Interface System Research Articles

Electrotunable superlubricity of two-dimensional ZIF-8

Electric field control can actively, dynamically, and repeatably influence the interface friction behavior. The unique properties of two-dimensional (2D) ZIF-8 make it a promising lubricating material for electromechanical devices. The study on the electrotunable superlubricity of 2D ZIF-8 is carried out under longitudinal and transverse electric fields respectively, resulting in an order of magnitude variation in friction coefficient (μ: 0.0037–0.0124). Through the experiments and simulation, the regulation mechanism of electric fields on the lubricating properties of 2D ZIF-8 is attributed to the coupling effect of adhesion regulation and out-of-plane deformation regulation: The weakening of anchoring effect reduces the adhesion between probe and 2D ZIF-8; the tight binding of interfacial charge under longitudinal electric field as well as the increase in surface stiffness caused by lattice tension under transverse electric field, both restrain the out-of-plane deformation during friction. The electrotunable superlubricity of 2D ZIF-8 helps achieve rapid and flexible adjustment of the friction interface in electro-mechanical systems under charged conditions, illuminating the future development prospects for intelligent control.

Gas-liquid interface properties of fluorocarbon and hydrocarbon surfactants and their mixed foams: Molecular dynamics simulations and experimental studies

This paper focuses on the gas-liquid interface characteristics of short-chain fluorocarbon surfactant FS-50 and hydrocarbon surfactant sodium dodecyl sulfate (SDS) and their mixed systems. The foam properties of SDS, FS-50, and their mixed solutions were experimentally determined. Then, based on the molecular dynamics simulation method, the interaction mechanism of surfactant molecules at the gas-liquid interface was simulated and analyzed by constructing a foam-liquid membrane system containing surfactant molecules and water molecules. The experimental results show that FS-50 can effectively enhance the foam stability when the concentration is lower than 0.16 wt% and the mixing ratio with SDS is not more than 1:1; the simulation results show that FS-50 can change the arrangement of SDS molecules at the gas-liquid interface. Simultaneously, there is a competitive adsorption phenomenon between FS-50 and SDS at the gas-liquid interface. The low mixing ratio FS-50 can increase the thickness of the gas-liquid interface of the mixed system and the ability of SDS to form hydrogen bonds with water, enhance the water-locking ability of SDS, and further improve the stability of the foam liquid film. The interaction mechanism between short-chain fluorocarbons and SDS surfactants at the gas-liquid interface was revealed by simulation, which provided theoretical guidance for optimizing the performance of the foam liquid film.

Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.

We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients. Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset. Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations. These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.

Enhancing Real-Time Cursor Control with Motor Imagery and Deep Neural Networks for Brain–Computer Interfaces

This paper advances real-time cursor control for individuals with motor impairments through a novel brain–computer interface (BCI) system based solely on motor imagery. We introduce an enhanced deep neural network (DNN) classifier integrated with a Four-Class Iterative Filtering (FCIF) technique for efficient preprocessing of neural signals. The underlying approach is the Four-Class Filter Bank Common Spatial Pattern (FCFBCSP) and it utilizes a customized filter bank for robust feature extraction, thereby significantly improving signal quality and cursor control responsiveness. Extensive testing under varied conditions demonstrates that our system achieves an average classification accuracy of 89.1% and response times of 663 milliseconds, illustrating high precision in feature discrimination. Evaluations using metrics such as Recall, Precision, and F1-Score confirm the system’s effectiveness and accuracy in practical applications, making it a valuable tool for enhancing accessibility for individuals with motor disabilities.

Motor Imagery Classification Improvement of Two-Class Data with Covariance Decentering Eigenface Analysis for Brain–Computer Interface Systems

This study is intended to improve the motor imagery classification performance of two-class data points using newly developed covariance decentering eigenface analysis (CDC-EFA). When extracting the classification for the given data points, it is necessary to precisely distinguish the classes because the left and right features are difficult to differentiate. However, when centering is performed, the unique average data of each feature are lost, making them difficult to distinguish. CDC-EFA reverses the centering method to enhance data characteristics, making it possible to assign weights to data with a high correlation with other data. In experiments with the BCI dataset, the proposed CDC-EFA method was used after preprocessing by filtering and selecting the electroencephalogram data. The decentering process was then performed on the covariance matrix calculated when acquiring the unique face. Subsequently, we verified the classification improvement performance via simulations using several BCI competition datasets. Several signal processing methods were applied to compare the accuracy results of the motor imagery classification. The proposed CDC-EFA method yielded an average accuracy result of 98.89%. Thus, it showed improved accuracy compared with the other methods and stable performance with a low standard deviation.

Steady-State Visual Evoked Potential-Based Brain-Computer Interface System for Enhanced Human Activity Monitoring and Assessment.

Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.

Self-distillation with beta label smoothing-based cross-subject transfer learning for P300 classification

Background:The P300 speller is one of the most well-known brain-computer interface (BCI) systems, offering users a novel way to communicate with their environment by decoding brain activity. Problem:However, most P300-based BCI systems require a longer calibration phase to develop a subject-specific model, which can be inconvenient and time-consuming. Additionally, it is challenging to implement cross-subject P300 classification due to significant inter-individual variations. Method:To address these issues, this study proposes a calibration-free approach for P300 signal detection. Specifically, we incorporate self-distillation along with a beta label smoothing method to enhance model generalization and overall system performance, which can not only enable the distillation of informative knowledge from the electroencephalogram (EEG) data of other subjects but effectively reduce individual variability. Experimental results:The results conducted on the publicly available OpenBMI dataset demonstrate that the proposed method achieves statistically significantly higher performance compared to state-of-the-art approaches. Notably, the average character recognition accuracy of our method reaches up to 97.37% without the need for calibration. And information transfer rate and visualization further confirm its effectiveness. Significance:This method holds great promise for future developments in BCI applications.

Investigation of Cross‐Sectional Characteristics of Internal Meshing Screw Mixing Flow Field for Dough Paste

ABSTRACTThe internal meshing screw mixer, driven by extensional rheology, demonstrates excellent mixing efficiency for high‐viscosity materials while minimizing fiber damage. This study developed a computational model for the mixing process of high‐viscosity fluids based on the motion equations of the internal meshing screw. The model was validated through experiments involving the mixing of corn syrup, flour and water. The results reveal the flow field's cross‐sectional streamlines and confirm the presence of chaotic mixing states at the system interface through horseshoe mapping and the existence of hyperbolic points. By comparing the extensional and shear rates, as well as analyzing the distribution of mixing indices, this paper establishes that the primary mixing mechanism within the cross‐section is extensional force, highlighting the role of the internal meshing screw as a mixer dominated by extensional flow fields. We investigate the variations in fluid velocity, velocity uniformity index, extensional rate, shear rate, and mixing index over time under different conditions of eccentricity, rotor radius, and rotational speed. The findings indicate that while eccentricity has a limited impact on average velocity, it significantly enhances velocity disturbances and increases the ratio of extensional effects within the fluid domain. In contrast, rotor radius and speed lead to a linear increase in average velocity but have little effect on velocity disturbances and the extensional effects in the fluid domain. This study provides valuable insights into how various cross‐sectional parameters influence the flow field of the internal meshing screw mixing process, offering crucial support for its application in mixing high‐viscosity non‐Newtonian fluids within the food industry.

Intelligent control algorithms to ensure the flight safety of aerospace vehicles

An overview of the architectures, functions and algorithms of aircraft-type aerospace vehicles equipped with fly-by-wire (FBW) digital integrated control systems (ICS) is given. The ICS has a hierarchical architecture, including the primary, backup and emergency control systems with a sophisticated control reconfiguration logic in the event of failures. Digital control computers have dissimilar redundancy of equipment and software. At the atmospheric flight stage, integrator control laws provide the specified control characteristics, flight envelope protection, automatic trim of vehicle and crew workload alleviation. Further integration of manual and automatic control, implementation of the control philosophy as a continuation of the pilot's actions, more intellectual control system and human-machine interface, use of multiple control surfaces to ensure optimal vehicle configuration and alleviate structural loading together with basic control functions are considered as promising areas for ICS improvement.

Exploring the Impact of Automatic Speech Recognition on Students' Speaking Skills and Perceptions

The speaking application developed in this research by using the technology of Automatic Speech Recognition (ASR) aims to help students practice speaking autonomously in English. The main objective is to form an interactive application named LINKS, which enhances their pronunciation, fluency, and vocabulary. The design, development, and evaluation of the application were based on survey findings from 28 participants along with principles from the ADDIE model. The findings showed that 82% of the students reported improvement in their pronunciation and speaking skills, while 96% rated the user interface and feedback system positively. The participants also found that the application gave corrective feedback and helped with self-assessment, and real-time scores presented to students which enabled them to handle mistakes much better and even boosted their confidence. Furthermore, the interactive features raised learner autonomy and engagement, demonstrating that ASR-based applications can be an effective tool for enhancing independent learning. This research, concludes that the application can be a useful tool to overcome students' problems in speaking as well may help the language learning process.

Organizing the automated system of dispatch control over pump units at water pumping stations

The design and operation of modern dispatch control systems for pumping units involves comprehensive solution to separate engineering, technical, and scientific problems. The object of research is information processes enabling the operational modes of electrically driven pumping units at water pumping stations. This paper solves the scientific and technical task related to developing topology, hardware, and software tools, control algorithms, dispatch interface, and researching the modes of operation of pumping units in dispatch control systems at water pumping stations. Algorithms for controlling pumping units have been implemented, the advantage of which is the possibility of automated calculation of parameters for technological PID-controllers, taking into account the current electrical parameters of the frequency-controlled electric drive of pumping units and operating conditions. The WEB-oriented dispatch interface of the SCADA-based pumping unit control system was designed and tested, which enables control process in real time. Features of the developed dispatch control system are improved topology, expanded functionality, energy-saving control modes, and the possibility of further modernization based on the principles of standardization and unification of hardware and software tools and design procedures. The developed dispatch control system for pumping units at water pumping stations has been implemented and is successfully operated at an industrial water supply enterprise. The results provided for an increase in technical and economic indicators during the operation of technological equipment due to the efficiency of control and management procedures, energy-saving operational modes of the frequency-controlled electric drive of pumping units.

A Simulation and Experimental Platform for Communication Courses Based on Digital Signal Processor

In this paper, a simulation and experimental platform is designed and implemented to overcome the shortcomings of the existing experimental platform for communication related courses. Firstly, the overall scheme of the simulation and experimental platform are introduced, including the specific hardware composition and supporting software. Secondly, the implement details are described in the following order: system composition, peripherals, hardware accelerator, and interface to the host computer. The main system is based on an independently developed digital signal processor, which may relieve the bottleneck problem of imported chips. Using FPGA to implement peripherals enhances the flexibility of the entire system and provides the ability for further development. The feasibility of reducing the cost is then discussed. Thirdly, the design and implementation of the simulator, compiler and assembler, debugger and other tools are introduced respectively. The simulator can operate under normal mode and debugging mode. The former can be used for ordinary experiments for teaching purpose, while the latter is for further development of graduated students. Finally, the prepared experimental projects are shown, including 6 basic experiments and 6 communication algorithm experiments. With the help of these experiments, students can better understand the principles and implementation of corresponding communication algorithms, and experience how different parameters may impact on the communication results, by modifying the configuration of the experimental system. The demonstrations of prototype prove that the simulation and experimental platform can meet the needs of communication related courses. The future work includes developing more experimental projects, raising integration to reduce the costs, and improving the entire system based on student feedback.

Pengembangan Antarmuka Pengguna (User Interface) Sistem Pengelolaan Beasiswa Berbasis Website dengan Pendekatan Cognitive Walkthrough

An effective user interface (UI) is crucial in a web-based scholarshipmanagement system to ensure a good user experience and easy accessibility toinformation. However, many users still encounter difficulties using the existingsystem, necessitating a better design. This study aims to evaluate and improve theuser interface of the scholarship management system using the CognitiveWalkthrough approach. The Cognitive Walkthrough approach is used to identifyusability issues in the user interface. A prototype of the design solution was thentested through usability testing involving seven evaluators and five different taskscenarios using Maze. The results show that all evaluators were able to completethe task scenarios with a category 1 rating (no issues), although four evaluators experienced minor issues in task scenarios 1 and 2. Evaluators were slightlyconfused when searching for the latest requirements information and the ineffectiveuse of buttons. By implementing the suggested improvements based on the findingsfrom the Cognitive Walkthrough, the user interface in the scholarship managementsystem can be significantly enhanced, facilitating users in accessing the requiredinformation.

Morphodynamics of interface between dissimilar cell aggregations

Tissue interfaces are essential for development and their disruption often leads to diseases such as tumor invasion. Here, we combine experiments, theoretical modeling, and numerical simulations to quantify the morphodynamics of interface in a biphasic system composed of Madin Darby canine kidney (MDCK) and mouse myoblast (C2C12) cells. We show that cellular activity regulates the interface morphodynamics and drives wave propagation along the interface. Based on the dispersion relationship, we identify that the wave dynamics results from the activity-mediated instability of the interface and coherent flow. It is found that the topological defects accumulate around and destabilize the interface and +1/2 topological defects are more likely to aggregate in MDCK cell clusters. A biphasic active nematic theory is employed to reproduce our experimental observations and decipher the underlying mechanisms. These findings provide physical insights into the interfacial evolution that could be implicated in tissue morphogenesis and tumor invasion.

Rancangan Sistem Profil Desa Sei Rotan Berbasis Website

In Sei Rotan Village, the village profile data processing process is carried out manually. The process of processing village profiles like this can result in unsafe storage of village profiles. So the researcher designed a website-based sei rattan village profile system, In this design there is a system design and a system interface design, the programming language used in this system is the php programming language and for the database is stored in MySQL Xampp. This research aims to design a profile system for Sei Rotan Villages, so that the data processed or input is stored safely. This research was conducted in Sei Rotan Village, Percut Sei Tuan District. The method used in this research is a qualitative method with data collection techniques through literature studies and observations. For the system development method, the researcher uses prototype. Judging from the above problems, there is a solution to overcome it so that the data before it is processed insecurely becomes safe and can be applied to the design of the village profile system of sei rattan. With this study, can conclude that the design of the village profile system made by the researcher is very helpful for Sei Rotan Village to access the village profile system quickly and safely in data storage.

A Comprehensive Analysis of the Design of Brain-Computer Interface Systems Utilizing Electroencephalography as a Means of Measurement: A Survey

A Comprehensive Analysis of the Design of Brain-Computer Interface Systems Utilizing Electroencephalography as a Means of Measurement: A Survey

Efficient security interface for high-performance Ceph storage systems

Ceph portrays a resilient clustered storage solution with supporting object, block, and file storage capabilities with no single point of failure. Despite these qualifications, data confidentiality defines a concern in the system, as authentication and access control are the only data protection security services in Ceph. CephArmor was proposed as a third-party security interface to protect data confidentiality by adding an extra protection layer to data at rest. Despite the added layer, the initial design of the API needed to be more efficient in addressing security and performance simultaneously. In this study, we propose a new architectural design to address the associated issues with the preliminary prototype. Comprehensive performance and security analysis verify the improvement of the proposed method compared to the initial approach. The benchmark result has indicated a 37% improvement on average in IOPS, elapsed time, and bandwidth for the write benchmark compared to the initial model.

3D Neurovascular Unit Tissue Model to Assess Responses to Traumatic Brain Injury.

The neurovascular unit (NVU) is a critical interface in the central nervous system that links vascular interactions with glial and neural tissue. Disruption of the NVU has been linked to the onset and progression of neurodegenerative diseases. Despite its significance the NVU remains challenging to study in a physiologically relevant manner. Here, a 3D cell triculture model of the NVU is developed that incorporates human primary brain microvascular endothelial cells, astrocytes, and pericytes into a tissue system that can be sustained invitro for several weeks. This tissue model helps recapitulate the complexity of the NVU and can be used to interrogate the mechanisms of disease and cell-cell interactions. The NVU tissue model displays elevated cell death and inflammatory responses following mechanical damage, to emulate traumatic brain injury (TBI) under controlled laboratory conditions, including lactate dehydrogenase (LDH) release, elevated inflammatory markers TNF-α and monocyte chemoattractant cytokines MCP-2 and MCP-3 and reduced expression of the tight junction marker ZO-1. This 3D tissue model serves as a tool for deciphering mechanisms of TBIs and immune responses associated with the NVU.

Design and Development of Interactive Moodle using Design Thinking to Support Online Learning

Moodle is an open-source learning management system (LMS) or e-learning platform that is designed to help educators create online courses with various features to facilitate learning. Interest in this research area has increased significantly in recent years, leading to an increasing number of schools employing this LMS. In the early stages of the development process, schools need to gain insights into user needs and generate new ideas for designing and developing interactive LMS. In this light, Design Thinking (DT) is viewed as an important approach. This study aimed to design and develop an online learning system based on Moodle to facilitate online learning for elementary school students. The objectives were to (1) design a system diagram, system architecture, and functional structure for facilitating online learning and (2) develop an interactive LMS using Moodle. The research methodology was conducted following the DT process. An evaluation was made to assess the usability of the system using the Computer System Usability Questionnaire (CSUQ). Across dimensions such as system quality, information quality, and interface quality, scores ranged between 5 and 7, highlighting a positive user perception across various aspects of the evaluation. These findings, therefore, offer practical guidance for educational stakeholders in adopting and promoting Moodle-based LMS.

A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.