A Resource-Based Model of IT Usage in Shanghai Higher Education Institutions

On the basis of resource-based view, this paper analyzes the impacts of IT resource on different levels of IT usage in Shanghai higher education institutions. By analyzing the survey data from 40 Shanghai institutions, the study contributes several insights to China-context IT usage research and practice in higher education system. First of all, this study sheds lights on the impacts of IT resource on deep IT usage in Shanghai higher education system. Second, the findings suggest that organizational support has significant positive impact on higher education institutions’ managerial IT usage. The study is the first few attempts to explore the process model of IT usage in China higher education institutions

Examining Drivers and Impacts of Informatization in Shanghai Manufacturing Firms

With careful theoretical development and empirical data examination, this paper investigates several key factors that influence the IT usage in Shanghai firms: technology resource, human resource and environment resource. On the basis of the resource-based view and the process model, the study imports government regulation policies, as well as e-government actions, as environmental resource to affect firms’ IT usage. By surveying 398 manufacturing firms in Shanghai and statistically analyzing the field data using structural equation modeling technique, the study contributes several insights to the IT usage in Chinese firms. First of all, this study sheds lights on the value creation process of firms’ informatization in Shanghai manufacturing industry and validates the route from IT investment to value realization. Second, the findings suggest that government promotion policies have significant impacts on manufacturing firms’ technology infrastructure and IT management decision. However, there is no evidence showing the government impact on firms’ IT usage level

Scientific Knowledge Communication in Online Q&A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contribution

With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before. However, those contributing high-quality professional scientific knowledge are confronted with the challenge of making their contributions popular, since non expert readers may not recognize the importance of their contributions given the massive amount of information available online. In this study, we show that non expert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts. However, we discovered that a salient discrepancy exists between the content non experts favor and the content they perceive as professional. In line with studies that have suggested that writing techniques play an important role in how expert content is received by lay persons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities. Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism. Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context. Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use. The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communitie

W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for multi-variable weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We pre-train W-MAE using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours and using only two years of data. Under the same training data conditions, we compare W-MAE with FourCastNet, and W-MAE outperforms FourCastNet in precipitation forecasting. In the setting where the training data is far less than that of FourCastNet, our model still performs much better in precipitation prediction (0.80 vs. 0.98). Additionally, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting (i.e., forecasting time ranges from 6 hours to one week), and the longer the prediction time, the more evident the performance advantage of W-MAE, further proving its robustness

Optimal CHP Planning in Integrated Energy Systems considering Use-of-System Charges

This paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges