基于DSGE模型的科技信贷激励政策研究

通过构建基于全要素生产率厂商异质性的NK-DSGE(新凯恩斯—动态随机一般均衡)模型,模拟科技信贷激励政策的传导机制与政策效果,研究发现政策效果从大到小依次是常规货币政策、政府部门贴息、中央银行再贷款利率、中央银行定向降准、中央银行再贷款抵押率,与我国科技信贷激励政策的实践效果相吻合。进一步研究发现,政府贴息、再贷款利率的外生冲击波动幅度对政策效果影响弹性更大。建议地方政府应和银行加强联动,完善科技信贷风险补偿机制、贷款担保体系;另外,中国人民人行应加强常规货币政策实施精准性,并探索更加灵活的结构型货币政策机制。

Some non-linear height–diameter models performance for mixed stand in forests in Northwest Iran

This study evaluated the total height of trees based on diameter at breast height by using 23 widely used height-diameter non-linear regression models for mixed-species forest stands consisting of Caucasian oak,field maple,and hornbeam from forests in Northwest Iran.1920 trees were measured in 6 sampling plots(every sampling plot has 1 ha area).The fit of the best height–diameter models for each species were compared based on R2,Root Mean Square Error(RMSE),Akaike information criterion(AIC),standard error,and relative ranking performance criteria.In the final step,verification of results was performed by paired sample t-test to compare the observed height and estimated height.Results showed that among 23 height-diameter models,the best models were obtained from the top five ones including Modified-logistic,Prodan,Sibbesen,Burkhart,and Exponential.Comparison between the actual observed height and estimated height for Caucasian oak showed that Modified–Logistic,Prodan,Sibbesen,Burkhart,and Exponential performed better than the others,respectively(There were no statistically significant differences between observed heights and predicted height(p≥0.05)).Prodan,Modified-Logistic,Burkhart,and Loetch evaluated field maple tree height correctly,and Modified-Logistic,Burkhart,and Loetch had better fitness compared to the others for hornbeam,respectively.Although other models were introduced as appropriate criteria,they could not reliably predict the height of trees.Using the Rank analysis,the Modified-Logistic model for the Caucasian oak and Prodan model for field maple and hornbeam had the best performance.Finally,to complement the results of this study,it is suggested to assess how environmental factors such as elevation,climate parameters,forest protection policy and forest structure will modify height-diameter allometry models and will enhance the prediction accuracy of tree heights prediction in mixed stands.

Heavy metals adsorption by banana peels micro-powder: Equilibrium modeling by non-linear models

In this study the copper and lead adsorption efficiency onto banana peels powder was investigated. The agroindustrial waste recovery represents one of the Circular Economy pillars. In the view of the synthesis of an environmentally friendly adsorbent material, the powder was used without any preliminary chemical or thermal activation, but only after simple washing, drying and grinding. The bio-adsorbent was characterized by the FTIR technique and tested in batch mode on synthetic aqueous solutions containing Pb and Cu in the range 10–90 mg·L^(-1). A selection of two(Langmuir, Freundlich) and three(Sips, Redlich–Peterson, Koble–Corrigan) parameter isotherm models was chosen to fit adsorption equilibrium data by non-linear regression procedure. The best fit isotherm model was selected relying on the error function with the lowest average percentage error(APE) value, among those characterized by the highest R^2 values. As expected, the three-parameter models are found to better represent both metals bio-adsorption, with APE and R^2 values always lower and higher, respectively, than the corresponding values obtained for the two-parameter models.

Non-Linear Mathematical Model of the Interaction between Tumor and Oncolytic Viruses

A mathematical modeling of tumor therapy with oncolytic viruses is discussed. The model consists of two coupled, deterministic differential equations allowing for cell reproduction and death, and cell infection. The model is one of the conceptual mathematical models of tumor growth that treat a tumor as a dynamic society of interacting cells. In this paper, we obtain an approximate analytical expression of uninfected and infected cell population by solving the non-linear equations using Homotopy analysis method (HAM). Furthermore, the results are compared with the numerical simulation of the problem using Matlab program. The obtained results are valid for the whole solution domain.

A Statistical Model with Non-Linear Effects and Non-Proportional Hazards for Breast Cancer Survival Analysis

The Cox proportional hazard model is being used extensively in oncology in studying the relationship between survival times and prognostic factors. The main question that needs to be addressed with respect to the applicability of the Cox PH model is whether the proportional hazard assumption is met. Failure to justify the subject assumption will lead to misleading results. In addition, identifying the correct functional form of the continuous covariates is an important aspect in the development of a Cox proportional hazard model. The purpose of this study is to develop an extended Cox regression model for breast cancer survival data which takes non-proportional hazards and non-linear effects that exist in prognostic factors into consideration. Non-proportional hazards and non-linear effects are detected using methods based on residuals. An extended Cox model with non-linear effects and time-varying effects is proposed to adjust the Cox proportional hazard model. Age and tumor size were found to have nonlinear effects. Progesterone receptor assay status and age violated the proportional hazard assumption in the Cox model. Quadratic effect of age and progesterone receptor assay status had hazard ratio that changes with time. We have introduced a statistical model to overcome the presence of the proportional hazard assumption violation for the Cox proportional hazard model for breast cancer data. The proposed extended model considers the time varying nature of the hazard ratio and non-linear effects of the covariates. Our improved Cox model gives a better insight on the hazard rates associated with the breast cancer risk factors.

Load Frequency Control of Small Hydropower Plants Using One-Input Fuzzy PI Controller with Linear and Non-Linear Plant Model

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two- input fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A non- linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si- mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

商业银行普惠金融如何助力共同富裕目标实现?——引入异质性企业和家庭的BGG-DSGE模型分析

经济增长和收入分配改善是共同富裕的应有之义,也是实现共同富裕的重要途径。商业银行普惠金融以中小微企业和农民等弱势群体为主要服务对象,是推进共同富裕的重要力量。本文在包含BGG金融加速器的DSGE模型中引入异质性企业和异质性家庭,从经济增长和收入分配角度分析商业银行普惠金融政策对共同富裕的动态一般均衡影响。校准参数后模拟发现,商业银行普惠金融政策会促进共同富裕。对比分析发现,宽松货币政策虽然会增加总产出,但会加剧收入不平等;同时,商业银行普惠金融政策和小企业技术进步的组合冲击更有利于实现共同富裕。

Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models

This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a non-linear model. On the basis of the error definition,this paper puts forward a new adjustment criterion, SGPE.Last,this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Power Allocation for Wireless Powered MIMO Transmissions with Non-Linear RF Energy Conversion Models

We study a radio frequency(RF) wireless energy transfer(WET) enabled multiple input multiple output(MIMO) system. A time slotted transmission pattern is considered. Each slot can be divided into two phases, downlink(DL) WET and uplink(UL) wireless information transmission(WIT). Since energy conversion efficiency of the energy harvesting circuits are non.linear, the conventional linear model leads to a mismatch for resource allocation. In this paper, the power allocation algorithm considering the practical non.linear energy harvesting circuits is studied. The optimization problem is formulated to maximize the energy efficiency of system with multiple constraints, i.e., the transmission power, the received power and the minimum harvested energy, which is a non.convex problem. We transform the objective function from fractional form into an equivalent objective function in subtractive form and provide an iterative power allocation algorithm to achieve the optimal solution. Numerical results show that our proposed algorithm with the non.linear RF energy conversion models can achieve much better performance than the algorithm with the conventional linear model.

NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Adsorption of Naphthol Green B on unburned carbon：2-and3-parameter linear and non-linear equilibrium modelling

The study deals with adsorption of Naphthol Green B on two unburned carbons and the parent coal,from which the UCs have been created in a fluidised-bed power station.Particular attention has been paid to the adsorption equilibrium modelling:experimental data has been analysed using 2-parameter(Langmuir,Freundlich) and3-parameter(Redlich-Peterson) isotherms — both linear and non-linear regressions have been used for the estimation of the isotherm parameters.In the case of both UCs,the Langmuir isotherm model provides the worst fit,whereas 2-parameter Freundlich and 3-parameter Redlich-Peterson models are both good,from which 3-parameter Redlich-Peterson isotherm provides slightly better results(despite the penalty used for the higher number of parameters).In the case of both UCs,the linear regression of Freundlich and Redlich-Peterson models provides good results(comparable with non-linear regressions).Unlike both UCs,the best fit of the experimental data from the adsorption on the coal has been achieved by the Langmuir isotherm model.The results based on the Freundlich or Redlich-Peterson model were(in this case) somewhat worse.

碳中和目标下负排放策略的综合效应——基于N‑DSGE模型的数值模拟分析

全球气候变化已达“气候紧急状态”,包括中国在内的诸多国家和地区先后宣布碳中和目标和净零排放承诺。在气候变化减缓和传统的适应措施之外,自然科学领域和气候经济学领域探讨负排放技术经济可行性的研究越来越多,但从可持续发展经济学视角对负排放技术综合效应的评估还比较少。该研究基于可持续发展经济学的生态价值理论,将气候变化综合评估模型与宏观经济学动态随机一般均衡(DSGE)模型相结合,构建包含自然生态系统部门的N‑DSGE模型,对负排放技术的气候效应、生态效应、经济效应以及福利效应进行综合评估。评估结果表明:①当可用技术条件下传统减排措施达到最大减排潜力后,可以依靠负排放技术实现净零排放乃至净负排放。②低强度、小规模实施负排放技术具有正向的气候、生态和经济效应,然而随着负排放技术实施强度增大,气候效应增强,经济效应削弱,社会综合福利明显降低。③敏感性分析证明了评估结果的稳健性,并进一步表明负排放技术的实施效果会受到传统减排水平和自然碳汇能力的影响。基于评估结果,应谨慎扩大负排放技术实施规模和实施强度,加强科学技术研发,降低技术成本;对不同负排放技术采取差异化发展策略;加强负排放技术实施的治理,尽可能避免和最小化其负面影响,促进气候、生态和经济等可持续发展目标的协同;部署负排放技术不应放松传统减排的努力,应降低碳达峰峰值,同时提升自然生态系统的碳汇能力。

灾难冲击、宏观经济稳定与货币政策选择——基于DSGE模型的决策效应分析

基于中国1992-2021年宏观经济统计数据,构建了包含全要素生产率灾害影响的新凯恩斯经济体动态随机一般均衡(DSGE)模型,并引入Calvo价格粘性、投资调整成本等因素,分析了数量型和价格型货币政策对我国宏观经济应对灾难冲击的调节效应。在灾难冲击下,我国宏观经济呈现短期下行态势,两种货币政策短期对经济均具有明显改善效应,长期均能促进经济恢复均衡状态。前者短期促进经济水平显著上升,但恢复均衡状态耗时较长。后者调节效应更加缓和,并且经济能够快速恢复其均衡值。因此,政府优化经济政策,相互协调配合,以应对灾难对宏观经济的冲击影响。

Assessing the effects of model parameter assumptions on surface-wave inversion results

Surface-wave inversion is a powerful tool for revealing the Earth's internal structure.However,aside from shear-wave velocity(v_(S)),other parameters can influence the inversion outcomes,yet these have not been systematically discussed.This study investigates the influence of various parameter assumptions on the results of surface-wave inversion,including the compressional and shear velocity ratio(v_(P)/v_(S)),shear-wave attenuation(Q_(S)),density(ρ),Moho interface,and sedimentary layer.We constructed synthetic models to generate dispersion data and compared the obtained results with different parameter assumptions with those of the true model.The results indicate that the v_(P)/v_(S) ratio,Q_(S),and density(ρ) have minimal effects on absolute velocity values and perturbation patterns in the inversion.Conversely,assumptions about the Moho interface and sedimentary layer significantly influenced absolute velocity values and perturbation patterns.Introducing an erroneous Mohointerface depth in the initial model of the inversion significantly affected the v_(S) model near that depth,while using a smooth initial model results in relatively minor deviations.The assumption on the sedimentary layer not only affects shallow structure results but also impacts the result at greater depths.Non-linear inversion methods outperform linear inversion methods,particularly for the assumptions of the Moho interface and sedimentary layer.Joint inversion with other data types,such as receiver functions or Rayleigh wave ellipticity,and using data from a broader period range or higher-mode surface waves,can mitigate these deviations.Furthermore,incorporating more accurate prior information can improve inversion results.

Influence of Confined Concrete Models on the Seismic Response of RC Frames

In this study, the influence of confined concrete models on the response of reinforced concrete structures is investigatedat member and global system levels. The commonly encountered concrete models such as Modified Kent-Park, Saatçioğlu-Razvi, and Mander are considered. Two moment-resisting frames designed according to thepre-modern code are taken into consideration to reflect the example of an RC moment-resisting frame in thecurrent building stock. The building is in an earthquake-prone zone located on Z3 Soil Type. The inelasticresponse of the building frame is modelled by considering the plastic hinges formed on each beam and columnelement for different concrete classes and stirrups spacings. The models are subjected to non-linear static analyses.The differences between confined concrete models are comparatively investigated at both reinforced concretemember and system levels. Based on the results of the comparative analysis, it is revealed that the column behaviouris mostly influenced by the choice of model, due to axial loads and confinement effects, while the beams areless affected, and also it is observed that the differences exhibited in the moment-curvature response of columncross-sections do not significantly affect the overall behaviour of the global system. This highlights the critical roleof model selection relative to the concrete strength and stirrup spacing of the member.

基于DREAM算法的DSGE模型参数估计精度研究

文章将人工智能算法中的DREAM算法首次应用到动态随机一般均衡模型的参数估计中,并以动态随机一般均衡模型LS(2005)为例对该算法的估计精度进行了系统分析,研究结果表明:根据待估参数随机抽样序列的箱线图来看,由DREAM算法产生的待估参数随机抽样序列的箱体长度均比RWMH和IMH算法产生的箱体长度要长,说明由DREAM算法产生的参数估计序列的分散程度比RWMH和IMH算法要大,表明了DREAM算法遍历参数空间范围更为广泛,算法逃逸局部最优值的能力更强。另外,从箱线图中的中位数数值来看,除了5个参数以外,由DREAM算法产生的参数估计序列的中位数相比RWMH和IMH算法,与真实数据生成过程更为接近,说明由DREAM算法产生的参数估计值大部分都集中在参数的真值附近。由于DREAM算法依据IQR统计方法除去无用链,故由DREAM算法产生的参数估计序列的异常值也明显降低,而RWMH算法产生的参数估计序列的异常值尤其多。从待估参数的90%最大后验密度可信区间来看,DREAM算法产生的待估参数90%最大后验密度可信区间除了3个参数以外,其余全部包含了真值,而传统的RWMH和IMH算法分别只有7个和1个区间包含了真值,表明DREAM算法的估计不确定性远小于传统的RWMH和IMH算法。最后,根据待估参数的无效因子来看,DREAM算法产生的待估参数序列与传统的RWMH和IMH算法相比,其相关性更弱,即无效因子数值更小,这一点进一步验证了DREAM算法遍历整个参数空间的能力更强。

开放经济条件下三类农业补贴政策的影响效应研究——基于农业开放DSGE模型

随着高水平对外开放的持续推进和农产品国际贸易环境的日趋复杂,厘清开放经济条件下农业补贴政策的影响效应对于保障粮食安全具有重要意义。本文基于农业开放DSGE模型,比较分析了开放经济条件下农业固定资本补贴政策、农业短期资本补贴政策和农地流转补贴政策的影响效应,并进一步阐释了“三量齐增”问题产生的深层原因。研究结果表明,农业固定资本补贴政策和农业短期资本补贴政策是激发农业生产潜力和促进农民增收的重要支持政策。然而,这两类补贴投入的持续增加也会引起农业生产成本的不断攀升和农产品比较优势的持续削弱,从而引起农产品进出口的结构失衡和“三量齐增”问题的进一步加剧。农地流转补贴政策能够有效促进农地流转,通过推动农业经营规模化,实现对农业生产要素资源配置结构的优化,是缓解农业生产成本上升压力和提高农产品比较优势的重要引导政策。因此,合理搭配使用各类农业补贴政策,构建以优化农业生产要素配置结构为目标的农业补贴政策体系是农业农村现代化顺利实现的关键政策保障。

Extracting Eye Models from MRI Scans Using U-Net-Based Deep Learning Framework

.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.