Feedback System Research Articles

Distect: automatic sample-position tracking for X-ray experiments using computer vision algorithms.

Soft X-ray spectroscopy is an important technique for measuring the fundamental properties of materials. However, for measurements of samples in the sub-millimetre range, many experimental setups show limitations. Position drifts on the order of hundreds of micrometres during thermal stabilization of the system can last for hours of expensive beam time. To compensate for drifts, sample tracking and feedback systems must be used. However, in complex sample environments where sample access is very limited, many existing solutions cannot be applied. In this work, we apply a robust computer vision algorithm to automatically track and readjust the sample position in the dozens of micrometres range. Our approach is applied in a complex sample environment, where the sample is in an ultra-high vacuum chamber, surrounded by cooled thermal shields to reach sample temperatures down to 2.5 K and in the center ofa superconducting split coil. Our implementation allows sample-position tracking and adjustment in the vertical direction since this is the dimension where drifts occur during sample temperature change in our setup. The approach can be easily extended to 2D. The algorithm enables a factor of ten improvement in the overlap of a series of X-ray absorption spectra in a sample with a vertical size down to 70 µm. This solution can be used in a variety of experimental stations, where optical access is available and sample access by other means is reduced.

AI-powered assessment tools for E-learning: Enhancing feedback and grading systems

Recently, there has been increased incorporation of artificial intelligence in the education system, which has impacted the practice and use of assessment in learning. To this end, the following is a paper discussing the role of AI in grading, instant feedback, and personalized adaptive testing. The paper also covers the ability of AI to minimize bias and introduce fairness while grading, as well as the ability that comes with the mass LMS platforms. Moreover, this paper discusses the issues and guidelines regarding the ethical use of AI in education: data protection, the problem of automated prejudice in AI, and the replacement of human tutors. When these seemingly unimportant problems are solved, and institutions admit that ethical practices are critical, such opportunities for Artificial Intelligence can be harnessed to make learning more efficient and fairer. At last, integrating AI into learning benefits teachers and students: it enhances the teaching and learning processes. It creates the prospects for the future global exposition of technology with human creativity to enrich learners.

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.

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Implementation of Measurement-Based Care in Mental Health Service Settings for Youth: A Systematic Review.

Measurement-Based Care (MBC) is the systematic use of patient-reported data to inform care decisions and monitor treatment progress. MBC has been shown to improve patient outcomes across medical and mental health treatment settings for adults and youth. While many studies have examined the use of MBC in specific care settings, few have focused on the implementation of MBC among youth populations or across care settings. While a review has shown that use of MBC benefits youth, no published reviews exist that summarize the successful strategies and barriers to implementation models across studies in youth service settings. To address these gaps, the present systematic review (N = 25 studies) focuses on the implementation of MBC across four youth service settings, including outpatient mental health centers, medical centers/pediatric clinics, schools, and clinical psychology training clinics. Results suggest that few studies employ consistent implementation models or strategies to guide efforts. Further, there is significant overlap in the successful strategies employed as well as the barriers to implementation of MBC across youth service settings, at the client, clinician, and organizational levels. Broadly, the authors recommend on the basis of findings that future implementation work in youth service settings: incorporate comprehensive training in the use of MBC for clinicians; incorporate stakeholder feedback into the implementation process for initial and sustained use; employ digital measurement feedback systems to deliver MBC that allow for real-time feedback and continuous technical support; and employ a health equity lens in implementation efforts to help address disparities in access to and use of MBC so that all youth and families may benefit from this evidence-based practice.

Dynamic interface printing

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

Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation

Abstract Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.

Innovative Application of Optoelectronic Control Engineering for Safety Assurance of Surgical Robots

As an emerging technology, optoelectronic control engineering has demonstrated a wide range of application potential in surgical robot safety and security. Through a literature review, this paper provides an in-depth discussion on the innovative applications of optoelectronics technology in enhancing the positioning accuracy, environment sensing, supple control and remote operation of surgical robots. At the same time, this paper summarizes the current research hotspots and future research directions, with a view to providing a reference for promoting the technological progress in this field. The current research focuses on improving the operating precision of surgical robots, enhancing the environment sensing ability and the application of force feedback system, while the future research will mainly focus on interdisciplinary cooperation and new technology development.

A Comparative Study on ERNIE Bot 4.0 Turbo and ChatGPT 4o’s Performance in Evaluating First-Year Undergraduate Persuasive Essays

This study compares the performance of two prominent AI language models, ERNIE Bot 4.0 Turbo and ChatGPT 4o, in evaluating first-year undergraduate persuasive essays within the social sciences domain. Drawing from the Louvain Corpus of Native English Essays, a comprehensive collection of academic writings by British and American university students, this study aims to examine the models’ capabilities in assessing the grammatical correctness, vocabulary usage, coherence, content depth, and writing style of the essays. This study adopts a structured evaluation framework based on IELTS writing criteria to assess the models’ performance. A 40 persuasive essays from the Louvain Corpus were evaluated by both AI models and compared with human raters’ evaluations to ensure validity. The findings reveal distinct differences in the assessment styles of the two models. ChatGPT 4o exhibits a more critical approach, pinpointing areas for improvement, such as lack of argument development, coherence issues, and grammatical errors. Conversely, ERNIE Bot 4.0 Turbo offers a more balanced assessment, acknowledging essays’ strengths and suggesting improvement areas. Notably, ERNIE Bot’s evaluation highlights potential biases in AI-based assessment systems, particularly in its unequal emphasis on viewpoints. This comparative examination offers valuable perspectives on the advantages and constraints of AI models in assessing scholarly compositions, underscoring the significance of amalgamating varied AI functionalities to establish more all-inclusive and efficient feedback systems for learners. By understanding these differences, researchers and educators can better utilize AI-assisted essay evaluation systems to enhance student learning experiences.

Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

Addressing Sexual Harassment in Nigerian Higher Institutions Through the Feasibility of a Digital Anonymous Feedback System

Sexual harassment remains a persistent and pervasive issue in Nigerian higher institutions, yet incidents often go unreported due to fear, stigma, and distrust in existing reporting mechanisms. This study investigates the feasibility and effectiveness of introducing a digital anonymous feedback system for students as a proactive measure to curb sexual harassment on university campuses. The aim is to provide students with a safe and confidential platform to report incidents of harassment, thereby empowering them to speak out without fear of retaliation. Using a quantitative research approach, a questionnaire was administered to students from selected Nigerian federal universities to gather data on their perceptions of sexual harassment reporting, the potential impact of an anonymous digital feedback system, and recommendations for addressing the issue. Findings reveal significant barriers to traditional reporting systems, including fear of reprisal and lack of trust in institutional responses. However, there is strong support among students for implementing an anonymous digital feedback system, with confidentiality and accessibility cited as key benefits. Based on the findings, it is recommended that Nigerian higher institutions prioritize the introduction of digital anonymous feedback systems, dedicate sections of their websites for lecturer ratings and anonymous reporting, and conduct awareness and training programs on sexual harassment prevention, including claims verification measures. By implementing these strategies, universities can create safer and more inclusive learning environments where students feel empowered to report sexual harassment without fear of reprisal.

Fighting HAIs by monitoring hand hygiene compliance in the Umberto I teaching Hospital in Rome

Abstract Background Healthcare-associated infections (HAIs) are one of the most common preventable complications in hospitalised patients related to increased morbidity and mortality for patients. Proper hand hygiene (HH) can decrease the prevalence of HAIs within care settings. The aim of the study was to evaluate the effects of a monitoring and feedback system on HH compliance (HHC). Methods Three monitoring and feedback studies have been carried out in Umberto I teaching hospital of Rome since 2021. Healthcare workers (HCWs), were trained on how to carry out the direct observation on the 5 WHO moments on HH. The data collection lasted two weeks each year. Following data collection, they were analysed and discussed annually with HCWs. A multivariable logistic regression model was constructed to identify predictors of HH compliance. Results A total of 15874 HH were observed, of which 40.1% in 2023. The recorded HHC was 71.0% in 2021, 75.4% in 2022 and 71.6% in 2023. Multivariable analyses showed that HHC was positively associated with female sex of observed (aOR:1.3, 95% CI:1.1-1.4), during weekend days or holidays (aOR:1.3 95% CI:1.1-1.4) and all indications with respect to ‘before touching a patient’. Regarding the professional category of the observer, compared to physicians, midwives were more likely to report compliant observations (aOR=2.4, 95% CI: 1.4-4.1). On the other hand, a negative association was found with external ward staff and other HCWs and no-HCWs compared to physicians. Ward area and year of observation did not influence the probability of the outcome. Conclusions The results of this study revealed sub-optimal adherence to HH, with the lowest rates observed prior to patient interaction. That, coupled with the variability seen among HCWs, underlines the difficulties in achieving a uniform level of compliance. Therefore, ongoing training to raise awareness among HCWs and to repeat the survey over time identifying any major problems is essential. Key messages • Monitoring the reasons for non-adherence to hand hygiene in healthcare institutions in order to formulate strategies to improve HH adherence among hospital staff is essential. • Hand hygiene (HH) monitoring systems can produce measurable improvements in HH adherence by healthcare personnel, resulting in a reduction in the incidence of healthcare-associated infections.

An Automated Word Spelling Error Detection System

Accurate spelling has extensive consequences for written communication's clarity, credibility, and general effectiveness in several contexts, including academic and professional writing and personal communications. The frequency of spelling errors has increased significantly recently, mainly owing to the widespread use of digital media and society's dependence on written communication. Misspelled words can diminish the reliability of written correspondence and result in misinterpretations. Hence, there is an urgent requirement for an innovative and advanced solution to improve the identification of spelling errors. This research seeks to address these challenges and develop a system that significantly enhances the quality of written communication by improving the accuracy and relevance of spelling error detection and correction suggestions. According to this study, the implemented model is a promising technique to correct spelling problems in legal terminology, has been praised for its effectiveness and usability. Its recommendations include continuous research and development, improving its ability to use contextual cues and domain-specific information for error correction, emphasizing user-centric customization, implementing collaborative feedback systems, offering extensive training and support, prioritizing ethical and regulatory considerations, and fostering interdisciplinary collaboration. These suggestions aim to optimize the Spello model's performance, resilience, and scalability, and make it a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society. The model can become a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society through combined efforts and collaboration.

Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

This paper concentrates on the design problem of linear active disturbance rejection control (LADRC) for uncertain systems, whose order and parameters are unknown. In general, LADRC has diversified structural forms due to different order and parameters selection. To clarify the prerequisites for constructing the LADRC strategy, a Laguerre-based LADRC (LLADRC) method is proposed, which provides controller design guidance for systems with unknown models by capturing the impulse response. Then, intensive attention is focused on establishing internal stability conditions of the feedback system and parameter tuning rules of the prescribed controller. To gain a better understanding, the internal correlation between LLADRC and standard LADRC of the same order is analyzed. It is shown that observer and controller gains of the standard LADRC can be tuned via a workable LLADRC to achieve better control performance. Finally, a numerical example and an application design of DC motor speed control are studied, both of which demonstrate the feasibility and validity of the proposed method.

Anisotropic piezoresistive response of 3D-printed pressure sensor based on ABS/MWCNT nanocomposite

Nanocomposites based on carbon nanotubes (CNTs) are suitable for sensors, due to matrix ability to incorporate nanotube properties. Thus, we developed a low-cost, nanostructured poly(acrylonitrile-butadiene-styrene) (ABS) polymer piezoresistive sensor produced by additive manufacturing. For this, solution layers of acetone, dimethylformamide and CNTs functionalized with carboxylic acid were pulverized on an ABS substrate using an aerograph. Electrical characterization revealed an anisotropic piezoresistive response of the material, induced by the printing lines direction. Field Emission Gun-Scanning Electron Microscopy showed the nanostructured film spreading after five layers of CNTs as well as the random entanglement of nanotubes on parallel and perpendicular 3D-printed ABS substrates. Raman spectroscopy indicated compression and p-type doping of CNTs in interaction with the polymer, as seen mainly by the blueshift of the G and 2D subbands. The results show that the material is promising for pressure sensors, with potential applications in robotic haptic feedback systems.

Enhancing network security with hybrid feedback systems in chaotic optical communication

This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.

Ergonomic Improvement at a Distribution Center Using a Voice-Directed Feedback System to Manage Loading Trucks

This field study reports on a wireless, wearable, automated voice-directed system where truck loaders at a distribution center wear a headset to listen to instructions and speak into a microphone to load pallets of product into delivery trucks accurately. The system was designed to provide immediate and corrective feedback to load pallets in reverse-stop sequence for all multi-stop routes. The voice-directed system intervention was instrumental in reducing the number of pallets loaded out of sequence to 0.015%, based on approximately 175 weekly routes, amounting to a decrease of an out-of-sequence percentage error by 99%.

Strategies to develop an efficient feedback system in medical education and its outcome – An interventional study

Objective The main aim of this study is to know the loopholes in the feedback system, strategies to remove these loopholes, and to measure the effects of an efficient feedback system in an institution. Methods This research has three stages. In the first stage, we tried to find out the flaws in the feedback system of the institution based on published benchmarks. Then we tried to remove these flaws with the collaboration of different stakeholders. In the last stage of the study, we tried to measure the effectiveness of these corrective procedures by certain key performance indicators (KPIs). Results After improvement strategies, there was a significant improvement in the KPIs like, the knowledge and attitude toward the feedback improved from 3.1 to 4.23 in students, and from 3.46 to 4.34 in faculty members, on a five-point scale, and the teaching standards improved as measured by students’ performance in different courses in terms of learning objectives achievement, for example in internal medicine from 70.8% to 75.3%. Similar improvement was recorded in other KPIs as well. Conclusion An efficient feedback system is the backbone of education and by adopting effective feedback, individuals and institutions can excel in the field of medical education.

Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production

In response to the problems of low reliability and long time consumption in traditional research on sound production, this article used electromyography (EMG) signals as input signals to build a high-precision sound production mechanics model. A Proportional-Integral-Derivative (PID) controller was used to dynamically adjust the model and develop a real-time feedback system. This article established a detailed three-dimensional (3D) finite element model including the vocal cords and throat, defined the nonlinear elastic properties and linear elastic properties of different tissues, and used tetrahedral grid partitioning technology to improve the computational accuracy of the model. Through a EMG sensor, an individual EMG was collected and filtered to remove noise. The processed EMGs were used as input parameters for the finite element model to drive the muscle units in the model. By using a PID controller to receive real-time EMG input, the error was calculated and the model was adjusted, enabling accurate simulation of the mechanical properties of vocal cord vibration under different vocal states and achieving real-time feedback. Considering the complexity of vocal cord vibration driven by biological signals, this article simulated and analyzed the modal characteristics of vocal cord vibration, and analyzed the differences in vocal cord vibration characteristics under different vocal states. The sound pressure distribution and resonance frequency were simulated and analyzed to understand the propagation characteristics of sound. Finally, by comparing and analyzing the simulated data with the actual collected data, it was found that the maximum relative error rate of the model under different sound states was 6.14%, and the overall error rate of the model was relatively low, which verified the reliability of the model. The findings demonstrated that the feedback delays of the model in different sound states were all within 100 milliseconds, indicating that the system had high real-time performance and accuracy, which was promising in practical applications.

Implementing Problem-Based Learning in Marketing Education: A Systematic Review and Analysis

The marketing industry is rapidly evolving, prompting educational programs to adapt and provide graduates with relevant skills and knowledge. This systematic literature review and bibliometric analysis (SLRBA) explores the incorporation of problem-based learning (PBL) in marketing education. PBL is a teaching approach that encourages students to solve real-world problems by integrating theory and practice, thereby improving their critical thinking and decision-making skills. Our review synthesizes data from 71 sources, detailing the definition of PBL, strategies for its implementation in marketing education, and the associated benefits and challenges. The findings show that PBL enhances student engagement, provides hands-on experience, and equips students for real-world marketing challenges. Effective strategies for PBL adoption include leveraging social media, serious games, E-learning, and feedback systems to facilitate active learning and student participation. However, PBL poses challenges, such as high resource requirements, student resistance, and concerns over information accuracy. This review underscores the importance of PBL in closing the gap between theoretical concepts and practical skills, helping marketing students better meet industry expectations. The paper concludes with suggestions for future research and best practices for integrating PBL in marketing education.