Feedback Capabilities Research Articles

Preparation and properties of metal-core piezoelectric fibers for dynamic sensing using the double heat-shrinkage method

Metal-core piezoelectric fibers (MPF)are coaxial fibers composed of a metal core, piezoelectric material, conductive material, and insulating material. They possess piezoelectric effects and hold great application potential in sensors and intelligent products. The most commonly used preparation method for metal-core piezoelectric fibers is electrospinning. However, this method is extremely sensitive to process parameters. It demands high voltage control, and it is difficult to precisely adjust the solution concentration and viscosity. This makes it challenging to manufacture piezoelectric fibers with global dynamic sensing capabilities, resulting in unstable product quality, such as uneven fiber diameters and internal structural defects, which restricts the application of metal-core piezoelectric fibers. In this research, metal-core piezoelectric fibers were prepared via a double heat-shrinkage thermoplastic approach, employing single thermoplastic piezoelectric polyvinylidene fluoride (PVDF) tubes and thermoplastic insulating silicone tubes. This study constructed a testing platform for metal-core piezoelectric fibers to explore their piezoelectric, impact, vibration, and durability attributes. The stability and consistency of the piezoelectric properties throughout the entire domain were also evaluated. The experimental outcomes demonstrate that the metal-core piezoelectric fibers fabricated herein not only possess a straightforward manufacturing process and low equipment cost but also display high manufacturing efficiency and excellent cross-sectional coaxiality. They exhibit remarkable response and feedback capabilities and can be applied to monitor the dynamic performance of piezoelectric fibers across the whole domain. This offers a novel concept for the large-scale and efficient production of metal-core piezoelectric fibers with stable performance.

Research on Algorithm Optimization Driven by Big Data

To address the performance degradation and limited adaptability of traditional optimization algorithms in big data environments, this paper proposes an optimization framework that integrates data representation, feedback adjustment, and model fusion. Through feature compression and structural modeling, data characteristics are embedded in the objective function expression, a search space compression mechanism with dynamic feedback capability is constructed, and the robustness of the algorithm is improved by combining parameter adaptation strategies. In the scenario of multi-source heterogeneous data, an integrated optimization scheme is further introduced to improve the generalization ability. Comparative and ablation experiments are carried out based on three types of real data sets, and the superior performance of the proposed method in terms of accuracy, convergence and resource control is systematically verified.

Exploring the influencing factors affecting the operational effectiveness of public health emergency response mechanism: a DEMATEL-ISM-MICMAC mixed methods study

ObjectivesThis study aims to explore key factors and identify root factors influencing the Public Health Emergency Response Mechanism (PHERM) to ensure timely and effective responses to emerging infectious disease crises and enhance the efficiency of emergency operations.MethodsWe employed a mixed-method approach using DEMATEL-ISM-MICMAC to analyse the interrelationships among factors affecting PHERM. The DEMATEL method established the hierarchical structure of the factors, ISM determined the relational paths, and MICMAC further characterized the attributes of the factors.ResultsThe analysis revealed that PHERM's influencing factors are organized into four levels, with the conscientiousness of emergency leadership action (X15) identified as the most profound and influential factor, exhibiting a strong causality with a high driving force. The decision-making and command feedback capability (X8) emerged as a significant outcome factor in the transition layer, highly influenced by other factors and with the highest node degree.ConclusionsThe proactive emergency response awareness and actions of leaders is crucial for the mechanism's smooth and efficient operation. It is essential to prioritise ideological education and simulation training to instill such awareness. Moreover, proactive preparation for factors associated with decision-making and command capabilities is necessary to mitigate potential hesitation and panic during actual epidemic prevention, thereby enhancing the operational effectiveness of PHERM.

Methodology for Enablement of Human Digital Twins for Quality Assurance in the Aerospace Manufacturing Domain

This paper will examine a methodology to enable the usage of Human Digital Twins (HDTs) for Quality Assurance in the aerospace manufacturing domain. Common-place hardware and infrastructure, including cloud-based facility security cameras, cloud-based commercial virtual environments, a virtual reality (VR) headset, and artificial intelligence (AI) detection algorithms, have been connected via application programming interfaces (API) to enable a 24-h surveillance and feedback capability for a representative aerospace manufacturing cell. Human operators who perform defined manufacturing assembly operations in real life in the cell can utilize this methodology to digitize their performance and provide objective evidence of conformity and safety messaging for their human-centric manufacturing operation in real time. The digitization of real human-centric performance using this methodology creates the foundation for a HDT. This paper will present the application of HDTs in a manner that can easily be scaled across manufacturing operations while utilizing technologies that are already commonly inserted into existing manufacturing operations, which facilitates the exploration of HDT concepts without the need for expensive capital purchases and emerging technologies.

Intuitive and Experiential Approaches to Enhance Conceptual Design in Architecture Using Building Information Modeling and Virtual Reality

The conceptual design phase in architecture requires both intuitive and iterative approaches, which traditional Building Information Modeling (BIM) workflows fail to support properly. BIM provides data-driven decision-making and project coordination but does not offer affective or experiential feedback capabilities. BIM and Virtual Reality (VR) integration offers a promising solution to improve user-focused spatial assessments during initial design phases. The research follows three distinct phases, including a Systematic Literature Review to identify BIM-based conceptual workflow limitations, semi-structured interviews with architects to understand practical challenges and expectations, and the development of a BIM-based framework combining immersive VR for affective and visuospatial evaluation. A testing phase of the proposed framework occurred in the pilot study. The current BIM workflows show significant deficiencies in their ability to support creative flexibility, user engagement, and experiential validation. The BIM-VR framework implemented in the pilot study showed improvements in spatial cognition, emotional engagement, and iterative design decision-making during the conceptual design phase. Early-stage architectural design evaluation becomes more effective through VR integration into BIM workflows because it provides real-time immersive user feedback. The proposed framework helps develop BIM tools that are more intuitive for humans while advancing user-informed design practices in the architecture, engineering, and construction industries.

GammaSTAR: A framework for the development of dynamic, real-time capable MR sequences.

To present the real-time capability and advanced MR sequence library of the MR sequence development framework gammaSTAR. The presented platform consists of four different components: (1) a frontend for sequence development combined with a Python backend for sequence generation; (2) a Lua backend for the creation of hardware instructions; (3) a vendor-specific driver for translation of these instructions into scanner-specific objects; and (4) an interface for real-time feedback capability. In vivo measurements of the same volunteer were performed for comparison of imaging and spectroscopy sequences implemented in this framework with those of one main vendor (Siemens Healthineers) at magnetic field strengths of 3 T and 1.5 T. Prospective motion correction was integrated into a spin echo EPI sequence to demonstrate the real-time feedback capability. The imaging and spectroscopy results of the gammaSTAR sequences show very similar image contrasts and qualities compared to those by the vendor. ADC maps were calculated and show values of (0.80 ± 0.14)10-3 mm2/s in white matter. Results of pseudo-continuous spin-echo (pCASL GRASE) and 3D radial UTE imaging demonstrate the ability to run complex sequences without long sequence preparation times. Prospective motion correction is possible by means of real-time feedback and shows much fewer movement artifacts with mean voxel displacement of 1.63 mm (uncorrected) versus 0.37 mm (corrected). All images were reconstructed using the vendor's reconstruction pipeline. The platform gammaSTAR allows for MR sequence development with real-time feedback capability demonstrated by a large number of MR sequences and applications.

Enhancing Oral English Proficiency Through Human-Computer Interaction

Human-Machine Interaction (HMI) technology has revolutionized the landscape of oral English education, offering new possibilities for improving learning efficiency and experiences. This paper presents an innovative teaching system that integrates real-time speech recognition and feedback capabilities with advanced natural language processing (NLP) and machine learning algorithms. The system is designed to provide personalized learning paths based on learners' performance data, ensuring tailored resources and guidance. Emphasizing user experience and interactive design, it aims to stimulate learner interest and motivation. Research findings indicate significant improvements in students' pronunciation, fluency, and grammar, alongside high levels of user satisfaction. However, challenges remain in fully replicating genuine human interactions and addressing technical limitations. Future work will focus on enhancing conversational abilities, personalization, and multimodal feedback mechanisms to better prepare students for real-world communication scenarios.

Leveraging Interactive Learning by Integrated Assessment Software in EdTech: Enhancing Sustained Learning Outcomes with Mentimeter

The advent of Educational Technology (EdTech) has transformed traditional pedagogical approaches, introducing innovative tools that enhance engagement and learning outcomes. This paper explores the application of Mentimeter, an interactive presentation software, in educational settings. By integrating features such as live polls, quizzes and word clouds, Mentimeter fosters a more interactive and participatory learning environment. The study investigates how real time feedback and immediate assessment capabilities enable educators to adjust their teaching strategies dynamically, thus catering to individual student needs and promoting active engagement in learning. Additionally, the paper examines Mentimeter’s role in increasing inclusivity and accessibility, allowing anonymous participation and accommodating both in person and remote learners. The analysis includes data-drive insights derived from student responses, highlighting the tool’s effectiveness in improving student engagement, collaboration and overall learning outcomes. This research contributes to the broader understanding of how interactive presentation software can be utilized within the EdTech framework to enhance educational experiences and support data driven instructional practices. The findings underscore the potential of interactive presentation tools in digitizing modern education, making a compelling case for their widespread adoption in classrooms and beyond.

The linguistic feedback of tourism robots significantly influences visitors’ ecotourism behaviors

With the extensive application of artificial intelligence technology in the tourism industry, robot-assisted tourism has become a vital strategy for enhancing tourist experiences and promoting sustainable tourism practices. This study aims to explore the impact of language feedback from tourism robots on tourists’ ecotourism behavior and analyze potential mediating and moderating mechanisms. Through three experimental studies, we found that robot guides with language feedback capabilities significantly improve tourists’ ecotourism behavior. Specifically, environmental responsibility acts as a moderator between the robot’s language feedback and tourists’ ecotourism behavior, indicating that the robot’s language feedback is more effective when tourists have a higher sense of environmental responsibility. Furthermore, the robot’s language feedback enhances tourists’ environmental awareness and responsibility by increasing cognitive trust and feedback propensity. The findings have practical implications for tourism destinations and operators in designing and implementing intelligent tourism services to promote tourists’ ecological engagement.

An intelligent federated learning boosted cyberattack detection system for Denial-Of-Wallet attack using advanced heuristic search with multimodal approaches

In the modern digital era, owing to technological progressions, the diversification and intensity of cyber-attacks have attained an extraordinary level. Unlike network users, intruders use technological developments and implement attacks to cause operational disruptions, data breaches, and financial losses. The Denial-of-Wallet (DoW) attack adapts the standard Denial-of-Service (DoS) attack. The principle of either attack is equivalent: to use the feedback capability to flood requirements to a service, making it unable to utilize it correctly. The DoW attack goal is to use the limitation of the calculating capability dealing with the cloud service, trying to cause direct financial loss. Federated Learning (FL) has been developed as a guaranteed solution for detecting DoW. This model deals with safety concerns, minimizes the data breach risk, and improves scalability. This manuscript presents a Cyberattack Detection Model for Denial-Of-Wallet Using Advanced Metaheuristic Optimization Algorithms in Federated Learning (CDMDoW-AMOAFL) model. The proposed CDMDoW-AMOAFL model aims to detect and mitigate malicious activities in a network. The z-score normalization is initially applied in the data normalization stage to transform input data into a beneficial format. Furthermore, the proposed CDMDoW-AMOAFL method utilizes the Harris hawk optimization (HHO) model for the feature selection process to identify and select the most relevant features from a dataset. For cyberattack detection, the ensemble models, namely the gated recurrent unit (GRU), temporal convolutional network (TCN), and convolutional autoencoder (CAE) models, are employed. Finally, the modified marine predator algorithm (MMPA) optimally adjusts ensemble models’ hyperparameter values, resulting in better classification performance. A wide-ranging experimentation was performed to prove the performance of the CDMDoW-AMOAFL method under the DoW attack detection dataset. The performance validation of the CDMDoW-AMOAFL technique illustrated a superior accuracy value of 98.12% over existing models.

Augmented reality for upper limb rehabilitation: real-time kinematic feedback with HoloLens 2

Exoskeletons for rehabilitation can help enhance motor recovery in individuals suffering from neurological disorders. Precision in movement execution, especially in arm rehabilitation, is crucial to prevent maladaptive plasticity. However, current exoskeletons, while providing arm support, often lack the necessary 3D feedback capabilities to show how well rehabilitation exercises are being performed. This reduces therapist acceptance and patients’ performance. Augmented Reality technologies offer promising solutions for feedback and gaming systems in rehabilitation. In this work, we leverage HoloLens 2 with its advanced hand-tracking system to develop an application for personalized rehabilitation. Our application generates custom holographic trajectories based on existing databases or therapists’ demonstrations, represented as 3D tunnels. Such trajectories can be superimposed on the real training environment. They serve as a guide to the users and, thanks to colour-coded real-time feedback, indicate their performance. To assess the efficacy of the application in improving kinematic precision, we conducted a feasibility study with 15 healthy subjects. Comparing user tracking capabilities with and without the use of our feedback system in executing 4 different exercises, we observed significant differences, demonstrating that our application leads to improved kinematic performance. 12 clinicians tested our system and positively evaluated its usability (System Usability Scale score of 67.7) and acceptability (4.4 out of 5 in the ’Willingness to Use’ category in the relative Technology Acceptance Model). The results from the tests on healthy participants and the feedback from clinicians encourage further exploration of our framework, to verify its potential in supporting arm rehabilitation for individuals with neurological disorders.

Integrated AI Technologies in Sports: A Technical Framework for Advanced Athletic Training Systems

This article presents a comprehensive technical framework for implementing advanced artificial intelligence technologies in athletic training systems. The article integrates multiple AI components, including computer vision, deep learning, and reinforcement learning, to create a scalable and accessible training platform. The article utilizes a multi-modal approach, combining real-time video analysis with IoT sensor data to provide comprehensive movement assessment and personalized coaching feedback. The architecture implements edge computing and cloud-based processing to ensure optimal performance across various training environments. The article incorporates advanced security measures and privacy-preserving techniques while maintaining real-time feedback capabilities through mobile devices. The article's modular design enables adaptation across different sports disciplines, utilizing sport-specific analytical modules and specialized AI components. This article addresses the significant gap between elite and grassroots level training opportunities by democratizing access to professional-grade training methodologies through innovative AI applications. The article details the technical implementation, validation methodologies, and future development roadmap for this integrated sports training system.

Research on the Impact of Digital Transformation on The Production Efficiency of My Country's Manufacturing Enterprises

With the continuous development of the global economy, digital transformation has become a key means for manufacturing enterprises to improve production efficiency and competitiveness. This paper systematically combs through relevant literature to explore the impact of digital transformation on the production efficiency of China's manufacturing enterprises. First, this paper explains the connotation and main technologies of digital transformation, including intelligent manufacturing, the Internet of Things, big data analysis, artificial intelligence and robotics. These technologies have played an important role in production process optimization, cost control, product quality improvement and supply chain management. Secondly, this paper analyzes the specific application of digital transformation in the manufacturing industry and demonstrates the significant impact of digital transformation on enterprise production efficiency through case studies. Case studies show that the application of digital technology not only improves the real-time monitoring and feedback capabilities of the production process, but also optimizes production scheduling, reduces operating costs and improves resource utilization. At the same time, digital transformation has also significantly improved product quality. Through quality control and improvement and customized production, enterprises can better meet market demand. In addition, the implementation of digital supply chain management has improved the collaborative efficiency of the supply chain and enhanced the overall competitiveness of enterprises. Finally, this paper summarizes the comprehensive impact of digital transformation on the production efficiency of China's manufacturing enterprises and puts forward corresponding policy recommendations. It is recommended that the government strengthen its support for digital transformation and formulate relevant policies to guide enterprises to carry out digital upgrades. At the same time, enterprises should actively embrace digital technology, formulate practical transformation strategies, and continuously improve their production efficiency and market competitiveness. This paper provides theoretical support and practical reference for future research, and points out potential directions for further research.

Enhanced Ultra-narrowband Fast Response Ultraviolet Photodetector based on GaN Homojunction with a Carbon-Doped Semi-insulating Intermediate Layer.

A narrowband detection photodetector (PD) serves as a rapid identifier of specific wavebands, holding immense significance in secure communication and spectral recognition. Herein, after carbon is doped into GaN, an acceptor energy level emerges in its band structure, which will compensate with donor states in GaN to reduce carrier concentration and make GaN semi-insulating, and it affected the dark current and electric field distribution of the GaN p-i-n PD. Operating at a bias of 0 V, the C-doped GaN p-i-n PD demonstrates an ultralow dark current density and a high light-to-dark current ratio compared to the undoped intrinsic i-layer GaN p-i-n PD. Moreover, the narrowband response's full width at half-maximum of the PD is only 8.11 nm and displays rapid signal feedback capabilities. Consequently, this prepared C-doped GaN p-i-n PD, which obviates the need for integrating optical filters or employing sophisticated processes, stands to be capable of accurately distinguishing UVA radiation.

Microfluidic Biosensors for the Detection of Motile Plant Zoospores.

Plant pathogen zoospores play a vital role in the transmission of several significant plant diseases, with their early detection being important for effective pathogen management. Current methods for pathogen detection involve labour-intensive specimen collection and laboratory testing, lacking real-time feedback capabilities. Methods that can be deployed in the field and remotely addressed are required. In this study, we have developed an innovative zoospore-sensing device by combining a microfluidic sampling system with a microfluidic cytometer and incorporating a chemotactic response as a means to selectively detect motile spores. Spores of Phytophthora cactorum were guided to swim up a detection channel following a gradient of attractant. They were then detected by a transient change in impedance when they passed between a pair of electrodes. Single-zoospore detection was demonstrated with signal-to-noise ratios of ~17 when a carrying flow was used and ~5.9 when the zoospores were induced to swim into the channel following the gradient of the attractants. This work provides an innovative solution for the selective, sensitive and real-time detection of motile zoospores. It has great potential to be further developed into a portable, remotely addressable, low-cost sensing system, offering an important tool for field pathogen real-time detection applications.

Analysis of Teaching Materials for Teaching Spanish to Arabic Speakers"

This study examines instructional resources employed to educate Spanish for the Arabic-speaking clientele based onlinguistic, cultural and pragmatic perspectives. Self administered questionnaires, videotaped classroom observation,and semi structured interviews were used with teachers in different Arab countries. The outcomes show that thereexists a number of important deficits in the present teaching-learning resources. These learning materials were alsoflagged to omit particular phonological difficulties of Arabic speakers such as the trilled ‘r’ and other features as thephonemic grammar of Arabic does not support it. These quantitative results were echoed in contrastive linguisticanalysis that received a slightly lower Mean of 2.80 (SD=1.15) further suggesting exclusionary evidence foraddressing cross-language interference in phonological adequacy with a mean of 3.40 (SD= 1.10). Lessons supportedthese outcomes; 87% taught extensively from printed texts and 40% of classes used technology, which reduced theinteraction and feedback capabilities. Specifically, teachers focused on pronunciation correction in 87% of classes,and the remaining 60% of grammatical mistakes, which required delayed feedback, in environments that do not allowreal-time feedback provision. However, cultural content integration was incomplete; only 67% of classes containedthem, while most of the teachers reported themselves in making changes to their lesson plans

Systematic review of motion capture in virtual reality: Enhancing the precision of sports training

In the modern era of sports training, the synergy between motion capture and Virtual Reality (VR) offers an innovative approach to enhancing training precision. This systematic review delves into the application of motion capture within VR for sports training, highlighting its transformative potential. Through a comprehensive literature search, we examined the myriad applications, from physical conditioning enhancements to accelerated rehabilitation processes. Our findings underscore the capability of real-time feedback, immersive training environments, and tailored regimes that this fusion provides. However, despite its promise, challenges such as hardware constraints, data processing complexities, and interaction interface limitations persist. Future trajectories indicate an increasing influence of AI and deep learning, promising more sophisticated hardware and a broader spectrum of applications, including niche sports disciplines. The review concludes with an emphasis on the wider societal implications, suggesting a shift towards a holistic athlete well-being approach.

Embedded Smart Sensor Network Architecture Based on Edge Computing

This paper focuses on the integration of edge computing in smart sensor networks. In Internet of Things (IoT) applications, traditional cloud computing models cause data processing delays due to long-distance data transmission. This transmission delay significantly impairs real-time data processing and feedback capabilities, particularly in time-sensitive applications. Edge computing can transfer data to local devices for processing so that data that is affected by network congestion does not have to be transmitted long distances to the cloud for processing. It can greatly reduce the delay caused by network congestion and reduce energy consumption. In addition, it also provides certain guarantees for data security. Building upon these characteristics, edge computing offers several key advantages. First, it enables efficient local data processing for real-time operation. In some industrial applications such as smart grids and intelligent transportation systems, edge computing can operate highly autonomously. This avoids the delay caused by network fluctuations when transmitting to the cloud. This paper explores the integration of lightweight machine learning models, particularly TinyML, in edge computing applications. These models will improve the data processing capabilities of edge devices. Finally, the article also proposes further ideas for this new architecture to make up for the shortcomings of the architecture in task allocation and data processing.

Manatee: A multicore interference analysis tool for embedded soc evaluation

Interferences on shared resources are the main factor limiting the employment of multicore architectures in many embedded use cases. Research on these interferences and enhancements, for example in memory hierarchies, could alleviate this restriction. This however requires more awareness of contention for shared resources during the design and development process of System on Chips (SoCs). As an answer we present the concept of a tool which brings this awareness to the RISC-V hardware development framework Chipyard. It extends Chipyard?s agile development focus by adding the capabilities for quick feedback on changes regarding shared resource contention. A partial realisation further allows first tests and evaluation on use case basis.

Construction of Full-Space State Model and Prediction of Plant Growth Information

HighlightsThis research proposes a model based on DTs and BPNN and accurately predicts the growth indexes and state of lettuce.Abstract. This research proposed a full-space state prediction model based on Digital Twins (DTs) for intelligent prediction and optimization control of environmental parameters and crop growth in plant factories. Compared with traditional prediction models, this model significantly improved production efficiency and resource utilization in plant factories by dynamically adjusting environmental control strategies through real-time data collection and feedback. The model employed a Back Propagation Neural Network (BPNN) for accurate prediction of crop growth indexes, with experimental results showing a Root Mean Squared Error (RMSE) of 0.868 and a Mean Absolute Error (MAE) of 0.625 on the test dataset, indicating high prediction accuracy. The innovative aspect of this model lies its integration of DTs technology, enabling full-cycle monitoring and intelligent regulation of the crop growth process, addressing the limitations of existing models in dynamic feedback and real-time adjustment capabilities. Future extensive validation and optimization of the model across different crop types and environmental conditions will further enhance its potential for application in plant factory management. Keywords: Back propagation neural network, Digital twins technology, Lettuce, Plant factory, State prediction.