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In order to address global warming, most countries in the world have established carbon neutral targets and are continuously taking action to achieve carbon neutrality. The building sector accounts for 36% of end-use energy consumption and 37% of energy-related CO2 emissions globally, so carbon mitigation in building sector is considered to be the most critical step in completing the "last mile" of global carbon neutrality. Low-carbon buildings and communities are the foundation for achieving low-carbon cities and the key transition to reach the goal of carbon neutrality. Therefore, this review aimed to: (a) provide a comprehensive review of countries' policies on low-carbon buildings and communities and a theoretical basis for improving the corresponding laws and regulations; (b) investigate low-carbon technologies related to building and community construction and operation, as well as identify the current technology gaps; (c) provide a comprehensive overview of low-carbon buildings and communities assessment systems to analyze and evaluate the economic, technical, environmental and social benefits of current building and community energy systems; and (d) identify enablers and barriers in low-carbon buildings and communities to provide direction for future research. The results of this paper can provide comprehensive insights in to further achieving low-carbon buildings and communities. [ABSTRACT FROM AUTHOR]

The development of emerging technologies not only has recently affected current industrial production but also has generated promising manufacturing opportunities that impact significantly on social and economic factors. Exploring upcoming renovation tendencies of technologies prematurely is essential for governments, research and development institutes, and industrial companies in managing strategies to achieve dominant advantages in business competitiveness. Additionally, the prospective changes, the scientific research directions, and the focus of technologies are crucial factors in predicting promising technologies. On the other hand, Industry 4.0 revolutionizes standards and models by accompanying significant technology developments in numerous sectors, including the sector of Smart energy. Moreover, asset performance management is always a prominent topic that has attained prevalence over the last decade because numerous challenges force all industrial companies to optimize their asset usability. However, to the best of our knowledge, no study reported an analysis of technology trends of asset performance management in the Smart energy sector by using proper data mining methods. Hence, this paper aims to fill in this gap and provide an analysis of technology trends of asset performance management in the Smart energy sector by structuring and exploring research subjects, considering problems, and solving methods with numerous experiments on scientific papers and patent data. [ABSTRACT FROM AUTHOR]

Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology. [ABSTRACT FROM AUTHOR]

In order to address global warming, most countries in the world have established carbon neutral targets and are continuously taking action to achieve carbon neutrality. The building sector accounts for 36% of end-use energy consumption and 37% of energy-related CO2 emissions globally, so carbon mitigation in building sector is considered to be the most critical step in completing the “last mile” of global carbon neutrality. Low-carbon buildings and communities are the foundation for achieving low-carbon cities and the key transition to reach the goal of carbon neutrality. Therefore, this review aimed to: (a) provide a comprehensive review of countries’ policies on low-carbon buildings and communities and a theoretical basis for improving the corresponding laws and regulations; (b) investigate low-carbon technologies related to building and community construction and operation, as well as identify the current technology gaps; (c) provide a comprehensive overview of low-carbon buildings and communities assessment systems to analyze and evaluate the economic, technical, environmental and social benefits of current building and community energy systems; and (d) identify enablers and barriers in low-carbon buildings and communities to provide direction for future research. The results of this paper can provide comprehensive insights in to further achieving low-carbon buildings and communities.

Cyber security is one of the topics that gain importance today. It is necessary to determine the basic components, basic dynamics, and main actors of the Cyber security issue, which is obvious that it will have an impact in many areas from social, social, economic, environmental, and political aspects, as a hot research topic. When the subject literature is examined, it has become a trend-forming research subject followed by institutions and organizations that produce R&D policy, starting from the level of governments. In this study, cybersecurity research is examined in the context of 5 basic cyber security functions specified in the cyber security standard (CSF) defined by the National Institute of Standards and Technology (NIST). It is aimed to determine the research topics emerging in the international literature, to identify the most productive countries, to determine the rankings created by these countries according to their functions, to determine the research clusters and research focuses. In the study, several quantitative methods were used, especially scientometrics, social network analysis (SNA) line theory and structural hole analysis. Statistical tests (Log-Likelihood Ratio) were used to reveal the prominent areas, and the text mining method was also used. we first defined a workflow according to the "Identify", "Protect", "Detect", "Respond" and "Recover" setups, and conducted an online search on the Web of Science (WoS) to access the information on the publications on the relevant topics It is seen that actors, institutions and research create different densities according to various geographical regions in the 5 functions defined within the framework of cybersecurity. It is possible to say that infiltration detection, the internet of things and the concept of artificial intelligence are among the other prominent research focuses, although it is seen that smart grids are among the most prominent research topics. In the first clustering analysis we performed, we can say that 17 clusters are formed, especially when we look under the definition function. The largest of these clusters has 32 data points, so-called "decision making models". [ABSTRACT FROM AUTHOR]

Understanding the link between research done at universities and the implication for industrial applications is a recurrent topic in innovation. Non-Patent Literature (NPL) citations found in patents have been seen as a tool to examine this relationship. The study shows that for certain technology sectors NPL citation plays no role as universities do not research in this area or innovation does not require scientific knowledge. For those sectors in which NPL citation plays a more important role, like patents of the Fourth Industrial Revolution, bibliographic data of cited NPL may be used to understand the geographical and institutional origin of these documents to have a clearer idea of knowledge flows between universities, research institutions and firms. NPL is shown to be mostly developed by universities and research institutions whereas firms are normally behind patents, reinforcing patents as an indicator of industrial knowledge development and NPL as an indicator for academic knowledge development. Technological landscapes should consider both to assess possible links and similarity among both. In the case of 3D printing both NPL and patent literature (PL) developments show to share a common long run trend, evidenced by a cointegration relationship, showing that for this specific technology both innovative actors: universities and firms play an important role in innovation development. [ABSTRACT FROM AUTHOR]

The development of emerging technologies not only has recently affected current industrial production but also has generated promising manufacturing opportunities that impact significantly on social and economic factors. Exploring upcoming renovation tendencies of technologies prematurely is essential for governments, research and development institutes, and industrial companies in managing strategies to achieve dominant advantages in business competitiveness. Additionally, the prospective changes, the scientific research directions, and the focus of technologies are crucial factors in predicting promising technologies. On the other hand, Industry 4.0 revolutionizes standards and models by accompanying significant technology developments in numerous sectors, including the sector of Smart energy. Moreover, asset performance management is always a prominent topic that has attained prevalence over the last decade because numerous challenges force all industrial companies to optimize their asset usability. However, to the best of our knowledge, no study reported an analysis of technology trends of asset performance management in the Smart energy sector by using proper data mining methods. Hence, this paper aims to fill in this gap and provide an analysis of technology trends of asset performance management in the Smart energy sector by structuring and exploring research subjects, considering problems, and solving methods with numerous experiments on scientific papers and patent data.

Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology.

As a key supporting technology in the fields of life sciences and medicine, high-throughput sequencing has developed rapidly and become increasingly mature. The workflow of this technology can be divided into nucleic acid extraction, library construction, sequencing, and data analysis. Among these, library construction is a pivotal step that bridges the previous and subsequent stages. The effectiveness of library construction is contingent on the quality of upstream samples and also impacts the data analysis following sequence data output. The selection and implementation of library construction quality control techniques are crucial for enhancing the reliability of results and reducing errors in sequencing data. This review provides an in-depth discussion of library construction quality control techniques, summarizing and evaluating their principles, advantages and disadvantages, and applicability. It also discusses the selection of relevant technologies in practical application scenarios. The aim is to offer theoretical foundations and references for researchers, disease prevention and control personnel, and others when choosing library quality control techniques, thereby promoting the quality and efficiency of high-throughput sequencing work.

Identifying technologies is a key element for mapping a domain and its evolution. It allows managers and decision makers to anticipate trends for an accurate forecast and effective foresight. Researchers and practitioners are taking advantage of the rapid growth of the publicly accessible sources to map technological domains. Among these sources, patents are the widest technical open access database used in the literature and in practice. Nowadays, Natural Language Processing (NLP) techniques enable new methods for the analysis of patent texts. Among these techniques, in this paper we explore the use of Named Entity Recognition (NER) with the purpose to identify the technologies mentioned in patents' text. We compare three different NER methods, gazetteer-based, rule-based and deep learning-based (e.g. BERT), measuring their performances in terms of precision, recall and computational time. We test the approaches on 1600 patents from four assorted IPC classes as case studies. Our NER systems collected over 4500 fine-grained technologies, achieving the best results thanks to the combination of the three methodologies. The proposed method overcomes the literature thanks to the ability to filter generic technological terms. Our study delineates a valid technology identification tool that can be integrated in any text analysis pipeline to support academics and companies in investigating a technological domain. • Leveraging patents as a valuable data source for technology identification • A text mining method enabling the technological Named Entity Recognition (NER) • 4500 fine-grained technologies collected over 1600 patents • An approach to support accurate forecasting and effective foresight [ABSTRACT FROM AUTHOR]