Understanding Demographic and Behavioral Determinants of Engagement in Plastic Tableware Reduction: Behavior, Support, and Price Sensitivity by Sai-Leung Ng, Yu-Chieh Hsieh

“…Using survey data from Hong Kong residents and a Multivariate Analysis of Variance (MANOVA) approach, this study analyzes how age, gender, education, income, housing type, order frequency, opt-out effectiveness, and their interactions influence the four dimensions of engagement, namely plastic tableware opt-out behavior, support for government policies, support for plastic-free restaurants, and price sensitivity. …”
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Impact of smart healthcare-based behaviors of elderly patients with chronic diseases on physicians’ behavioral adaptations by Nan Ji, Mao Wu, Yong Liu

“…A total of 100 physicians and 100 of their patients were enrolled. Data were collected using a general information questionnaire, the Chinese version of the Self-Efficacy in Patient-Centeredness Questionnaire (SEPCQ), the Chinese version of the Wake Forest Physician Trust Scale (WFPTS-C-10), the Health Information Seeking Behavior (HISB) scale, and the Cloud Follow-up Service Experience Scale for Patients with Chronic Diseases.ResultsThe mean scores were as follows: SEPCQ (50.54 ± 6.16), WFPTS-C-10 (107.82 ± 5.16), HISB (31.96 ± 4.94), and the Cloud Follow-up Service Experience Scale for Chronic Disease Patients (26.11 ± 3.16). …”
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Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras by Alejandro Dionis-Ros, Joan Vila-Francés, Rafael Magdalena-Benedito, Fernando Mateo, Antonio J. Serrano-López

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Changing practice in cystic fibrosis: Implementing objective medication adherence data at every consultation, a learning health system and quality improvement collaborative by Carla Girling, India Davids, Nikki Totton, Madelynne A. Arden, Daniel Hind, Martin J. Wildman

“…Thirteen healthcare practitioners participated in semi‐structured interviews, before and after establishing the QIC. Qualitative data were analyzed using the behavior change model. …”
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Behavioral Biases in Investor Decision-Making: A Comparative Meta-Analysis of Behavioral Finance Research by Seyed Amir Sabet, Saeed Aibaghi esfahani, Abdolmajid Abdolbaghi Ataabadi

“…This study synthesizes research on twelve key biases through meta-analysis, confirming their significant impact across markets, where cognitive biases include overconfidence (overestimating knowledge), anchoring (relying on initial reference points), herd behavior (following crowds), representativeness (using stereotypes over data), availability heuristic (overweighting recent information), and mental accounting (categorizing money subjectively), while emotional biases feature loss aversion (fearing losses more than valuing gains), regret aversion (avoiding potential regret), self-attribution (blaming failures on externals), optimism bias (overestimating success), and self-control issues (failing long-term planning). …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

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Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data by Jyoti U. Devkota

“…The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.…”
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A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers by Rui Fu, Tong Liu, Yuxi Guo, Shiwei Zhang, Wendong Cheng

“…This paper presents an investigation of the relationship between driver risk and factors indicating vehicle’s speed and driver’s acceleration behavior. The main objective is to examine whether GPS data and derivative indicator can be used to identify risky drivers by means of factor analysis. …”
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Key Drivers of Live Streaming Adoption: An Empirical Analysis Using the UTAUT Model by Retno Fuji Oktaviani

“…Both performance expectancy and effort expectancy significantly influence behavioral intention, and behavioral intention, in turn, positively affects the actual use of live streaming. …”
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Use of Computational Fluid Dynamics (CFD) Methods to Analyze Combustion Chamber Processes at HVOF Spraying and Their Comparison with Experimental Data by Bauyrzhan Rakhadilov, Nazerke Muktanova, Aidar Kengesbekov, Nurtoleu Magazov

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Abnormality detection of sliding surface and exploration suitable sensor data for condition monitoring by calculating contribution using machine learning by Ryo NAKASHIMA, Tomomi HONDA, Tomohiko KON

“…In this paper, we investigated a method to reduce the opacity of anomaly detection using machine learning and explored sensor data useful for condition monitoring of the sliding surface. …”
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On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach by Mahmoud El-Morshedy, Zahra Almaspoor, Gadde Srinivasa Rao, Muhammad Ilyas, Afrah Al-Bossly

“…The importance of statistical methods in various research fields is modeling the real data and predicting the future behavior of data. …”
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A Study on Using Transfer Learning to Utilize Information From Similar Systems for Data-Driven Condition Diagnosis and Prognosis by Marcel Braig, Peter Zeiler

“…A rolling bearing and a filter degradation data set are used to evaluate the diagnostic and prognostic performance. …”
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Capacity Forecasting of Lithium-Ion Batteries Using Empirical Models: Toward Efficient SOH Estimation with Limited Cycle Data by Kanchana Sivalertporn, Piyawong Poopanya, Teeraphon Phophongviwat

“…Among these, the linear and single-exponential models demonstrated strong performance in early-cycle predictions. It was found that using 30 to 40 cycles of data is sufficient for reliable forecasting within a 100-cycle range, reducing the mean absolute error by over 80% compared to using early-cycle data alone. …”
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Understanding the charactersitics and types of single-person households based on food purchase frequencies in Korea: a cross-sectional study using the 2023 Consumer Behavior Survey... by So-Yun Kim, Youngmin Nam, Jong-Youn Rha, Haerang Lee

“…Data were analyzed using Rao-Scott chi-square tests, ANCOVA, ANOVA, and K-modes hierarchical cluster analysis. …”
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Identifying time patterns in Huntington’s disease trajectories using dynamic time warping-based clustering on multi-modal data by Alexia Giannoula, Audrey E. De Paepe, Ferran Sanz, Laura I. Furlong, Estela Camara

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Reduced order data driven framework for computation of the unsteady lid-driven cavity flow using dynamic mode decomposition by Karim Mazaheri, Mohammad Kazem Moayyedi, Mohammad Hadi Dehghan

“…Validation of CFD computation was assessed, and independence of the DMD modes from the data collection process was investigated. A data set of 400 snapshots of the flow field at three different Reynolds numbers were used to identify the DMD modal structure. …”
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Dynamic AI-Enhanced Therapeutic Framework for Precision Medicine Using Multi-Modal Data and Patient-Centric Reinforcement Learning by R. Gayathri, S. K. B. Sangeetha, R. Sangeetha, G. Leena Rosalind Mary, Sandeep Kumar Mathivanan, Usha Moorthy

“…Here, genomic data integrated with EHR coupled with the metering from wearable devices are combined using an adaptive temporal-fusion mechanism to ensure it is context-aware and timely. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

“…This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. …”
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Uncovering the Uncertainties and Variability of Travel Time and Fuel Consumption Using Floating Car Data: A Case Study in Wuhan by Wenxin Teng, Fei Liu, Jianbing Yang, Chaoyang Shi, Yunfei Zhang, Fuqiang Wang

“…While previous studies have extensively examined travel time distributions, the joint distributional patterns of fuel consumption remain underexplored, limiting the effectiveness of green routing strategies. A novel data-driven framework, integrating map-matching, second-by-second trajectory interpolation, and microscopic fuel consumption estimation (CMEM), is developed for jointly analyzing the distributions and spatiotemporal variability characteristics of link-level travel time and fuel consumption using floating car data (FCD) in Wuhan, China. …”
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