Objective Feedback Research Articles

Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial.

Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention). This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2). Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization). In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001). This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

Perspectives on competency-based feedback for training non-specialists to deliver psychological interventions: multi-site qualitative study of the EQUIP competency-based approach.

The use of feedback to address gaps and reinforce skills is a key component of successful competency-based mental health and psychosocial support intervention training approaches. Competency-based feedback during training and supervision for personnel delivering psychological interventions is vital for safe and effective care. For non-specialists trained in low-resource settings, there is a lack of standardised feedback systems. This study explores perspectives on competency-based feedback, using structured role-plays that are featured on the Ensuring Quality in Psychosocial and Mental Health Care (EQUIP) platform developed by the World Health Organization and United Nations Children's Fund. Qualitative data were collected from supervisors, trainers and trainees from multiple EQUIP training sites (Ethiopia, Kenya, Lebanon, Peru and Uganda), from 18 key informant interviews and five focus group discussions (N = 41 participants). Qualitative analysis was conducted in Dedoose, using a codebook with deductively and inductively developed themes. Four main themes demonstrated how a competency-based structure enhanced the feedback process: (a) competency-based feedback was personalised and goal-specific, (b) competency-based feedback supported a feedback loop, (c) competency-based feedback supported a comfortable and objective feedback environment, and (d) competency-based feedback created greater opportunities for flexibility in training and supervision. A better understanding of the role of feedback supports the implementation of competency-based training that is systematic and effective for trainers and supervisors, which ultimately benefits the learning process for trainees.

Drone-Facilitated Real-Time Video-Guided Feedback Helps to Improve the Quality of Lay Bystander Basic Life Support. A Randomized Controlled Simulation Trial

Objectives Telephone instructions are commonly used to improve cardiopulmonary resuscitation (CPR) by lay bystanders. This usually implies an audio but no visual connection between the provider and the emergency medical telecommunicator. We aimed to investigate whether video-guided feedback via a camera drone enhances the quality of CPR. Methods We conducted a randomized controlled simulation trial. Lay rescuers performed 8 min of CPR on an objective feedback manikin. Participants were randomized to receive telephone instructions with (intervention group) or without (control group) a drone providing a visual connection with the telecommunicator after a 2-min run-in phase. Performed work (total compression depth minus total lean depth) was the primary outcome. Secondary outcomes were the proportion of effective chest compressions, average compression depth, subjective physical strain measured every 2 min, and dexterity in the nine-hole peg test after the scenario. Outcomes were compared using the t- and Mann Whitney-U tests. A two-sided p-value of <0.05 was considered significant. Results We included 27 individuals (14 (52%) female, mean age 41 ± 14 years). Performed work was greater in the intervention than in the control group (41.3 ± 7.0 vs. 33.9 ± 10.9 m; absolute difference 7.5, 95% CI 1.4 to 14.8; p = 0.046), with higher average compression depth (49 ± 7 vs. 40 ± 13 mm; p = 0.041), and higher proportions of adequate chest compressions (43 (IQR 14–60) vs. 3 (0–29) %; p = 0.041). We did not find any significant differences regarding the remaining secondary outcomes. Conclusion Video-guided feedback via drones might be a helpful tool to enhance the quality of telephone-assisted CPR in lay bystanders.

The Current State and Future Trends of AI-assisted Higher Vocational English Education

As information technology evolves, particularly with advancements in artificial intelligence (AI), higher vocational English education is witnessing significant transformations. This paper explores the integration of AI in teaching methods and predicts future trends. AI applications, such as automated assessments and personalized learning paths, enhance teaching efficiency and personalize education. Technologies like natural language processing (NLP) evaluate students' language skills in real-time, providing instant, objective feedback which lightens educators' workloads and increases assessment precision. AI-driven tools, including language learning software like Duolingo and virtual teaching assistants, utilize voice recognition and machine learning to simulate interactive learning environments. These technologies adjust teaching content dynamically and help students practice English in realistic scenarios, thereby improving language proficiency and motivation. Future advancements in AI are expected to introduce more sophisticated algorithms for complex tasks like discourse analysis and cultural education, supported by evolving big data technologies. This will enable deeper, more precise learning analytics and content customization, further enhancing educational outcomes. The integration of AI not only enriches resources but also fosters educational model innovation, supporting vocational colleges in training globally competent professionals. As AI continues to develop, its role in shaping higher vocational English education will grow increasingly vital.

External quality assessments for SARS-CoV-2 genome detection in Austria : Acomparison of the first postpandemic round with results from the pandemic era.

External quality assessment (EQA) schemes provide objective feedback to participating laboratories about the performance of their analytical systems and information about overall regional analytical performance. The EQAs are particularly important during pandemics as they also assess the reliability of individual test results and show opportunities to improve test strategies. With the end of the COVID-19 pandemic, the testing frequency significantly decreased in Austria. Here, we analyzed whether this decrease had an effect on participation and/or performance in SARS-CoV‑2 virus detection EQAs, as compared to the pandemic era. Identical samples were sent to all participating laboratories, and the EQA provider evaluated the agreement of the reported results with defined targets. The EQA was operated under two schemes with identical samples and therefore we analyzed it as asingle EQA round. The performance of testing was reported as true positive ratios, comparing the post-pandemic data to previous rounds. Furthermore, subgroups of participants were analyzed stratified by laboratory type (medical or nonmedical) and the test system format (fully automated or requiring manual steps). While the frequency of false negative results per sample did not change during the 3 years of the pandemic (5.7%, 95% confidence interval [CI] 3.1-8.4%), an average per sample false negative ratio of 4.3% was observed in the first post-pandemic EQA (0%, 1.8%, and 11% for the 3 positive samples included in the test panel, n = 109 test results per sample). In this first post-pandemic EQA medical laboratories (average 0.4% false negative across 3samples, n = 90) and automated test systems (average 1.2% false negative, n = 261) had lower false negative ratios than nonmedical laboratories (22.8%, n = 19) and manual test systems (16.7%, n = 22). These lower average ratios were due to alow concentration sample, where nonmedical laboratories reported 36.8% and manual test systems 54.5% true positive results. Overall ratios of true positive results were below the mean of all results during the pandemic but were similar to the first round of the pandemic. Alower post-pandemic true positive ratio was associated with specific laboratory types and assay formats, particularly for samples with low concentration. The EQAs will continue to monitor the laboratory performance to ensure the same quality of epidemiological data after the pandemic, even if vigilance has decreased.

Criticize my Code, not me: Using AI-generated Feedback in Computer Science Teaching

Large Language Models (LLMs) like ChatGPT can help teachers to tailor learning tasks for their students, combining learning objectives and storytelling to raise interest in the subject. AI-based learning task design can help to support competency-based learning, especially for girls in STEM courses like computer science, where otherwise the “Leaky STEM pipeline” (Speer 2023) leads to a constant loss of female students over school time. LLMs support many steps of the creation cycle of learning tasks. One important step is the feedback process between teachers and students during and after solving the tasks. Students need person-related as well as process-related feedback to make progress. Sometimes problems occur when teachers give feedback in a way that embarrasses or hurts the students. Especially female students often need more confirmation to make them aware of their progress, but studies show that boys demand and get more attention by teachers in this situation. This is one of the many reasons why girls lose motivation and interest in STEM courses over time. Since male and female teachers differ in expressing feedback without being aware of it, it is necessary to raise their consciousness. LLMs like ChatGPT can be used in two scenarios here. The first scenario is helping teachers to formulate objective feedback in a way that is adequate and understandable for the target group – e.g., young girls or boys - in a specific situation. The second scenario is training the teacher in a Socratic way, where the LLM simulates a student receiving the feedback and reacting to it according to established communication models like the Four Ears-model by Schulz von Thun (Schulz von Thun 1981) or Berne’s Transactional Analysis (Berne, 1964). This case study provides examples and prompting schemes for both scenarios and discusses the fragile balance between avoiding gender stereotypes in LLMs and giving more helpful and sustainable feedback for female students to foster self-esteem and competency-awareness.

Superhydrophobic Triboelectric Structural Materials Enabled by Hierarchical Spatial Assembly

AbstractNon‐contact intelligent sensing technology is a key player in the data acquisition and environmental monitoring framework of the Internet of Everything, laying the groundwork for the realization of highly integrated intelligent network systems. However, environmental factors continue to be a major challenge in the process of real‐time monitoring and feedback of objects, constraining the stability and accuracy of the sensing signals. In this study, a hierarchical spatial assembly strategy is implemented to effectively integrate an intermediate energy storage layer with superhydrophobic surfaces (CA = 162 ± 2.5°), resulting in the development of a moisture‐resistant triboelectric structural materials with stable monitoring and tracking capabilities. The moisture‐resistant self‐powered sensor constructed from this triboelectric structural material operates effectively in extreme humidity conditions (99% RH) with a full‐scale output retention rate of 95.2%, and is capable of accurately sensing human activity from the distance of 3 m. Importantly, the sensor can also effectively detect vehicle states during operation and perform real‐time monitoring of the vehicle's surrounding environment. This study not only overcomes the long‐standing challenges of accuracy and stability in high humidity for sensors but also advances the development of sustainable, interconnected intelligent systems for the Internet of Everything in extreme environmental conditions.

Enhancing English as a Foreign Language (EFL) Learners’ Writing with ChatGPT: A University-Level Course Design

This research explores the innovative integration of OpenAI’s GPT-3.5 within a university-level English as a Foreign Language (EFL) writing course, illustrating a novel approach to academic instruction. The course follows the ADDIE instructional design model, encompassing five systematic stages: analysis, design, development, implementation, and evaluation. This model serves as the backbone of the course structure, ensuring a comprehensive educational experience. The incorporation of the Technological Pedagogical Content Knowledge (TPACK) framework in this course facilitates the effective integration of GPT-3.5 by enabling instructors to align advanced AI capabilities with appropriate pedagogical strategies, thereby enhancing the learning experience. TPACK guides educators in applying GPT-3.5’s features in a manner that is contextually relevant and pedagogically sound, ensuring the technology’s use complements the course content. The findings from this research are significant. They reveal that GPT-3.5 addresses three fundamental challenges often encountered in academic writing courses. Firstly, it enhances efficiency by providing immediate feedback and generating content ideas, accelerating the writing process. Secondly, GPT-3.5 ensures cohesive organization within students’ writing, guiding them to structure their thoughts more logically. Lastly, it serves as a reliable substitute for traditional peer reviewers, offering critical and objective feedback that students can use to refine their drafts. As students engage with AI, they enter a dynamic partnership. This collaboration with GPT-3.5 fosters critical thinking and empowers students to develop a distinctive writing voice. Through this interaction, students are not merely passive recipients of knowledge but active participants in a learning process that is augmented by cutting-edge technology. This study not only provides insight into the potential of AI-augmented academic writing but also highlights GPT-3.5’s role in promoting writing proficiency. It demonstrates that the application of AI in education can enhance the learning experience without compromising the individuality of student expression.

An intraoperative accelerometry and real-time analysis tool for magnetic resonance-guided focused ultrasound thalamotomy.

Magnetic resonance-guided focused ultrasound (MRgFUS) is one of the newest surgical treatments for essential tremor (ET). During this procedure, a lesion is created within the thalamus to mitigate tremor. Targeting is done using a combination of stereotaxy, MR tractography, and sublesional heating, with tremor assessed during the procedure to gauge therapeutic effectiveness. Currently, tremor assessments are done qualitatively, but this approach requires the tremor change to be above a subjective threshold and provides no objective record of surgical tremor progression. Here, the authors present and demonstrate an MR-compatible accelerometer with custom MATLAB analysis code and graphical user interface to record, visualize, and quantify tremor in near real-time. Results can be exported and saved for future review. This method was used in 20 surgeries, with patients experiencing a 50.7% (95% CI -64.1% to -37.3%) improvement in the treated limb per the Clinical Rating Scale for Tremor. This method does not interrupt the surgery and is quantitative. As research on optimizing MRgFUS treatment for ET continues-for example, the refinement of targeting during sublesional sonications-such quantifying and recording of tremor changes will provide rapid and objective feedback.

Sensor-Based Discovery of Search and Palpation Modes in the Clinical Breast Examination.

Successful implementation of precision education systems requires widespread adoption and seamless integration of new technologies with unique data streams that facilitate real-time performance feedback. This paper explores the use of sensor technology to quantify hands-on clinical skills. The goal is to shorten the learning curve through objective and actionable feedback. A sensor-enabled clinical breast examination (CBE) simulator was used to capture force and video data from practicing clinicians (N = 152). Force-by-time markers from the sensor data and a machine learning algorithm were used to parse physicians' CBE performance into periods of search and palpation and then these were used to investigate distinguishing characteristics of successful versus unsuccessful attempts to identify masses in CBEs. Mastery performance from successful physicians showed stable levels of speed and force across the entire CBE and a 15% increase in force when in palpation mode compared with search mode. Unsuccessful physicians failed to search with sufficient force to detect deep masses ( F [5,146] = 4.24, P = .001). While similar proportions of male and female physicians reached the highest performance level, males used more force as noted by higher palpation to search force ratios ( t [63] = 2.52, P = .014). Sensor technology can serve as a useful pathway to assess hands-on clinical skills and provide data-driven feedback. When using a sensor-enabled simulator, the authors found specific haptic approaches that were associated with successful CBE outcomes. Given this study's findings, continued exploration of sensor technology in support of precision education for hands-on clinical skills is warranted.

Exploration of the “Four-Three-Four” Internship and Training Mode in Higher Vocational Colleges Under the Modern Vocational Education System

This article mainly introduces the innovative internship and training model of vocational colleges, including five aspects such as “four-party collaboration, three-stage progression, four-way integration, value-added evaluation, and double selection and promotion.” The model aims to improve students’ practical skills and professional quality to better adapt to market demand and social development. The article also presents the prospects of the future internship and training model, including strengthening cooperation and communication with industry enterprises, focusing on students’ personalized development and practical skill cultivation, and establishing a scientific and objective evaluation and feedback mechanism.

Application of video behavior fast detection based on wearable motion sensor devices in sports training

The sensors can collect the athletes' movement data in real time, providing more accurate and objective feedback for the training process. This study aims to explore the application of video behavior rapid detection based on sports wearable sensor devices in sports training. By integrating sensor data and video information, more comprehensive and accurate behavioral analysis is provided to help coaches better understand athlete performance and progress. The study uses an approach based on motion wearable sensor devices and video technology, in which athletes wear sensor devices and exercise during training. Sensors automatically record movement data, and cameras record video of the training process. Then, computer vision algorithms and machine learning techniques are used, combined with sensor data and video information, for rapid behavior detection. The experimental results show that the video behavior detection based on sports wearable sensor devices has high accuracy and efficiency in sports training. Through the fusion of sensor data and video information, the movement, posture and technical details of the athlete can be analyzed in real time, and accurate evaluation results can be obtained.

Utilization of 3D evaluation for assessing selective caries removal practice in pre-clinical dental students: a pilot study

BackgroundPracticing and assessment of selective caries removal techniques in dental students remain challenges in many dental schools. The aim of this study was to utilize a 3D assessment technique, within a designated acceptable range of deviation, to evaluate the tendency of dental students in performing selective caries removal (SCR). The correlation between 3D assessment results and the conventional rubric rated by an instructor was also determined.MethodsFifth-year dental students (n = 61) performed the SCR task on 3D-printed teeth containing simulated deep caries lesions in occlusal and proximal surfaces. One instructor assessed the results using a conventional analytic rubric. The excavated teeth were additionally evaluated using 3D analysis software with the designated acceptable range of deviations (± 0.5 mm) from the standard cavities. The average root mean square (RMS) value, representing the deviation between student-prepared cavities and the predefined standard cavities, was recorded. A tendency towards over-excavation was defined for RMS values > 0.5 mm, and towards under-excavation for RMS values < 0.5 mm.ResultsThe mean (min-max) of RMS was 0.27 (0.18–0.40) for occlusal and 0.29 (0.20–0.57)for proximal cavities. A tendency of dental students toward over-excavation was observed in both occlusal (74%) and proximal cavities (87%). There was a moderate negative correlation between the RMS values and the traditional rubric scores for both occlusal (R2 = 0.148, P = 0.002) and proximal cavities (R2 = 0.107, P = 0.010).ConclusionsThe 3D evaluation technique effectively revealed specific tendencies in dental students’ caries removal skills. The integration of computerized assessments with traditional methods could potentially assist the instructors in delivering more objective and specific feedback to students. Further research is encouraged to investigate the impact of this assessment technique on improving student performance in selective caries removal skills.

Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton

Assistive devices like exoskeletons undergo extensive testing not least because of their close interaction with humans. Conducting user studies is a time-consuming process that demands expert knowledge, and it is accompanied by challenges such as low repeatability and a potential lack of comparability between studies. Obtaining objective feedback on the exoskeleton’s performance is crucial for developers and manufacturers to iteratively improve the design and development process. This paper contributes to the concept of using robots for objective exoskeleton testing by presenting various approaches to a robotic-based testing platform for upper-body exoskeletons. We outline the necessary requirements for realistically simulating use cases and evaluate different approaches using standard manipulators as robotic motion generators. Three approaches are investigated: (i) Exploiting the anthropomorphic structure of the robotic arm and directly placing it into the exoskeleton. (ii) Utilizing a customized, direct attachment between the robot and exoskeleton. (iii) Attaching a human arm dummy to the robot end effector to simulate a more realistic interface with the exoskeleton. Subsequently, we discuss and compare the results against the aforementioned requirements of a systematic testing platform. Our conclusion emphasizes that achieving objective and realistic testing necessitates highly specialized hardware, algorithms, and further research to address challenging requirements.

Significance and application of quality improvement in clinical medicine

Quality improvement is a methodology which was initially developed and employed in the field of industrial manufacturing. This approach involves implementing a series of interventions aimed at elevating the existing quality standards to a higher level. In daily medical work, there are often spontaneous quality improvements. Medical quality improvements supported by scientific methodology can evaluate medical quality more scientifically and provide objective feedback on the quality of medical work for healthcare professionals. This article provides a concise introduction to quality improvement and shows its application and significance in the field of clinical medicine through examples.

Real-Time Classification of Anxiety in Virtual Reality Therapy Using Biosensors and a Convolutional Neural Network.

Virtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour-Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing and enhancing microsurgical anastomosis skills among surgical trainees. We hypothesize that this framework can differentiate the proficiency levels of microsurgeons, aligning with subjective assessments based on the ALI score.MethodsWe select relevant performance metrics from the literature on laparoscopic skill assessment and human motor control studies, focusing on time, instrument kinematics, and tactile information. This information is measured and estimated by a set of sensors, including cameras, a motion capture system, and tactile sensors. The recorded data is analyzed offline using our proposed evaluation framework. Our study involves 12 participants of different ages (35.42±9.78\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$35.42\\pm 9.78$$\\end{document} years) and genders (nine males and three females), including six novice and six intermediate subjects, who perform surgical anastomosis procedures on a chicken leg model.ResultsWe show that the proposed set of objective and quantitative metrics to assess skill proficiency aligns with subjective evaluations, particularly the ALI score method, and can effectively differentiate novices from more proficient microsurgeons. Furthermore, we find statistically significant disparities, where microsurgeons with intermediate level of skill proficiency surpassed novices in both task speed, reduced idle time, and smoother, briefer hand displacements.ConclusionThe framework enables accurate skill assessment and provides objective feedback for improving microsurgical anastomosis skills among surgical trainees. By overcoming the subjectivity and limitations of current assessment methods, our approach contributes to the advancement of surgical education and the development of aspiring microsurgeons. Furthermore, our framework emerges to precisely distinguish and classify proficiency levels (novice and intermediate) exhibited by microsurgeons.

Preface

The 4th Edition of the International Altamira Materials and Devices Encounter (EMDA 4) was successfully held in hybrid format, on November 16th and 17th, 2023 with 196 participants on-site at CICATA Altamira, and ca. 180 participants online. The Scientific Program included 13 invited conferences from around the globe, with researchers from Malaysia, India, Azerbaijan, Romania, United Kingdom, Morocco, USA, Honduras, Mexico, Colombia giving their talks in four sessions (Semiconductors, Modelling, Materials and Engineering and Physical Sciences).Six Workshops were imparted: Elemental Analysis by X-ray Fluorescence, Production of Biobatteries from Plants, Creation of Metamaterials by 3D Printing, Introduction to AI for Materials Science, Electrochemical Methods of Analysis, ANSYS Mechanic Introductory Training. Graduate and undergraduate students benefited from these Workshops, which were also transmitted by YouTube live as well as the rest of the activities of the Conference.https://youtube.com/playlist?list=PL9d20U28c14xN3a1uvGW8-Tbrulu8-HOD&si=-X2PIPFe5gMoh32XA hybrid poster session with 46 posters showing the groundbreaking results of researchers and students was done. The submissions provided the public with a broad view of physics, semiconductors, materials, batteries, engineering applications, mathematical and computational science methods, novel concepts, ideas, and tools. The best posters were judged by a multidisciplinary jury, in two categories: Materials and Devices. The list of winners is depicted in this book.There were 43 conference paper submissions, and after a strict, double-blinded peer review process, 23 papers were selected by the Editor to be published in this volume. A total of 11 reviewers participated in this conference, who provided their professional and objective feedback to the authors to ensure the quality of the manuscripts according to the IOP Editorial’s Guidelines. The accepted papers are grouped into three general topics, i.e. Engineering Applications, Batteries/Supercapacitors and Semiconductors.The papers entitled “nS1-xSex alloy deposition by RF magnetron co-sputtering for potential photoelectrochemical applications”, “Wide-angle microwave attenuation response of a multilevel metastructure based on rGO/Fe3O4/PPy: an in-silicon study”, “Wastewater treatment from graphene oxide synthesis process”, “Physicochemical characterization of urban dust in Southern Tamaulipas using X-ray fluorescence”, “Delignification and characterization of Tule (Typha Dominguensis) for its potential use in the production of green concrete”, won the best materials poster and the paper entitled “Open Source electronic system with OLED screen monitoring of Plant Based Batteries”, “One-bit register based on 180nm CMOS technology”, “Optoelectronic system for measuring sugar content in citrus fruit” won the best devices poster after being reviewed by the session’s jury at the conference.A special acknowledgement is given to Instituto Politecnico Nacional through its Secretaria de Investigacion y Posgrado, from which financial support was possible to waive the fees for this event including the fees for paper publication, which was of capital importance because the highest participation was from graduate and undergraduate students.The kind sponsorship of SCM, SATELSA, and Soporte Analitico Especializado for the Conference banners and specific logistic needs, is kindly granted.Acknowledgements are also given to the Conference Staff, composed of CICATA Altamira and UV students who were the success factor of the Conference.The Organizers also grant the support from CICATA Altamira for the use of their facilities and the all the event logistics, as well as to the effort of the Scientific Program and Technical Committees, who did a great job preparing the event, contacting the Lecturers, confectioning the Program, reviewing the posters and the papers and making valuable suggestions for the authors to improve their work. We also would like to express our gratitude to the external reviewers, who kept the quality of the submitted papers and qualified the presented posters.Finally, on behalf of the Organizing Committees, we would like to express our sincere gratitude to all participants and speakers who contribute to make this event a great success.Looking forward to seeing you in 2025!Organizing Committee of EMDA 4th 2023List of Organizing Comittee, Conference Chair, Program Chair, Program Co-chairs, Steering Committee, Support Committee/Conference Staff and Organizations are available in this pdf.

Constructive feedback – efficiency factor of the adult training process

Constructive feedback is an important way to support learning and development in adult education. Providing constructive feedback refers to the trainer’s creation of interactive educational contexts, by reinforcing or modifying the learners’ behaviors based on their needs. In this context, it is important for the trainer to provide constructive feedback in the training activity in order to maximize the benefits of the training and help the adult participants to achieve their intended goals. This article reflects a range of strategies for providing constructive feedback in the context of adult learning, namely: formulating a message relevant to a particular activity and learning objectives, using clear and precise language, giving specific and detailed feedback, providing objective feedback and focusing on behavior or performance, not the individual. Thus, the use of these strategies generates a friendly educational design, oriented towards improving the performance and personal and professional development of adults in the training process.

교사의 준언어·비언어 의사소통 교육 연구 -교수자와 학습자 인식 분석과 AI 면접 시스템 활용을 중심으로-

The purpose of this study is to examine the perceptions and current status of semi-verbal and non-verbal education in teaching speech education, and to explore the possibility of using the AI interview system in teaching speech education. In order to investigate the perception of teaching speech education, a survey was conducted on 13 professors majoring in Korean language education and 91 students attending the College of Education. In addition, four teachers were asked to directly experience the AI interview system to analyze the results and conduct interviews after the experience. As a result of the survey, semi-verbal and non-verbal communication feedback in teacher speech classes was passively conducted. There was a problem that students who accepted feedback accepted it emotionally, or that the evaluation criteria were ambiguous and the way of semi-verbal and non-verbal communication education was not systematic. The AI interview system can be used for teaching speech education in the following aspects. First, in teacher speech classes, this system can be used forfeedback on voices. Second, this system can be used or learners' own teaching speech practice. Third, this item can be usefully used to practice teaching speech in online classroom situations. If specific and detailed feedback made through teaching speech education and objective feedback in the AI interview system are properly utilized and practiced, it can create good synergy in improving teaching speech competency.