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  • Journal of Chinese Research Hospitals
    (Bimonthly,started publication in 2014)
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    Ministry of Civil Affairs of the People’s Republic of China
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    Chinese Research Hospital Association
    Science and Technology of China Press
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    Overseas Issue NO.:BM9207
    ISSN 2095-8781 CN 10-1274/R

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    China Swine Industry    2024, 19 (4): 3-11.   DOI: 10.16174/j.issn.1673-4645.2024.04.001
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    China Swine Industry    2024, 19 (3): 59-67.   DOI: 10.16174/j.issn.1673-4645.2024.03.006
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    China Swine Industry    2024, 19 (5): 3-10.   DOI: 10.16174/j.issn.1673-4645.2024.05.001
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    China Swine Industry    2024, 19 (5): 11-21.   DOI: 10.16174/j.issn.1673-4645.2024.05.002
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    Construction Process and Technological Prospects of Large Language Models in the Agricultural Vertical Domain
    ZHANG YuQin, ZHU JingQuan, DONG Wei, LI FuZhong, GUO LeiFeng
    Journal of Agricultural Big Data    2024, 6 (3): 412-423.   DOI: 10.19788/j.issn.2096-6369.000052
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    With the proliferation of the internet, accessing agricultural knowledge and information has become more convenient. However, this information is often static and generic, failing to provide tailored solutions for specific situations. To address this issue, vertical domain models in agriculture combine agricultural data with large language models (LLMs), utilizing natural language processing and semantic understanding technologies to provide real-time answers to agricultural questions and play a crucial role in agricultural decision-making and extension. This paper details the construction process of LLMs in the agricultural vertical domain, including data collection and preprocessing, selecting appropriate pre-trained LLM base models, fine-tuning training, Retrieval Augmented Generation (RAG), evaluation. The paper also discusses the application of the LangChain framework in agricultural Q&A systems. Finally, the paper summarizes some challenges in building LLMs for the agricultural vertical domain, including data security challenges, model forgetting challenges, and model hallucination challenges, and proposes future development directions for agricultural models, including the utilization of multimodal data, real-time data updates, the integration of multilingual knowledge, and optimization of fine-tuning costs to further promote the intelligence and modernization of agricultural production.

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    China Swine Industry    2024, 19 (4): 78-85.   DOI: 10.16174/j.issn.1673-4645.2024.04.010
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    Bulletin of Agricultural Science and Technology    2024, 0 (6): 156-159.  
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    Rice Yield Prediction UAV Remote Sensing Image Dataset of Heilongjiang Province in 2023
    YUAN JiangHao, ZHENG ZuoJun, CHU ChangMing, YAO HongXun, LIU HaiLong, GUO LeiFeng
    Journal of Agricultural Big Data    2024, 6 (4): 546-551.   DOI: 10.19788/j.issn.2096-6369.100031
    Abstract542)   HTML44)    PDF(pc) (3288KB)(206)       Save

    Rice is one of the three major grain crops in China, and accurate, efficient and timely prediction of rice yield is crucial for variety selection and optimization of field management. UAV remote sensing system is widely used in crop pest and disease identification, crop growth monitoring and crop phenotyping by virtue of its advantages of fast, non-destructive, low cost and high throughput. To explore the role of spectral data in estimating rice yield, this dataset used UAV remote sensing to collect multispectral images of rice growth process, 106 sample points of 1 m×1 m were selected for manual sampling and yield measurement, and at the same time, visible images were collected after the sampling to realize the correlation between spectral images and yield data. The dataset of this paper was constructed after manual checking and organizing. The data collection location was Heilongjiang Province, and the UAV collected the data under cloudless and light-sufficient conditions, and the collection time was from July to August in 2023, and a total of 3 days of multispectral data and 1 day of visible light data were collected with different varieties in the experimental field. The dataset in this paper was complete in all data and provided data support for research on yield estimation.

    Data summary:

    Items Description
    Dataset name Rice Yield Prediction UAV Remote Sensing Image Dataset of Heilongjiang Province in 2023
    Specific subject area Agricultural Science
    Research Topic computer vision
    Time range July 2023- August 2023
    Temporal resolution Day
    Data types and technical formats .tif,.xlsx,.jpg
    Dataset structure The dataset consists of three parts of data. The first part is the multispectral image data of the entire growth period of rice, including six spectral channels: blue (450nm), green (555nm), red (660nm), red edge 1 (720nm), red edge 2 (750nm), and near-infrared (840nm), with a total of 14226 images, approximately 32.6GB; The second part is production data, saved in. xlsx format; The third part is visible light image data used to annotate sampling points, totaling 746 images, approximately 18.9GB.
    Volume of dataset 51.5 GB
    Key index in dataset Gradient settings, plot labeling, yield, multispectral images, RGB images
    Data accessibility CSTR:https://cstr.cn/17058.11.sciencedb.agriculture.00131
    DOI:https://doi.org/10.57760/sciencedb.agriculture.00131
    NASDC Access link: https://agri.scidb.cn/, restricted access
    Financial support National Science and Technology Major Project(2021ZD0110901)
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    China Swine Industry    2024, 19 (6): 14-25.   DOI: 10.16174/j.issn.1673-4645.2024.06.005
    Abstract489)      PDF(pc) (2424KB)(45)       Save
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    China Swine Industry    2024, 19 (5): 90-100.   DOI: 10.16174/j.issn.1673-4645.2024.05.011
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    China Swine Industry    2024, 19 (3): 77-84.   DOI: 10.16174/j.issn.1673-4645.2024.03.008
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    China Swine Industry    2024, 19 (4): 25-36.   DOI: 10.16174/j.issn.1673-4645.2024.04.004
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    Survey of Differential Privacy Algorithms and Applications for High- Dimensional Data Publishing
    LONG Chun, QIN ZeXiu, LI LiSha, LI Jing, YANG Fan, WEI JinXia, FU YuHao
    Journal of Agricultural Big Data    2024, 6 (2): 170-184.   DOI: 10.19788/j.issn.2096-6369.200001
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    With the further development of big data and machine learning technologies, handling high-dimensional data with complex structures, relationships, and rich semantic information containing dozens to hundreds of features has become a challenge. Safely utilizing such high-dimensional data, while ensuring the privacy of individuals, has become a significant topic today. Upon reviewing existing literature, we found numerous reviews on differential privacy technology itself, but few on the algorithms and applications of differential privacy specifically tailored for high-dimensional data. Therefore, this paper provides a review of the application of differential privacy in the field of high-dimensional data, aiming to delve into the strengths and weaknesses of different methods in protecting the privacy of high-dimensional data and to guide future research directions for differential privacy algorithms tailored for high-dimensional data publishing. Firstly, this paper introduces the principles and characteristics of differential privacy, summarizing the current research work on the technology itself. Then, it analyzes the application of differential privacy in high-dimensional data environments from the perspectives of data dimensionality reduction and data synthesis, discussing the challenges and issues faced by differential privacy and proposing preliminary solutions to better address the issues of privacy protection and data analysis in the current high-dimensional data landscape. Lastly, potential future research directions are proposed to facilitate technological exchange and further advancements in the application of differential privacy in high-dimensional data settings.

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    A Review and Analysis of Keyword Search Technologies for Data Privacy Protection
    YANG Yu, WANG Wei, CHEN ShiWu
    Journal of Agricultural Big Data    2024, 6 (2): 185-204.   DOI: 10.19788/j.issn.2096-6369.000012
    Abstract369)   HTML28)    PDF(pc) (2950KB)(931)       Save

    In the modern information society, data privacy protection has become a focal point of public attention. As internet users increasingly prioritize personal information security, research on privacy protection in the field of information retrieval has become crucial. Privacy-protecting keyword search technology aims to provide secure and private search services without revealing users' query intentions. Although existing technologies have made progress in meeting basic needs, how to reduce the risk of privacy leaks while maintaining efficiency remains a challenge. For this purpose, this paper provides a detailed review of privacy-protecting keyword search technology, systematically analyzing the principles, strengths, and weaknesses of current mainstream technologies. The study finds that although existing technologies can encrypt user queries to prevent direct leakage of sensitive information, there is still a potential risk of privacy leakage between the query pattern, access mode, and returned results. In response to this issue, the paper proposes a series of improvement directions to enhance the effectiveness of privacy protection. Furthermore, current privacy protection technologies face numerous challenges in practical applications, involving aspects such as technological enhancement and privacy compliance. By integrating and innovating cutting-edge technologies related to privacy-protecting keyword search, new ideas and solutions are expected to resolve these technical problems and promote the development of privacy protection technology to a higher level. Finally, the paper provides an outlook on the future development directions and innovative application models of privacy-protecting keyword search technology.

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    Bulletin of Agricultural Science and Technology    2024, 0 (6): 61-64.  
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    China Swine Industry    2024, 19 (4): 12-18.   DOI: 10.16174/j.issn.1673-4645.2024.04.002
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    Progress of Agricultural Big Data Research (2024)
    Agricultural Information Institute of CAAS
    Journal of Agricultural Big Data    2024, 6 (4): 433-468.   DOI: 10.19788/j.issn.2096-6369.200003
    Abstract353)   HTML68)    PDF(pc) (10122KB)(1806)       Save
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    Image Dataset of Wheat, Corn, and Rice Seedlings in Heilongjiang Province in 2022
    QIN JiaLe, YUAN JiangHao, SONG GuoZhu, YAO HongXun, GUO LeiFeng, WANG XiaoLi
    Journal of Agricultural Big Data    2024, 6 (4): 558-563.   DOI: 10.19788/j.issn.2096-6369.100026
    Abstract352)   HTML27)    PDF(pc) (2655KB)(317)       Save

    During the cultivation process, most field crops are typically grown in open fields. The northeastern region of China experiences relatively low temperatures throughout the year. During the seedling stage of crops, significant fluctuations in sunlight and rainfall can easily lead to issues such as weak and stunted seedlings, poorly developed root systems, and slow growth. Timely monitoring and management of crops during the seedling stage can help in understanding their growth status and environmental conditions, enabling prompt decision-making.Experimental data was collected from May 9, 2022, to June 16, 2022. RGB cameras installed at 11 meteorological stations in the experimental fields collected data seven times a day at 6:00, 8:00, 10:00, 12:00, 14:00, 16:00, and 18:00. The images were captured at a height of 2.4 meters with a field of view angle of 90°, covering an area of 4.4 meters in length and 2.5 meters in width. Photography was mainly conducted through natural light conditions with a downward vertical perspective.After organizing and screening, the dataset comprises approximately 2.59 GB of data, including 1.48 GB of visible light RGB data and 1.11 GB of near-infrared spectral data. This dataset enables leaf age identification through visible light RGB data and near-infrared spectral data. Extracted features (color features, image features, texture features, vegetation indices) can be inputted into machine learning regression models for analysis and prediction. Moreover, this dataset is suitable for constructing convolutional neural network models for crop recognition or seedling identification, facilitating precise crop detection and further research on issues such as missed or replanted seedlings after transplanting.

    Data summary:

    Items Description
    Dataset name Image Dataset of Wheat, Corn, and Rice Seedlings in Heilongjiang Province in 2022
    Specific subject area Agricultural science
    Research Topic Computer vision
    Time range May 2022-July 2022
    Temporal resolution 1 day
    Data types and technical formats .jpg
    Dataset structure The dataset consists of two parts of data, one is the field crop visible light RGB image data set, and the other is the field crop multispectral near-infrared image data set, of which: 1. The field crop image data contains data within 38 days, and the data volume is 1.48G; 2. Daejeon near-infrared spectral data within 38 days, the data volume is 1.11G.
    Volume of dataset 2.59 GB
    Key index in dataset RGB images and near-infrared spectral images
    Data accessibility CSTR: https://cstr.cn/17058.11.sciencedb.agriculture.00092
    DOI: https://doi.org/10.57760/sciencedb.agriculture.00092
    hNASDC Access link: https://agri.scidb.cn/, restricted access
    Financial support National Key R&D Program of China (2021ZD0110901); Science and Technology Planning Project of Inner Mongolia Autonomous Region (2021GG0341)
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    China Swine Industry    2024, 19 (5): 83-89.   DOI: 10.16174/j.issn.1673-4645.2024.05.010
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    Research Progress on the Application of Intelligent Breeding Technology in Dairy Cattle Farming
    WEI Danni, GUO Yongqing
    China Dairy    2024, 0 (6): 31-39.   DOI: 10.12377/1671-4393.24.06.06
    Abstract333)      PDF(pc) (4637KB)(240)       Save
    With the development of large-scale dairy farms and modern information technology,intelligent breeding technology has been gradually applied to dairy cattle production,which has accelerated the transformation process of intelligent breeding. This paper reviewed the application and research progress of intelligent breeding technologies such as Internet of Things,big data,intelligent feeding,intelligent environmental control and remote monitoring in dairy cattle farming both domestically and internationally in recent years. The aim is to better utilize intelligent devices,improve production efficiency,achieve refined management of dairy farms,and provide references for the innovation and development of dairy cattle intelligent breeding technologies in China.
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    Financial Performance Evaluation of ROYAL GROUP Based on Factor Analysis
    DAI Wenbo
    China Dairy    2024, 0 (7): 21-27.   DOI: 10.12377/1671-4393.24.07.04
    Abstract318)      PDF(pc) (1484KB)(247)       Save
    This research selected the 2014-2022 financial data of ROYAL GROUP,eight representative indicators were selected from the four dimensions of solvency, profitability, operating ability and growth ability, and the IBM analysis was carried out by using SPSS Statistics 26.0 software. We should optimize the capital structure, stabilize the main business and improve the management ability. Through this study on the ROYAL GROUP of financial performance evaluation, for other regional dairy companies to provide reference for financial performance evaluation.
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    Exploration of Big Data Security Issues in the Field of Intelligent Agriculture
    WU YunKun, YANG Ying, LI Hao, XIONG Jian, CHEN XiangLing
    Journal of Agricultural Big Data    2024, 6 (3): 380-391.   DOI: 10.19788/j.issn.2096-6369.000029
    Abstract306)   HTML24)    PDF(pc) (1031KB)(588)       Save

    In the context of the rapid development of informatization, intelligent agriculture is an inevitable trend in agricultural development, and agricultural big data plays an important role in the realization of intelligent agriculture. Although agricultural big data has brought huge industrial momentum, many data security-related issues arose. It is crucial to handle the relationship between agricultural big data technology and data security effectively. First and foremost, this paper redefined the agricultural big data by analyzing various perspectives comprehensively, and elaborated on its promotion role in each aspect of the agricultural supply chain through a case study. Furthermore, it conducted an in-depth analysis on the distinctive attributes of agricultural big data, including its ubiquity, sociality, intersectionality, and more. Lastly, based on three fundamental elements of security (confidentiality, integrity and availability), three key functions of security (authentication, authorization and audit) and proprietary characteristics of agricultural big data, from the perspective of the seven-stage life cycle of the big data (data collection, data transmission, data storage, etc.), we proceed to construct a comprehensive framework for managing big data security risks in intelligent agriculture scenarios. The unique features of agriculture present particular obstacles within the broader context of big data. To address this issue, a customized solution has been devised, taking into account the specific needs and requirements of intelligent farming practices. This paper will introduce fresh insights and perspectives to address future data security issues in the field of intelligent agriculture, aiming to promote faster and safer development of intelligent agriculture.

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    China Swine Industry    2024, 19 (4): 70-77.   DOI: 10.16174/j.issn.1673-4645.2024.04.009
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    Agri-CBI: Agricultural Big Data Security Governance Model Leveraging Cloud-Blockchain Integration
    YUE RuiJun, HE Liang, TANG MinRui, YAN Wei, LIU ShengQuan, YANG WanXia, SUN WeiHong, HUANG YongFeng
    Journal of Agricultural Big Data    2024, 6 (3): 333-350.   DOI: 10.19788/j.issn.2096-6369.000039
    Abstract295)   HTML21)    PDF(pc) (5325KB)(222)       Save

    The current agricultural production model in China is transitioning from traditional to smart agriculture. In response to the continuous expansion of data scale in various agricultural organizations and the problem of "Data Silos" in data sharing, it is difficult to gather agricultural data on a large scale to guide precise agricultural decision-making. This study is based on Cloud-Blockchain Integration and data security governance related technologies in distributed agriculture scenarios to solve the above-mentioned problems and explore their practical application effects. In a distributed agricultural scenario, based on IPFS, blockchain, and cloud computing, design an agricultural big data governance algorithm that can be deployed on smart contracts, construct a multi-party agricultural data aggregation model, as well as a complete, secure, traceable data protection model and typical scenario application model. Taking the agricultural production of Huaxing Farm and its affiliated agricultural organizations in Changji, Xinjiang as an example, further build a Cloud-Blockchain Integration agricultural big data platform. By comparing the performance of the agricultural big data governance model based on Cloud-Blockchain Integration with two traditional models, the experiment shows that the comprehensive performance of the model in this article can achieve a better balance and achieved better performance compared to the traditional models.

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    Study on Nutritional Value,Health Function and Application of Camel Milk
    HUANG Xinhong, ZHANG Liru, ZHANG Zongjie, TAI Yanjie, FAN Tieliang, SHI Gang, ZHANG Lianchao
    China Dairy    2024, 0 (11): 167-175.   DOI: 10.12377/1671-4393.24.11.27
    Abstract293)      PDF(pc) (1374KB)(285)       Save
    Camel milk is a milk with high nutritional value and health benefits. Camel milk is rich in contains fat,protein,vitamins,lactose and other nutrients,with biologically active substances that play a vital role in the human body,helping to treat diabetes,antioxidants,antibacterials and anticancer. However,the production of camel milk is very limited because of the differences in compositional content with cow's milk lead to the production process of cow's milk not being applicable to camel milk. Therefore,this paper reviews the nutritional composition and functions of camel milk,discusses the processing and production technology of camel milk products,and analyses the research direction to improve its quality,so as to provide a reference for further related research and development and application of camel milk.
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    Bulletin of Agricultural Science and Technology    2024, 0 (7): 144-147.  
    Abstract289)      PDF(pc) (847KB)(13)       Save
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    China Swine Industry    2024, 19 (5): 33-42.   DOI: 10.16174/j.issn.1673-4645.2024.05.004
    Abstract282)      PDF(pc) (1752KB)(44)       Save
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    Bulletin of Agricultural Science and Technology    2024, 0 (6): 162-164.  
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    An Overview of Zero-Knowledge Proof Technology and Its Typical Algorithms and Tools
    WAN Wei, LIU JianWei, LONG Chun, LI Jing, YANG Fan, FU YuHao, YUAN ZiMeng
    Journal of Agricultural Big Data    2024, 6 (2): 205-219.   DOI: 10.19788/j.issn.2096-6369.200002
    Abstract278)   HTML29)    PDF(pc) (517KB)(1712)       Save

    In the context of the increasing importance of data security and privacy protection, Zero-Knowledge Proofs (ZKPs) have provided a powerful tool for protecting privacy. This article comprehensively discusses the technology of zero-knowledge proofs and their application in modern cryptography. First, the article introduces the basic concepts of zero-knowledge proofs, as well as different types of ZKPs such as Snarks and Starks, along with their technical characteristics and application scenarios. In particular, the article conducts an in-depth study of ZK-Snarks. At the same time, the article also discusses other proof mechanisms such as ZK-Stark and Bulletproofs, comparing their differences in design and performance. Then, it focuses on the application of ZKPs in the blockchain environment and analyzes the related tools for writing zero-knowledge proofs. Finally, it points out some potential problems and future research directions in the field of zero-knowledge proofs.

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    Research Progress on Quality and Safety Testing Techniques for Dairy Products
    SONG Yanjing, LI Juan
    China Dairy    2024, 0 (8): 119-123.   DOI: 10.12377/1671-4393.24.08.22
    Abstract274)      PDF(pc) (1262KB)(120)       Save
    As a source of high-quality protein food,dairy products have gradually become an important part of daily diet. With the growth of consumption,its safety has been more and more widely concerned,and testing requirements have become more stringent,but different testing methods for nutrients and harmful substances are different,this paper summarized and compared the application of conventional spectroscopic,chromatographic and mass spectrometric detection techniques and new detection techniques in nutrients such as pollutants and minerals in dairy products,to provide a theoretical reference for the quality and safety detection of dairy products.
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    Security Challenges and Countermeasures on Open Sharing of Scientific Data in the Context of Open Science
    LIAO FangYu, LI Jing, LONG Chun, YANG Fan, YUAN ZiMeng
    Journal of Agricultural Big Data    2024, 6 (2): 146-155.   DOI: 10.19788/j.issn.2096-6369.000027
    Abstract265)   HTML28)    PDF(pc) (487KB)(335)       Save

    Scientific data is a strategic and fundamental scientific and technological resource, profoundly impacting national security, economic development and technological progress. In the context of open science, scientific data, as the outcome and important support of data-intensive scientific research paradigms, also faces severe security challenges in terms of security and compliance, trusted and reliable sharing exchange. Focus on these challenges and aims to promote the open sharing of scientific data, the author propose several feasible strategies from the aspects of policy, management, technology, evaluation, and supervision, where the core is to construct a dynamic, fine-grained, and domain-applicable security classification and grading system, to promote the secure development and utilization of scientific data and accelerate transformation into a scientific and technological powerhouse.

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    Application Analysis of Blockchain and Confidential Computing Technology in Material Database Platform
    GONG HaiYan, MA FuQiang, ZHANG DaWei, LI XiaoGang
    Journal of Agricultural Big Data    2024, 6 (2): 241-252.   DOI: 10.19788/j.issn.2096-6369.000026
    Abstract261)   HTML12)    PDF(pc) (1260KB)(944)       Save

    With the rise of data-driven material design driven by artificial intelligence and materials science, material science data has become a focal point of production factors, national strategic resources, and international competition. However, as material data sharing increases, data security issues become increasingly important. Issues such as data leakage, misuse, and tampering threaten the competitiveness of enterprises. We first review mainstream data security protection technologies, including access control and encryption technologies, which constitute the traditional data security protection model, ensuring security during data transmission and storage. Next, the development of blockchain technology is introduced. Blockchain technology can achieve confidentiality, integrity, and availability during data transmission and storage, but these mechanisms still cannot address privacy issues during data usage, nor can they protect the confidentiality and integrity of data during usage. Then, the advantages of confidential computing technology are analyzed. By executing calculations in a hardware-based trusted execution environment, confidential computing technology minimizes the trusted computing base, providing comprehensive data protection and adhering to the concept of "data usability without visibility" to protect data during usage, thereby constructing end-to-end lifecycle data security. Finally, we combine the advantages of blockchain and confidential computing technology to propose a trustworthy infrastructure solution for material data based on blockchain and confidential computing, to achieve security throughout the data lifecycle and provide strong support for the secure application of material data.

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    The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022
    DING DaWei, YONG BeiBei, REN Wen, XIE Kun, ZHAO YongJian, WANG GuangShuai, CHEN JinPing, WANG MingHui
    Journal of Agricultural Big Data    2024, 6 (4): 552-557.   DOI: 10.19788/j.issn.2096-6369.100029
    Abstract261)   HTML8)    PDF(pc) (823KB)(185)       Save

    The farmland ecosystem of the Huanghuai Plain primarily cultivates winter wheat and summer maize, which have played a crucial role in ensuring national food security. To grasp the key phenological period of primary crops precisely is of great significance for estimating crop yield, improving the level of agricultural production management, and preventing agricultural meteorological disasters. The dataset integrates ecological observation data on the phenology of different crops in a two-cropping winter wheat-summer maize continuous cropping system in the alluvial plain of the Old Yellow River over the past 15 years (2008-2022). It mainly includes information on observation plots, winter wheat phenological period data, and summer maize phenological period data. It will serve as a valuable resource for regional agricultural quantitative remote sensing, crop growth model simulation, agricultural climate change research, and decision-making in agricultural production and management.

    Data summary:

    Items Description
    Dataset name The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022
    Specific subject area Agricultural Science
    Research topic Crop phenology period of winter wheat and summer maize
    Time range From 2008 to 2022
    Data types and technical formats .xlsx
    Dataset structure The dataset includes the variety and crop phenology period of winter wheat and summer maize in six long-term observation plots at the Alluvium Plain of the Old Yellow River from 2008 to 2022.
    Volume of dataset 21.7 kB
    Key index in dataset The crop phenological period of winter wheat and summer maize
    Data accessibility CSTR:https://cstr.cn/17058.11.sciencedb.agriculture.00034
    DOI:https://doi.org/10.57760/sciencedb.agriculture.00034
    NASDC Access link: https://agri.scidb.cn/, restricted access
    Financial support Central Public-Interest Scientific Institution Basal Research Fund (Y2024JC31, Y2024JC08, IFI2024-24); The Scientific and Technological Project of Henan Province(242102110222); National Agricultural Experimental Station for Agricultural Environment, Shangqiu (NAES038AE05).
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    Bulletin of Agricultural Science and Technology    2024, 0 (10): 185-187.  
    Abstract259)      PDF(pc) (1604KB)(22)       Save
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    Strongly Promote the Construction of Smart Agriculture, Accelerate the Formation of New Quality Productive Forces in Agriculture
    WANG XiaoBing, LIU Yang, LIANG Dong, KANG Ting, KANG ChunPeng, YIN RuiFeng, CHEN Sha, REN YuJue, XU Yang
    Journal of Agricultural Big Data    2024, 6 (4): 469-475.   DOI: 10.19788/j.issn.2096-6369.000054
    Abstract256)   HTML25)    PDF(pc) (363KB)(588)       Save

    Agriculture is the most fundamental and representative traditional industry, and accelerating the construction of an agricultural powerhouse requires the cultivation and development of new quality productive forces in agriculture. Digital technology is the leading force of the new round of scientific and technological revolution and industrial transformation, and comprehensively promoting the integration of digital agriculture will become an important focus for accelerating the formation of new quality productive forces in agriculture. This article studies the scientific connotation of new quality productive forces, the basic characteristics of agricultural new quality productive forces, the realistic needs, development trends, and key directions of smart agriculture construction, combining domestic and foreign literature, statistical data, and survey results. It proposes measures to promote the construction of smart agriculture and accelerate the formation of new quality productive forces in agriculture. China's smart agriculture construction is moving from "scenic spots" to "scenery," and has entered a new stage of synergistic and efficient integration of big data, IoT, blockchain, artificial intelligence, satellite remote sensing, and BeiDou Navigation Satellite System (BDS) and other modern information technologies.It is necessary to start from key industries and accelerate the formation of a favorable ecological environment for the development of smart agriculture. We should regard smart agriculture as an important lever for advancing the construction of an agricultural powerhouse, addressing the biggest constraint factor of data, promoting the deep integration of modern information technology with agricultural industries, cultivating a high-quality innovative workforce, fully leveraging the role of enterprises as the main innovation actors, strengthening key core technology R&D, aligning with the development trends of digitalization and greenization, and accelerating the cultivation and development of new agricultural productive forces.

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    China Swine Industry    2024, 19 (4): 19-24.   DOI: 10.16174/j.issn.1673-4645.2024.04.003
    Abstract253)      PDF(pc) (1420KB)(147)       Save
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    Optimization of Geber Method for Determination of Fat Content in Milk
    ZENG Yunfeng, ZHOU Zhenxin, LIU Zuo, CHEN Fang, LI Wenjing, CHEN Baoyu, LI Weiyin, BU Zeming
    China Dairy    2024, 0 (7): 98-101.   DOI: 10.12377/1671-4393.24.07.19
    Abstract250)      PDF(pc) (1301KB)(87)       Save
    [Objective] In order to improve the accuracy and reliability of the results of the determination of fat content in milk by Gerber method. [Method] This paper studied the fourth method of GB 5009.6—2016 Gerber method,analyzed the problems encountered in the operation process. [Result] The key nodes such as the total volume of the solution and the control of the adjustment time are optimized to clarify the parameters such as sulfuric acid concentration and standing time. [Conclusion] The test results are stable and accurate,and meet the daily testing of the laboratory,which is especially suitable for production enterprises and grassroots testing institutions.
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    China Swine Industry    2024, 19 (5): 22-31.   DOI: 10.16174/j.issn.1673-4645.2024.05.003
    Abstract247)      PDF(pc) (1962KB)(59)       Save
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    Bulletin of Agricultural Science and Technology    2024, 0 (8): 188-191.  
    Abstract246)      PDF(pc) (655KB)(19)       Save
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    Comparison of Infant Formula Food Standards between China, EU
    ZHOU Mi, MA Jiao, YAO Sai, YU Xiaojin, SHI Na, ZHENG Yue, GUO Rui, GENG Jianqiang
    China Dairy    2024, 0 (7): 83-87.   DOI: 10.12377/1671-4393.24.07.16
    Abstract243)      PDF(pc) (1189KB)(243)       Save
    In February 2021,China promulgated a series of new version standards for infant formulae and follow-on formulae,and it has been officially implemented since February 22,2023.The EU standard is the main reference standard for countries to formulate their own standard.The differences between new National Food Safety Standards 2021 and EU standard were compared,and the possible causes of these differences were analysized. Through comparative analysis,we can more intuitively understand the differences between the new national standard and EU standard,and understand the purpose and significance of the revision of the new national standard, and to provide a reference basis for the subsequent research and the continuous revision of the standards.
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