Moderate scale and realization potential of new citrus-planting business entities in hilly and mountainous areas in China

The natural and economic conditions of agricultural resources vary greatly in China,especially in hilly and mountainous areas.The phenomenon of land fragmentation has become increasingly prominent,so that large-scale operations cannot be fully achieved in a short period of time,and the centralized and continuous scale of operations cannot be realized in China.In addition,with increasingly strict farmland protection and agricultural land use control systems,the issues of nongrain production and nonagricultural cultivated land use have become increasingly restricted.Thus,it is quite necessary to determine the appropriate scale of operations and the means to achieve moderately scaled operations for the new business entities.On the basis of microsurvey data for 108 new citrus-planting business entities in the modern agricultural park in the Chongqing's Jiangjin District,an area with long citrus-planting history,we measured the moderate scale of new citrus-planting business entities to maximize profit using a translog growth model.According to the projection pursuit model,we evaluated the suitability conditions of citrus planting in garden land,woodland,grassland,and general cultivated land in the study area.We then explored the potential for new moderate-scale business entities within different farming radii.The results showed that large-scale planting conditions of citrus in garden land,forest land,grassland,and general cultivated land in the study area were suitable,and the proportion of high-suitable and mediumsuitable land was 73.42%.Under the existing social and economic conditions,the moderate scale of new citrus-planting business entities in the study area was 1.8–2.7 hm^(2).In particular,its rankings from large to small were agricultural enterprises(17.19–25.78 hm^(2)),farmer cooperatives(16.88–25.33 hm^(2)),big growers and breeders(6.39–9.59 hm^(2)),and family farms(5.02–7.53 hm^(2)).In the sample of 108 households,only 47.22%of the entities achieved moderate-scale operation,of which 25%of the entities achieved a scale greater than moderate operation.However,52.78%of the entities achieved a scale of less than moderate operation.These entities would have to transfer the surrounding adjacent garden land,forest land,grassland,or general cultivated land to achieve largescale land management.The land area of the adjacent gardens in a 1000 m farming radius could meet the moderate-scale operation demand of the vast majority of new business entities.For the vast majority of new business entities,the land area of the adjacent woodland,grassland,and general cultivated land could supplement the garden land to achieve moderate-scale operation in a 500 m farming radius.If the land area with moderate suitability and high suitability is prioritized,the land area in the adjacent gardens in the 1000 m farming radius could meet the moderate-scale operation demand for the vast majority of new business entities.Within the 500 m farming radius,the vast majority of new business entities have achieved moderate-scale operations if the land area adjacent to forest land,grassland,and general arable land was supplemented by gardens;however,a few entities could not achieve moderate-scale operations.From the village perspective,gardens were prioritized.Sixteen villages had planting areas and planting suitability that exceeded the average level of the study area,accounting for 23.53%.If combined with the reserve potential of the garden land,eight villages could improve their potential,accounting for 11.76%.Therefore,the conditions of large-scale citrus planting in the study area should be further improved,and the scale expansion of new citrus-planting business entities should receive additional scientific guidance.

Optimization of Rural Governance Structure under the Development of New Agricultural Management Entities

The positive impact of the development of new agricultural business entities and their institutional systems on the optimization of rural governance structures can be examined from various perspectives,including the state,market,rural society,urban-rural relations,and grassroots governance structure in rural communities.The development of these new agricultural business entities has not only redefined but also restructured the power distribution and governance patterns in rural developing countries,markets,and villages.The rural social order has evolved into a ternary mutual structure governance pattern,often referred to as the“state market rural”model.This transformation has prompted adjustments in the national economic and social policy structure and management systems at both macro and micro levels.It has led to the reshaping of power dynamics,benefit distribution,and governance structures in both urban and rural areas,resulting in significant changes to the economic and social fabric of rural regions.Furthermore,the grassroots governance structure in rural society,characterized by“township governance and village governance,”is undergoing continuous development and improvement.This transition is marked by a shift towards a collaborative governance structure that encourages diverse participation.Building upon the aforementioned optimizations,the rural governance structure now exhibits new characteristics.These include a more extensive and diverse range of rural governance mechanisms,increased openness in governance processes,and a heightened synergy among various governance mechanisms.This dynamic evolution reflects a richer,more diverse,and more open approach to rural governance,fostering a stronger collaborative effort in the pursuit of effective governance.

沉积模拟实验信息系统的设计与实现

多年来,国内外地质学家在沉积模拟实验研究中积累了海量的实验数据和资料,但目前缺少对此类信息进行规范化管理和共享应用的软件系统。对此,提出浏览器/服务器(B/S)模式的沉积模拟实验信息系统,实现了沉积模拟实验的实验设计方案、实验观测数据、实验结果等信息的集成管理、高效检索和可视化分析等功能,从而为沉积模拟实验的网上信息化管理与分析应用提供可行方案。最后,以某油田辫状河储层沉积模拟实验为例,介绍了系统中实验项目检索、实验详情浏览、砂体测量数据的统计分析等功能的应用效果,说明了本系统的实用性。本系统的研发与应用可极大地方便沉积模拟实验数据的管理、分享及分析工作。

基于ASP.NET MVC架构在精炼炉RH二级客户端设计与实现

为了实现基于B/S框架的精炼炉RH二级客户端系统,采用基于.NET平台的ASP.NET MVC的Web开发框架,使用了MVC(Model-View-Controller)三层模式,利用基于ADO.NET为基础的Entity Framework对象关系映射器(简称ORM)框架技术,与数据库之间进行交互,Web界面采用Razor视图引擎,使得代码开发更加简洁、规范。基于MVC分层模式开发的精炼炉RH二级客户端系统,具有较好的可复用性、可扩展性,并且易于部署和维护。

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

SciCN:A Scientific Dataset for Chinese Named Entity Recognition

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://github.com/yangjingla/SciCN.git.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

SHEL:a semantically enhanced hardware-friendly entity linking method

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

Implicit Modality Mining: An End-to-End Method for Multimodal Information Extraction

Multimodal named entity recognition(MNER)and relation extraction(MRE)are key in social media analysis but face challenges like inefficient visual processing and non-optimal modality interaction.(1)Heavy visual embedding:the process of visual embedding is both time and computationally expensive due to the prerequisite extraction of explicit visual cues from the original image before input into the multimodal model.Consequently,these approaches cannot achieve efficient online reasoning;(2)suboptimal interaction handling:the prevalent method of managing interaction between different modalities typically relies on the alternation of self-attention and cross-attention mechanisms or excessive dependence on the gating mechanism.This explicit modeling method may fail to capture some nuanced relations between image and text,ultimately undermining the model’s capability to extract optimal information.To address these challenges,we introduce Implicit Modality Mining(IMM),a novel end-to-end framework for fine-grained image-text correlation without heavy visual embedders.IMM uses an Implicit Semantic Alignment module with a Transformer for cross-modal clues and an Insert-Activation module to effectively utilize these clues.Our approach achieves state-of-the-art performance on three datasets.

基于MVVM架构的智能储库系统

智能储库系统具备对起重机进行智能调度的功能,通过减少或替代人工操作达到提高生产效率并降低运营成本的目的。文中详细叙述了智能储库系统强大的记录和追踪货物信息能力,并对系统中各功能进行创新设计,从而实现精准的库存管理以及根据需求变化智能调度起重机。在库位管理方面,仓库被划分为不同的区域,根据货物的大小分配库位,这样可以进一步提高存储和检索效率。调度系统掌握货物的位置信息,能够制定合理的路线规划,并将任务分配给起重机执行,以实现多台起重机的协同作业。

Extraction and Evaluation of Knowledge Entities from Scientific Documents

As a core resource of scientific knowledge,academic documents have been frequently used by scholars,especially newcomers to a given field.In the era of big data,scientific documents such as academic articles,patents,technical reports,and webpages are booming.The rapid daily growth of scientific documents indicates that a large amount of knowledge is proposed,improved,and used(Zhang et al.,2021).

Number Entities Recognition in Multiple Rounds of Dialogue Systems

As a representative technique in natural language processing(NLP),named entity recognition is used in many tasks,such as dialogue systems,machine translation and information extraction.In dialogue systems,there is a common case for named entity recognition,where a lot of entities are composed of numbers,and are segmented to be located in different places.For example,in multiple rounds of dialogue systems,a phone number is likely to be divided into several parts,because the phone number is usually long and is emphasized.In this paper,the entity consisting of numbers is named as number entity.The discontinuous positions of number entities result from many reasons.We find two reasons from real-world dialogue systems.The first reason is the repetitive confirmation of different components of a number entity,and the second reason is the interception of mood words.The extraction of number entities is quite useful in many tasks,such as user information completion and service requests correction.However,the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks.To address these problems,in this paper,we propose a comprehensive method for number entity recognition,which is capable of extracting number entities in multiple rounds of dialogues systems.We conduct extensive experiments on a real-world dataset,and the experimental results demonstrate the high performance of our method.

Target Entities of Total Quality Management Based on the New TQM and Three-Dimensional Unification Value Models

In recent years, the method of TQM (Total Quality Management) is widely recognized. However, contents of target entities for assessment are various, and it is very difficult to define the whole scope of TQM. On the other hand, it is very important to define the whole target entities of quality management about the TQM because lack of important target entities may cause significant risk of loss in future. Furthermore, target entities of TQM should correspond to needs and priority of an objective requirement and that should be based on the consideration of basic principle of quality management. In the previous study, we have proposed the “framework of new TQM” of assessment for a total quality management of organizations based on the original concept of “TQM matrix”. On the other hand, we have proposed the view point of “Three-Dimensional Unification Value Models” for an evaluation of system product. Therefore, in this paper, we propose the target entities of whole assessment of quality management of organization totally based on the consideration of “New TQM framework” and the view point of “Three-Dimensional Unification Value Models”. Also, this paper proposes the result of verification based on the result of comparison between proposed target entities of assessment and American Malcolm Baldrige Prize for Performance Excellence.

数据库查询中LINQ to Entities应用研究

Entity Framework是微软自.NET 3.5后力推的数据访问技术,其中的LINQ to Entities提供了查询关系数据库中的实体模型方式。主要介绍了使用LINQ to Entities进行数据库查询的不同方法,以及每种方法涉及到的查询语言的语法和程序调用的方法,并对这些方法的使用场景、执行效率进行了比较。

Analysis on Function Orientation and Development Countermeasures of New Agricultural Business Entities

Along with the rapid advance of industrialization and urbanization process, fostering new agricultural business entities become inevitable for agricultural transformation and the construction of agricultural modernization in China. The status of the new agricultural business entities determines the level of modern agricultural development. In recent years, new agricultural business entities have grew rapidly. However, there are still many problems including the difficulties in financing loans, inadequate agricultural insurance system, bad implementation of agricultural subsidies, jagged agricultural talents and so on. In order to foster new agricultural business entities, countermeasures should be carried out to ensure financial support, perfect the agricultural insurance, strengthen the level of agricultural subsidies, strive to develop the degree of specialization agricultural operators and so on.

Research on Financial Support for the New Types of Agricultural Management Entities from the Perspective of the Rural Revitalization Strategy

Both the escape from the predicament of traditional financial support in rural areas and the cultivation of new types of agricultural management entities underlie,at a micro level,the improvement of a new-type of agricultural management system,and offer an important guarantee for the implementation of a rural revitalization strategy.In reference to the demands of carrying out reform,activating factors,invigorating entities and stimulating markets during the implementation of this rural revitalization strategy,we are applying a financing preference theory that infers and analyzes the excessive preference for new-type agricultural management entities(family farms,specialized farmer cooperatives,specialized large family farms,and modern agricultural enterprises)regarding government subsidies(quasi-equity financing).Our analysis has identified crucial factors in the issue and predicts that government subsidies(quasi-equity financing)will crowd out financial support funding(quasi-debt financing),and we offer empirical proof obtained through statistical modeling.As our results indicate,financing costs,free cash flows,and the perceived income adequacy(PIA)of new-type agricultural management entities all have significant influence upon decision-making for debt financing by such entities.Therefore,with the concrete contents of the formulation of policies concerning the financial support for rural agricultural strategy,one not only needs to consider the further decrease of financing costs,but also should take into account both the designing of cash flow mechanism in the process of paying both principal and interest,and the improvement of bankruptcy rules for agricultural management entities to accelerate the transformation of family farms,specialized farmer cooperatives,and specialized large family farms,towards modern agricultural enterprises.Meanwhile,upgrades to the supply chains of the agriculture industry,improvements to the construction of the rural financial information system,building an accounting system that meets the requirements of the rural revitalization strategy,and giving full play to the policies for financial support,which assume an important role in activating factors and markets during the implementation of the rural revitalization strategy,are also anticipated.