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The purpose of this paper is to develop a transform method and a deep learning model to identify the inner surface shape based on the measurement temperature at the outer boundary of the pipe.

The training process is assisted by the finite element method (FEM) simulation which solves the direct problem for the data preparation. To avoid re-meshing the domain when the inner surface shape varies, a new transform method is proposed to transform the shape identification problem into the effective thermal conductivity identification problem. The deep learning model is established to set up the relationship between the measurement temperature and the effective thermal conductivity. Then the unknown geometry shape is acquired by the mapping between the inner shape and the effective thermal conductivity through the inverse transform method.

The new method is successfully applied to identify the internal boundary of a pipe with eccentric circle, ellipse and nephroid inner geometries. The results show that as the measurement points increased and the measurement error decreased, the results became more accurate. The position of the measurement point and mesh density of the FEM model have less effect on the results.

The deep learning model and the transform method are developed to identify the pipe inner surface shape. There is no need to re-mesh the domain during the computation progress. The results show that the proposed method is a fast and an accurate tool for identifying the pipe inner surface.

This paper aims to objectively present the research dynamics of China in the field of information behavior and its development trends. Firstly, it incorporates China’s research in the field of information behavior into the global research network of information behavior, analyzing the changes in the status of Chinese scholars and their research institutions in the global research network from 1991 to 2022, the trends in publication volume and the cooperation relationships with other countries. Then, it conducts a detailed analysis of China’s research categories, groups, theoretical models and hot topics in different information contexts in the past five years (2018–2022).

The study retrieved research literature related to information behavior in China from 1991 to 2022 in the Web of Science database. It then utilized a national/institutional cooperation network map to analyze the changes in the status of Chinese scholars/institutions in the global research network during this period, publication volume trends and cooperation relationships with other countries. Furthermore, it employed keyword co-occurrence network maps to analyze the key categories, groups, theories and models of China’s research in different information contexts in the past five years. Based on this, it used keyword clustering network maps to analyze the hot topics of China’s research in different information contexts in the past five years.

(1) China’s research in the field of information behavior started relatively late, but the volume of publications has grown rapidly since 2004, currently ranking second globally in cumulative publication quantity. However, the influence of the literature published by China is limited, and there is a lack of research institutions with global influence. (2) In the last five years, China has conducted extensive research in various information contexts. Among these, most research was conducted in work contexts, followed by healthcare contexts, especially studies related to epidemics. (3) Current research on information behavior in China is characterized by expanded and refined research groups, diversified research categories, continuous expansion and enrichment of research contexts, increased interdisciplinary nature of research and continuous innovation in research methods and theoretical models.

This study, utilizing a scientific knowledge map, elucidates China’s position in global information behavior research, with a specific emphasis on analyzing China’s research hot topics and trends in this field over the past five years. It aims to provide valuable resources for scholars interested in understanding the status of information behavior research in China and to offer some guidance for scholars currently or intending to engage in information behavior research.

The purpose of this paper is twofold: the authors initially survey a sample of literature published after the Great Recession that address macroeconomic and commercial real estate forecasting methods related to the Great Recession and compare significant lessons learned, or lack thereof. The authors then seek to identify new models to improve the predictability of commercial real estate early warning signals regarding cyclical turning points which result in negative appreciation rates.

The authors develop a probit model to estimate quarterly probabilities of negative office appreciation returns using an alternative methodology to Tsolaco et al. (2014). The authors’ alternative method incorporates generally publicly available macroeconomic and real estate variables such as gross domestic product, office-related employment sectors, cap rate spreads, and commercial mortgage flow of funds into a probit model in order to estimate the probability of future quarterly negative office appreciation rates.

The authors’ models demonstrate the predictive power of macroeconomic variables typically associated with office demand. The probit model specification shows probabilities of negative office appreciations rates greater than 50 percent either as the quarterly office returns become negative, or in some cases several quarters before office returns become negative, for both the Great Recession and the recession occurring in the early 1990s. The models fail to show probabilities greater than 50 percent of negative office returns until after they occur for the recession in 2001. While this indicates need for further improvement in early warning models, the models do predict the more severe periods of negative office returns in advance, indicating the findings useful to real estate investors to monitor the changes in economic and real estate data identified as statistically significant in the results.

The Great Recession is a unique laboratory of significant contractions, recessions, and recoveries that challenge pre-recessionary real estate cycle models. The models provide guidance on which historical economic indicators are important to track, and gives a framework with which to calculate the probability that office prices are likely to decline. Because the models use macroeconomic indicators that are publicly available from at least one quarter in the past, the models or variations of them may provide real estate professionals with some indication of an impending decrease in office prices, even if that indication comes only one quarter in advance. Armed with this information, property owners, investors, and brokers can make more informed decisions on whether to buy or sell, and how sensitive their real estate transactions may be to timing.

The authors introduce several new models that examine the ability of historical macroeconomic indicators to provide early warning signals and identify turning points in real estate valuations, specifically negative office appreciation rates caused by the Great Recession. Using data from at least one quarter in the past, all the data in the models are publicly available (excluding National Council of Real Estate Investment Fiduciaries data) at the observed return quarter being predicted, which gives practitioners rational insights that can provide at least one source of guidance about the likelihood of an impending decrease in office prices.