In the post-COVID-19 era, virology-related research, which not only depends on the governments as its main source of funding but also requires international and interdisciplinary collaborations, is recognized as an essential defense for sustainability. Few published studies have examined the trend, but only for certain viruses before the mid-2010s. Moreover, it is challenging to define generally accepted virology-related research fields due to its broad spectrum. Thus, it is time that we confront the unprecedented pandemic to understand the status of nationally supported projects in developed nations to establish international collaborative research strategies from an interdisciplinary perspective. In this study, 32,365 national-funded projects were collected from the US, EU, and Japan and assigned to five scientific fields to conduct a cluster analysis. Then, an expert-based approach was utilized to define an individual cluster. Moreover, a comparative analysis between nations was carried out to determine if there was a competitive edge for collaboration. As a result, a framework for virology-related research areas was constructed to provide the status quo and differences between nations’ research capabilities, thereby eliciting practical global research and development (R&D) cooperation to achieve a common agenda and a direction for goals in the post-COVID-19 era. These findings have implications for viral response R&D, policy, and practice for future pandemics. A systematic approach based on scientific evidence and an R&D collaboration strategy between industry and academia is essential to resolve the interdisciplinary barriers between countries and promote sustainable virus R&D collaboration.

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

Many countries define policies to manage oceans and coastal areas in order to utilize marine ecosystems strategically. When we reviewed the strategies and policies of various countries in relation to ocean sustainability, we found that biodiversity preservation is a key issue for policies related to sustainable marine development. We investigated the research trends and collaboration status of China, Japan and South Korea regarding marine biodiversity through the analysis of scientific articles using bibliometric analysis. The results showed that Japan collaborated the most with other countries compared to China and South Korea. All three countries collaborated with the Organization for Economic Cooperation and Development (OECD) and Association of Southeast Asian Nations (ASEAN) countries frequently. South Korea showed the strongest inter-collaboration amongst China, Japan and South Korea. Microorganism research is a common research topic in China, Japan and South Korea. Each country demonstrated its own prominent research area, such as local region research in China, deep-sea research in Japan and aquaculture research in South Korea.

Infectious diseases have been continuously and increasingly threatening human health and welfare due to a variety of factors such as globalisation, environmental, demographic changes, and emerging pathogens. In order to establish an interdisciplinary approach for coordinating R&D via funding, it is imperative to discover research trends in the field. In this paper, we apply machine learning methodologies and network analyses to understand how the European Union (EU) and the United States (US) have invested their funding in infectious diseases research utilising an interdisciplinary approach. The purpose of this paper is to use public R&D project data as data and to grasp the research trends of epidemic diseases in the US and EU through scientometric analysis.

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.