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The problem of weight degradation is inevitable in particle filtering algorithms, and the resampling approach is an important method to reduce the particle degradation phenomenon. To solve the problem of particle diversity loss in existing resampling methods, this paper proposes a new digital twin-based resampling algorithm to improve the accuracy of particle filter estimation based on the traditional resampling algorithm. The digital twin-based resampling algorithm continuously improves the resampling process through the data interaction between the data model and the physical model, and realizes the real-time correction capability of particle weights that traditional resampling methods do not have. The new algorithm calibration rules are divided according to the size of particle weights, with particles of large weights retained and particles of small weights selectively processed. Compared with the traditional resampling algorithm, the new resampling algorithm reduces the mean square error of the particle filter estimation results by 16.62 % , 16.49 % , and 13.86 % , and improves the computing speed by 7.67 % , 2.25 % , and 7.54 % , respectively, in the simulation experiments of nonlinear systems with univariate unsteady state growth model. The algorithm is experimentally demonstrated to accurately track a person in motion in an indoor building in a non-rigid target tracking application, which illustrates the effectiveness and reasonableness of the digital twin-based resampling algorithm. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

The Metaverse is currently becoming a massive technology platform and is considered to be the next significant development in global technology and business landscapes. The Metaverse is a digital platform that people can enter or transport virtual items with a device as a medium, implemented as virtual but very similar to the real world through the concept of the digital twin as used in smart cities. The Metaverse is currently in its infancy but is developing gradually. However, the potential threat of crime in this new world already has become a concern. As the Metaverse becomes more similar to the real world, the events that occur in it can affect the real world as well. Therefore, digital forensic research on the Metaverse is necessary to investigate crimes occurring in the Metaverse, such as money laundering, virtual burglaries, virtual theft, and fraud. In this paper, we present the conceptual architecture of the Metaverse and discuss what are termed metacrimes, crimes that may occur within the Metaverse, and address the need for research on digital forensic investigations of the Metaverse. Furthermore, we propose a Metaverse forensic framework for the first time; it consists of four phases based on the digital forensic guidance of NIST. These are data collection, examination and retrieval of evidence, analysis, and reporting. In the framework, we provide three different procedures in the data collection phase and examination phase by dividing them into three categories: user, service, and the Metaverse platform. Finally, we discuss the challenge of digital forensic investigations in the Metaverse from three standpoints: data possession, anti-forensics, and privacy. [ABSTRACT FROM AUTHOR]

In this article, a digital twin approach is proposed for modeling a pharmaceutical drying process using machine learning techniques, driven by data from different sensors captured in-line. The current difficulty with the drying process is mainly due to the manual operator control for choosing the end-point for terminating the drying step. This results in significant variability, depending on which human operator is supervising, and due to the multi-tasking nature of the job, generally allows a longer processing time to be sure the material is completely dry. The objective is to automate the end-point identification, thus optimizing the processing duration and therefore the overall energy consumption of the process. The point at which the drying is complete is indicated by the temperature difference between the ingoing and outgoing air flow. However, the stochastic nature of the process makes the data modeling a challenge. Firstly, a wide selection of supervised statistical and machine learning algorithms was benchmarked to find the one (CatBoost) which gave the best performance with the data. Next, the set of hyper-parameters was found for CatBoost which gave the optimum performance. This gave a best performance of 0.788 (R2) fitting of the drying end-point prediction with the real values, for a large number of batches (over 700 K records). This is considered a good result taking into account the high residual of data models for these data and the stochastic nature of the process. The approach has been deployed in a real setting digital twin to control the drying process cutoff in the production plant. The results show the viability of the approach for modeling the process and automatically identifying the optimum end-point for the drying process, thus achieving significant energy savings which have been quantified as approximately 3.7 MWh per year for the pharmaceutical company. [ABSTRACT FROM AUTHOR]