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Missing values is a crucial problem in the area of big data analysis, which hinders data integrity. Various regression methods have been employed for estimating missing values, but they exhibit significant prediction errors. To ensure the integrity of data collected from a wood sensor monitoring system and address the issue of data loss and anomaly, we propose a missing value estimation method based on the random forest regression model. This study focuses on the environmental data, including temperature, relative humidity, and absolute humidity surrounding the wood subjects. We simulate a number of methods on the data for comparison purpose. The experiment results in terms of prediction performance indicate that the random forest regression model algorithm we developed for estimating moisture content's missing values yields favourable outcomes with consistently low estimation errors. © 2024 IEEE.

This study conducts an in-depth verification and analysis of the authenticity of information in WeChat group chats by integrating Latent Dirichlet Allocation (LDA) topic modeling with advanced Natural Language Processing (NLP) techniques such as XLNet and BERT. Leveraging LDA, the thematic structure of group chat content is revealed, and through the integration of NLP technologies like XLNet and BERT, a comprehensive analysis of the information is achieved. Experimental results demonstrate that our developed model performs exceptionally well in identifying the authenticity of information, confirming the effectiveness of this method in the domain of social media information verification. This research not only deepens our understanding of the authenticity of information in WeChat group chats but also provides a more effective tool for social media platforms to detect and prevent the spread of false information. It opens up a new perspective on social media information authentication research and points out future research directions. © 2024 IEEE.

This study explores the complex relationship between wood moisture content and environmental factors, temperature and relative humidity. Utilizing a novel Autoregressive Polynomial Regression Model (APRM), data from sensors placed in reconstituted bamboo and pine planks at various positions were analyzed. The APRM, adept at handling polynomial and interaction terms, revealed a nuanced, non-linear relationship between moisture content and environmental conditions. The research findings underscore significant material-specific differences in response to environmental changes. This study not only contributes to the understanding of wood-environment interactions but also demonstrates the efficacy of APRM in environmental science, providing a foundational approach for future research in this field. © 2024 IEEE.

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.

Due to the uncertainty and inconsistency of measurement data from multiple sensors in the same space, a multi-sensor data fusion algorithm is used to fuse the measurement data of multiple nodes. We propose a multi-Bayesian estimation method for fusing multi-sensor data, and combine Bayesian estimation with ARIMA model to predict the ambient temperature of bamboo and wood building materials. It can utilize the redundancy of data to reduce this uncertainty and improve the reliability of subsequent predictions. © 2023 ACM.

This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.

In the era of big data, it is indispensable to apply data science and technology for big data analysis to solve the big data problems. With the advancement of the big data technologies, we are also facing many problems when dealing with the big data and their studies. It is obvious that big data become "bigger"and "bigger", more complex than before, with a good number of attributes and features in various formats and styles. On the other hand, many data analysis techniques have been proposed for various application domain problems in different purposes. This worsens the situation of choosing an appropriate method for a right problem of right data. In this paper, the author intends to propose an approximation approach toward this problem, through discussing the ways of identification of patterns of the original data, be they of data features or analysis methods. The author attempts to apply the idea to a case of fault detection of a household photovoltaic system. © 2023 IEEE.

As a stochastic optimiser, the firefly algorithm (FA) has been successfully and widely used in the solutions to various optimisation problems. Recent related research shows that the standard FA does not sufficiently balance between exploration and exploitation. Especially in high-dimensional problems, it is easy for the standard FA to fall into the local optimum and lead to premature convergence. To overcome the problems as mentioned above, DMTgFA uses three strategies: dynamic step length setting strategy (DS), non-elite two-way guidance model (TG) and elites dimensional mutation strategy (DM). The dynamic step length setting strategy makes the algorithm convergence speed faster. The non-elite two-way guidance model and the elite dimensional mutation strategy cooperate to solve the balance problem between global search and local search. Experimental results show that DMTgFA has stronger optimisation ability and faster convergence speed than other state-of-the-art FA variants.