公民與社會

補正完畢TW

An Augmented User Context-Awareness Recommendation System

台灣網路智能學會補正完畢國內雲林縣，臺灣TW

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data

High-resolution real-time air quality forecasting can alert decision-makers and residents about forthcoming air pollution events and refine air quality management. The Environmental Protection Administration in Taiwan has deployed numerous low-cost air quality microsensors near industrial zones lately to facilitate local air quality monitoring. Nevertheless, the frequent occurrence of missing sensor data due to problems of mobile transmission, frontend/backend device malfunction, or other unforeseen issues would raise difficulty in making quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP) collaborating an Autoencoder (AE), a Convolutional Neural Network (CNN), and a Back Propagation Neural Network (BPNN) to effectively extract crucial features from big data for making successive high-spatiotemporal-resolution forecasts of PM2.5 concentrations 4 h ahead. The proposed model was trained and tested in three industrial zones densely installed with microsensors in Kaohsiung City of Taiwan. A high pollution incident was selected to evaluate model performance. The results show that the proposed model could reliably produce nice high-spatiotemporal-resolution forecasts for 12 air quality monitoring stations and 485 microsensors, with Coefficient of Determination (R2) values and Root Mean Squared Error (RMSE) of 0.82 (0.76) and 11.05 (12.75) μg/m3 in the training (testing) stage, respectively. For the selected incident, the Mean Absolute Percentage Error (MAPE) values of the proposed model were 22.3% and 27.1% at T+1 and T+4, respectively. This study demonstrates that the proposed deep learning model based on ensemble datasets of sparsely distributed monitoring stations and densely deployed microsensors can offer reliable high-spatiotemporal-resolution air quality forecasts, benefiting environmental studies and informed policymaking by accounting for local-scale variations in PM2.5 concentrations.補正完畢US

The Impact of Corporate Tax Evasion on Tax Avoidance Behavior of Industrial Peer Firms.

補正完畢國際Chicago，AmericaUS

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

Accurately forecasting riverbed aeolian dust emissions (PM10) in complex watershed environments is a critical engineering challenge, shaped by the intricate interdependencies among hydrometeorological factors, land surface dynamics, and anthropogenic pollution sources. Traditional models often struggle to capture these nonlinear interactions, limiting their utility for real-time environmental decision-making. This study presents a novel hybrid deep learning framework—combining a 3D Convolutional Neural Network (CNN), dual 1D CNNs, and a Transformer architecture—to enhance the predictive accuracy and interpretability of PM1110 forecasts in Taiwan’s Jhuoshuei River Basin. The model harnesses the spatial feature extraction of the 3D CNN, temporal pattern recognition of the 1D CNNs, and long-range dependency modeling of the Transformer to learn complex, multiscale relationships across diverse environmental variables. Extensive quantitative and qualitative evaluations demonstrate the model’s superior performance over conventional approaches, particularly in capturing seasonal variability and the mitigating effects of water infrastructure (e.g., Jiji Weir discharge) on dust emissions. The model effectively anticipates pollution peaks, offering critical lead time for the implementation of targeted interventions such as reservoir releases or dust suppression. Beyond technical innovation, this research provides actionable insights into the dynamic coupling of atmospheric, hydrological, and operational factors. The model’s scalability and generalizability position it as a robust decision-support tool for engineers, environmental managers, and policymakers. By bridging AI-driven modeling with practical engineering applications, this study advances the field of environmental informatics and supports the development of adaptive, knowledge-based systems for sustainable air quality and watershed management.補正完畢GB

Computational Testing Procedure for the Overall Lifetime Performance Index of Multi-Component Exponentially Distributed Products.

Download PDFsettingsOrder Article Reprints Open AccessArticle Computational Testing Procedure for the Overall Lifetime Performance Index of Multi-Component Exponentially Distributed Products by Shu-Fei Wu *ORCID andChia-Chi Hsu Department of Statistics and Data Science, Tamkang University, New Taipei City 251301, Taiwan * Author to whom correspondence should be addressed. Stats 2025, 8(4), 104; https://doi.org/10.3390/stats8040104 Submission received: 7 September 2025 / Revised: 15 October 2025 / Accepted: 23 October 2025 / Published: 2 November 2025 Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract In addition to products with a single component, this study examines products composed of multiple components whose lifetimes follow a one-parameter exponential distribution. An overall lifetime performance index is developed to assess products under the progressive type I interval censoring scheme. This study establishes the relationship between the overall and individual lifetime performance indices and derives the corresponding maximum likelihood estimators along with their asymptotic distributions. Based on the asymptotic distributions, the lower confidence bounds for all indices are also established. Furthermore, a hypothesis testing procedure is formulated to evaluate whether the overall lifetime performance index achieves the specified target level, utilizing the maximum likelihood estimator as the test statistic under a progressive type I interval censored sample. Moreover, a power analysis is carried out, and two numerical examples are presented to demonstrate the practical implementation for the overall lifetime performance index. This research can be applied to the fields of life testing and reliability analysis.補正完畢CH

The Moderate Role of Perceived Surveillance for Value Perception in SOLOMO Services Continuance

The full-fledged Social-Local-Mobile (SoLoMo) services appear recently as the form of app for Android or iOS system which include Facebook, Instagram, LINE, Google maps, etc. However, no study has attempted to understand the continuance intention among SoLoMo services. Besides, SoLoMo services have provided more powerful means of surveillance to track and profile their users, which might arouse negative feeling. In this study, we apply the consumption value theory to explore the value drivers and investigate the moderating effect of users’ perceived surveillance. The results indicate that social value, emotional value, and functional value are significant drivers for continuance intention. Perceived surveillance moderates the relationship of social value and functional value on continuance intention.補正完畢國際Chiayi, TaiwanTW

From purchase to return: How personalized E-commerce recommendations shape consumer behavior

As fashion e-commerce grows, personalized recommendation systems (PRS) are increasingly influential in shaping consumer decisions. Based on the DeLone and McLean IS Success Model, this study investigates how information, system, and service quality-along with immersion-affect perceived value, purchase intention, post-purchase satisfaction, and return intention. Using data from 299 online fashion shoppers and PLS-SEM analysis, the findings highlight perceived value as a key dual mediator: it partially mediates the effects of PRS quality on purchase intention and fully mediates the effect of immersion on purchase intention. While PRS quality and immersion enhance perceived value, only system and service quality directly influence purchase intention. However, neither perceived value nor purchase intention significantly affects post-purchase satisfaction, revealing a gap between expectation and experience. Post-purchase satisfaction is the sole significant predictor of return intention, with dissatisfaction driving returns. These results underscore the need for e-commerce platforms to align recommendation strategies with accurate product representation to reduce returns and build lasting trust.補正完畢GB

Articulating the Archives Use Abilities Required for History Researchers: Comparing the User Expertise in Archives Model and Five Archival Literacy Guidelines

補正完畢TW

Advancing Shared Micromobility in Smart Cities Through Spatial Analysis and Optimization

The rapid growth of urban populations and private vehicle ownership has exacerbated many cities' traffic congestion and environmental degradation. Shared micromobility services, including bike-sharing (BSS) and electric moped-sharing systems (EMSS), have emerged as viable solutions to these issues by providing sustainable and flexible transportation options. However, these systems face significant operational challenges, including spatial and temporal imbalances in supply and demand, as well as suboptimal infrastructure placement. While existing research has explored the influence of built environment factors on shared micromobility usage, most studies rely on global models that assume spatially uniform relationships, often overlooking the impact of spatial heterogeneity. In addition, existing spatial optimization models for locating shared micromobility stations typically evaluate demand coverage based on individual stations, without considering the dual-node nature of user trips. This simplification may result in substantial overestimation of system performance. Moreover, for EMSS, existing research has primarily focused on placing battery swapping stations, with limited attention given to the integration of parking and battery-swapping functions. Such integration is crucial for enhancing both user experience and operational efficiency, yet it remains underexplored in existing research. This dissertation addresses these gaps through three empirical studies on BSS and EMSS in Taipei. The first study examines the impact of built environments on shared micromobility usage using the Multiscale Geographically Weighted Regression model to capture spatial heterogeneity. The second study develops the Flow Termini Coverage Model (FTCM), incorporating origin-destination flow dynamics to improve the siting of bike-sharing stations. By accounting for both trip ends, the FTCM offers a more accurate representation of travel behavior than conventional single-node coverage models. The third study introduces the concept of EMSS hubs, multifunctional facilities that integrate moped rental, return, and battery-swapping, and proposes the EMSS Hub Location Problem (EHLP) model to support strategic hub placement. The EHLP provides a comprehensive framework for EMSS infrastructure planning by incorporating three types of demands. Collectively, these studies advance the shared micromobility field by developing novel spatial analytical methods and optimization models. The findings offer important implications for policy and practice, demonstrating how advanced spatial methods can support more effective and sustainable shared micromobility planning.電子版US