Search Results (9)

Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity prediction, and recycling, drawing on a dataset of over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations and evolving research themes in battery-related technologies, capacity degradation factors, and recycling methods. The literature is structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” and (2) “Capacity Prediction and Influencing Factors”. DTM revealed pivotal findings: advancements in lithium-ion and solid-state batteries for higher energy density, improvements in recycling technologies to reduce environmental impact, and the efficacy of machine learning-based models for real-time capacity prediction. Gaps persist in scaling sustainable recycling methods, developing cost-effective manufacturing processes, and creating standards for life cycle impact assessment. Future directions emphasise multidisciplinary research on new battery chemistries, efficient end-of-life management, and policy frameworks that support circular economy practices. This review serves as a resource for stakeholders to address the critical technological and regulatory challenges that will shape the sustainable future of electric vehicles. Full article

Quantitative research on the evolution and transformation of topics in China’s energy policy can enhance the theoretical and methodological framework of policy document analysis. Utilizing dynamic topic modeling (DTM) and social network analysis, this study examined 1872 energy policy documents issued in China between 1980 and 2023, focusing on detecting hot topics and analyzing trend evolution. DTM identified five core topics: State Grid and new energy, comprehensive energy conservation and emission reduction, intelligent building energy management, promotion of energy-saving products and new energy vehicles, and standardization of energy industry management. Temporal analysis of these core topics reveals a shift in policy focus over time, moving from infrastructure development and standardization management to new energy development and modernization of the energy system. The co-occurrence network of thematic terms transitions from an “independent and loose” structure to a “concentrated and balanced” one, with increasing network scale and frequency. The conclusions of this study offer valuable insights for establishing a dynamic monitoring and real-time updating mechanism for energy policies, enhancing the integration and coordination of energy policy topics, and effectively supporting national energy strategies in response to global energy market challenges. Full article

Drawing on data from 133 Chinese New Energy Vehicle (NEV) policy documents from 2007 to 2023, this study utilizes Dynamic Topic Modelling (DTM), social network analysis and a quantitative model to investigate the evolutionary path of policy themes and the coordination effects. The following results were obtained. (1) A thematic cross-sectional analysis identified six core policy themes, namely, coordinated promotion of technology and finance, industry development and safety standardisation, market service and technical support systems, promotion strategy and urban cluster development, industrial capital and safety supervision mechanisms, and policy support and market expansion. The analysis also mapped the distribution of hot spots within these themes. (2) The keyword co-occurrence network of the NEV policy indicated that the network structure evolved from an initial ‘overall dispersion–theme concentration’, comprising 16 policy themes, to an ‘overall stability–theme coordination’, consisting of 14 policy themes. (3) The coordination degrees across the three types of policies exhibited a consistent upward spiral, with the comprehensive coordination index surging from 30 in 2007 to 951 in 2023, underscoring the complementary effects among policy instruments. These conclusions offer valuable insights for government departments to understand NEV development trends and dynamically adjust policy themes accordingly. Full article

The hydrophilic vegetation from reservoir deltas sustains rapid expansions in surface and important increases in vegetal mass against a background of a significant influx of alluvium and nutrients from watercourses. It contributes to reservoir water quality degradation and reservoir silting due to organic residues. In this paper, we propose an evaluation method of two-dimensional and three-dimensional parameters (surfaces and volumes of vegetation), using the combined photogrammetric techniques from the UAS category. Raster and vector data—high-resolution orthophotoplan (2D), point cloud (pseudo-LIDAR) (3D), points that defined the topographic surface (DTM—Digital Terrain Model (3D) and DSM—Digital Surface Model (3D))—were the basis for the realization of grid products (a DTM and DSM, respectively). After the successive completion of the operations within the adopted workflow (data acquisition, processing, post-processing, and their integration into GIS), after the grid analysis, the two proposed variables (topics) of this research, respectively, the surface of vegetation and its volume, resulted. The data acquisition area (deriving grids with a centimeter resolution) under the conditions of some areas being inaccessible using classical topometric or bathymetric means (low depth, the presence of organic mud and aquatic vegetation, etc.) has an important role in the reservoirs’ depth dynamics and reservoir usage. After performing the calculations in the abovementioned direction, we arrived at results of practical and scientific interest: Cut Volume = 196,000.3 m³, Cut 2D Surface Area = 63,549 m², Fill Volume = 16.59998 m³, Fill 2D Surface Area = 879.43 m², Total Volume Between Surfaces = 196,016.9 m³. We specify that this approach does not aim to study the vegetation’s diversity but to determine its dimensional components (surface and volume), whose organic residues participate in mitigating the reservoir functions (water supply, hydropower production, flash flood attenuation capacity, etc.). Full article

Nature-based solutions (NbS) is an approach to solving climate change and social issues based on nature. Despite NbS being widely studied as an effective method to solve social problems, the trends in NbS research have hardly been analyzed. Therefore, this study examined change patterns in NbS-related research topics over time and analyzed the interactions of NbS research and relevant activities in various fields. After reviewing research papers based on the search term ‘nature-based solutions’ on Scopus, and collecting 1567 research papers, we conducted dynamic topic modeling (DTM) and network analysis. The papers were classified into 19 topics via DTM. Water, forest, and urban topics made up the greatest portion of NbS research, while NbS topics in the forest sector showed a steady increase over time. This study also found close connections between NbS studies on forests and other sectors and confirmed that the forest sector can become an integrated means of contributing to climate change responses and other resultant social issues. This study demonstrates that DTM and network analysis are useful tools for understanding the trends in NbS research and finding the linkages between various fields. Full article

Peatlands are wetlands with an accumulation of peats, partially decomposed organisms, under waterlogged and anoxic conditions. Despite peatlands being extensively studied due to their wide distribution and various functions, the trends in peatland research have hardly been analyzed. We performed dynamic topic modeling (DTM) and network analysis to investigate the changes in the global trends in peatland research. Among the searched studies using the keyword ‘peatland’ from ScienceDirect, titles and abstracts from 9541 studies (1995–2022) were used for the analysis. They were classified into 16 topics via DTM (geomorphology, land use and land cover, production, greenhouse gas, habitat, permafrost, management, deposit, fire, soil organic matter, peatland formation, forest, past environmental change, microbe, metal, and hydrology). Among these, the proportion of ‘management’ was the largest and increased the fastest, showing the transition of research trends toward the sustainable management of peatlands under climate change. The keywords used within topics tended to change dynamically when related to a large number of studies and increasing trends. Network analysis among topics suggested that studying peatlands as a response measure to climate change will promote overall peatland research because the greenhouse gases topic had the greatest impact on other topics. Despite increasing research on peatland management under climate change, a gap between academia and policies was found in the field of using peatlands as a response measure to climate change, indicating the necessity for effective policies, research, and technology. This study demonstrates that DTM and network analysis are useful tools for understanding the temporal shift of views on peatlands and finding a gap we need to focus on in the near future. Full article

Chinese generation Z (post-00s) are about to confront career decisions as the first batch of post-00s graduates. However, current career studies rarely take the post-00s, the liveliest group with characteristics of the era, as research subjects to investigate their beliefs, attitudes, values, motivation, career behavior, etc. Existing studies focused on the status quo of post-00s career education without dynamically studying the career development process from college to graduation. This study performed big data analysis, using the dynamic topic model (DTM), combing the golden triangle theory to study the career development of the post-00s in China. We summarized the “connection between individuals and others” as a new dimension and tried to propose a corrected theoretical model of the “golden triangle” that can help the post-00s make sustainable career decisions. Full article

Social unrest events have been an issue of concern to people in various countries. In the past few years, mass unrest events appeared in many countries. Meanwhile, social media has become a distinctive method of spreading event information. It is necessary to construct an effective method to analyze the unrest events through social media platforms. Existing methods mainly target well-labeled data and take relatively little account of the event development. This paper proposes an entity-based integration event detection framework for event extraction and analysis in social media. The framework integrates two modules. The first module utilizes named entity recognition technology based on the bidirectional encoder representation from transformers (BERT) algorithm to extract the event-related entities and topics of social unrest events during social media communication. The second module suggests the K-means clustering method and dynamic topic model (DTM) for dynamic analysis of these entities and topics. As an experimental scenario, the effectiveness of the framework is demonstrated using the Lihkg discussion forum and Twitter from 1 August 2019 to 31 August 2020. In addition, the comparative experiment is performed to reveal the differences between Chinese users on Lihkg and Twitter for comparative social media studies. The experiment results somehow indicate the characteristic of social unrest events that can be found in social media. Full article

Recently, as core technologies leading the fourth industrial revolution, such as the Internet of Things (IoT), 5G, the cloud, and big data, have promoted smart convergence across national socio-economic infrastructures, cyber systems are expanding and becoming complex, and they are not effective in responding to cyber safety risks and threats using security technology solutions limited to a single system. Therefore, we developed cyber security technology that combines machine learning and AI technology to solve complex problems related to cyber safety. In this regard, this study aims to identify technology development trends to prevent the risks and threats of various cyber systems by monitoring major cyber security convergence fields and technologies through the symmetrical thesis and patent analysis. Because thesis information can explain the superiority of technology and patent information can explain the usefulness of a technology, they can be effectively used for analyzing and predicting technology development trends. Therefore, in this study, latent Dirichlet allocation is applied to extract text-document-based technical topics for the symmetrical thesis and patent information to identify security convergence fields and technologies for cyber safety. In addition, it elucidates cyber security convergence fields and technology trends by applying a dynamic topic model and long short-term memory, which are useful for analyzing technological changes and predicting trends. Based on these results, cyber security administrators, system operators, and developers can effectively identify and respond to trends in related technologies to reduce threats, and companies and experts developing cyber security solutions can present a new security approach. Full article