods are composed of complex operations with poor reproducibility, flexibility, and real-time responsiveness. This study presents a new method for measuring the contractile force generated by artificial muscles. A measurement system was constructed, wherein artificial muscles were patterned by UV laser scanning in an oil-sealed microchamber, and the contractile force was measured in real time using a microforce sensor extended by a 3D-printed microcantilever. The measurement accuracy of the sensor was ensured through calibration and correction. For demonstration purposes, a series of contractile measurements were carried out using the proposed system. The relationship between contractile force and the dimensions of the activation space of the artificial muscles, as well as the tensile properties of the contracted muscle chain were evaluated. The results will help characterize the contractile properties of the artificial muscle and lay the foundations for its further application in biohybrid robotics.