Relationship Between Agricultural Credits and Agricultural Economy Based on Error Correct Model in Heilongjiang Province

Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.

A Brief Analysis of the Self-correction Model——from the View of Linking Theory

Based on the theory of the post method pedagogy and the teaching practice，this paper proposes a new teaching model，the self-correction model，and explains it from the point of view of linking theory.

Analysis of the Self-correction Model——from the view of cognitive psychology

In view of the learners Chinglish,this paper puts forward a new teaching model-the Self-correction Model and makes an analysis of it from the view of cognitive psychology.

An Online Model Correction Method Based on an Inverse Problem:Part I—Model Error Estimation by Iteration

Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.

Research on the Relationship between Income and Consumption of the Urban Residents in Hunan Province on the Basis of Error Correction Model

By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.

Parameters Sensitivity Analysis and Correction for Concrete Damage Plastic Model

In order to understand the effect of hardening ductility parameters and softening ductility parameters of the concrete damage plastic model in LS-DYNA,a sensitivity and reliability analysis of these parameters through a convenient cube unit test was conducted. The results showed that the peak strength strain was independent of the hardening ductility parameter DH,but affected by AH,BH,and CH. The softening ductility was mainly related to the softening ductility parameter AS,but not affected by the damage ductility exponent BS. In case that the model with default parameters failed to match the AS-controlled damage softening phase,an optimized model with an AS correction was developed. The corrected model with the AS value of 2 matched well with the code model,and exhibited good feasibility in predicting the stress-strain curve of different grades of concrete. Moreover,the practicability of the corrected model was further validated by the conventional triaxial test. The simulated curve exhibited favorable consistence with the trial curve. Therefore,the model with parameter correction could provide a prospective reference for predicting the mechanical properties of concrete.

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.

On the Community Correction Model of the State and Domain Jurisprudence

The"state and domain phenomenon"emerged at the modeling period of the crowd system,and with the development of cities,communities are extruded by the multivariate space and the crowd system,forming the social need of a basic guarantee of community correction. After the expansion of communities,the functional integration period begins,which leads to the period of urban transition,when community and the jurisprudential basis begin to coincide with each other,bringing the community correction model. In the industrialized period,the urban population made great contribution to the community correction of cities. Fromthe perspective of globalization,urban integration will experience the anguish of community correction,and crime and punishment will enter communities of the whole society,forming normal community correction model,so the service systemof community correction begins with the formation of the community-based management system,the crowd-based management range,the fundamental organization structure and the coincidence of the organizing function.

A MAXIMUM ENTROPY CHUNKING MODEL WITH N-FOLD TEMPLATE CORRECTION

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction model.First two types of machine learning models are described.Based on the analysis of the two models,then the chunking model which combines the profits of conditional probability model and rule based model is proposed.The selection of features and rule templates in the chunking model is discussed.Experimental results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score:92.93%.Compared with the ME model and ME Markov model,the new chunking model achieves better performance.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

The Correctional Model of Population Development Equation

The problem of population development has always been the key problem of restricting the development of our country. In order to increase the prediction accuracy, we analyze the exponential model, logistic model and continuous model. Also, the improved discrete population development model is provided to control the quantity and improve the quality of population.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

Application of MODTRAN Model in Atmospheric Correction of HJ/CCD Data

MODTRAN model was used for the atmospheric correction of one HJ-1B / CCD2 image,and the effect of atmospheric correction was evaluated from the changes of spectral characteristics of typical ground objects,the comparison with the MODIS surface reflectance product,and the effect on normalized differential vegetation index( NDVI). The results show that atmospheric correction eliminated the increase effect in visible bands and the absorption in near-infrared band. Atmospheric correction results and the MODIS surface reflectance product with high accuracy were highly consistent in the reflectance of vegetation,water and residents,and the average error of vegetation was 12.8%. According to the comparison of changing characteristics of NDVI before and after atmospheric correction,it could be found that atmospheric correction had corrected NDVI of mixed pixels and made it more reasonable. NDVI of each kind of ground objects improved,among which NDVI of vegetation increased most greatly,which can help differentiate vegetation from other ground objects. In a word,MODTRAN model has a good effect on atmospheric correction of HJ /CCD images.

无缝钢管张力减径工艺减径率分配修正模型理论研究

无缝钢管张力减径工艺减径率分配是孔型分配合理与否的关键步骤,但其分配仍依赖于经验公式。从金属流动的角度分析了减径率的分配规律,在经验的基础上提出了“三点两段统合式”减径率分配修正模型。将修正模型得到的结果与传统模型计算结果以及企业生产数据相比,发现变化趋势相近,减径率和减径量变化曲线更加光滑,应用于生产有利于金属流动。

侧部注浆对已建盾构隧道受荷变形影响试验研究

采用侧部注浆法整治盾构隧道横椭圆变形超限过程中,盾构隧道周围附加土压力及隧道变形特性暂不明晰。通过几何相似比为1∶10的缩尺寸模型试验开展了侧部注浆对已建盾构隧道的影响试验研究,试验结果分析表明:在注浆挤压作用下,将导致隧道周围的土压力在注浆点处局部增大,两边有一定程度的减小,产生了明显的水平被动土拱现象;在注浆附加荷载影响下,模型盾构隧道在注浆点附近发生了竖椭圆变形,而在离注浆点较远处则发生了横椭圆变形,且隧道发生了反弯现象;注浆过程中发生了“渗透→挤密→劈裂”扩散模式与“挤密→劈裂→渗透”扩散模式,并阐明了两种浆液扩散模式的扩散机制;隧道外壁附加荷载主要由浆液通过渗透扩散与劈裂扩散的浆液压力所形成,同时浆液挤压土体变形对隧道周围的浆液也将形成一定的挤压荷载。

上市公司影子银行化对金融稳定的影响与机制研究

影子银行因其规模的日益扩大及其带来的巨大影响而受到政府与学术界的广泛关注。本文基于上市公司财务数据和金融稳定相关数据,运用CRITIC指数构建法和误差修正模型,对上市公司影子银行化对金融稳定的影响与机制进行了理论分析与实证检验。研究发现:上市公司影子银行化提高了经济金融化程度,进而增加金融脆弱性而影响金融稳定。金融稳定更多受到东部、中部地区上市公司影子银行化的影响。相较于规模较小与盈利能力较弱上市公司,规模较大上市公司与盈利能力较强上市公司影子银行化对金融稳定造成的影响更大。研究结果有助于深化影子银行发展对金融稳定影响的理解,对强化上市公司金融化行为监管,维护金融稳定,实现金融高质量发展具有重要的政策意义。

一体化视角下涉毒类社区矫正对象矫正工作的实践难点及破解路径——兼论社区矫正与戒毒模式之间的协作

涉毒类社区矫正对象是社区矫正对象中的特殊群体,具有身患疾病多、毒瘾戒断难、再犯风险高、社会排斥大等特点,需要予以特别关注。我国现行社区矫正法律规范并未专门规定涉毒类社区矫正对象及特别的矫正措施,社区矫正制度与不同戒毒模式之间存在适用界限,加之现有社区矫正工作人员数量有限及戒毒专业知识不足,导致实践中矫正此类群体工作难度大,增加了监管压力与社会风险。立法机关适时完善法律规范,增加针对涉毒类社区矫正对象的特殊条款及相应的矫正措施,为矫正特殊群体提供指导与保障;同时,从一体化视角出发,社区矫正机构整合不同戒毒模式的优势资源,促进社区矫正与戒毒模式间的协作,以提高教育矫正质量与刑事执行效果。

广东人口规模预测及其对经济增长的影响研究

以1995-2021年广东省常住人口数据为基础,采用多种模型对2025-2040年广东常住人口规模进行了综合预测。利用人口惯性因子、政策调整因子、经济发展因子对预测结果进行修正,广东常住人口规模在2040年将达到1725842万人。计量实证分析表明,广东常住人口对经济增长存在显著的促进作用,其发挥作用的渠道既包括供给侧的劳动就业和人力资本,也包括需求侧的消费与投资。政策上应注重吸引外来人口持续流入,并将现有人口优势转化为经济增长优势,从供给侧与需求端双管齐下,充分释放人口红利。