The high-gravity carbonation process for CO2 mineralization and product utilization as a green cement was evaluated using field operation data from the steelmaking industry. The effect of key operating factors, including rotation speed, liquid-to-solid ratio, gas flow rate, and slurry flow rate, on CO2 removal efficiency was studied. The results indicated that a maximal CO2 removal of 97.3% was achieved using basic oxygen furnace slag at a gas-to-slurry ratio of 40, with a capture capacity of 165 kg of CO2 per day. In addition, the product with different carbonation conversions (i.e., 0%, 17%, and 48%) was used as supplementary cementitious materials in blended cement at various substitution ratios (i.e., 0%, 10%, and 20%). The performance of the blended cement mortar, including physicochemical properties, morphology, mineralogy, compressive strength, and autoclave soundness, was evaluated. The results indicated that the mortar with a high carbonation conversion of slag exhibited a higher mechanical strength in the early stage than pure portland cement mortar, suggesting its suitability for use as a high early strength cement. It also possessed superior soundness compared to the mortar using fresh slag. Furthermore, the optimal operating conditions of the high-gravity carbonation were determined by response surface models for maximizing CO2 removal efficiency and minimizing energy consumption.

Well-being has rarely been used to discuss the post-adoption behavior of information technology users. Currently, satisfaction is the primary predictor of user behavior in IT post-adoption research. We live in an age when social media, mobile devices, the Internet, and other information technologies have virtually fused with our lifestyles. In discussing post-adoption behavior, focusing only on satisfaction might no longer be satisfactory. We should consider other constructs that might capture additional post-adoption factors, such as the concept of affect. In this study, we examined the influence of well-being on continuance intention and on loyalty. We compared well-being’s impact with that of satisfaction. A survey of 297 college students supplied the data that was entered into a structural equation model on social network site usage. The results showed strong support for satisfaction and well-being as influential factors for continuance intention and loyalty. Moreover, relative to satisfaction, we found well-being to have a greater impact on continuance intention and loyalty.

Dangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers' dangerous driving degree based on a fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), a variant of LSTM recurrent neural networks, which can predict drivers' dangerous driving behaviors and risk degree. We elucidate how a macroscopic fuzzy inference dangerous driving behavior system is designed based on various driving behavior factors and the neuron architecture of the fuzzy-macro LSTM network. We collect real driving behavior data of drivers on the road and conduct a series of experimental analyses. Compared with five other commonly used time-series forecasting neural network models, our fuzzy-macro LSTM model performs best in terms of prediction error. Experimental results verify the effectiveness of the proposed method for macroanalysis and prediction of dangerous driving behavior.INDEX TERMS Data analysis, time series, fuzzy rules, driving behavior, prediction, fuzzy neural network.

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This article describes how personality traits have rarely been used to discuss the emerging topic of information systems continued usage. Confirmation and perceived usefulness constructs are the most salient factor in post-adoption researches and represents the essence of IS continuance model. This article proposes to connect personality traits to IS continued usage through exploring the relation of five-factor models to confirmation and perceived usefulness using appraisal theory of emotion as a lens. The results would be useful to IS designers, administrators or policy makers. A survey of 293 college students was examined using structural equation modeling analysis. The initial results showed some promising support for the effect of personality traits on the factor of confirmation but not on perceived usefulness.