Feedback System Research Articles

Research on Precise Servo Control of Hand Motion Real-time Detection Based on Surface Electromyography Signals Integration and Analysis of Muscle Response to Workout Exercises

This study explores the integration of surface electromyography (sEMG) signals for precise servo control in real-time hand movement detection. It also further utilizes the feedback system designed to measure effects of workout exercises on forearm sEMG signals and delves into how different time and types of workouts affect forearm muscle response. Medical electrodes captured sEMG signals from forearm muscles and converted them into digital signals using an Arduino UNO R3 microcontroller board and an Olimex EMG shield to monitor hand pose variations. The digital signals were then processed to calculate the root mean square (RMS) voltage, which was then used to control the position of a servo motor. The implementation of a proportional-derivative (PD) controller further enhanced the accuracy and stability of the servo movements. Then the feedback system was used to measure how sEMG signals vary due to hand opening and closing before and after a range of workout exercises with varying time and workout types, and the results were compared to reach the conclusion that both workout time and type influences how forearm muscles respond to hand closing and opening. Future work may explore the effect of more time and types of workouts on muscle response, expand the range of test subjects, and improve the accuracy and stability of the test system

Significance of thermoregulatory homeostasis in sustaining human physiological equilibrium

Physiological control is based on homeostasis, which is the outcome of intricate interactions and competition between several negative and positive feedback systems. The dynamic process of thermoregulatory homeostasis may modify internal circumstances to withstand shocks from the outside world. Homeostatic management of the internal environment is responsible for the organism's health and vitality; knowledge of this idea is essential to a comprehension of normal physiology. In contrast, sickness is caused by disruption of homeostatic mechanisms, and effective therapy must try to re-establish these homeostatic conditions, working with rather than against nature.

Research on force feedback technology for industrial robot

Force feedback technology plays a crucial role in the field of robot control, especially in scenarios involving precise task execution. With the increasing application of robots in industrial production and automation, traditional control methods based on preset paths can no longer meet the requirements of complex and changing working environments. The force feedback system senses the force information of the robot during operation, adjusts the robot's actions and force in real time, ensures that appropriate force can be maintained during operation, and prevents damage to the working object or reduced task accuracy. While this technology primarily enhances robotic flexibility in complex operations, it has also become instrumental in advancing human-robot collaboration. By combining human perception and regulation abilities, robots are able to perform more challenging tasks. In recent years, force feedback systems that combine multi-sensor fusion, visual control, and remote operation technologies have gradually become one of the core solutions for solving force control problems in robot task execution, providing strong guarantees for safety, accuracy, and efficiency in automated production.

A Mini-Hydro Turbine as a Solution to Power Challenges in a Society with plenty of Water

The mini hydro turbine research is aimed at designing and constructing a hydro-electric power plant model that can generate electric power, which can be used at the domestic level to power electrical appliances. There are three main sections for the hydro turbine, such as the pelton wheel which rotate due to falling water from the water storage tank through the penstock, the alternator, been made of a permanent magnet rotor and conducting coil windings on the stator connected to the turbine through a runner, and the feedback system for the continuous flow of water. The result shows that the construction of mini hydro turbine plant is feasible and there were no major problems apparent at the design and implementation stages of the mini hydro turbine power plant. Keywords- Head, Pelton Turbine, Efficiency, Reliability, Alternator

Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback

Job interviews play a critical role in shaping one's career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback , a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user's responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent's guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using LLM-supported interview simulation and dialogic feedback. Our findings show that participants valued the adaptive follow-up questions from LLMs, as they enhanced the realism of interview simulations and encouraged deeper thinking. Participants also appreciated the AI-assisted annotation, as it reduced their cognitive burden and mitigated excessive self-criticism in their own evaluation of their interview performance. Moreover, participants found the LLM-supported dialogic feedback to be beneficial, as it promoted personalized and continuous learning, reduced feelings of judgment, and allowed them to express disagreement.

High-quality targeted temperature management combined with decompressive craniectomy in patients with poor-grade aneurysmal subarachnoid hemorrhage: a secondary analysis of a multicenter prospective study

BackgroundThe effect of targeted temperature management (TTM) combined with decompressive craniectomy (DC) on poor-grade aneurysmal subarachnoid hemorrhage (aSAH) has not been previously addressed in the literature. This study aims to investigate the therapeutic outcomes of the combination of TTM and DC in patients with poor-grade aSAH.MethodsThis study represents a secondary analysis of the Multicenter Clinical Research on Targeted Temperature Management of Poor-grade Aneurysmal Subarachnoid Hemorrhage (High-Quality TTM for PaSAH), a multicenter prospective study conducted in China. The High-Quality TTM for PaSAH study enrolled patients aged 18 years and older who were transported to the intensive care units (ICU) of three tertiary care hospitals in China between April 2022 and April 2024. Among these patients, those who underwent DC were included in the present analysis. Patients were divided into two groups: the DC-alone group and the TTM combined with the DC (TTM-DC) group. The DC-alone group maintained normothermia. The TTM-DC group used automated devices with a temperature feedback system (TFS). TTM was initiated with core temperatures between 36°C-37°C immediately after diagnosing poor-grade aSAH, and concurrent emergency aneurysm repair. This was followed by a rapid induction to 34°C-35°C, maintained for a minimum of 72 h. Subsequently, a slow rewarming process reached 36°C-37°C, which was maintained for at least 48 h. Primary outcomes were evaluated using the Modified Rankin Scale (mRS) score at 3 months. Secondary outcomes included the Glasgow Coma Scale (GCS) at discharge, ICU stay duration, length of hospitalization, proportion of external ventricular drainage (EVD), mechanical ventilation time, tracheostomy, midline shift, hydrocephalus, and delayed cerebral ischemia (DCI) on the 7th day. Safety outcomes comprised the incidence of pneumonia, myocardial infarction, stress hyperglycemia, thrombocytopenia, acute liver injury, hypokalemia, hypoproteinemia, and death at 90 days.ResultsOf the 141 patients enrolled in the High-Quality TTM for PaSAH study, 43 (25 in the TTM-DC group and 18 in the DC-alone group) were eligible for this secondary analysis. The TTM-DC group had a higher proportion of favorable outcomes (mRS 0–3: 56% vs. 22%, aOR 5.97, 95%CI 0.96–52.2, p = 0.071). After propensity score matching, the TTM combined with DC improved favorable outcome at 3 months (mRS 0–3: 61% vs. 22%, OR 5.50, 95%CI 1.36–26.3, p = 0.022). In addition, the TTM-DC group increased GCS score at discharge compared with the DC-alone group (9 vs. 3, β 2.58, 95%CI 0.32–4.84, p = 0.032). The incidence of safety outcomes was not increased in the TTM-DC group.ConclusionTTM combined with DC can improve clinical conditions at discharge and ameliorate short-term neurological outcomes in poor-grade aSAH patients. TTM should be considered one of the main treatments for poor-grade aSAH patients who underwent DC.

Biosensor technology for adaptive intelligent education systems to enhance personalized English learning

The integration of biosensor technologies, like electroencephalography (EEG), has extended the limits of adaptive, intelligent education systems, offering real-time, personalized learning knowledge. This study explores the use of EEG to track and assess students’ cognitive states, allowing for the improvement of an active, adaptive English learning system that tailors content according to every student’s participation and improvement. Students’ cognitive states serve as the foundation for personalized education responses that motivate and enhance their participation. EEG data are gathered during English language testing to assess the correlation between learners’ cognitive states and their performance. Noise reduction is one of the preprocessing stages that ensures clear and pertinent data for analysis. Power spectral density (PSD) for feature extraction approaches is used to identify key cognitive patterns. Based on real-time EEG data, the personalized education feedback system constantly modified the course material, enhancing motivation and learning results. This research proposed a novel Dynamic Osprey Optimized Intelligent Gradient Boosting Machines (DOO-IGBM) to assess and improve the efficiency of an adaptive intelligent education system. The findings suggest that EEG-based adaptive systems make it possible to significantly progress English learning by offering personalized education paths based on brain activity to other conventional algorithms with 98.5% accuracy, 97.7% precision, 98% recall, and 98.6% F1-score. These outcomes provide precious insights and data to support the future development of adaptive, intelligent education systems for language learning.

Development and testing of a dual-frequency real-time hardware feedback system for the hard X-ray nanoprobe beamline of the SSRF.

A novel dual-frequency real-time feedback system has been developed to simultaneously optimize and stabilize beam position and energy at the hard X-ray nanoprobe beamline of the Shanghai Synchrotron Radiation Facility. A user-selected cut-off frequency is used to separate the beam position signal obtained from an X-ray beam position monitor into two parts, i.e. high-frequency and low-frequency components. They can be real-time corrected and optimized by two different optical components, one chromatic and the other achromatic, of very different inertial mass, such as Bragg monochromator dispersive elements and a pre-focusing total external reflection mirror. The experimental results shown in this article demonstrate a significant improvement in position and energy stabilities. The long-term beam angular stability clearly improved from 2.21 to 0.92 µrad RMS in the horizontal direction and from 0.72to 0.10 µrad RMS in the vertical direction.

Two-phase reactive transport modeling of heterogeneous gas production in a low- and intermediate-level radioactive waste repository

Deep geological disposal is a widely proposed approach for safe storage of low- and intermediate-level radioactive waste (L/ILW) for tens to hundreds of thousands of years. Although the use of cement materials will maintain a high pH environment that is favorable for radionuclide retention, slows down metal corrosion, and suppresses microbial activity, different gases may still be produced by chemical reactions, such as pH-dependent anoxic corrosion of metals, and the degradation of organic matter as well. Both reactions consume water and may lead to the formation of a free gas phase within and around the repository.In order to investigate the controlling factors of this gas production processes, a coupled reactive transport model of component-based two-phase flow in the OpenGeoSys framework is adopted here. Building on the previous work by Huang et al. (2021), this work extends the geometric configuration of the model from a single drum to the scale of a waste container (12 drums) in order to more realistically model the water and gas fluxes. The geochemical evolution of a container filled with cemented steel drums and surrounded by mortar and host rock is simulated in a two-dimensional setup over 500 years. The relative humidity in the pore space as measure of water availability determines the chemical reactivity.We have conducted a sensitivity study for the influence of mortar capillarity and permeability on gas production over time in the waste drums. As expected, the amount of gas produced within the first several years depends primarily on the initial water content within the waste drum and the amount of fast degradable waste stored in a drum. Later, this feedback system is forced into a new equilibrium, where the water transport has to match the water consumption rate at the waste package. Both, the mortar capillarity and permeability control the point in time when the equilibrium is reached and the subsequent rate of gas production.This study shows how reactive transport models are capable to help with the understanding of couplings between chemical and transport processes that control gas generation in L/ILW waste repositories, especially in barrier systems with different material properties. Future studies could benefit from more accurate parameterization of the chemical reactions, depending on the availability of new experimental data and/or more realistic process-based model approaches.

Research on linewidth narrowing and frequency control technology of quantum cascade lasers based on optical feedback and optical phase-locked loops

The mid-infrared (MIR) spectral region, which corresponds to molecular vibrational and rotational energy level transitions, contains a wealth of molecular energy level information. By employing techniques such as cavity ring-down spectroscopy (CRDS), the MIR spectra can be precisely measured, thereby validating fundamental physical laws, the inversion of fundamental physical constants, and the detection of trace gases. However, technical noise from temperature fluctuations, mechanical vibrations, and current noise causes free-running quantum cascade laser (QCL) to suffer high-frequency noise, typically broadening the linewidth to the MHz range, thus reducing spectral resolution. Moreover, long-term drift in the laser frequency due to temperature and current fluctuations hinders high-precision spectroscopy, particularly for narrow-linewidth nonlinear spectroscopy, such as saturated absorption and multiphoton absorption spectroscopy. This work presents a method of combining optical feedback with an optical phase-locked loop (OPLL) for offset frequency locking, aiming to generate a mid-infrared (MIR) laser with excellent frequency characteristics. Strong optical feedback is employed to narrow the linewidth of the quantum cascade laser (QCL) acting as a slave laser, thereby alleviating the challenges associated with phase locking. The OPLL uses frequency-offset to lock the slave laser to the ultra-narrow laser. By adjusting the offset frequency, fine control of the slave laser is achieved. To ensure tight phase locking, the OPLL is based on the ADF4007, and combines a phase lead circuit to compensate for phase lag, effectively expanding the loop bandwidth of the system. In this work, the fundamental principles of the optical phase-locked loop are theoretically analyzed, and a basic model is established. The influence of loop bandwidth on locking performance is also investigated. Upon achieving phase locking using the combined optical feedback and OPLL system, the magnitude of the beat note of the two lasers is improved by 66 dBm, with phase noise suppressed to -81 dBc/Hz@2 kHz in the low-frequency region and -101 dBc/Hz@2MHz in the high-frequency region.The frequency noise power spectral density of both the master laser and slave laser is obtained via the error signal in the closed-loop system. Significant suppression of frequency noise is observed for the slave laser across both low- and high-frequency region, with suppression ratio reaching 86 dB at 100 Hz and 55 dB at 400 kHz. The frequency noise of the slave laser in the low-frequency domain is found to be comparable to that of the master laser. Based on the white noise response region in the frequency noise spectrum (from 200 Hz to 400 kHz), the locked slave laser linewidth is determined to be approximately 3 Hz, narrowing the initial MHz-level linewidth to match the Hz-level linewidth of the master laser. Finally, the locked laser is used to conduct cavity ring-down spectroscopy, achieving an improvement factor of 5 in the signal-to-noise ratio of the ringdown signal. This frequency-stabilized laser will be applied to high-precision spectroscopy for detecting radiocarbon isotopes in future.

A method for determining stability range of grid‐side converter parameters of PMSG based on negative feedback modelling of phase‐locked loop and outer control loop

AbstractDirect‐drive wind farms are integrated into power grid through power electronic devices, extensively utilizing voltage source converters (VSCs) for grid connection, which has precipitated frequent stability challenges within the grid integration systems of wind farms; it is imperative to properly tune the VSC parameters consequently. In grid‐connected VSC system, the interaction between phase‐locked loop (PLL) and outer control loop (OCL) has a significant impact on system stability. Therefore, it is essential to account for the interactions when establishing the stability boundaries for VSC parameters. This article proposes a new method to determine the parameter stability range in grid‐side converter of direct‐drive permanent magnet synchronous generator (PMSG), based on the interactions between PLL and OCL. This method simplifies the modelling process and improves the accuracy of stability range calculations compared to traditional methods. The system is modelled as a negative feedback system with PLL as the forward path and OCL as the feedback path. Both paths include grid dynamics. This modelling approach not only captures the interaction between PLL and OCL, but is also straightforward and clear. Using the Bode diagram, stability mechanism of the system considering interaction between PLL and OCL is studied. The VSC parameter stability criterion of the system is analysed based on the open‐loop frequency characteristics of the system. Then an analytical description of the stability range for parameters is provided. Simulation results confirm the effectiveness of our proposed method.

Non-monotonic frictional behavior in the lubricated sliding of soft patterned surfaces.

We study the lubricated contact of sliding soft surfaces that are locally patterned but globally cylindrical, held together under an external normal force. We consider gently engineered sinusoidal patterns with small slopes. Three dimensionless parameters govern the system: a speed, and the amplitude and wavelength of the pattern. Using numerical solutions of the Reynolds lubrication equation, we investigate the effects of these dimensionless parameters on key variables such as contact pressure and the coefficient of friction of the lubricated system. For small pattern amplitudes, the coefficient of friction increases with the amplitude. However, our findings reveal that increasing pattern amplitude beyond a critical value can decrease the friction coefficient, a result that contradicts conventional intuition and classical studies on the lubrication of rigid surfaces. For very large amplitudes, we show that the coefficient of friction drops even below the corresponding smooth case. We support these observations with a combination of perturbation theory and physical arguments, identifying scaling laws for large and small speeds, and for large and small pattern amplitudes. This study provides a quantitative understanding of friction in the contact of soft, wet objects and lays theoretical foundations for incorporating the friction coefficient into haptic feedback systems in soft robotics and haptic engineering.

Combining simulation and reinforcement learning to reduce food waste in food retail

Extraordinary amounts of fresh produce are never purchased and are discarded as waste. Reinforcement learning (RL) could serve as a means to improve business profits while reducing food waste via control of store pricing and ordering decisions. We present a discrete-event-based simulation framework for food retail which simulates wholesaler, store, and customer interactions. This simulator is critical for driving development and testing of future RL methods. It provides an efficient learning feedback system across a wide gamut of possible scenarios, which cannot be replicated from live observations or pure historical data alone. This is crucial as RL agents cannot learn robust decision-making policies without exposure to many unique scenarios. We evaluate our simulator on a demonstrative case generated from historical consumption and price data using a provided methodology for synthesizing daily demand from monthly and yearly stats. In this demonstrative case, we investigate proximal policy optimization, soft actor–critic, and deep Q networks trained with different reward formulations to decrease food waste and improve profits. These RL methods reduced food waste by 78%–92% on average on an unseen 3-year test period as compared to a baseline mimicking typical food retail waste. Compared to a second popular baseline in literature, the best performing RL algorithm was able to improve profits by up to 12.3%.

Changes in heart rate variability at rest and during exercise in patients after a stroke: a feasibility study

The aim of this study was to evaluate the feasibility of using a biofeedback-enhanced robotics-assisted tilt table (RATT) to investigate time- and intensity-dependent changes in heart rate variability (HRV) at rest and during heart rate-controlled exercise in patients recovering from a stroke. Twelve patients (age 55.3 years ± 15.6 years, 7 women) completed two separate measurement sessions. The first involved familiarization and system identification to determine parameters of a feedback system for automatic control of heart rate (HR). The second comprised 14 min of rest and 21 min of active exercise during which HR was held constant using feedback control to eliminate cardiovascular drift. HR data were collected using a chest-belt HR sensor, and raw RR intervals were employed for HRV analysis during periods of rest (0–7 min and 7–14 min) and exercise (5–13 min and 13–21 min). A biofeedback-enhanced, robotics-assisted tilt table can be successfully employed to perform heart rate-controlled exercises in patients after a stroke. All HRV metrics were substantially lower during exercise compared to rest. In the rest period, HRV values during 0–7 min were lower than during 7–14 min, in line with a slight HR decrease over the entire rest period. During exercise, HRV values during 5–13 min were higher than during 13–21 min, suggesting a time-dependent HRV decrease. All HRV metrics exhibited intensity- and time-dependent changes: higher HRV at rest and decreasing HRV over time. Understanding these HRV characteristics will support the development of heart rate-controlled exercise regimens and protocols for examining HRV changes during exercise in patients.

Integration of Automated Feedback Tools in EFL Academic Writing Classes: Teachers’ Perspective

With the introduction of AI-driven automatic feedback systems like QuillBot, Grammarly, ProWritingAid, and the Hemingway App, the field of teaching academic writing and English as a Foreign Language has been revolutionized. The current research explores the teachers’ perspectives on the integration and efficacy of these tools in enhancing learners’ writing abilities, motivation, and engagement. Within the quantitative approach, the online survey was administered to investigate the perceived advantages and challenges of employing automated feedback technologies in English as a Foreign Language (EFL) and academic writing classes in Higher Education Institutions (HEI) of Georgia. The questionnaire focused on teachers’ views on what areas of writing may each aforementioned tool improve. It also investigated the participants’ overall perceptions of automated feedback and its benefits. The findings of the study revealed that HEI teachers perceive automated feedback technologies very positively and see the benefits the latter bring to their classrooms. They find it effective due to its nature to be personalized, instant, precise and clear. They also find AI-driven feedback tools beneficial for enhancing their feedback skills and enabling them to address students’ needs timely and effectively. This research adds to the expanding corpus of research on educational technology by emphasizing the crucial role that teacher perception plays in the effective integration of automated feedback technologies into writing instruction.

ANALYTICAL REVIEW OF THE METHODS AND MEANS OF CONTROLLING ELECTRIC DRIVES OF ELECTROMECHANICAL SYSTEMS OF APC

Control of electric drives in electromechanical systems of the agro-industrial complex (APC) is a critical factor in increasing the efficiency and reliability of technological processes that directly affect the productivity of agricultural production. In connection with the introduction of the latest technologies, the growth of product quality requirements and the need to automate processes, modern agricultural technologies require the improvement of electric drive control methods. The relevance of this topic is due to the need to optimize management processes in the agricultural sector to increase productivity, reduce energy consumption, and increase system reliability. The review examines classical and modern approaches to the control of electric drives. Traditional proportional, integral and differential methods ensure the stability of systems and minimize errors, adapting to the conditions of agricultural production. An important role is played by feedback systems that automatically adjust the operating parameters of electric drives in response to changes in external factors. Fuzzy management systems are also an important part, allowing decisions to be made based on expert judgments in cases where accurate data is difficult to obtain. Adaptive management systems capable of changing their parameters in conditions of variability are considered separately, which is especially important in the agricultural sector with its changing conditions. The article also highlights modern intelligent control systems using artificial intelligence, machine learning and remote monitoring, which allow analyzing large volumes of data, predicting resource needs and increasing energy efficiency. Internet-based systems such as SCADA provide operators with the ability to remotely monitor and control equipment in real time, an essential element of modern agricultural production. In conclusion, the control of electric drives in agriculture is aimed at automating and increasing the efficiency of production processes, optimizing the use of energy resources and ensuring the safety of the systems, which makes them an integral part of modern agricultural technologies.

Utilizing Artificial Intelligence in Writing Feedback: Benefits and Challenges for First-Year Students at Hanoi University of Industry

This paper analyses the application of AI technology to the process of delivering writing feedback to first-year students of Hanoi University of Industry (HaUI). AI brings an effective solution to improve writing skills since one wants to receive individual and effective instruction. In this article, the authors discuss how there are opportunities for the use of AI to improve feedback quality, time, and flexibility in line with the various AI tools and platforms that cater to young writers. It also explores the implications and difficulty of incorporating AI-driven feedback systems for the classroom, like having issues with technology use and the roles and participation of teachers. This research uses qualitative data collection techniques, interviewing 10 teachers and focusing group discussions with 50 students majoring in business. Drawing on the analytical framework outlined above, this article examines the possible benefits and limitations of AI written feedback applicable to HaUI and offers directions for teachers and administrators in comparable contexts who want to utilize AI technology for writing feedback to support learners.

AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms

The rapid evolution of educational technologies has led to a shift toward personalized and adaptive learning experiences. A critical component of such systems is the ability to provide timely and relevant feedback to students. This paper presents an AI-driven real-time feedback system designed to enhance student support through the integration of sentiment analysis and machine learning algorithms. The system leverages sentiment analysis to gauge the emotional tone of student interactions, such as forum posts, assignment submissions, and feedback. Machine learning algorithms, including decision trees, support vector machines (SVM), and deep learning models, are used to analyze and predict student engagement, performance, and emotional states. By combining both cognitive and emotional insights, the system delivers personalized, context-sensitive feedback that helps students overcome learning challenges and improve academic outcomes. The effectiveness of the system is evaluated using multiple datasets, showing significant improvements in student engagement, satisfaction, and performance.

Human AI collaboration for unsupervised categorization of live surgical feedback

Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts. Our discovered categories are rated highly for clinical clarity and are relevant to practice, including topics like “Handling and Positioning of (tissue)” and “(Tissue) Layer Depth Assessment and Correction [during tissue dissection].” These AI-generated topics significantly enhance predictions of trainee behavioral change, providing insights beyond traditional manual categorization. For example, feedback on “Handling Bleeding” is linked to improved behavioral change. This work demonstrates the potential of AI to analyze surgical feedback at scale, informing better training guidelines and paving the way for automated feedback and cueing systems in surgery.

Model-based conceptualization of thyroid hormone equilibrium via set point and stability behavior

The HPT complex, consisting of the hypothalamus, pituitary and thyroid, functions as a regulated system controlled by the respective hormones. This system maintains an intrinsic equilibrium, called the set point, which is unique to each individual. In order to optimize the treatment of thyroid patients and understand the dynamics of the system, a validated theoretical representation of this set point is required. Therefore, the research field of mathematical modeling of the HPT complex is significant as it provides insights into the interactions between hormones and the determination of this endogenous equilibrium. In literature, two mathematical approaches are presented for the theoretical determination of the set point in addition to a time-dependent model. The two approaches are based on the maximum curvature of the pituitary response function and the optimal gain factor in the representation of the HPT complex as a closed feedback system. This paper demonstrates that all hormone curves described by the model converge to the derived set point with increasing time. This result establishes a clear correlation between the physiological equilibrium described by the set point and the mathematical equilibrium with respect to autonomous systems of differential equations. It thus substantiates the validity of the theoretical set point approaches.