Effects of Expert- and User-Generated Evaluations on Food Product Choices via a Food Literacy App

With the proliferation of mobile apps to promote healthy diets, it is important to understand the influence of evaluation information presented through these apps on users\u27 decisions. Deriving from information processing concepts, we examine the influence of information cues (authority and social proof cues) obtained via food literacy apps on users’ food product choices. We employ clickstream data from such an app that provides labeling information, expert grades, and user ratings/reviews of food products. We use a regression discontinuity design to uncover the effects of authority cue (expert grade) and Poisson regression to analyze the effects of social proof (user rating, review) on food product choices. The initial results add to our knowledge of the influence of these two key cues (authority and social proof) on food product choice. There are also salient implications for the app providers, for experts evaluating food products, for users, and for public health

FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization

As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating serving a robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.Comment: 13 pages, 9 figure

Excess mortality and the COVID-19 pandemic: causes of death and social inequalities

Background During the coronavirus diseases 2019 (COVID-19) pandemic, population’s mortality has been affected not only by the risk of infection itself, but also through deferred care for other causes and changes in lifestyle. This study aims to investigate excess mortality by cause of death and socio-demographic context during the COVID-19 pandemic in South Korea.  Methods Mortality data within the period 2015–2020 were obtained from Statistics Korea, and deaths from COVID-19 were excluded. We estimated 2020 daily excess deaths for all causes, the eight leading causes of death, and according to individual characteristics, using a two-stage interrupted time series design accounting for temporal trends and variations in other risk factors. Results During the pandemic period (February 18 to December 31, 2020), an estimated 663 (95% empirical confidence interval [eCI]: -2356–3584) excess deaths occurred in South Korea. Mortality related to respiratory diseases decreased by 4371 (3452–5480), whereas deaths due to metabolic diseases and ill-defined causes increased by 808 (456–1080) and 2756 (2021–3378), respectively. The increase in all-cause deaths was prominent in those aged 65–79 years (941, 88–1795), with an elementary school education or below (1757, 371–3030), or who were single (785, 384–1174), while a decrease in deaths was pronounced in those with a college-level or higher educational attainment (1471, 589–2328). Conclusion No evidence of a substantial increase in all-cause mortality was found during the 2020 pandemic period in South Korea, as a result of a large decrease in deaths related to respiratory diseases that offset increased mortality from metabolic disease and diseases of ill-defined cause. The COVID-19 pandemic has disproportionately affected those of lower socioeconomic status and has exacerbated inequalities in mortality.This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime.", funded by Korea Ministry of Environment (MOE) (2022003570006)

ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale, real-world interactions between students and AI systems still remain limited. In this study, we present ChEDDAR, ChatGPT & EFL Learner's Dialogue Dataset As Revising an essay, which is collected from a semester-long longitudinal experiment involving 212 college students enrolled in English as Foreign Langauge (EFL) writing courses. The students were asked to revise their essays through dialogues with ChatGPT. ChEDDAR includes a conversation log, utterance-level essay edit history, self-rated satisfaction, and students' intent, in addition to session-level pre-and-post surveys documenting their objectives and overall experiences. We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction. As a foundational step, we establish baseline results for two pivotal tasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. We finally suggest further research to refine the integration of generative AI into education settings, outlining potential scenarios utilizing ChEDDAR. ChEDDAR is publicly available at https://github.com/zeunie/ChEDDAR

Momu: A mobile music toolkit

ABSTRACT The Mobile Music (MoMu) toolkit is a new open-source software development toolkit focusing on musical interaction design for mobile phones. The toolkit, currently implemented for iPhone OS, emphasizes usability and rapid prototyping with the end goal of aiding developers in creating real-time interactive audio applications. Simple and unified access to onboard sensors along with utilities for common tasks found in mobile music development are provided. The toolkit has been deployed and evaluated in the Stanford Mobile Phone Orchestra (MoPhO) and serves as the primary software platform in a new course exploring mobile music

センリャク ガクシュウ ニ オケル ケイエイ トップ ノ ヤクワリ ニ ツイテ ノ イチコウサツ ユウリョウ ロウジン ホーム 4 シャ ノ ジレイ ブンセキ オ トオシテ

本論文は， 医療・福祉の市場化が進展している中で， 持続的な成長と存続を図るための経営戦略を展開しながら様々な試行錯誤を積み重ねている有料老人ホームに着目し， 組織学習理論に基づいて事例研究を行ったものである． 有料老人ホームにおける戦略的行動に注目し， 環境変化に適応するためにはどのような戦略を立て， どのように実行すべきかについて， その解を究明するとともに， 戦略学習 （戦略の形成および実行プロセス） における経営トップの果たすべき役割を明らかにするよう努めている． 事例分析では， 各社が単純， 深層， 変革レベルでの様々な学習を繰り返すことによって明確な戦略的違いを形成しており， 各社の経営トップが執事， 教師， 設計者の役割を果たしながら戦略学習を促進していることが明らかになった． さらに， 本稿では， 戦略学習における経営トップの新たな役割として， 芸術家的な役割を提示し， その仮説モデルを構築している

Meta-Qtest: meta-analysis of quadratic test for rare variants

Background In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level. Results Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT. Conclusion As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did.Publication costs are funded by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) grant (HI16C2037). Also, this work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165, HI16C2048

Direct cell-to-cell transfer in stressed tumor microenvironment aggravates tumorigenic or metastatic potential in pancreatic cancer.

Pancreatic cancer exhibits a characteristic tumor microenvironment (TME) due to enhanced fibrosis and hypoxia and is particularly resistant to conventional chemotherapy. However, the molecular mechanisms underlying TME-associated treatment resistance in pancreatic cancer are not fully understood. Here, we developed an in vitro TME mimic system comprising pancreatic cancer cells, fibroblasts and immune cells, and a stress condition, including hypoxia and gemcitabine. Cells with high viability under stress showed evidence of increased direct cell-to-cell transfer of biomolecules. The resulting derivative cells (CD4

RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Appropriateness of the anxiety subscale of the Hospital Anxiety and Depression Scale for Koreans to measure preoperative anxiety and the effect of preoperative anxiety on postoperative quality of recovery

Background The reliability and validity of the anxiety subscale of the Hospital Anxiety and Depression Scale for Koreans (K-HADS-A) has not been studied in Korean surgical patients. This study aimed to validate the usefulness of K-HADS-A for measuring preoperative anxiety in Korean surgical patients. Additionally, the effect of preoperative anxiety on postoperative quality of recovery was evaluated. Methods Preoperative anxiety in 126 inpatients with planned elective surgery was measured using the K-HADS-A. The postoperative quality of recovery was measured using the Korean version of the Quality of Recovery-15. The validity and reliability of the K-HADS-A were evaluated. The differences in quality of recovery on the first and seventh day postoperatively were then compared between the anxious and non-anxious groups. Results There was a statistical correlation between the K-HADS-A and Anxiety Likert Scale. The goodness-of-fit indices of the structural equation model showed how well the data from the K-HADS-A match their concept. The Kaiser-Meyer-Olkin value was 0.848, and the P value of Bartlett’s test of sphericity was < 0.001. Cronbach’s alpha was high at 0.872. The K-HADS-A had an acceptable level of validity and reliability. Postoperative quality of recovery was significantly lower in the anxious group (postoperative day 1: t = 2.058, P = 0.042; postoperative day 7: t = 3.430, P = 0.002). Conclusions The K-HADS-A is an acceptable tool for appropriately assessing preoperative anxiety in Korean surgical patients. Assessing preoperative anxiety is valuable, because preoperative anxiety affects the postoperative quality of mental and physical recovery