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In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air quality. Accurate and efficient ozone prediction is of great significance to the prevention and control of ozone pollution. The air quality monitoring network provides multisource pollutant concentration monitoring data for ozone prediction, but ozone prediction based on multisource monitoring data still faces the challenges of each station's series of data. Aiming at the problems of low prediction accuracy and low computational efficiency in traditional atmospheric ozone concentration prediction, ozone concentration prediction using dual series decomposition was proposed by variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory (LSTM). First, the historical data series of Nanjing air quality monitoring stations is decomposed by VMD, and then the EEMD algorithm is applied to the residual of VMD to obtain several characteristic intrinsic mode function (IMF) components; each characteristic IMF component is trained by LSTM to obtain the prediction result of each component, and then the final result can be obtained by linear superposition. The proposed method achieved the best results with R2 = 99%, MSE = 5.38, MAE = 4.54, and MAPE = 3.12. Because LSTM has strong adaptive learning ability and good memory function, it has the learning advantage of long-term memory for long-term data, and the prediction results are more accurate. According to the data, the proposed method is superior to the baseline models in terms of statistical metrics. As a result, the proposed hybrid method can serve as a reliable model for ozone forecasting. [ABSTRACT FROM AUTHOR]

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision-motion correlation. Network-based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning-based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM's output weights. Experimental results conducted on a digital twin operating platform for 4-DOF robot with an eye-in-hand configuration demonstrate better performance than classical visual servoing and traditional ELM-based visual servoing in various cases. [ABSTRACT FROM AUTHOR]

The world has been going through the global crisis of the coronavirus (COVID-19). It is a challenging situation for every country to tackle its healthcare system. COVID-19 spreads through physical contact with COVID-positive patients and causes potential damage to the country's health and economy system. Therefore, to overcome the chance of spreading the disease, the only preventive measure is to maintain social distancing. In this vulnerable situation, virtual resources have been utilized in order to maintain social distance, i.e., the telehealth system has been proposed and developed to access healthcare services remotely and manage people's health conditions. The telehealth system could become a regular part of our healthcare system, and during any calamity or natural disaster, it could be used as an emergency response to deal with the catastrophe. For this purpose, we proposed a conceptual telehealth framework in response to COVID-19. We focused on identifying critical issues concerning the use of telehealth in healthcare setups. Furthermore, the factors influencing the implementation of the telehealth system have been explored in detail. The proposed telehealth system utilizes artificial intelligence and data science to regulate and maintain the system efficiently. Before implementing the telehealth system, it is required that prearrangements be made, such as appropriate funding measures, the skills to know technological usage, training sessions, and staff endorsement. The barriers and influencing factors provided in this article can be helpful for future developments in telehealth systems and for making fruitful progress in fighting pandemics like COVID-19. At the same time, the same approach can be used to save the lives of many frontline workers. [ABSTRACT FROM AUTHOR]

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that "Artificial Intelligence" and "Information Storage and Retrieval", "Natural Language Processing and Information Storage and Retrieval", and "Human-Computer Interface" and "Database Applications" were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. [ABSTRACT FROM AUTHOR]