Quick Learning Research Articles

Evaluating the User Experience and Usability of the MINI Robot for Elderly Adults with Mild Dementia and Mild Cognitive Impairment: Insights and Recommendations.

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot's usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot's speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the "good" category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.

The Impact of Epistemological Beliefs and School Climate on the Sustainability of Critical Thinking Dispositions in Middle School Students

The objective of this study is to ascertain whether school climate and students’ epistemological beliefs serve as predictors of sustainable critical thinking dispositions in middle school students. This research is designed as a correlational study. A total of 585 students from four middle schools in Çanakkale, Turkey, participated in the research. To this end, the researchers administered the Critical Thinking Disposition Scale, the Middle School Students’ Epistemological Belief Scale, and the School Climate Survey, gradually collecting the raw data. Following the collection of the data, they were subjected to analysis using multiple linear regression. The results of the statistical analysis demonstrated that the students’ critical thinking dispositions were significantly predicted by both epistemological beliefs and school climate. In other words, belief in the omniscient power of authority and quick learning were found to have a negative correlation with critical thinking, whereas belief in simple knowledge was observed to have an inverse positive relationship. The dimension of the school climate, relating to positive student–teacher relations, appeared to constitute a prospective positive predictor, while authoritarian student–teacher relations and the lack of resources were identified as the main negative classroom environment factors. The study highlights the complex mutual influence of individual beliefs and the learning environment in the development of critical thinking dispositions.

A model-free method to learn multiple skills in parallel on modular robots

Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model and its dynamics, are necessary. In this paper, we present a generic method based on Central Pattern Generators, that enables the acquisition of basic locomotion skills in parallel, through very few trials. The novelty of our approach, underpinned by a mathematical analysis of the controller model, is to search for good initial states, instead of optimising connection weights. Empirical validation in six different robot morphologies demonstrates that our method enables robots to learn primary locomotion skills in less than 15 minutes in the real world. In the end, we showcase our skills in a targeted locomotion experiment.

A Lightweight Task-Agreement Meta Learning for Low-Resource Speech Recognition

Meta-learning has proven to be a powerful paradigm for transferring knowledge from prior tasks to facilitate the quick learning of new tasks in automatic speech recognition. However, the differences between languages (tasks) lead to variations in task learning directions, causing the harmful competition for model’s limited resources. To address this challenge, we introduce the task-agreement multilingual meta-learning (TAMML), which adopts the gradient agreement algorithm to guide the model parameters towards a direction where tasks exhibit greater consistency. However, the computation and storage cost of TAMML grows dramatically with model’s depth increases. To address this, we further propose a simplification called TAMML-Light which only uses the output layer for gradient calculation. Experiments on three datasets demonstrate that TAMML and TAMML-Light achieve outperform meta-learning approaches, yielding superior results.Furthermore, TAMML-Light can reduce at least 80 %\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} of the relative increased computation expenses compared to TAMML.

Ultrasound-guided approach to surgery for nodal recurrence following lateral neck dissection for differentiated thyroid carcinoma. A single institution experience.

Total thyroidectomy (TT) and central neck dissection (CND) had a significant effect on the reduction of local recurrence compared with TT alone. Lateral Neck Dissection (LND) was performed in all the cases with therapeutic intent. The suspicion of nodal recurrence is provided by the appearance of one or more enlarged nodes in the central and/or laterocervical compartment during the follow up period. From January 2018 to November 2023, 16 patients at the University General Surgery unit of the Polyclinic of Foggia underwent reoperation due to nodal recurrence after previously undergoing total thyroidectomy with central and lateral cervical dissection. All surgical interventions were approached with intraoperative ultrasound performed by the operating surgeon. In all cases, ultrasound identification of the suspicious lymph node led to histological confirmation of malignancy. In only two cases it was necessary to carry out an extemporaneous intraoperative histological examination. No complications were recorded during the operations. Surgical reintervention in patients with nodal recurrence is challenging and requires an assessment by members of the interdisciplinary team. The ideal method should be economically convenient, easy to practice, with a quick learning curve, easily reproducible, and safe for patients. Intraoperative, ultrasound-guided, is a safe and effective technique. It facilitates tumor localization and removal, especially in patients requiring re-operative neck surgery.

Cutting-edge AI apply un-supervised learning to BDA

In order to take advantage of modern computing power, we shall apply AI to Big Data Analysis (BDA) partially from the so-called Art, Culture, Science, and Technology (ACST) --- “experience of Homosapien”, recorded at the 20 Smithsonian Museums and at the National Library of Medicine affiliated with dozens National Institutes of Medicine as well as non-profit Max-Planck ResearchGate.net. We furthermore define unsupervised learning capability that is innate to the Homosapien based on (1) ionic current responsible for Rational IQ and (2) chemical signals supporting Emotional IQ. Thus, we assume that AI computing tools must incorporate thermal temperature to the computer machine, but not operate at low temperature Electromagnetic physics. We begin by reviewing Homosapien intrinsic characteristics: (1) warm blooded brains that provide steady kinetic transport for efficient cellular operations, and (2) the ‘power of paired sensors’ (pops) which gather vector time series order set data Xp(t) for self-referenced unsupervised learning. Likewise, Homosapien have pair time series sensors (ears, eyes, nostrils, olfactory bulbs, taste buds, limbs extremities) which communicate with each other through the nervous system. The nervous system of human brain must be kept in reference at thermodynamics equilibrium, known in biology as homosapiens= . These are necessary but not sufficient conditions for intelligent beings. A higher temperature does not necessarily imply smarter or quicker learning. For example, the chicken’s brain is in equilibrium at but they lack hands, tools and past experience keeping through evolution to be our growth human beings.

Proficiency Development and Learning Curve in Robot-Assisted Spine Surgery Using the ExcelsiusGPS® System: Experience From a Single Institution.

Retrospective Cohort Study Objectives: Robot-assisted spine surgery (RASS) is a rapidly evolving technique with potential benefits for improving surgical outcomes. A number of studies on RASS learning curve have focused on early iterations of the Mazor robot. Limited research exists on the learning curve associated with using the Globus Medical ExcelsiusGPS® system. In this retrospective study, we aimed to evaluate the learning curve of RASS using the ExcelsiusGPS® system at a single institution. A total of 95 patients (541 screws) who underwent RASS between 2021 and 2022 were included. Variables including operative time, robot registration time, screw placement time, fluoroscopy utilization, and complications were analyzed. Statistical analysis was performed using descriptive statistics and two-sample t-tests. The average operative time significantly decreased after the first 14 cases, indicating a learning curve. However, no significant improvement was observed in robot registration time. Notably, screw placement time significantly improved after approximately 13 cases. When controlling for the number of levels fused, the trends remained consistent. Our study confirmed the presence of a learning curve in RASS using the ExcelsiusGPS® system and demonstrated rapid proficiency development. Our findings highlight the relatively quick learning curve of 1 RASS system.

Shifting gears: Creating equity informed leaders for effective learning health systems.

Leadership is vital to a well-functioning and effective health system. This importance was underscored during the COVID-19 pandemic. As disparities in infection and mortality rates became pronounced, greater calls for equity-informed healthcare emerged. These calls led some leaders to use the Learning Health System (LHS) approach to quickly transform research into healthcare practice to mitigate inequities causing these rates. The LHS is a relatively new framework informed by many within and outside health systems, supported by decision-makers and financial arrangements and encouraged by a culture that fosters quick learning and improvements. Although studies indicate the LHS can enhance patients' health outcomes, scarce literature exists on health leaders' use and incorporation of equity into the LHS. This article begins addressing this gap by examining how equity can be incorporated into LHS activities and discussing ways leaders can ensure equity is considered and achieved in rapid learning cycles.

The Impact of BABA: Mean Absolute Deviation (MAD) Tool: Beyond Spreadsheet Simple Statistics

This study evaluates the effectiveness and user experience of the Business Analytic Basic Aid (BABA) Mean Absolute Deviation (MAD) tool compared to manual MAD calculations using Excel among students. Initially, students were taught to perform MAD calculations manually with Excel, followed by an introduction to the BABA automated MAD tool. A survey was conducted to assess various aspects of usability, complexity, and overall experience with both methods. Survey results indicated a general preference for the BABA automated MAD tool, with high ratings for ease of use (mean: 3.96) and quick learning (mean: 4.06). However, the tool was perceived as somewhat complex (mean: 2.88), and students expressed a need for technical support (mean: 3.36). Despite these challenges, confidence in using the tool was high (mean: 3.78), and students appreciated its well-integrated functions (mean: 3.88).The findings suggest that while the BABA automated MAD tool provides significant advantages over manual calculations, further refinements and user support are needed to address its complexities. The study highlights the potential for improved learning outcomes and efficiency in statistical analysis through the adoption of automated tools in educational settings, offering valuable insights for educators and tool developers.

An Intelligent Adaptive Neuro-Fuzzy for Solving the Multipath Congestion in Internet of Things

The Internet of Things (IoT) has recently become a significant focus in research circles. IoT facilitates the integration of numerous physical entities with the Internet. Adhering to a standardized structure is imperative to manage the vast amount of information effectively. Although many researchers in the field of IoT have proposed various layered architectural designs, none have yet fulfilled all the requisite architectural criteria. Network congestion occurs when the volume of data packet traffic surpasses the network's handling capacity. Apart from addressing congestion issues, it is crucial to harmonize network resources like energy, bandwidth, and latency. The Quality of Service (QoS) in IoT applications chiefly depends on proficient congestion management, which is the central subject of this research. The research employs the Adaptive Neuro-Fuzzy Inference System (ANFIS) to regulate congestion, while the Membership Function (MF) undergoes adjustments through the application of the Modified Squirrel Search Algorithm (MSSA). This ANFIS amalgamates the advantages of Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to form a unique framework. Utilizing ANFIS, adaptive analysis services are available to interpret complex patterns and nonlinear interactions, featuring quick learning capabilities. The MSSA aids in tweaking the Membership Function within the ANFIS model, achieving a successful global convergence rate. An adaptive method considering predator presence probability is employed to harmonize the algorithm's exploration and exploitation functionalities, further bolstered by a dimensional search approach. The simulation results demonstrate that the proposed Swarm Intelligence Adaptive Neuro-Fuzzy Inference System (SI-ANFIS) method significantly reduced traffic overhead and attained an impressive accuracy rate of 93.58%.

Personal Writing Activity: Investigation of Students’ Oldest Childhood Recollections

The personal writing genre can provide valuable space for students to start from what they know best, themselves. In this qualitative study, middle school students’ earliest memories and the kinds of remembered memories were tried to be investigated. Students’ earliest memories were analysed based on their written personal writings. In addition, how well students remembered the names of the objects shown to them in a short time was analysed. Thus, the study also aimed to investigate which objects were remembered by students in a quick learning activity. The participants of the study were 21 struggling middle school students participated into a summer project titled as “Let's Discover Our Thoughts: What Do I Know and How Do I Use My Knowledge?” Data was collected from students’ writings reflecting their earliest memories and lists of remembered items’ names. The study's findings yielded that the most remembered feeling was being sad. Other reflected emotions were feeling happy, embarrassed, and tired. Analyses of the data also showed that the most remembered items were oranges, playing cards, and lemons. According to the used memorization test, among struggling middle school students only two students had good memorization skills.

Artificial Visual Systems Fabricated with Ferroelectric van der Waals Heterostructure for In-Memory Computing Applications.

Rapid developments in artificial neural network techniques and retina-inspired artificial visual systems are required to realize the sensing, processing, and memorization of an optical signal in a single device. Herein, a ferroelectric field-effect transistor fabricated with CuInP2S6 and α-In2Se3 van der Waals heterostructures is proposed and demonstrated for the development of an artificial visual system. The dipole polarizations are coupled and bidirectionally locked inside the ferroelectric α-In2Se3 along the in-plane and out-of-plane directions and are controlled by the gate voltages. Furthermore, light-induced polarization can change the order of polarization of the dipoles inside α-In2Se3. We demonstrate that using the combined control of these electrical and optical signals, the device may function like a retina-inspired vision system. The device can operate across a wide wavelength range (405-850 nm) and detect very low incident light (0.03 mW/cm2). Color recognition, high paired-pulse facilitation (∼170%), and short- to long-term memory transitions through quick learning are observed using this device. Additionally, this device demonstrates different complex processing abilities, including pattern recognition, light adaptation, optical logic operation, and event learning. The proposed ferroelectric heterostructure-based artificial visual system can serve as an essential bridge for fulfilling the future requirements of all-in-one sensing and memory-processing devices.

Best practice guidelines for the dynamic mode decomposition from a wind engineering perspective

This paper distills the serial work of Li et al. (2022a, 2022b, 2022c, 2023b, 2023a, 2021, 2020a) into a practice guide for the Dynamic Mode Decomposition (DMD) with a particular emphasis on the quick learning and adaptation of the technique from a wind engineering perspective. The guide begins with discussions of the DMD's connections with the Koopman, Fourier & Laplace, the Proper Orthogonal Decomposition, and machine learning theories. Then, based on 1.5 × 106 core hours of parametric investigations on paradigmatic cases, recommendations that broadly apply to bluff-body wind engineering problems—especially regarding the input sequence—are formulated. In the temporal aspect, results highlight the DMD's preference for steady, stationary, equilibrium, or quasi-equilibrium systems, and suggest a sampling resolution 15–20 times the frequency of the dynamics of interest. Emphasis is also placed on the necessity of the Stabilization state for sampling convergence while avoiding the input of overdetermined systems. In the spatial aspect, high-dimensional, mean-subtracted data with minimal interpolation and noise proved to yield better Koopman models. Different input variables, though derived from the same system, may also result in non-unique Koopman modes depending on their nonlinear richness. Finally, two open-source MATLAB algorithms have been discussed and made open-source to facilitate the easy dissemination and usage for wind engineers. Via pedagogical examples, Algorithm 1 outlines a standard procedure to check for sampling convergence, and Algorithm 2 performs the similarity-expression DMD and visualizes the outputs.

Effectiveness of Distance Education During Covid-19 at Higher Education Level: Perceptions of Students

In the present era of information technology, distance education or online education has been referred to as the method of content dissemination and quick learning technique. Distance education is a key component of education in the 21st century. The birthplace of distance education is the United States of America (USA) and then expanded over worldwide after 1998. The main purpose of this study was to investigate the effectiveness of distance education during COVID-19 at higher education and perceptions of students. This research was conducted to achieve objectives which were: to study the students’ perceptions of effectiveness about distance education during the outbreak of COVID-19 at the higher education, to find out the effect on students perceptions about effectiveness of distance education during the outbreak of COVID-19 at the higher education, and to determine different problems faced by students of distance education during the outbreak of COVID-19 at higher education. The quantitative research approach was used to analyze this research study and the population of the study was comprised of 153 students of BS level Spring Semester 2020 at Faculty of Social Sciences, National University of Modern Languages Islamabad. The sample has been collected through Universal Sampling techniques. Closed-ended five points Likert Scale questionnaire was used to collect data from the selected population. The reliability of the instruments was checked through Cronbach's Alpha which was 0.81. Data were collected through online Google survey forms. Collected data were analyzed through specific statistical software, IBM-SPSS (Version 23). The Frequency and Percentage techniques were used to analyze the data. The results were obtained in the form of tables. The final results showed that respondents had positive perceptions about distance education and it may be continue, respondents indicated the issue of non-availability of distance learning resources and gadgets and these may be provided. Poor internet connection, load shedding, lack of social interactions during distance learning and health risks were also identified in the finding and these may be countered by proper planning and training

Motion capture device reveals a quick learning curve in vascular anastomosis training.

Surgical procedures are often evaluated subjectively, and an objective evaluation has been considered difficult to make and rarely reported, especially in open surgery, where the range of motion is wide. This study evaluated the effectiveness of surgical suturing training as an educational tool using the Leap Motion Controller (LMC), which can capture hand movements and reproduce them as data comprising parametric elements. We developed an off-the-job training system (Off-JT) in our department, mainly using prosthetic grafts and various anastomotic methodologies with graded difficulty levels. We recruited 50 medical students (novice group) and 6 vascular surgeons (expert group) for the study. We evaluated four parameters for intraoperative skills: suturing time, slope of the roll, smoothness, and rate of excess motion. All 4 parameters distinguished the skill of the novice group at 1 and 10h off-JT. After 10h of off-JT, all 4 parameters of the novices were comparable to those of the expert group. Our education system using the LMC is relatively inexpensive and easy to set up, with a free application for analyses, serving as an effective and ubiquitous educational tool for young surgeons.

Mouth of the Donkey: Re-imagining Biblical Animals

Mouth of the Donkey: Re-imagining Biblical Animals

Spatial visualization for development of visual thinking and cognitive abilities among mechanical engineering students through tool design ideation

With the exposure of the mechanical engineering undergraduate students to the concepts of spatial visualization, this work assesses the effect of spatial visualization skills on the visual thinking abilities. Influence of spatial visualization on visual thinking and cognitive abilities like reasoning, abstract thinking, planning, comprehension of complex ideas, quick learning and learning from experience, is analysed through FMEA (failure modes and effects analysis). The RPNs (risk-priority-numbers) have been calculated and priority is set for proposing the corrective actions. Further to this, case studies in tool design have been picked for exhibition of spatial visualization skill set. Influence of tool design on visual thinking capability of the students is understood through various case-studies. Thus, tool, die and mold design ideation is used as a platform for the proposing improvements in visual thinking and cognitive abilities among mechanical engineering students.

Using a Spreadsheet Program to Fit a Gaussian Function

Due to their accessibility and relatively quick learning curve, spreadsheet programs (such as Microsoft Excel and Google Sheets) are a common choice for students when faced with data analysis work. As is well known by most physicists, however, these spreadsheet programs cannot always perform all the tasks that may be necessary for analyzing data. One such area where these programs tend to lag is in nonlinear curve fitting. Most of these programs contain a limited basic set of fit functions, such as linear, polynomial, and exponential fits, to name a few. Outside of these basic functions, one either needs to get creative or turn to a more complex data analysis platform that may have a steep learning curve or cost a considerable amount of money, making it inaccessible to students. In this paper, I will present a method to fit a specific nonlinear curve, a Gaussian function, to a set of collected data using Microsoft Excel.

Musicians’ views on the role of reading music in learning, performance, and understanding

Reading music notation is not an easy skill to acquire and can take years of training to master. In addition, it is not strictly necessary to be able to read music to make music. Nevertheless, music teaching and learning in the West has traditionally centered around the skill of reading music. This study explored participants’ reasons for learning to read music and the reported benefits of this skill for musical activities. We developed an online questionnaire with open-ended questions to gather the views of 64 people, both music readers and non-readers, on their musical activities using or not using notated scores. Their responses were analyzed thematically. The analysis showed that participants believe that learning and engaging with music notation adds to their multisensory experience of music involving vision, sound, and action; that, compared to learning by ear, the visual aspects of notation support the quick learning and improved theoretical understanding of a musical work: and that the skill of reading music is valuable in the context of group music making. They also believe, however, that notation can inhibit expressive performance or improvisation, and non-readers believe that sound technologies can be used as an alternative to notation. Finally, reading music was seen by readers, but not non-readers, as integral to playing an instrument. This may reflect differences between the practices and genres of music favored by the two groups.

Communication between Autonomous Vehicles and Pedestrians: An Experimental Study Using Virtual Reality.

One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians' reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.