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Grounded in signaling theory, this study explores the influence of user-generated content (UGC) within online healthcare communities on patient purchasing behavior, with the overarching goal of advancing the development of online medical consultation services and contributing to the sustainable evolution of the online healthcare community. Leveraging publicly available data from the "Haodf.com", we construct an empirical model of online medical consultation purchases, integrating principles from signaling theory and trust theory. Our analysis scrutinizes the effects of various forms of UGC on patient purchasing behavior, alongside the moderating influence of associated signals. The results demonstrate that knowledge-sharing articles authored by doctors and patient ratings positively impact consultation service purchases, whereas public displays of doctors' past consultation records impede such transactions. Furthermore, external signals were found to moderate the relationship between UGC and consultation service purchases. The implications of these findings offer actionable insights for stakeholders invested in online healthcare communities. [ABSTRACT FROM AUTHOR]

Ancient canal towns in Jiangnan have become important tourist destinations due to their unique water town scenery and historical value. Creating a unique tourist image boosts these ancient towns' competitive edge in tourism and contributes significantly to their preservation and growth. The vast amount of data from social media has become an essential source for uncovering tourism perceptions. This study takes two ancient towns in Shanghai, Zhaojialou and Fengjing, as case study areas. In order to explore and compare the destination images of the towns, in the perception of tourists and in official publicity, machine learning approaches like word embedding and K-means clustering are adopted to process the comments on Sina Weibo and publicity articles, and statistical analysis and correspondence analysis are used for comparative study. The results reveal the following: (1) Using k-means clustering, destination perceptions were categorized into 16 groups spanning three dimensions, "space, activity, and sentiment", with the most keywords in "activity" and the fewest in "sentiment". (2) The perception of tourists often differs significantly from the official promotional materials. Official promotions place a strong emphasis on shaping the image of ancient towns based on their historical resources, presenting a more general picture. Tourist perception, which is fragmented, highlights emerging elements and the experiential activities, along with the corresponding emotional experiences. (3) Comparing the two towns, Fengjing Ancient Town stands out, with more diverse tourist perceptions and richer emotional experiences. This underscores the effectiveness of tourism activities that use space as a media to evoke emotions, surpassing the impact of the spaces themselves. [ABSTRACT FROM AUTHOR]

According to the natural language perspective, UGC has been significantly used for the screening of key nodes in knowledge discovery and strategic investment. This article presents a new research framework that is proposed for the decomposition of UGC knowledge feature extraction into topic recognition and language analysis, mainly. For visual analysis of associated topics, the LDAvis approach is utilized. Then, risk features of UGC knowledge are assigned according to language attribution. Based on previous studies, the risk attribute lexicon is further updated by judging semantic distance through word vectors. This research uses platform data and individual stock data as samples for subject recognition and knowledge feature extraction. A regression model is constructed based on the panel data after natural language processing to verify the feedback effect of the market at strategic risk measurement. It can be found from the conclusion that the change in market behavior is regular and correlates with the change in the UGC risk degree of individual stocks. The purpose of this paper is to examine the value of UGC in investment decision-making from the perspective of knowledge discovery. The research content can provide a reference for data mining, fintech, strategic risk monitoring, and other related works. [ABSTRACT FROM AUTHOR]

We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned into microzones, given the smallest areas in which a census of the urban system is taken in terms of resident population, type and number of buildings and properties, and industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert, which characterize the type of urban green area and the relationship with the residents and buildings, are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called urban green contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the urban green context. We test our framework on the municipality of Pozzuoli, Italy, comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the urban study area based on his knowledge of it. [ABSTRACT FROM AUTHOR]

Even though destination image is an important expression of discovering the local landscapes and place significance, the construction and measurement of destination image neglect the place component. This research explores the image of health destinations, as well as its representation mechanism, combining the triadic structure of tourism image proposed by Marine-Roig et al. with the theory of discourse power put forward by Michel Foucault, taking Bama, Guangxi as a case. In addition, this paper uses the IPA matrix to visually unveil the pronounced gap between the projected image by Destination Management Organizations (DMOs) and the perceived image of tourists and suggests strategies that DMOs should adopt in the different dimensions. [ABSTRACT FROM AUTHOR]

Recently, user-generated content (UGC) has been in the limelight. This study investigates why Internet users share their own UGC and reveals how the motives behind UGC sharing affect UGC sharing intentions both quantitatively and qualitatively. Based on motivations established in existing online communication literature, theoretical UGC motives are identified. Using online surveys administered to 300 users in South Korea, factor analysis is performed to identify empirical UGC sharing motives, and regression analyses shows how UGC sharing motives affect UGC sharing intention in terms of quality and quantity. A total of 10 theoretical UGC motives are consequently factorized into five motives. It is revealed that three motives—self-creation, self-expression, and reward—are related to individual purposes. Users get enjoyment from creating content, they want to be recognized by others, and further expect to be rewarded socially and economically. The other two motives, community commitment and social relationships, are related to social purposes. Users share UGC as a means of communication, desire feedback from others, and want to feel a sense of belonging within certain communities. All of these motives positively affect UGC sharing intention. This is the first study to empirically clarify UGC sharing motives. In addition, this study reveals UGC-centric self-creation and self-expression motives, which have not been the focus of previous online communication studies. Finally, the research results suggest how UGC site managers can adopt practical strategies related to UGC management. [ABSTRACT FROM AUTHOR]

This paper investigates the maximum photovoltaic (PV) penetration limits on both overhead lines and underground cables medium voltage radial distribution system. The maximum PV penetration limit is estimated considering both bus voltage limit (1.05 p.u.) and feeder current ampacity (1 p.u.). All factors affect the max PV penetration limit are investigated in detail. Substation voltage, load percentage, load power factor, and power system frequency (50 Hz or 60 Hz) are analyzed. The maximum PV penetration limit associated with overhead lines is usually higher than the value associated with the underground cables for high substation voltage (substation voltage = 1.05 and 1.04 p.u.). The maximum PV penetration limit decreases dramatically with low load percentage for both feeder types but still the overhead lines accept PV plant higher than the underground cables. Conversely, the maximum PV penetration increases with load power factor decreasing and the overhead lines capability for hosting PV plant remains higher than the capability of the underground cables. This paper proved that the capability of the 60-Hz power system for hosting the PV plant is higher than the capability of 50 Hz power system. MATLAB software has been employed to obtain all results in this paper. The Newton-Raphson iterative method was the used method to solve the power flow of the investigated systems. [ABSTRACT FROM AUTHOR]