Chinese Academy Of Sciences Research Articles

A bibliometric analysis of the literature on crop yield prediction: insights from previous findings and prospects for future research.

This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.

Conservativeness Study on the Seismic Analysis Method for Research Reactor Plant Structure Based on TMSR-LF1

The seismic performance analysis of research reactor plants is crucial for ensuring the safety of the entire reactor system. This paper analyzes the plant structure seismic performance of the 2WMt Thorium Molten Salt Reactor-Liquid Fuel 1 (TMSR-LF1) at the Shanghai Institute of Applied Physics, Chinese Academy of Sciences under the action of frequent earthquakes, fortification earthquakes, and rare earthquakes was analyzed by finite element software PKPM based on the seismic design method of civil code +1°. On this basis, a comparison was made between the seismic responses of structural pushover analysis and elastoplastic time history analysis under the action of rare earthquakes, and the conservatism of these two commonly used methods for elastoplastic analysis was systematically analyzed. The results indicate that the TMSR-LF1 plant structure exhibits well bearing and deformation capacity. It meets the seismic design goal of “no damage under small earthquakes,” “no unrepairable damage under medium earthquakes,” and “no collapse under large earthquakes (using static pushover analysis),” and the critical regions are entirely within the elastic range. Furthermore, the analysis reveals that the elastoplastic time history analysis method produced a significantly higher seismic response than the pushover analysis method, which indicates the conservativeness of the time history method. Therefore, it is recommended to use the elastoplastic time history analysis method to evaluate the seismic performance of research reactor plants under rare earthquake actions. The research in this paper provides important references for the seismic performance analysis of other Class II research reactor plants.

Visualising Current Research Trends in Class Imbalance using Clustering Approach: A Bibliometrics Analysis

In recent years, extensive research has been carried out on big data class imbalance problems using the clustering approach. The bibliometric analysis employs statistical techniques to map and assess trends in a specific research domain based on keywords, author affiliations, and citations. Bibliographic analysis assists us in comprehending unstructured big data. This study aims to present a comprehensive literature review on class imbalance problems using the clustering approach and identify gaps in the research domain using bibliometric analytical techniques. The Scopus and Web of Science databases were used to extract 663 articles on class imbalance data using a clustering approach published between 2010 and 2021. We used the VOS (Visualisation of Similarities) viewer to visualise the bibliometric analytical outcomes. Co-citation and co-word analysis were used to visualise the publication trend and identify areas of current research interest. The study's key findings evidenced a growing interest in the research domain. Herrera, f., and Chawla N. V. are dominant authors in this field, and China is leading the publication in the clustering approach for the big data imbalance problem. The top three affiliations are from China: Tsinghua University, the Chinese Academy of Sciences, and Beihang University. Conducting an in-depth bibliometric analysis using other databases such as Science Direct, IEEE, and Emerald is recommended. This study may assist researchers in understanding the nature of the big data imbalance problem using a clustering approach and providing insights for future research derived from these worldwide databases.

Exploration of the ground state properties of neutron-rich sodium isotopes using the deformed relativistic mean field theory in complex momentumrepresentations with BCS pairings* *Partly supported by the National Natural Science Foundation of China (11935001, 11575001), the Natural Science Foundation of Anhui Province (2008085MA26), Anhui project (Z010118169), the Heavy Ion Research Facility in Lanzhou (HIRFL) (HIR2021PY007), and the project of Key

This study explores the ground-state characteristics of neutron-rich sodium isotopes, encompassing two-neutron separation energies, root-mean-square radii, quadrupole moments of proton and neutron distributions, single-particle levels of bound and resonant states, and neutron density distributions and shapes. Simultaneously, special attention is paid to the distinctive physical phenomena associated with these isotopes. The deformed relativistic mean field theory in complex momentum representations with BCS pairings (DRMF-CMR-BCS) employed in our research provides resonant states with real physics, offering insights into deformed halo nuclei. Four effective interactions (NL3, NL3*, PK1, and NLSH) were considered to assess the influence of continuum and deformation effects on halo structures. Calculations for odd-even nuclei 35–43Na revealed the dependence on the chosen effective interaction and number of considered resonant states. Neutron occupation patterns near the Fermi surface, particularly in orbitals and , were determined to be crucial in halo formation. The study provided detailed insights into the density distributions, shape evolution, and structure of neutron-rich sodium isotopes, contributing valuably to the field of nuclear physics.

Advances in Studies on Heavy Metals in Urban Soil: A Bibliometric Analysis

Recent advancements in urban economies and population growth have led to an escalation in urban soil heavy metal pollution, thereby posing significant threats to human life and health. This paper presents a comprehensive bibliometric analysis, detailing the developmental trajectory, research hotspots, and emerging trends in the field of urban soil heavy metal pollution research. Utilizing the CiteSpace and VOSviewer software tools, we conducted an analysis of 1247 articles sourced from the Web of Science Core Collection Database (WoSCC) spanning the period from 2000 to 2022. Our analysis revealed a significant upward trend in the number of publications during the period 2000–2022, a trend expected to persist. Ahmad Kafeel, Khan Zafar Iqbal, and Huang Biao emerged as the leading authors in this domain. The journal, Science of the Total Environment, held the most influence, while China led in the number of publications, with the Chinese Academy of Sciences as the foremost contributor. The research predominantly focused on source apportionment of urban soil heavy metal pollution, pollution risk assessment, and the application of environmental magnetism. Future research priorities include assessing the human health risks from diverse sources of heavy metal pollution, a key aspect of ensuring urban safety and soil health. Our findings delineate the evolutionary trajectory of urban soil heavy metal pollution research over the past two decades and underscore the viability of employing a dynamic bibliometric approach to investigate this research discipline.

Reference Evapotranspiration Modeling Using Artificial Intelligence-Based Approaches: A Bibliometric Analysis

A bibliometric analysis was performed over the period 1992–2023, to pinpoint important trends, emphasis, and geographic distribution of international reference evapotranspiration (ETo) modeling research using intelligence-based approaches. Data was mined from the databases of the online version of Web of Science. The data was analyzed using the Excel program, and the bibliometric mapping was performed using the VOSviewer software. A total of 1148 articles met the required criteria. The findings indicated that the number of articles had increased rapidly over the past five years and that English was the prevalent language (99.5%). Researchers in 99 countries have published in this field of research. China ranked first with 356 articles (31.0%), followed by the United States of America with 193 articles (16.8%). Egypt and Saudi Arabia are two of the top 15 countries in the world. These articles were published in 289 journals; the Journal of Hydrology was the most productive journal (95 articles, 8.3%), followed by Computers and Electronics in Agriculture (67, 5.8%). The most productive author is Kisi Ozgur from Turkey (68 articles, 4.2%). Taking into account all the institutions working on ETo modeling (1555 institutions), the Chinese Academy of Science was ranked first (80 articles, 7.0%), followed by the Egyptian Knowledge Bank (55 articles, 4.8%). Artificial neural networks and machine learning were the most commonly used intelligence-based approaches for ETo modeling. Reinforcement learning was the least popular technique for ETo modeling, which could be due to its complexity and data requirements.

Mapping knowledge landscapes and emerging trends of non-contact vital signs monitoring: A bibliometric and visualization analysis from 2002 to 2023

This study makes a comparative analysis based on knowledge mapping of Chinese and English publications on non-contact vital signs monitoring and provides a reference for future research and development. Publications were retrieved from Web of Science (WoS) and PubMed, China National Knowledge Infrastructure (CNKI), and Wanfang databases from January 1, 2002 to October 31, 2023. Bibliometric analysis was performed using CiteSpace software. Information from 612 English and 543 Chinese original research articles was collected and analyzed. The maximum number of English appeared in 2022 and the maximum number of Chinese articles appeared in 2020, and the number of articles published afterward declined slightly. For the articles published in English, USA and China shared the highest number of publications. Chinese Academy of Sciences was the most published institution. Steffen Leonhardt was the highest-published author. Meanwhile, within the Chinese articles, the most productive province was Beijing. The most productive institution was the Air Force Medical University. The most productive author was Weixue Liu. The research institutions and authors of English and Chinese articles were decentralized. The hotspots of the English articles mainly were “heart rate” and “respiratory rate”, and the Chinese articles focused on “temperature” and “heart rate”. Concluding, the research on non-contact vital signs monitoring is in a booming phase of development at present. Non-contact heart rate monitoring is a popular research direction in English and Chinese research. In the future, research institutions and authors should strengthen their cooperation and communication to promote the development of this research area.

European Journal of Geography (Year 2023): Reviewer Appreciation & Publication Recap

European Journal of Geography (Year 2023): Reviewer Appreciation & Publication Recap

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment (LSA). The study area is the Feiyun catchment in Wenzhou City, Southeast China. Two types of landslides samples, combined with seven non-landslide sampling strategies, resulted in a total of 14 scenarios. The corresponding landslide susceptibility map (LSM) for each scenario was generated using the random forest model. The receiver operating characteristic (ROC) curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy. The results showed that higher accuracies were achieved when using the landslide core as positive samples, combined with non-landslide sampling from the very low zone or buffer zone. The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA, which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Spatial Distribution Characteristics of Soil Carbon and Nitrogen in Citrus Orchards on the Slope of Purple Soil Hilly Area

Since the 1990s, a large area of sloping farmland in a purple soil hilly region of southwest China was converted into an orchard to prevent soil erosion, increase soil fertility, and elevate economic benefits for farmers. In order to explore the spatial distribution of soil carbon (C) and nitrogen (N) fractions on the slope of returning arable lands to citrus orchards in purple soil hilly areas, a soil sampling event was carried out in a citrus orchard at the Yanting Agro-ecological Experimental Station of Purple Soil, Chinese Academy of Sciences, to examine the differences in soil C and N fractions and their influencing factors. The results showed that the slope position had significant effects on the contents of soil total nitrogen (TN), nitrate nitrogen (NO3--N), and dissolved organic carbon (DOC) (P < 0.05), but the effects were not obvious regarding the total organic carbon (SOC) and ammonia nitrogen (NH4+-N) of the soil (P > 0.05). For topsoil (0-30 cm), the variation trend of soil NO3--N content along the slope was upper slope < middle slope < lower slope, whereas the TN and DOC contents along the slope exhibited the trend of upper slope > middle slope > lower slope. The contents of soil C and N in each slope position generally showed a downward trend with increasing soil depth (0-30 cm). The contents of soil TN, SOC, NO3--N, and DOC were significantly affected by soil depth (P < 0.05). The TN storage (0-30 cm) significantly decreased from the top to the bottom within the soil slope, with a value of 2.37, 1.89, and 1.62 t·hm-2 (reported as N) for the upper slope, middle slope, and lower slope, respectively. There was no significant difference in SOC reserves along the slope, with a range from 56.12 to 58.48 t·hm-2 (reported as C). Our results provide scientific basis for understanding the spatial distribution of soil nutrients of the restored farmland in purple soil hilly areas. Our research suggests that the spatial distribution of soil carbon and nitrogen storage should not be ignored when predicting the response of soil nutrients to land use change.

Global trends in research of melanoma differentiation-associated gene 5: a bibliometric analysis from 2002 to 2022.

Melanoma differentiation-associated gene 5 (MDA5), as a cytoplasmic sensor for viral double-stranded RNAs, has received increasing attention in recent years. Although considerable headway has been made on the functional role of MDA5 in antiviral immunity and autoimmune disease, the available literature is insufficient to assess the vast field. This study performed a bibliometric analysis to investigate current hotspots in the global scientific output of MDA5 over the past two decades. Related publications and recorded information from 2002 to 2022 in the Web of Science Core Collection (WoSCC) database were retrieved. VOSviewer and CiteSpace were used for quantitative evaluation and visualization. A total of 2267 original articles and reviews were obtained, and the annual number of publications related to MDA5 was increasing rapidly. China has published the most papers, while the USA was the most influential country with the most citations and the highest H-index. The Chinese Academy of Sciences, the United States Department of Health and Human Services, and the Journal of Virology were the most prolific research affiliation, funding source, and journal, respectively. Fujita T (Kyoto University) was the most productive author with the highest H-index and had close cooperation with Kato H and Yoneyama M. The keywords "RIG-I," "MDA5," "innate immunity," "double-stranded-RNA," and "recognition" had the highest frequency, while "dermatomyositis" as well as "autoantibody" seemed to be the emerging hotspots. This study comprehensively demonstrated the research frontiers of MDA5 and will provide a useful resource for scholars to conduct future decisions. We conducted the first in-depth survey of the research frontiers on melanoma differentiation-associated gene 5 (MDA5) over the past two decades via bibliometric analysis. We found that many early breakthroughs have been made in the mechanism of MDA5-mediated antiviral immune responses, and the role of MDA5 in autoimmune and autoinflammatory diseases has raised the recent concern. We identified that the virus infection-associated pathogenesis and effective therapeutic strategy of anti-MDA5 antibody-positive dermatomyositis will remain the hotspots in the future.

SMILE Winter Campaign

ABSTRACT This white paper is highly topical as it relates to the upcoming solar wind magnetosphere ionosphere link explorer (SMILE) mission: SMILE is a joint mission between the European Space Agency and the Chinese Academy of Sciences and it aims to build a more complete understanding of the Sun–Earth connection by measuring the solar wind and its dynamic interaction with the magnetosphere. It is a fully funded mission with a projected launch in 2025. This paper outlines a plan for action for SMILE’s first Northern hemisphere winter campaign using ground-based instruments. We outline open questions and which data and techniques can be employed to answer them. The science themes we discuss are: (i) Earth’s magnetosheath, magnetopause, and magnetic cusp impact on the ionospheric cusp region; (ii) defining the relationship between auroral processes, solar wind, and magnetospheric drivers; (iii) understanding the interhemispheric properties of the Earth’s magnetosphere–ionosphere system. We discuss open questions (different to the mission goals) which may be answered using existing ground-based instrumentation together with SMILE data to leverage the maximum scientific return of the mission during the first winter after launch. This paper acts as a resource for planning, and a call to collaborative action for the scientific community.

A bibliometric analysis of deep learning applications in climate change research using CiteSpace

<p>In recent years, artificial intelligence, particularly deep learning, has garnered significant attention among practitioners and scholars in meteorology and atmospheric sciences, leading to a substantial body of literature. This study aims to delineate the present research status and trends in climate innovation through CiteSpace visual analysis. To comprehend the current landscape, prevalent terms, and research frontiers of deep learning for climate change research (DLCCR) within meteorology and atmospheric applications, we gathered 256 published papers spanning from 2018 to 2022 from the Web of Science (WOS) core database. Employing these articles, we conducted co-authorship, co-citation, and keyword co-occurrence analyses. The findings unveiled a steady rise in DLCCR publications over the last five years. However, the correlation between high yield and high-citation authorship appears inconsistent and weak. Notably, prolific authors in this domain included Zhang Z.L. and Bonnet P. Furthermore, leading institutions such as the Chinese Academy of Sciences (China), le Centre National de la Recherche Scientifique (France), and Nanjing University of Information Science and Technology (China) have played pivotal roles in advancing DLCCR. The primary contributors among high-yield countries primarily cluster in a select group comprising China, the USA, South Korea, and Germany. Identifying significant information gaps in numerical weather, atmospheric physics and processes, algorithm parametrizations, and extreme events, our study underscores the necessity for future researchers to focus on these and related subjects. This study provides valuable insights into research hotspots, developmental trajectories, and emerging frontiers, thereby delineating the knowledge structure in this field and highlighting directions for further climate innovation research.</p>

A new species of Elimaea (Poaefoliana) Ingrisch (Orthoptera: Tettigoniidae: Phaneropterinae) from China.

One new species of leaf katydids, Elimaea (Poaefoliana) kiukiangensis sp. nov. from the Himalaya-Hengduan Mountains region, is described, based on recently collected material. Illustrations of mounted specimens including male genitalia, and habitat images are provided. A distribution map is also provided. The types are deposited in Insect Collection of Institute of Zoology, Chinese Academy of Sciences, Beijing, China (IZCAS).

Two new species of Amaurobius C. L. Koch, 1837 (Araneae, Amaurobiidae) from Sichuan Province, China.

In this paper, two new species of the genus Amaurobius C. L. Koch, 1837 of the family Amaurobiidae Thorell, 1869 are reported from Sichuan Province, China: A. danba Lin & Li, sp. nov. () and A. yaan Lin & Li, sp. nov. (). Photos and morphological descriptions of the new species are presented; the type specimens of the new species are deposited in the Institute of Zoology, Chinese Academy of Sciences (IZCAS), Beijing.

CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.

Research Progress on Soil Security Assessment in Farmlands and Grasslands Based on Bibliometrics over the Last Four Decades

Soil security assessments are an important part of the green development of agriculture and animal husbandry. To explore the research progress and development trends in the field of farmlands and grasslands soil security assessments, a bibliometric study was conducted using VOSviewer software to visually analyze 3618 papers from the Web of Science Core database on the topic of “soil security assessment” published from 1979 to 2023. The results revealed the following: (1) Research started in 1979; the number of papers can be divided based on germination, start-up, and rapid development stages. China published the most articles, the Chinese Academy of Sciences had the highest number of publications, and Science of the Total Environment issued the most publications (247). (2) Based on keywords, the research frontier can be divided into a distinct time sequence: the initial exploratory period (1979–2008), wherein relevant research focused on resource development and management; the rapid development period (2009–2015), wherein research focused on sustainable development and efficient farmland use; and the comprehensive development period (2016–2023), wherein research focused on the assessment, measurement, and evolution of cultivated land. (3) Related researches at home and abroad focus on land development and utilization, highlighting the rational development and efficient use of land; the security of industrial and supply chains, underlining risk assessment and promotion strategies; ecological security, emphasizing the ecological security assessments of agricultural production and the water environment; and ecosystem service value, underscoring spatiotemporal evolution and driving factors, evolution mechanisms, value prediction, and compensation strategy. Currently, there is an urgent need to develop soil security assessment models based on regional development, soil biology, spatial metrology, and other parameters, to establish an index system, and to analyze the evolution rules of soil security at different scales and investigate the scale effect of soil quality evaluations.

Upper Cambrian–Lower Ordovician conodonts from the southern margin of the Kopet-Dagh Basin, Saluk Mountains, Iran

A detailed Furongian to Tremadocian conodont biostratigraphy is established from the Saluk Mountains located in southern Kopet-Dagh, northeast Iran. Thirty species assigned to 17 genera were recovered from decimetre-scale limestone beds in a fine-grained siliciclastic succession, deposited in an intraplate rift system. The following biozones are established for the region, in ascending order: Proconodontus muelleri, Eoconodontus, Cordylodus intermedius, Cordylodus lindstromi, Cordylodus angulatus, Paltodus deltifer and Acodus triangularis interval zone. This low-to medium-richness conodont fauna comprises cosmopolitan euconodont species of Proconodontus, Eoconodontus, Cordylodus, Teridontus nakamurai, Semiacontiodus nogamii and a few long- ranging proto- and paraconodont species, which inhabited a narrow marine basin flanked by landmasses and formed by active rifting. It lacks shallow water tropical taxa and probably belongs to the Cold Domain of the Shallow Sea Realm. The occurrence of conodonts of the Paltodus deltifer Biozone in the uppermost part of the Qyzlar Formation in the Ghelli section suggests that the major episode of flood basalt volcanism in south Kopet-Dagh commenced in the late Tremadocian. Hadi Jahangir [Jahangir.hadi@nigpas.ac.cn] and Guanzhou Yan [gzyan@nigpas.ac.cn], State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Paleontology, Chinese Academy of Sciences, Nanjing 210008, PR China.

CBAS: An International Platform of Digital Technologies Facilitating Sustainable Development Goals

The Chinese Academy of Sciences (CAS) launched the Big Earth Data Science Engineering Program (CASEarth) in 2018, which laid the foundation for the International Research Center of Big Data for Sustainable Development Goals (CBAS). Building on CASEarth’s achievements, CBAS integrates advanced digital technologies to advance the UN Sustainable Development Goals (SDGs) through five key missions: (1) developing SDG data infrastructure and information products via its SDG Big Data Platform, utilizing advanced computing, cloud services, and AI; (2) developing and launching a series of SDG satellites, including SDGSAT-1, which provides crucial Earth observation data through its Open Science Program; (3) providing new knowledge for SDG monitoring and evaluation through annual reports (Big Earth Data in Support of the Sustainable Development Goals, published since 2019) and guiding the development of big data-driven technical solutions and theoretical systems; (4) establishing a think tank for science, technology, and innovation, promoting SDGs through initiatives like the annual International Forum on Big Data for Sustainable Development Goals (FBAS) and the CBAS Fellowship Program; and (5) providing capacity development for SDGs in developing countries through international collaborations, such as the Digital Belt and Road Program (DBAR), which offers professional education and training in big data. Future CBAS efforts will focus on expanding data access, enhancing AI capabilities for SDG indicator monitoring, and strengthening international partnerships to address data gaps and ensure equitable access to technology and expertise for achieving global sustainability.

Science-management Partnerships as Catalysts for Governance Reform of National Parks in China

Protected areas have long been considered a cornerstone of biodiversity conservation by policy-makers and the conservation community. Yet, despite ongoing efforts, many countries, including China, continue to face persistent challenges in effectively managing these areas. Here we examine how science-management partnerships, particularly those headed by Chinese Academy of Sciences (CAS), have catalyzed governance reforms of protected areas in China. The integration of scientific research with policy action has enabled the establishment of a unified national park system, mitigating management inefficiencies and enhancing ecosystem resilience. We propose strategies for CAS to consolidate its leadership in this domain. These partnerships might offer global insights into evidence-based protected area management.