Re-measuring International Short-term Capital Flow into China with Varying Methods

This paper.fi＇rst conducts a systematic review of domestic and foreign scholars＇ approaches to predicting short-term capital flows, then employs a combination of both direct and indirect methods to carry out its analysis. Three kinds of indicators, both specific and general, are applied in both methods. Thorough consideration is given to short-term international capital inflow from trade, other current account items, capital account, and errors and omissions, as well as other channels through which short term capital might accrue to a nation＇s balance. Based on a comprehensive comparison of year-on-year data, this paper also estimates monthly data using a simplified, indirect calculation approach. Estimates show that, despite a degree of difference in results between methods, most estimates are highly consistent for a given period. Based on monthly estimates, we conclude that turbulence in international financial markets （i.e., the United States subprime mortgage crisis and the European sovereign debt crisis） has had a major impact on China ＇s short-term capital flow.

How Demographic Structure Determines International Capital Flows

This paper investigated the relationship between demographic structure and international capital flows with panel data of 190 countries over the past 60 years＇ and projection data for the 21st century. As found, from a global perspective, the current account balance （CAB） is negatively related to the dependency ratio, and orresponding to continuous change, international eapital flows tend to move from ＂adult countries＂ to ＂aged or young countries.＂ Since the middle of the 20th century, the U.S., Europe, Japan, China, Southeast Asia, Central Asia, South Asia, West Asia and Africa took turns in exporting capital to other countries. In the 2lst century, Europe, the U.S., Australia and Singapore will keep importing capital, while China in the 2030s, and Southeast Asia in the 2050s will in turn become the main capital importers. Given the demographic structure of China and the world, the future pattern of the international capital flows requires more serious concern and responses.

The Effect of Net Working Capital on Jordanian Industrial and Energy Sectors＇ Net Cash Flows Employed in Operating Activities

The primary objective of the cash flow statement is to provide useful, meaningful, and relevant information about the cash receipts and cash payments of a firm during a given period of time. Decision makers can achieve extra features of the change in net assets, the firms＇ financial position （liquidity and solvency）, and the firm＇s ability to adapt to changing circumstances by affecting the amount and timing of cash flows. Cash flow statements improve comparability as they are not affected by differing accounting policies used for the same type of transactions or events. This study aims to verify the effect of net working capital （NWC）, as an indicator of a company＇s short-term liquidity or its ability to meet short-term obligations, on Jordanian industrial and energy sectors＇ net operating cash flows （NOCF）. A simple liner regression is used to test a period of 2008-2011 in order to conclude the extent of the effect on industrial and energy sectors. The study showed that there is a significant effect of the independent variable NWC on the dependent variable NOCF among industrial and energy sectors in Jordanian market. Whereas the adjusted R-squared of test is 0.854, changes in NOCF in Jordanian industrial and energy sectors have been described by NWC. Also, the study reached that the utilities and energy sector has the lowest NWC, while the mining and extraction sector has the highest. And, electrical industries sector has the lowest NOCF, while the mining and extraction sector has the highest. Moreover, the study showed that the printing and packaging sector has the lowest NWC, while the mining and extraction sector has the highest. And, glass and ceramic industries sector has the lowest NOCF, while the utilities and energy sector has the highest.

The Economic Growth with the Flows of Capital ＆ Technology

The difference of regional economy comes from capital dissymmetry, technology dissymmetry, manpower dissymmetry and the information dissymmetry. In the knowledge-based economic ages, globalization and information exceed any age of the history. It provides the new terrace for the balanced development of global economy. The flows of capital and technology improve the regional dissymmetry of production factor. By establishing circulating channels, the flows of the production factor will be enlarged. This will raise the distribution efficiency of global resources and lead to the global economic growth.

ANALYSIS OF CAPITAL FLOW WITH GIS——Two Approaches for Regional Investment in China

GIS technology has been mostly concerned with handling physical data andmodeling physical environment. However, the retirements of GIS for handling socio-economicinformation in many cases are different from those concerning phenomena in the physical environment.Analysis of capital flow among regions requires the transitions both from economic values tophysical landscape and from physical surface to economic explanation. Rapid growth of Chineseeconomy comes mainly from investment. There are two main ways for obtaining high growth ofinvestment. One is government expenditure which usually invests in regional facility and amenityblock, which is regarded as stimulus for attracting investment. The other is the creation ofinvesting center and corresponding capital source areas, both of which need the central city withthe highest growth rate of investment among regions. This paper presents the cluster areas of bothgovernment revenue and total investment, the potential situation of capital flow between centralcity Shanghai and its neighbor provinces by using Classification' and Interpolation' functions ofArcView GIS.

Schlieren imaging and video classification of alphabet pronunciations:exploiting phonetic flows for speech recognition and speech therapy

Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns.The schlieren imaging technique visualizes airflows with subtle density variations.It is hypothesized that speech flows captured by schlieren,when analyzed using a hybrid of convolutional neural network(CNN)and long short-term memory(LSTM)network,can recognize alphabet pronunciations,thus facilitating automatic speech recognition and speech disorder therapy.This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets:/A/,/B/,/C/,and/D/.A schlieren optical system was developed,and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second.A total of 640 video clips,each lasting 1 s,were utilized to train and test a hybrid CNN-LSTM network.Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets.The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes(i.e.,20,30,40,50 videos per alphabet),all achieving over 95%accuracy in classifying videos of the same participant.However,the network’s performance declined when tested on speech flows from a different participant,with accuracy dropping to around 44%,indicating significant inter-participant variability in alphabet pronunciation.Retraining the network with videos from both participants improved accuracy to 93%on the second participant.Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy.These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows,although challenges remain in expanding the alphabet set and participant cohort.

The Policy Effects of Capital Flow on Foreign Trade： An Empirical Analysis

Today, intemational capital flows faster and faster. Thus, the supervision of capital flow becomes one of the key problems need to be solved every country, and how to supervise it is the focus in theoretical circle. Especially, China is currently facing international pressure to revalue the RMB, and a large number of free foreign capitals is flowing into China. So it is much more important to supervise it. By using the theory of foreign trade, we analyze the policy effects of foreign trade in the process of capital flow, and hope that the policy of foreign trade will exert positive effects on the supervision of capital flow.

Financial Vulnerability, Capital Flow Reversals, and Divergence Macro-Prudential Policies Posing Challenges to the ASEAN Banking Integration Framework （ABIF）

Against the prevailing background of an unusual capital flow reversal which is posing immense challenges to the integration of the region＇s banking sector, this study measures macro-prudential instruments affecting the implementation of an integrated financial service industry. This study is important at times when domestic and country-based financial policies are directed at competing goals. The interaction of macro-prudential policies with other policies, in particular monetary policies and micro-prudential policies is crucial to address systemic risk involved. There is growing recognition that prudential policies tools interact and coordinate with one another. To utilize multiple instruments seems to provide a greater assurance of effectiveness by tackling risk from various angles. As such, this study also assesses the interactions of the policies. The study also proposes a baseline model to capture systemic risk due to liquidity risk and risk because of currency devaluation.

Global Risk Appetite,US Economic Policy Uncertainties and Cross-Border Capital Flow

Based on the global asset portfolio model,this paper created a panel threshold model using EPFR fund data to empirically test the non-linear spillover effects of US economic policy uncertainties on cross-border capital flow for emerging economies.Our study led to the following findings:(1)When the level of global investor risk tolerance is high,rising US EPU will induce a capital inflow into emerging economies,as manifested in the“portfolio rebalancing effect.”When the level of global investor risk tolerance is below a critical threshold,this gives rise to risk aversion and emerging economies will experience net capital outflow,i.e.the“flight to quality effect”.(2)Equity fund investors have a lower risk tolerance threshold than bond fund investors.(3)According to our heterogeneity analysis,more attention should be paid to monitoring capital flow through actively managed funds,ETF funds,and retail investor funds.The economy should increase financial efficiency and economic resiliency to mitigate capital outflow pressures from the external environment.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

Flow Structure and Short-Term Riverbed Evolution in Curved Flumes

River bending is the major effect responsible for bed topography and bank changes.In this study,fluid velocity(measured by a three-dimensional Doppler advanced point current meter)and bed topographical data have been collected in 40 sections of an experimental model.The whole flume was composed of an organic glass bend,upstream and downstream water tanks,two transition straight sections,a circulation pump,and a connection pipeline.Each section has been found to be characterized by a primary circulation and a small reverse circulation,with some sections even presenting three more or more circulation structures.The minimum circulation intensity has been detected in proximity to the top of the curved channel,while a region with small longitudinal velocity has been observed near the concave bank of each bend,corresponding to the flat bed formed after a short period of scouring.The maximum sediment deposition and scour depth in the presence of a uniform distribution of living flexible vegetation within 10 cm of the flume wall have been found to be smaller than those observed in the tests conducted without vegetation.

Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.

跨境资本极端流动与中国金融部门系统性风险传染

随着全球金融一体化程度提高,跨境资本大幅度异常流动越来越频繁,而跨境资本高频次极端流动可能会加剧金融部门脆弱程度、提高系统性金融风险。本文实证检验了涌入、骤停、外逃、回撤四种类型跨境资本极端流动对我国金融部门系统性风险的影响,得出以下结论:(1)我国跨境资本极端流动的持续期和发生频次均较高,说明我国跨境资本流动具有“大进大出”“急进急出”的特征,且跨境资本极端流动类型频繁转换区间与各金融部门系统性风险的局部峰值具有较高的同步性。(2)不同类型跨境资本极端流动对金融部门系统性风险的影响具有异质性。跨境资本涌入和外逃会增加金融部门的系统性风险,且此时保险部门是各金融部门的风险源;跨境资本骤停和回撤会降低金融部门系统性风险,但资本骤停期间,证券部门是风险源,而资本回撤期间,房地产部门则构成了金融系统的风险隐患。

资金流量核算:演进、模型及应用

围绕资金流量核算的演进历程、资金流量表的编制、模型以及应用四个方面,对资金流量核算研究进行评述。首先,从资金流量核算的起源、发展和完善三个阶段厘清资金流量表的发展脉络;其次,系统梳理国内和国际资金流量表的编制,比较分析主要经济体资金流量表的编制情况,并介绍资金流量表的预测方法;再次,介绍资金流量表的相关分析模型及原理;最后,重点归纳总结资金流量表的应用。

人力资本流动对区域经济增长的驱动作用——基于Uzawa-Lucas模型

以户籍制度改革为背景,研究人力资本流动对经济增长的作用机制,将人力资本流动要素纳入到Uzawa-Lucas两部门生产模型中。首先,在人力资本流动条件下建立Uzawa-Lucas两部门生产模型,并且依据Uzawa-Lucas两部门生产模型推导出在人力资本流动情况下人力资本与经济增长之间的关系,从理论上分析人力资本流动对经济增长的影响。其次,利用明瑟方程和引力模型测算人力资本与人力资本流动。最后,依据经济增长模型构建人力资本与经济增长关系研究的空间计量模型,检验我国2005—2022年人力资本流动条件下人力资本与经济增长的关系。实证结果表明,人力资本存量确实促进了我国经济增长,流动人力资本为我国经济增长带来积极影响。

Evolution of gas kick and overflow in wellbore and formation pressure inversion method under the condition of failure in well shut-in during a blowout

With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick.A Roe scheme was used for numerical calculation based on the finite volume method.The changes of bottom-hole pressure,casing pressure,the development law of cross-sectional gas holdup,and gas velocity,along with the vertical well depth,were analyzed through simulation examples.The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter.The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established.The long short-term memory network(LSTM)of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in.Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method,demonstrating that this method is reliable,with greatly improved prediction accuracy.This approach provides theoretical support for the early monitoring of gas kick during drilling,and for well-killing design and construction after uncontrolled blowout.

Traffic flow prediction based on BILSTM model and data denoising scheme

Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.

Short-time prediction for traffic flow based on wavelet de-noising and LSTM model

Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.

A Solution to Two Paradoxes of International Capital Flows

International capital flows from rich to poor countries can be regarded as either too small(the Lucas paradox in a one-sector model)or too large(when compared with the logic of factor price equalization in a two-sector model).To resolve the paradoxes,we introduce a non-neoclassical model which features financial contracts and firm heterogeneity.In our model,free trade in goods does not imply equal returns to capital across countries.In addition,rich patterns of gross capital flows emerge as a function of financial and property rights institutions.A poor country with an inefficient financial system may simultaneously experience an outflow of financial capital but an inflow of FDI,resulting in a small net flow.In comparison,a country with a low capital-to-labor ratio but a high risk of expropriation may experience an outflow of financial capital without a compensating inflow of FDI.