Development of a therblig-based evaluation methodology for accessible product: a case study of spinal-cord impaired users.

PURPOSE: This study proposes a therblig-based evaluation technique as a new accessibility tool for physical products like home appliances that spinal cord injured users occasionally use. MATERIAL AND METHODS: This study recruited nine spinal cord injured users for the interview and observation regarding home appliance usage and analytically structured their usage behaviors using therbligs. The therblig notations eventually referred to actual and potential accessibility issues that spinal cord injured users would encounter when using the home appliances. RESULTS: The primary therblig operations causing accessibility issues for spinal cord injured users were 'reach,' 'move,' 'grasp,' 'position,' and 'use', corresponding to their disability characteristics. In addition, this study proposed a new effective therblig called "hook," which is suitable for better representation of user behavior and accessibility evaluation of spinal cord users. CONCLUSION: This study provided an interaction-based accessibility evaluation technique, which is easy to learn and apply, especially for physical products.

IMPLICATIONS FOR REHABILITATIONThe therblig-based accessibility evaluation method describes the user behavior on a micro-scale and localizes a problematic operation throughout the task process during home appliance usage.Reach, Move, Grasp, Position and Use were the primary problematic therblig operations regarding home appliance usage of spinal cord injured users, resulting in low accessibility.A new therblig 'hook' was developed to describe the user behavior of spinal cord injured users.Therblig evaluation offers a more standardized, easy-to-learn accessibility evaluation method that does not require expertise but provides the latent needs of users.

Unveiling the impact of lysosomal ion channels: balancing ion signaling and disease pathogenesis.

Ion homeostasis, which is regulated by ion channels, is crucial for intracellular signaling. These channels are involved in diverse signaling pathways, including cell proliferation, migration, and intracellular calcium dynamics. Consequently, ion channel dysfunction can lead to various diseases. In addition, these channels are present in the plasma membrane and intracellular organelles. However, our understanding of the function of intracellular organellar ion channels is limited. Recent advancements in electrophysiological techniques have enabled us to record ion channels within intracellular organelles and thus learn more about their functions. Autophagy is a vital process of intracellular protein degradation that facilitates the breakdown of aged, unnecessary, and harmful proteins into their amino acid residues. Lysosomes, which were previously considered protein-degrading garbage boxes, are now recognized as crucial intracellular sensors that play significant roles in normal signaling and disease pathogenesis. Lysosomes participate in various processes, including digestion, recycling, exocytosis, calcium signaling, nutrient sensing, and wound repair, highlighting the importance of ion channels in these signaling pathways. This review focuses on different lysosomal ion channels, including those associated with diseases, and provides insights into their cellular functions. By summarizing the existing knowledge and literature, this review emphasizes the need for further research in this field. Ultimately, this study aims to provide novel perspectives on the regulation of lysosomal ion channels and the significance of ion-associated signaling in intracellular functions to develop innovative therapeutic targets for rare and lysosomal storage diseases.

Multi-order graph attention network for water solubility prediction and interpretation.

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

Invariable resistance of conductive nanocomposite over 30% strain.

The dependence of the electrical resistance on materials' geometry determines the performance of conductive nanocomposites. Here, we report the invariable resistance of a conductive nanocomposite over 30% strain. This is enabled by the in situ-generated hierarchically structured silver nanosatellite particles, realizing a short interparticle distance (4.37 nm) in a stretchable silicone rubber matrix. Furthermore, the barrier height is tuned to be negligible by matching the electron affinity of silicone rubber to the work function of silver. The stretching results in the electron flow without additional scattering in the silicone rubber matrix. The transport is changed to quantum tunneling if the barrier height is gradually increased by using different matrix polymers with smaller electron affinities, such as ethyl vinyl acetates and thermoplastic polyurethane. The tunneling current decreases with increasing strain, which is accurately described by the Simmons approximation theory. The tunable transport in nanocomposites provides an advancement in the design of stretchable conductors.

Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring.

Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as 'HAR', is the prediction of common human measurements, which consist of movements such as walking, running, drinking, cooking, etc. It is extremely advantageous for services in the sphere of medical care, such as fitness trackers, senior care, and archiving patient information for future use. The two types of data that can be fed to the HAR system as input are, first, video sequences or images of human activities, and second, time-series data of physical movements during different activities recorded through sensors such as accelerometers, gyroscopes, etc., that are present in smart gadgets. In this paper, we have decided to work with time-series kind of data as the input. Here, we propose an ensemble of four deep learning-based classification models, namely, 'CNN-net', 'CNNLSTM-net', 'ConvLSTM-net', and 'StackedLSTM-net', which is termed as 'Ensem-HAR'. Each of the classification models used in the ensemble is based on a typical 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network; however, they differ in terms of their architectural variations. Prediction through the proposed Ensem-HAR is carried out by stacking predictions from each of the four mentioned classification models, then training a Blender or Meta-learner on the stacked prediction, which provides the final prediction on test data. Our proposed model was evaluated over three benchmark datasets, WISDM, PAMAP2, and UCI-HAR; the proposed Ensem-HAR model for biomedical measurement achieved 98.70%, 97.45%, and 95.05% accuracy, respectively, on the mentioned datasets. The results from the experiments reveal that the suggested model performs better than the other multiple generated measurements to which it was compared.

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Electron tunneling of hierarchically structured silver nanosatellite particles for highly conductive healable nanocomposites.

Healable conductive materials have received considerable attention. However, their practical applications are impeded by low electrical conductivity and irreversible degradation after breaking/healing cycles. Here we report a highly conductive completely reversible electron tunneling-assisted percolation network of silver nanosatellite particles for putty-like moldable and healable nanocomposites. The densely and uniformly distributed silver nanosatellite particles with a bimodal size distribution are generated by the radical and reactive oxygen species-mediated vigorous etching and reduction reaction of silver flakes using tetrahydrofuran peroxide in a silicone rubber matrix. The close work function match between silicone and silver enables electron tunneling between nanosatellite particles, increasing electrical conductivity by ~5 orders of magnitude (1.02×103 Scm-1) without coalescence of fillers. This results in ~100% electrical healing efficiency after 1000 breaking/healing cycles and stability under water immersion and 6-month exposure to ambient air. The highly conductive moldable nanocomposite may find applications in improvising and healing electrical parts.

Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach.

The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust. In particular, ability (as a trustworthiness facet) was a dominant factor affecting trust in HRI. Integrity was found to mediate the relationships between robot- and human-related metrics and trustworthiness. This study also showed a correlation between intelligence and trustworthiness, as well as between PAS and trustworthiness. The findings of the present study have significant implications for both theory and practice on factors and levels that affect trust in HRI.

A comparative study on the statistical modelling for the estimation of stature in Korean adults using hand measurements.

In forensic research, stature is an important indicator in the identification of humans. There are numerous methods for estimating stature, and their goal is to determine the optimal variables for delivering the most accurate predictions. The purpose of this study is to compare the predictive algorithms for stature based on various hand dimensions. The selected hand variables can be separated into four categories-length, breadth, wrist, and thickness-and 18 variables were eventually selected in this research. The hand dimension data were analyzed by descriptive statistics. In the Korean population, there were significant differences found within genders in terms of hands and stature. Two predictive algorithms, regression and artificial neural network, were compared on the basis of their coefficient of determination (R2) and root mean square error (RMSE). In the single linear regression, hand length (R2 = .386) and palm length (R2 = .349) were found to be the most relevant variables in stature prediction for males. For females, hand length (R2 = .286) and inner grip circumference (R2 = .261) scored the highest R2. In the multiple linear regression, an R2 of .659 was obtained for both males and females, with an RMSE of 5.38 cm. In the artificial neural network, the value of R2 was .05, along with an RMSE of 5.17 cm. Overall, this study proposes the artificial neural networks as an improved predictive algorithm for stature, and hand length and inner grip circumference were found to be the most relevant variables to predict stature.

Mining affective experience for a kansei design study on a recliner.

As the technical performance of products progresses, it is becoming more important to design products that satisfy customers' affective experiences. Hence, many studies about Kansei engineering or Kansei design have been conducted to develop products that can satisfy customers' affective experiences. In the Kansei design method, it is important to select affective variables related to the design elements of the product in order to accurately grasp the emotions of customers. Therefore, this study seeks to develop an affective variable extraction methodology that can reflect users' implicit needs effectively and efficiently. In this study, users' affective variables were extracted from online reviews and classified using a self-organizing map (SOM). For verification, the study selected the Amazon e-commerce service and performed a product experiment on recliners. The experimental results show that the most frequently used affective variable in the use of recliners is 'comfort', which is related to various affective variables. In addition, 15 clusters for affective experiences of recliners extracted from Amazon.com were classified through the SOM. The findings suggest that text mining techniques and the SOM can be used to gather and analyze customers' affective experiences effectively and efficiently. The results of this study can also enhance an understanding of customers' emotions regarding recliners.

Estimation of stature from hand and foot dimensions in a Korean population.

The estimation of stature using foot and hand dimensions is essential in the process of personal identification. The shapes of feet and hands vary depending on races and gender, and it is of great importance to design an adequate equation in consideration of variances to estimate stature. This study is based on a total of 5,195 South Korean males and females, aged from 20 to 59 years. Body dimensions of stature, hand length, hand breadth, foot length, and foot breadth were measured according to standard anthropometric procedures. The independent t-test was performed in order to verify significant gender-induced differences and the results showed that there was significant difference between males and females for all the foot-hand dimensions (p<0.01). All dimensions showed a positive and statistically significant relation with stature in both genders (p<0.01). For both genders, the foot length showed highest correlation, whereas the hand breadth showed least correlation. The stepwise regression analysis was conducted, and the results showed that males had the highest prediction accuracy in the regression equation consisting of foot length and hand length (R2=0.532), whereas females had the highest accuracy in the regression model consisting of foot length and hand breadth (R2=0.437) The findings of this study indicated that hand and foot dimensions can be used to predict the stature of South Korean in the forensic science field.

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

Measuring user similarity using electric circuit analysis: application to collaborative filtering.

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user-item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems.

Health-related Quality of Life and Related Factors in Full-time and Part-time Workers.

BACKGROUND: There has been a rapid increase in the number of part-time workers in Korea with little information available on associated changes in quality of life. This study was designed to compare part-time and full-time workers in terms of the quality of life and related factors. METHODS: Data were extracted from the 4th Korea National Health and Nutrition Examination Survey, conducted in 2008. Of the 1,284 participants selected, 942 were females (range, 20 to 64 years). Based on the information provided by self-administered questionnaire, subjects were categorized according to the working pattern (full-time and part-time) and working hours (<30 and ≥30 hours). Differences in socio-demographic characteristics, health-related behaviors, and job characteristics were assessed by t-test and chi-square test. EuroQol-five dimensions (EQ-5D) index was implemented in order to measure the quality of life. Differences in the EQ-5D index scores between the groups were compared by t-test, stepwise multivariate logistic regression analyses. RESULTS: Quality of life did not differ by work patterns. In males, the Organization for Economic Cooperation and Development part-time group was associated with poorer quality of life (odds ratio [OR], 0.49; P = 0.028). For both sexes, the non-stress group was linked with superior quality of life in comparison to the stress group (OR, 2.64; P = 0.002; OR, 2.17; P < 0.001). Female employees engaged in non-manual labor had superior quality of life than those engaged in manual labor (OR, 1.40; P = 0.027). CONCLUSION: This study concludes that working less than 30 hours per week is related to lower quality of life in comparison to working 30 hours or more in male employees in Korea.