Dynamic scaling characteristics of single-phase natural circulation based on different strain transformations

To understand the dynamical system scaling(DSS)analysis theory,the applicability of DSSβ-andω-strain transformation methods for the scaling analysis of complex loops was explored.A simplified model consisting of two loops was established based on the primary and secondary sides of a nuclear reactor,andβ-andω-strain transformation methods were used to ana-lyze the single-phase natural circulation in the primary circuit.For comparison with the traditional method,simplified DSSβ-andω-strain methods were developed based on the standard scaling criterion.The strain parameters in these four methods were modified to form multiple groups of scaled-down cases.The transient process of the natural circulation was simulated using the Relap5 code,and the variation in the dynamic flow characteristics with the strain numbers was obtained using different scaling methods.The results show that both the simplified and standard DSS methods can simulate the dynamic characteristics of natural circulation in the primary circuit.The scaled-down cases in the simplified method exhibit the same geometric scaling and correspond to small core power ratios.By contrast,different scaled-down cases in the standard DSS method correspond to different geometric scaling criteria and require more power.The dynamic process of natural circula-tion can be simulated more accurately using the standard DSS method.

Two‑parameter dynamical scaling analysis of single‑phase natural circulation in a simple rectangular loop based on dilation transformation

Scaling analysis is widely used to design scaled-down experimental facilities through which the prototype phenomena can be effectively evaluated.As a new method,dynamic system scaling(DSS)must be verified as a rational and applicable method.A DSS method based on dilation transformation was evaluated using single-phase natural circulation in a simple rectangular loop.The scaled-down cases were constructed based on two parameters—length ratio and dilation number—and the corresponding transient processes were simulated using the Relap5 computational code.The results show that this DSS method can simulate the dynamic flow characteristics of scaled-down cases.The transient deviation of the temperature difference and mass flow rate of the scaled cases decrease with increases in the length ratio and dilation number.The distortion of the transient temperature difference is smaller than that of the mass flow;however,the overall deviation is within a reasonable range.

Transformations of Hematite-Magnetite in Natural System: A Review

Magnetite and hematite,which are main existing forms of iron oxides in nature,could transform differently in various geological environment.Traditionally,the transformation of magnetite and

基于Transformer的司法文书命名实体识别方法

命名实体识别是自然语言处理领域的关键任务之一,是实现下游任务的基础。目前针对司法领域的相关研究相对较少,司法系统的信息化和智能化转型仍有许多问题亟需解决。相比其他领域的文本,司法文书存在专业性强、语料资源少等局限,导致现有的司法文书识别结果较低。因此,从以下3方面开展研究:首先,提出了一种多标签层级迭代的文本标注方式,可以对原始司法文书文本进行自动化标注,同时有效地提升司法文书命名实体识别任务的实体识别效果;其次,提出了一种交融式的Transformer神经网络模型,对汉字固有属性的深层特征进行了充分利用,用于对司法文书进行命名实体识别;最后,对所提出的标注方法和模型与其他神经网络模型进行了对比实验。所提出的文本标注方式可以较为准确地实现司法文书的标注任务;同时,所提出的模型在通用数据集中相对于对照模型有较大的提高,并在司法领域数据集中取得了良好的效果。

规则压缩模型和灵活架构的Transformer加速器设计

基于注意力机制的Transformer模型具有优越的性能,设计专用的Transformer加速器能大幅提高推理性能以及降低推理功耗。Transformer模型复杂性包括数量上和结构上的复杂性,其中结构上的复杂性导致不规则模型和规则硬件之间的失配,降低了模型映射到硬件的效率。目前的加速器研究主要聚焦在解决模型数量上的复杂性,但对如何解决模型结构上的复杂性研究得不多。该文首先提出规则压缩模型,降低模型的结构复杂度,提高模型和硬件的匹配度,提高模型映射到硬件的效率。接着提出一种硬件友好的模型压缩方法,采用规则的偏移对角权重剪枝方案和简化硬件量化推理逻辑。此外,提出一个高效灵活的硬件架构,包括一种以块为单元的权重固定脉动运算阵列,同时包括一种准分布的存储架构。该架构可以高效实现算法到运算阵列的映射,同时实现高效的数据存储效率和降低数据移动。实验结果表明,该文工作在性能损失极小的情况下实现93.75%的压缩率,在FPGA上实现的加速器可以高效处理压缩后的Transformer模型,相比于中央处理器(CPU)和图形处理器(GPU)能效分别提高了12.45倍和4.17倍。

基于语言和视觉融合Transformer的指代图像分割

针对指代图像分割任务中存在语言表达歧义、多模态特征对齐不充分、对图像整体理解不全面等问题,提出一种基于Transformer特征融合与对齐的多模态深度学习模型。该模型使用优化的Darknet53图像特征提取骨干网络,加强了对全局特征理解能力。使用了卷积神经网络结构、双向门控循环单元Bi-GRU结构和自注意力机制相互结合的语言特征提取结构,挖掘深层次语义特征,消除语言表达的歧义性。构建了基于Transformer的特征对齐结构,以提升模型的分割细节和分割精度。最后,采用平均的交并比mIoU和在不同阈值的识别精度作为模型评估指标,通过实验证明所提模型可以充分融合多模态的特征,理解多模态特征的深层语义信息,模型识别结果更加准确。

A novel system for Agrobacterium-mediated transformation of wheat(Triticum aestivum L.) cells

A new approach for transforming the cultured cells of wheat(Triticum aestivum L. cv. Ganmai 8) was developed using Agrobacterium tumefaciens. The features of the optimum procedure were: (a) both combined synthetic signal molecules and multiple natural extracts from susceptible plants were used to pretreat the primary vigorous Agrobacterium(PVA) cells for approximately 16 h; (b) the gyratory magnetic field condition was used during cocultivation; (c) the cocultivating period and selecting condition were modified; (d) the recipient cells were at exuberant metabolism and active division while infected with Agrobacterium. Both neomycin phosphotransferase and nopaline synthase assays demonstrated the expression of NPT II and NOS genes, located on the T-DNA segment of chimaeric plasmid pGV3850::1103neo, in transformed wheat cell colonies by adopting the techniques of dot blot, ndPAGE or high voltage paper electrophoresis. Integration of the foreign genes into wheat genome was confirmed by Southern blot hybridization. Moreover, a relatively rational method was described for the estimation of transformation frequencies from cultured cell levels.

基于TF-IDF和多头注意力Transformer模型的文本情感分析

文本情感分析旨在对带有情感色彩的主观性文本进行分析、处理、归纳和推理,是自然语言处理中一项重要任务。针对现有的计算方法不能充分处理复杂度和混淆度较高的文本数据集的问题,提出了一种基于TF-IDF(Term Frequency-Inverse Document Frequency)和多头注意力Transformer模型的文本情感分析模型。在文本预处理阶段,利用TF-IDF算法对影响文本情感倾向较大的词语进行初步筛选,舍去常见的停用词及其他文本所属邻域对文本情感倾向影响较小的专有名词。然后,利用多头注意力Transformer模型编码器进行特征提取,抓取文本内部重要的语义信息,提高模型对语义的分析和泛化能力。该模型在多领域、多类型评论语料库数据集上取得了98.17%的准确率。

Transformation of Erosion Types on Loess Slope by REE Tracking

REE Tracking was adopted to study the transformation of erosion types on loess slope and the results indicated that the slope runoffs was correlated with the erosion ratios at the coefficients ranging within 0.84～0.94 and this indicated the variation tendency of erosion ratios basically agreed with the runoffs; the major erosion type is sheet erosion during the initial slope runoff generation and the sheet erosions separately accounted for 71%, 48% and 49% of the total; as rainfalls went on, the rill erosions continuously increased, indicating gradual transformation from sheet erosion into rill erosion. The examination of the origins of erosion silts revealed the relative erosion was always below 10% in the upper slope section; comparison of the relative rill erosions in different slope sections showed that rill erosions mainly originated in the section spanning from 0 to 4 m away from slope foot.

Natural Transform for Solving Fractional Models

In this paper, we present a novel technique to obtain approximate analytical solution of fractional physical models. The new technique is a combination of a domain decomposition method and natural transform method called a domain decomposition natural transform method (ADNTM). The fractional derivatives are considered in Caputo sense. To illustrate the power and reliability of the method some applications are provided.

The Natural Transform Decomposition Method for Solving Fractional Klein-Gordon Equation

In this paper, a coupling of the natural transform method and the Admoian decomposition method called the natural transform decomposition method (NTDM), is utilized to solve the linear and nonlinear time-fractional Klein-Gordan equation. The (NTDM), is introduced to derive the approximate solutions in series form for this equation. Solutions have been drawn for several values of the time power. To identify the strength of the method, three examples are presented.

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

This research investigates the dielectric performance of Natural Ester(NE)using the Partial Differential Equation(PDE)tool and analyzes dielectric performance using fuzzy logic.NE nowadays is found to replace Mineral Oil(MO)due to its extensive dielectric properties.Here,the heat-tolerant Natural Esters Olive oil(NE1),Sunflower oil(NE2),and Ricebran oil(NE3)are subjected to High Voltage AC(HVAC)under different electrodes configurations.The break-down voltage and leakage current of NE1,NE2,and NE3 under Point-Point(P-P),Sphere-Sphere(S-S),Plane-Plane(PL-PL),and Rod-Rod(R-R)are measured,and survival probability is presented.The electricfield distribution is analyzed using the Partial Differential Equation(PDE)tool.NE shows better HVAC with stand capacity under all the electrodes configuration,especially in the S-S shape geometry.The exponential function is developed for the oils under different elec-trode geometry;NE shows a higher survival probability.Likewise,the most influ-ential dielectric properties such as breakdown voltage,kinematic viscosity,and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model.The surface model indi-cates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity.Based on the surface models of PDE and fuzzy,it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content.Hence the application of NE in the transformer application is highly safe and possesses extended life.

Nature and Kinetic Architecture： The Development of a New Type of Transformable Structure for Temporary Applications

The contemplation of contemporary architectural designs shows an increasing demand for the development of more adaptable, flexible and transformable structures. This type of structures can adapt with different environmental conditions and meet different functions. This can help in reducing environmental waste and pollution associated with many buildings and above all can save on cost and time. Natural systems have inspired human being, since they began to build and design. Architects and designers have utilized nature as one of the main resources of information for the creation of innovative architectural spaces. One of the unique features of natural structures is the way that their components open and close in order to respond to a particular requirement or the environmental changes. This aspect has inspired many designers for the development of transformable architectural structures that can change their shape and geometry to be able to adapt with specific conditions. To make a way toward the design of transformable structure for temporary applications, the authors have developed a new type of adaptable structures according to natural forms. The proposed design applies the transformation principles that exist in potato＇s flower and the movement mechanisms used in a spider＇s leg. The design is able to fit to different topographies and have a potential to be folded to a very compact state in a very short period of time. The detailed design and the different configurations of the system applications will be presented in this paper. The result of the study shows that using modular triangular plates can create a changeable module that is not only able to respond to different functions and environmental changes but it is also able to shape different configuration to be able to respond to different user＇s ambitions. The compactability of this structure into 1/3 of its base dimensions; makes its transportation fast and with minimum costs. These capabilities make this structure suitable for temporary buildings such as exhibitions, temporary settlements or hospital in damaged areas.

Helmholtz Theorems, Gauge Transformations, General Covariance and the Empirical Meaning of Gauge Conditions

It is well known that the use of Helmholtz decomposition theorem for static vector fields , when applied to the time dependent vector fields , which represent the electromagnetic field, allows us to obtain instantaneous-like solutions all along . For this reason, some people thought (see e.g. [1] and references therein) that the Helmholtz theorem cannot be applied to time dependent vector fields and some modification is wanted in order to get the retarded solutions. However, the use of the Helmholtz theorem for static vector fields is correct even for time dependent vector fields (see, e.g. [2]), so a relation between the solutions was required, in such a way that a retarded solution can be transformed in an instantaneous one, and conversely. On this paper we want to suggest, following most of the time the mathematical formalism of Woodside in [3], that: 1) there are many Helmholtz decompositions, all equally consistent, 2) each one is naturally related to a space-time structure, 3) when we use the Helmholtz decomposition for the electromagnetic potentials it is equivalent to a gauge transformation, 4) there is a natural methodological criterion for choosing the gauge according to the structure postulated for a global space-time, 5) the Helmholtz decomposition is the manifestation at the level of the fields that a gauge is involved. So, when we relate the retarded solution to the instantaneous one what we do is to change the gauge and the space-time. And, if the Helmholtz decompositions are related to a space-time structure, and are equivalent to gauge transformations, each gauge transformation is natural for a specific space-time. In this way, a Helmholtz decomposition for Euclidean space is equivalent to the Coulomb gauge and a Helmholtz decomposition for the Minkowski space is equivalent to the Lorenz gauge. This leads us to consider that the theories defined by different gauges may be mathematically equivalent, because they can be related by means of a gauge transformation, but they are not empirically equivalent, because they have quite different observational consequences due to the different space-time structure involved.

Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

Analysis of Economic Benefits Brought by Industrialization of Natural Enemy Insects

In the field of biological control of agricultural and forestry pests,natural enemy insects play an important role,constituting the core of modern integrated pest management(IPM)strategy,providing solid support for the zero growth goal of chemical pesticide use,and helping the sustainable development of green agriculture.The rise of the natural enemy insect industry has become a key driver of the transformation of green agriculture,injecting vitality into the sustainability and ecological protection of agriculture.With the increasing concern about food safety,environmental protection and ecological balance all over the world,the application scope of natural enemy insects as biological control means is constantly expanding.Its unique ecological adaptability and environmentally friendly characteristics help to reduce dependence on chemical pesticides,ensure the safety of agricultural products and maintain ecological diversity.The purpose of this study is to analyze the current situation of natural enemy insect industry,evaluate its cost and benefit,emphasize its great significance in promoting the transformation of green agriculture,improving production efficiency and promoting ecological protection by drawing lessons from foreign successful experiences,and explore an efficient and sustainable new agricultural development model.

Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

Arabic Dialect Identification in Social Media:A Comparative Study of Deep Learning and Transformer Approaches

Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.

Multifaceted natural resources and green energy transformation for sustainable industrial development

Climate change is the most phenomenal challenge to humanity,and its roots are intervened with unsustainable industrialization,exercising overexploitation of natural resources.Therefore,the departure from non-renewable to renewables has become inevitable,though thought-provoking.In this respect,we explore how green energy transformation moderates the impacts of multifaceted natural resources on sustainable industrial development in the presence of other covariates involving technological progress,financial development,and economic progress.We compiled data from Group of Seven(G-7)members over the 1995
2018 period and applied panel quantile regression(PQREG)to capture the effects across varying levels of quantiles of sustainable industrial development.Results revealed a positive role of natural gas rents,while coal,forest,and total natural resource rents contributed adverse implications for sustainable industrial development.However,the green energy transformation proved to be the game changer because it not only directly induced sustainable industrial development improvement but also turned the unfavorable effects of coal,forest,and total natural resources into favorable ones by interacting with those multifaceted natural resources.Technological,financial,and economic progress supported sustainable industrial development in G-7 nations,particularly in members with existing middle and upper scales of sustainable industrial development.These findings are robust enough when subjected to different estimation tools.In light of these outcomes,the interaction between green energy transformation and natural resource policy is inevitably critical to attaining natural resource efficiency for sustainable industrial development.Therefore,it is imperative to establish a close policy coordination between advancing green energy technology and allocating natural resource revenue to achieve sustainable development goals(SDGs),with a particular emphasis on SDG-7 and SDG-13。

基于Transformer模型的轨道交通机器翻译系统设计

针对商用机器翻译引擎安全性无法保证、专业化领域翻译精准度低、翻译服务方式单一等问题,通过神经网络模型、知识蒸馏方法、专业语料库构建等技术,设计基于Transformer模型的轨道交通机器翻译系统——“铁译通”,实现文本翻译、文档翻译、Office插件翻译、浏览器插件翻译等多元化功能。应用表明,该系统可为行业用户提供专业化、多元化、定制化、安全性强的机器翻译服务。