WOLNOŚĆ JAKO NIEMOŻNOŚĆ, JĘZYK MILCZENIA: PONOWNIE CZYTAJĄC POEZJĘ AUTORSTWA KIM SUYǑNG

This article examines several works written by Kim Suyǒng in the 1960s with a focus on negation as the poetic method in accordance with revolution. He lived through a late colonial period, the Korean War, the April Revolution, and Park Chung Hee’s regime and he was keenly aware Koreans had not spoken of liberty as the invention of modernity in our mother tongue throughout our history. He dedicated all his poems to demonstrating why liberty was impossible to be spoken in Korean. In the course of his writing, his authentic poetic language developed into silence as a martyr, the language of death and love. In so doing, he could “live liberty” through his poetry in accordance with his conscience in the authoritarian society.김수영은 ‘자유’의 시인이다. 그에게 근대의 산물인 자유는 한국인들이 모국어로서 발화하지 못한 것이었다. 1960년대 한국에는 박정희 정권이 집권했다. 군사 독재 정권 하에서 자유에 대한 발언은 억압당했는데, 김수영은 그러한 사태를 ‘불가능’으로 접수했다. ‘불가능’으로 존재하는 자유를 시로써 발화하는 것은 혁명의 수행을 의미한다. 그는 시작 과정에서 혁명을 부정의 문법으로 실천했고, 그것은 시에서 화자의 자기 폭력으로 나타난다. 그러한 시적 방법은 화자의 죽음으로 메지 나며, 이 때 침묵이 발화된다. ‘침묵’은 자유를 시로써 말할 수 있는, 그의 ‘양심’에 근거한 독특한 언어이다. 침묵은 그가 스스로를 끝까지, 무한히 부정함으로써 다른 이를 위해 자유를 상상할 수 있게 했던 ‘사랑의 언어’이다.W tym artykule rozważa się kilka prac napisanych przez Kim Suyǒng w latach sześćdziesiątych, w których występuje negacja jako metoda poetycka zgodna z trendem rewolucyjnym. Autor przeżył późny okres kolonialny, wojnę koreańską, kwietniową rewolucję i reżim Parku Chung Hee'a, i był w pełni świadomy, że Koreańczycy nie mówili o wolności jako w swoim języku ojczystym. Swoje wszystkie wiersze poświęcił uświadomieniu ludziom, dlaczego o wolności nie można mówić po koreańsku. W toku pracy twórczej ukształtował się jego prawdziwy język poetycki stanowiący pomnik milczenia męczennika, język śmierci i miłości. Dzięki temu to mógł „żyć wolnością” tworząc poezję zgodnie ze swoim sumieniem jednocześnie żyjąc w społeczeństwie autorytarnym

The Ghost of Modernity: Normative Power of Modernity as Propaganda

This thesis explores how domestic factions and authoritarian regimes in Japan and Korea in the period from the 1850s to 1970s appropriated the concept of “modernity” to gain normative superiority over their competitors. The appropriating entity revised the concept of modernity to suit its own worldview. Across the case studies, the propaganda of modernity created a hierarchy that privileged those who are “more modern,” encouraged martial masculinity, and attached itself to existing domestic norms, such as ethno- nationalism. Under authoritarian regimes, modernity helped justify the mobilization of capital, manpower, and other critical resources in the name of nation-building or defense. Many factions and demagogues may have initially used the concept of modernity for domestic gains, but using this narrative later devolved into foreign conquests and imperialist expansion, for otherwise, their call for modernization would have become an empty promise in the eyes of the masses. This paper examines five cases along these dimensions, namely the rise of reformist samurais in feudal-era Japan, the failure of Joseon Korea’s ruling regime to adopt modernity in a timely manner, Imperial Japan’s colonial practices in Korea and Manchuria, the ideological divergences among factions in Colonial Korea, and a South Korean dictator’s attempts to gain legitimacy following a coup d’etat. Each case follows how domestic factions or individuals were motivated by an inferiority complex and how they produced their own version of modernity that favored their ascendance

Applying Constructivism in Neurodiverse Classrooms

Cognitive development theories differ on how young students can meaningfully process new information and retain that information for future knowledge-building through scaffolding within their zone of proximal development. More traditional theories like the cognitive load theory adhere to the rote memorization approach by categorizing students as passive learners and the teachers as initiators who provide information in a structured, often rigid format, to be stored and retrieved for future application using their working memory. In contrast, the more progressive theories, like constructivism, are premised on the belief that students should proactively initiate their own learning while teachers act more as facilitators. The current trend in government policy under ESSA is to embrace the latter approach in the classroom, which is also more inclusive of all types of students, especially neurodiverse students. Moreover, teachers can utilize the wider range of assistive technology tools to accommodate and support their students’ unique learning styles

Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies–Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as ‘witnessed e-cigarette use in their household’ and ‘perception of their tobacco use’, could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers

A Novel Simplified System to Estimate Lower-Limb Joint Moments during Sit-to-Stand

To provide effective diagnosis and rehabilitation, the evaluation of joint moments during sit-to-stand is essential. The conventional systems for the evaluation, which use motion capture cameras, are quite accurate. However, the systems are not widely used in clinics due to their high cost, inconvenience, and the fact they require lots of space. To solve these problems, some studies have attempted to use inertial sensors only, but they were still inconvenient and inaccurate with asymmetric weight-bearing. We propose a novel joint moment estimation system that can evaluate both symmetric and asymmetric sit-to-stands. To make a simplified system, the proposal is based on a kinematic model that estimates segment angles using a single inertial sensor attached to the shank and a force plate. The system was evaluated with 16 healthy people through symmetric and asymmetric weight-bearing sit-to-stand. The results showed that the proposed system (1) has good accuracy in estimating joint moments (root mean square error < 0.110 Nm/kg) with high correlation (correlation coefficient > 0.99) and (2) is clinically relevant due to its simplicity and applicability of asymmetric sit-to-stand. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

The effect of sex and physical frailty on incident disability after 2 years among community-dwelling older adults: KFACS study

Background : This study investigated the impact of physical frailty on the development of disabilities in mobility, activities of daily living (ADL), and instrumental activities of daily living (IADL) according to sex among community-dwelling Korean older adults. Methods : We used data of 2,905 older adults aged 70-84 years from the Korean Frailty and Aging Cohort Study (KFACS) at baseline (2016-2017) and Wave 2 (2018-2019). Fried’s physical frailty phenotype was used to identify frailty. Results : After adjustment, frailty showed a higher impact for women than men on developing mobility disability (odds ratio [OR]=14.00, 95% confidence interval [CI]=4.8–40.78 vs. OR=9.89, 95% CI=4.28–22.86) and IADL disability after two years (OR=7.22, 95% CI=2.67–19.56 vs. OR=3.19, 95% CI=1.17–8.70). Pre-frailty led to mobility disability for women and men (OR=2.77, 95% CI=1.93–3.98 vs. OR=2.49, 95% CI=1.66–3.72, respectively), and IADL disability only for women (OR=3.01, 95% CI=1.28–7.09). Among the IADL components, both men and women who were prefrail or frail showed increased disability in ‘using transportation’. Among men, pre-frailty was significantly associated with disability in “going out” and “shopping”. In women, frailty was significantly associated with disability in “doing laundry,” “performing household chores,” “shopping,” and “managing money”. Conclusions : Physical frailty increased disability over 2 years for women more than men. Physical frailty increased disability in outdoor activity-related IADL components in men and household work-related IADL components in women. This study highlights the need for gender-specific policies and preventative programs for frailty, particularly restorative interventions that focus on women who are physically frail.This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), which is funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI15C3153), and the Research Program funded by the National Institute of Health, Korea Disease Control and Prevention Agency (2021-ER060500). The funding bodies had no role in the study design the collection, analysis and interpretation of the data; the writing of the report; or the decision to submit this article for publication

Exploring Intention to Adopt Mobile Tv Services In the U.S.: Toward A New Model With Cognitive-Based and Emotional-Based Constructs

This study focuses on U.S. mobile phone users’ intention to utilize mobile TV services. Existing theories and models in the field of information technology and communications were applied to predict users’ intentions to employ mobile TV service. The constructs used in the research model were Perceived Usefulness and Perceived Ease of Use (from TAM), Perceived Behavioral Control (from TPB), Social Influences (from UTAUT), and Innovativeness (from Diffusion of Innovation). Most of the constructs in technology acceptance models are based on consumers’ cognitive judgments of the service or the technology. Considering that mobile phones are highly personal devices and that people tend to always carry their mobile phones with them, mobile phone users are likely to possess personal memories related to the phones and to demonstrate attachmentto their mobile phones (Blom & Monk, 2003; Vincent, 2006; Wehmeyer, 2007). Therefore, scales of mobile phone users’ attachment, which focus on the emotional aspects of users, were added to the research model. Along with cognitive- and emotional-based constructs, individual characteristics such as age, gender, and experiences were hypothesized to moderate the relationships between the previously mentioned constructs and the dependent variable, user intention to adopt mobile TV service. The hypothesized research model was tested among the student sample using structural equation modeling (SEM). Perceived usefulness (PU), perceived ease of use EOU), perceived behavioral control (PBC), social influences (SI), and personal innovativeness (PIIT) were found to be the significant determinants of intention to use mobile TV service. Furthermore, the results showed that mobile phone users’ gender, age, and experiences moderated the relationship between some of the determinants and behavioral intention. Mobile phone user-device attachment, comprised of four subdimensions – symbolism, fashion, possession, and needs – negatively influenced users’ intention to use mobile TV service. This study is significant because it extended the technology acceptance models by adding the factor of users’ attachment to mobile phones and incorporated the moderating effects of the individual characteristics of gender, experience, and age. Managerial implications, limitations of the study, and suggestions for future study are discussed in the last chapter

A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure

The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM’s lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker’s implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended

Toward clinically-relevant joint moment estimation during sit to stand: A feasibility study

Conventional motion analysis system to measure joint moment during sit to stand is impractical to be used in clinics. Inertial sensor-based motion analysis system has been proposed to improve usability. In this study, we propose a simple system that can predict joint moment, especially maximum joint moment, with a minimum number of an inertial sensor attached and force plate; shank and thigh angles were estimated with a kinematic model and an effective joint moment analysis period. Through a validation experiment with eight subjects, the estimated joint moments were comparable to be actual joint moment measured by the conventional system, also the previous study with the inertial system. © 2019 IEEE

A Systematic Review and Meta-Analysis of the Effectiveness of Virtual Reality-Based Rehabilitation Therapy on Reducing the Degree of Pain Experienced by Individuals with Low Back Pain

Background: The concept of virtual reality (VR)-based rehabilitation therapy for treating people with low back pain is of growing research interest. However, the effectiveness of such therapy for pain reduction in clinical settings remains controversial. Methods: The present study was conducted according to the reporting guidelines presented in the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. We searched the PubMed, Embase, CENTRAL, and ProQuest databases for both published and unpublished papers. The Cochrane risk of bias tool (version 2) was used to evaluate the quality of the selected studies. GRADEprofiler software (version 3.6.4) was used to evaluate the level of evidence. We analyzed the included research results using RevMan software (version 5.4.1). Results: We included a total of 11 articles in the systematic review and meta-analysis, with a total of 1761 subjects. Having assessed the quality of these studies, the risk of bias was generally low with high heterogeneity. The results revealed a small to medium effect (standardized mean difference = ±0.37, 95% confidence interval: 0.75 to 0) based on evidence of moderate overall quality. Conclusion: There is evidence that treatment using VR improves patients’ pain. The effect size was small to medium, with the studies presenting evidence of moderate overall quality. VR-based treatment can reduce pain; therefore, it may help in rehabilitation therapy