Real-time Feedback Research Articles

Upgrade of crystallography beamline BL19U1 at the Shanghai Synchrotron Radiation Facility

BL19U1, an energy-tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure-based drug design utilizing X-ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low-temperature or room-temperature data collection modes. The control system has been upgraded from Blu-Ice to MXCuBE3, which supports website-mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real-time feedback on data quality.

Smart Gloves for Blind Person using Ultrasonic Sensor & Arduino Nano

This paper presents the design and development of smart gloves aimed at enhancing the mobility and independence of blind individuals. The proposed system integrates an Arduino Nano microcontroller with an ultrasonic sensor to detect obstacles in the user's path. The ultrasonic sensor continuously scans the surroundings and provides real-time feedback to the wearer through haptic feedback mechanisms incorporated into the gloves. The system employs vibration patterns to convey the proximity and location of obstacles, allowing the user to navigate safely and confidently in various environments. Additionally, the compact and lightweight nature of the Arduino Nano ensures portability and ease of use. The effectiveness of the smart gloves was evaluated through user testing, demonstrating promising results in improving the mobility and autonomy of visually impaired individuals. This innovative solution has the potential to significantly enhance the quality of life for blind individuals by providing them with greater freedom of movement and increased safety in their daily activities. Keywords: Ultrasonic Sensors Arduino Nano, Vibrator motor, Speaker, Connecting wires, JQ6500 voice module.

Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization

The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse educational materials. Deep Learning, acting in tandem with Handwriting Recognition, amplifies these advantages manifold. Deep learning powered study assistants offer personalized learning experiences tailored to individual needs, adapting to varied learning styles. Additionally, these assistants facilitate collaborative opportunities, providing real time feedback and evaluation tools that revolutionize the learning process. Key Words: Image recognition, CNN, RNN, LSTM, Neural Networks, SVM, Deep Learning, Convolutional layer, PreLU, ReLU, Text Summarization, Extractive text summarization, Tokenization.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

REVOLUTIONIZING EDUCATION THROUGH AI: A COMPREHENSIVE REVIEW OF ENHANCING LEARNING EXPERIENCES

Artificial Intelligence (AI) is transforming the landscape of education, offering innovative solutions to enhance learning experiences. This review provides a comprehensive overview of how AI is revolutionizing education, focusing on its impact on learning outcomes, teaching methodologies, and the overall educational ecosystem. The adoption of AI in education has led to personalized learning experiences tailored to individual student needs. AI-powered adaptive learning systems analyze student performance data to create customized learning paths, ensuring that students receive content at their pace and level of understanding. This personalized approach improves student engagement and academic performance. AI is also reshaping teaching methodologies, providing educators with tools to streamline administrative tasks and enhance instructional strategies. AI-powered tools can automate grading, create interactive lessons, and provide real-time feedback to students. This allows teachers to focus more on facilitating learning and developing critical thinking skills in students. Furthermore, AI is revolutionizing the assessment process, moving beyond traditional exams to more dynamic and insightful evaluation methods. AI-powered assessment tools can analyze student responses in real-time, providing immediate feedback and insights into student comprehension and learning progress. The integration of AI in education also extends to administrative functions, such as student enrollment, scheduling, and resource allocation. AI-powered systems can optimize these processes, leading to more efficient and effective management of educational institutions. Despite the numerous benefits of AI in education, challenges remain, including concerns about data privacy, algorithmic bias, and the need for teacher training. Addressing these challenges will be crucial to maximizing the potential of AI in education and ensuring equitable access to quality education for all. In conclusion, AI is revolutionizing education by enhancing learning experiences, transforming teaching methodologies, and optimizing administrative processes. As AI continues to evolve, its impact on education is expected to grow, offering new opportunities to improve learning outcomes and prepare students for success in the digital age.
 Keywords: Revolutionizing, AI, Enhancing, Learning, Experiences.

Intraoperative computed tomography for the management of traumatic complex facial fractures: a narrative review

Successful facial reconstruction requires accurate restoration of the concealed three-dimensional facial skeleton. The key rationale for intraoperative computed tomography (iCT) is that it facilitates the most comprehensive assessment of fracture reduction. Real-time visual feedback is now considered an essential component in hand, spine and general orthopaedic surgery. Frequently proposed as the new gold standard of care, iCT has been widely adopted in maxillofacial surgery, especially internationally. In contrast, our Australian experience is that access to iCT and therefore uptake is relatively lacking. The craniofacial skeleton is a complex structure owing to its various convexities and concavities, sinonasal elements, biomechanical buttresses, visceral and occlusal components and neurovascular structures. Classic approaches to open reduction internal fixation aim to reduce incisions to prevent complications such as ectropion, salivary leak or nerve damage. However, intraoperative assessment of fractures in minimally-invasive techniques are hindered by the limited extent of exposure, especially in the setting of complex fracture patterns or those that involve dynamic or functional endpoints. With iCT, surgeons may visualise, adjust and revise fixation without wide exposure while the patient is on the operating table. This obviates an additional operation that may be required due to an unsatisfactory fixation. iCT is cost-effective and reduces radiation exposure. Challenges in integrating iCT include competing for availability with other specialties, training staff and lack of awareness about the benefits this technology offers. This narrative review discusses the role of iCT in the modern Australian operating theatre.

Can Robotic-Arm Assistance Decrease Iatrogenic Soft-Tissue Damage During Direct Anterior Total Hip Arthroplasty?

Manual techniques for total hip arthroplasty (THA) have been widely utilized and proven to be clinically successful. However, the use of advanced computed tomography (CT) scan-based planning and haptically-bounded reamers in robotic-arm assisted total hip arthroplasty (RTHA) holds promise for potentially limiting surrounding soft-tissue damage. This cadaver-based study aimed to compare the extent of soft-tissue damage between a robotic-arm assisted, haptically-guided THA (RTHA) and a manual, fluoroscopic-guided THA (MTHA) direct anterior approach. There were six fresh-frozen torso-to-toe cadaver specimens included, with two surgeons each performing three RTHA and three MTHA procedures. One hip underwent an RTHA and the other hip received an MTHA in each cadaver. Postoperatively, one additional surgeon, blinded to the procedures, assessed and graded damage to nine key anatomical structures using a 1 to 4 grading scale: (1) complete soft-tissue preservation to <5% of damage; (2) 6 to 25% of damage; (3) 26 to 75% of damage; and (4) 76 to 100% of damage. Kruskal-Wallis hypothesis tests were used to compare soft-tissue damage between RTHA and MTHA cases and adjusted for ties. Pooled analysis of the gluteus minimus, sartorius, tensor fascia lata, and vastus lateralis muscle grades demonstrated that cadaver specimens who underwent RTHA underwent less damage to these structures than following MTHA (median, IQR: 1.0, 1.0 to 2.0 vs. 3.0, 2.0 to 3.0; p=0.003). Pooled analysis of the calculated volumetric damage (mm3) for the gluteus minimus, sartorius, tensor fascia lata, and vastus lateralis muscles demonstrated that the cadaver specimens that underwent RTHA underwent less damage to these structures than those that followed MTHA (median, IQR: 23, 2 to 586 vs. 216, 58 to 3,050; p=0.037). This cadaver-based study suggests that utilizing RTHA may lead to reduced soft-tissue damage compared with MTHA, likely due to enhanced preoperative planning with robotic-arm assisted software, real-time intraoperative feedback, haptically-bounded reamer usage, reduced surgical steps, as well as ease of use with reaming. These findings should be carefully considered when evaluating the utilization of robotic-arm assisted THA in practice.

Developing a Gantry Robot with Pre-calculated pure S-curve Motion Profiles for Delicate Egg Handling: Utilizing ESP32, FreeRTOS, and AS5600 Encoders

The poultry industry faces challenges due to egg breakage during transfer. This paper describes a gantry robot specifically designed for delicate egg handling. The robot utilizes pre-calculated pure S-curve motion profiles to achieve smooth and precise movements, minimizing stress on the eggs. This approach leverages the computational efficiency of pre-calculation, making it suitable for low-power microcontrollers like the ESP32. FreeRTOS(Free Real-Time Operating System) ensures real-time task management for profile execution and data collection every 4 milliseconds from the AS5600 encoders. These encoders provide high-resolution angular position feedback, allowing for comparison with the planned S-curve profile after each movement step. This system offers advantages such as reduced egg breakage, improved transfer efficiency, and a simpler design compared to real-time control. However, limitations include limited adaptability to significant environmental changes and disturbances. Future work may investigate incorporating real-time feedback control for enhanced robustness. 

AI Trainer  : Video-Based Squat Analysis

This research proposes a video-based system for analyzing human squats and providing real-time feedback to improve posture. The system leverages MediaPipe, an open-source pose estimation library, to identify key body joints during squats. By calculating crucial joint angles (knee flexion, hip flexion, ankle dorsiflexion), the system assesses squat form against established biomechanical principles. Deviations from these principles trigger real-time feedback messages or visual cues to guide users towards optimal squat posture. The paper details the system architecture, with a client-side application performing pose estimation and feedback generation. The methodology outlines data collection with various squat variations, system development integrating MediaPipe, and evaluation through user testing with comparison to expert evaluations. Key features include real-time feedback and customizable thresholds for user adaptation. Potential applications encompass fitness training, physical therapy, and sports training. Finally, the paper explores future work possibilities like mobile integration, advanced feedback mechanisms, and machine learning for automatic threshold adjustments. This research offers a valuable tool for squat analysis, empowering users to achieve their fitness goals with proper form and reduced injury risk.

Robust multiple focusing through scattering media via feedback wavefront shaping

The control of multiply-scattered light has greatly boosted the understanding of optics in complex photonic systems, leading to many applications including imaging, information processing, random lasing, to name just a few. Here, we demonstrate a real-time feedback approach to realize multiple focusing of multiply-scattered light, which is more robust to the inherent background field compared to the time reversal approach. Our method retrieves the optimized wavefront by aligning the high-dimensional vector of the measured pattern with that of the target pattern. Our theoretical simulations and experimental results show that the feedback approach is capable of constructing homogeneous diffraction-limited spots at predefined positions. Moreover, the peak-to-background ratio exhibits a sub-1/m decay with m being the number of focuses, indicating the excellent capability of the proposed approach to focus scattered light at multiple positions. This efficient approach provides a promising avenue toward complex control on scattered light in complex photonic media, and therefore would be useful in relevant applications such as optical imaging and optical manipulation.

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

Automated Teaching Weighted Recurrent Neural Network (Atwrnn) Model: Analysis of Badminton Teaching Mode Based on Online Teaching Platform

An online teaching platform serves as a virtual environment where educators and learners interact, collaborate, and engage in various educational activities. Through a combination of multimedia resources, interactive tools, and communication features, online teaching platforms facilitate effective remote learning experiences. Educators can create and deliver engaging instructional content, including video lectures, presentations, quizzes, and assignments, tailored to the needs and preferences of learners. These platforms often incorporate features such as discussion forums, live chat, and virtual classrooms, enabling real-time communication and collaboration among students and instructors. This paper presents an analysis of the badminton teaching mode facilitated by an online teaching platform, augmented with an Automated Teaching Weighted Recurrent Neural Network (ATwRNN). Through this innovative approach, the traditional method of badminton instruction is enhanced by leveraging advanced machine learning techniques to personalize and optimize the teaching process. The online teaching platform provides a virtual environment where instructors can deliver interactive lessons, instructional videos, and practice exercises tailored to individual learner needs. ATwRNN, a specialized neural network model, dynamically adjusts the teaching weightings based on student performance data, providing personalized feedback and adaptive instruction in real time. Simulation results stated that learners who received instruction through the ATwRNN-enhanced platform demonstrated a 25% increase in shot accuracy compared to those in traditional teaching modes. Additionally, the adaptive nature of ATwRNN led to a 30% reduction in the time required for skill acquisition, as learners received personalized feedback tailored to their individual learning pace and preferences. Moreover, user feedback surveys revealed high levels of satisfaction and engagement among participants, with 90% reporting that the ATwRNN-enhanced teaching mode improved their overall learning experience.

Intelligent Construction of University Music Education Teaching System Based on Artificial Intelligence Technology

The university music education teaching system plays a vital role in nurturing aspiring musicians and music enthusiasts by providing a comprehensive framework for musical learning and development. This system typically encompasses a diverse range of courses, workshops, and performance opportunities designed to cultivate students' musical talents, theoretical knowledge, and practical skills. Through a combination of classroom instruction, ensemble rehearsals, private lessons, and hands-on experiences, students receive a well-rounded musical education that covers various genres, styles, and traditions. Moreover, university music education teaching systems often incorporate state-of-the-art facilities, including rehearsal rooms, recording studios, and performance venues, to support students' artistic growth and creative expression. This paper presents an innovative approach to the intelligent construction of university music education teaching systems, leveraging artificial intelligence (AI) technology with Intelligent Fuzzy Regression Classification (IFRC). Recognizing the complexity and diversity of music education, this research aims to optimize teaching methodologies and enhance learning outcomes through AI-driven strategies. The proposed approach integrates AI technology, particularly IFRC, into the design and implementation of university music education teaching systems. IFRC combines fuzzy logic with regression analysis and classification techniques to model and predict complex relationships within music education datasets, enabling the system to adapt and respond dynamically to student needs and preferences. The IFRC-enhanced teaching system can generate personalized learning pathways, recommend tailored resources, and provide real-time feedback to students and instructors alike. This intelligent adaptation to individual learning styles and progress fosters a more engaging, effective, and inclusive music education environment. The IFRC-enhanced teaching system achieves an average improvement of 25% in student performance compared to traditional teaching methods. Moreover, specific musical skills, such as sight-reading, ear training, and music theory comprehension, exhibit notable enhancements, with average score increases of 30%, 20%, and 35%, respectively.

Application of Computer-Aided Technology in Teaching Spoken English

Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.

Development of real-time density feedback control on MAST-U in L-mode

In this paper we report on the development and demonstration of density feedback control for MAST-U. Sinusoidal perturbations are used to measure the frequency response from a deuterium gas valve (actuator) to line-integrated core electron density measured by the interferometer (sensor). In the frequency range relevant for control design, only two system-identification experiments were needed to regress a first-order dynamic model. This control-oriented model informs the offline design of a proportional integral controller with the established loop-shaping controller design method. After offline verification of the controller implementation, control is demonstrated by experimentally tracking a staircase reference for the line-integrated electron density. This paper demonstrates the efficiency of controller design using system-identification and loop-shaping, providing reliable density control for MAST-U.

Patient-centered perspectives on perioperative care

IntroductionThe collection and evaluation of patient-reported outcomes is essential to the development of patient and family centered care. Current patient surveying techniques are limited by delayed response times and restriction to specific health systems. The use of random-domain intercept technology (RDIT), by Real-Time Interactive World-Wide Intelligence (RIWI, Toronto, ON, Canada) mitigates current barriers by creating a dynamic real-time feedback environment and providing a mass sampling technique.MethodsRDIT was employed to survey a wide sample of respondents across the United States (US). Respondents who self-identified as having had a surgical procedure or cared for someone having a surgical procedure were included in the analysis.Results1,004 participants completed the survey and answered questions regarding demographics, perioperative details, sentiments on postoperative recovery, postoperative clinical endpoints, sentiments on healthcare professionals, and opinions on future surgical care.DiscussionThe results of this cross-sectional study identified areas with potential for improvement in the patient perioperative experience that could improve the patient experience. This novel use of RDIT provided a valuable tool for real-time feedback and mass sampling allowing the creation of a dynamic healthcare environment that fosters timely and targeted improvements to patient experiences and outcomes.

Real-time feedback control of β p based on deep reinforcement learning on EAST

Recently, with the advancement of the AI field, reinforcement learning (RL) has increasingly been applied to plasma control on tokamak devices. However, possibly due to the generally high training costs of reinforcement learning based on first-principle physical models and the uncertainty in ensuring simulation results align perfectly with tokamak experiments, feedback control experiments using reinforcement learning specifically for plasma kinetic parameters on tokamaks remain scarce. To address this challenge, this work proposes a novel design scheme including the development of a low computational cost environment. This environment is derived from EAST modulation experiments data through system identification. To tackle issues of noise and actuator limitations encountered in experiments, data preprocessing methods were employed. During training, the agent collected data across multiple plasma scenarios to update its strategy, and the performance of the RL controller was fine-tuned by adjusting the weight of the integral term of the error in the reward function. The effectiveness and robustness of the proposed design were then validated in a simulated environment. Finally, the scheme was successfully implemented on EAST, effectively tracking the β p target with lower hybrid wave (LHW) at 4.6 GHz as the actuator, and providing reference for implementing feedback control based on reinforcement learning in tokamaks.

How to HEEAL: A Patient and Peer-Centric Simulation Curriculum for Medical Error Disclosure.

Medical errors are an unfortunate certainty with emotional and psychological consequences for patients and health care providers. No standardized medical curriculum on how to disclose medical errors to patients or peers exists. The novel HEEAL (honesty/empathy/education/apology-awareness/lessen chance for future errors) curriculum addresses this gap in medical education through a multimodality workshop. This 6-hour, two-part curriculum incorporated didactic and standardized patient (SP) simulation education with rapid cycle deliberate practice (RCDP). The morning focused on provider-patient error disclosure; the afternoon applied the same principles to provider-provider (peer) discussion. Summative simulations with SPs evaluated learners' skill baseline and improvement. Formative simulations run by expert simulation educators used RCDP to provide real-time feedback and opportunities for adjustment. Medical knowledge was measured through pre- and postintervention multiple-choice questions. Learners' confidence and attitude towards medical errors disclosure were surveyed pre- and postintervention with assistance of the Barriers to Error Disclosure Assessment tool, revised with the addition of several questions related to provider-provider disclosure. Fourteen medical students participated in this pilot curriculum. Statistical significance was demonstrated in medical knowledge (p = .01), peer-disclosure skills (p = .001), and confidence in medical error disclosure (p < .001). Although there was improvement in patient-disclosure skills, this did not reach statistical significance (p = .05). This curriculum addresses the need for designated training in medical error disclosure. Learners gained knowledge, skills, and confidence in medical error disclosure. We recommend this curriculum for medical students preparing for transition to residency.

Effects of Real-Time Feedback Methods on Static Balance Training in Stroke Patients: A Randomized Controlled Trial.

The purpose of this study was to investigate the effects of real-time feedback methods on static balance training in stroke patients. There are two types of real-time feedback methods, as follows: one is Knowledge of Result (KR), and the other is Knowledge of Performance (KP). Thirty stroke patients participated in this study and were randomly assigned to the KR group (n = 15) or the KP group (n = 15). All of the groups underwent real-time feedback training for four weeks (30 min per session, five sessions per week). The primary outcomes were sway length, sway velocity, and area 95%, which were assessed before and after the intervention. The secondary outcomes included the Berg Balance Scale, the Fugl Meyer Assessment for Lower Extremity, the Postural Assessment Scale for Stroke Trunk Impairment Scale, and the Fall Efficacy Scale. A group × time interaction was assessed using two-way ANOVA with repeated measures. There was a significant increase over time in all outcomes (p < 0.05). Significant differences were observed for a group × time interaction in sway length and area 95% (p < 0.05). Real-time feedback training for static balance enhanced stroke patients' static balance abilities, clinical outcome assessments, and promoted self-efficacy against falls.

Progress of CO2 Dispersion Interferometer on EAST

A CO2 Dispersion Interferometer (DI) system on the Experimental Advanced Superconducting Tokamak (EAST) was successfully operated, providing plasma electron density measurements. The DI system utilizes a continuous-wave 9.3 μm CO2 laser source to measure line-averaged electron densities. This offers significant advantages, including the ability to minimize fringe jumps and insensitivity to mechanical vibrations. These characteristics are well-suited for future high-density, long-pulse plasma discharges. The DI system provides a real-time density feedback signal to the plasma control system for routine density control during long-pulse operation. Experiments with EAST demonstrated good agreement between the density obtained by the DI system and the preset densities. The DI system also exhibited stability during long-pulse discharge. Moreover, the DI system was stable during rapid density changes and high-density pellet injections. In shot No. 120594, the DI system exhibited stable density feedback during continuous projectile injection lasting over 50 seconds; the line-averaged electron density is approximately 4×1019 m-3. In contrast to the long-wavelength source interferometer, which may deflect light from the detector owing to excessive refraction angles in larger density-gradient discharges, the DI ensured accurate density measurements. The DI system on EAST is dependable for accurately measuring the electron density.