Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles

The emergence of automated vehicles (AVs) is expected to have a huge impact on traffic safety and environmental improvement. In order to promote the sustainable development of AVs, it is urgent to study the public’s acceptance of and willingness-to-pay for automated vehicles and their influencing factors. Based on a questionnaire survey and descriptive research, this paper investigates the public’s general views on AVs. A psychological model considering technical trust (TT), perceived benefit (PB), perceived risk (PR), and perceived ease of use (PU) was constructed to study the factors that influence the public’s acceptance of and willingness-to-pay for AVs. Logistic regression models based on demographic factors such as monthly income (MI) and driving experience (DE) and psychological factors were established to predict end-users’ acceptance and willingness-to-pay. The accuracy of the two models is 93.2% and 87.9%, respectively. Based on the results, the following policies can be put forward to promote the development of AVs: (1) more information to enhance TT; (2) pricing and easy maintenance based on PU; (3) education and training based on TT and PB; and (4) personalized sales based on DE and MI.