Short-term Estimates Research Articles

Investigating Asymmetric Effects of Electronic Payment on Money Supply for India using Non-linear ARDL Model

Money supply in India is analysed using a non-linear autoregressive distributed lag (NARDL) model to see how several factors, such as electronic payments, exchange rate, interest rates and inflation, affect the money supply. The Augmented Dickey–Fuller unit root test and the Phillips–Perron unit root test are utilized to confirm that the variables are stationary. In the bounds test of the NARDL as well as Johansen–Juselius cointegration test, this study finds evidence of cointegration amongst variable used in the study. NARDL testing model is employed to investigate the dissimilar influences on variables with short- and long-run dynamics. Short-term estimates suggest that the money supply ( M1) is most responsive to positive shocks involving retail payments, the exchange rate and interest rates, but negative shocks involving card payment and prepaid payment instruments. A reduction in card payment, as well as a positive increase in retail payment, the prepaid payment instrument, the exchange rate and the interest rate, would all have major effects on M3 in short run. In addition, a long-term finding demonstrates that all of the variables have a significant influence on the money supply ( M1) in its mixed form while all of the factors, with the exception of the one involving card payments, have a considerable impact on the money supply ( M3). Wald test results confirm the existence of asymmetric influences which can have significant lessons for policymakers.

Macroeconomic Variables, Global Stock, and Financial Sector Stock Indices in Indonesia

This study aims to analyze the impact of macroeconomic variables and global stock indices on the financial sector stock index in Indonesia. The data used is secondary data in the form of monthly time series from January 2010 to December 2019, which comes from Bank Indonesia, the Central Bureau of Statistics (BPS), and Yahoo Finance. The variables of this study include the Jakarta Islamic Index (Indonesia), IDX Sectoral Index (Indonesia), Money Supply Inflation, Rupiah against USD Exchange Rate, Crude Palm Oil Price, Industrial Production Index, KL Financial Sectoral Index (Malaysia), SET Financial Sectoral Index (Thailand) and S&P 500 Stock Index (USA). The analysis method used is Autoregressive Distributed Lag (ARDL). The results concluded that the short-term estimation resulted in the exchange rate variable, the Industrial Production Index (IPI), and the Thailand SET financial sector stock index having a significant effect on the JII index, while the money supply variable, the Malaysia KL financial sector stock index, the Thailand SET financial sector stock index, and the US S&P 500 stock index had a significant effect on the IDX financial sector index. Then, the long-term estimation found that the Thailand SET financial sector stock index variable has a significant effect on the JII. Then, in the IDX financial sector index model, there are two significant variables, namely money supply and Industrial Production Index.

Pengaruh BI-7 Day Repo Rate dan Nilai Tukar terhadap Nilai Aktiva Bersih Reksa Dana Saham Periode 2018 - 2022

Mutual funds are a good place to invest for novice investors to get profits in the future. Therefore, mutual funds really need to be studied by including macroeconomic variables, namely BI-7 day repo rate and exchange rates. The purpose of this study is to analyze the effect of BI-7 day repo rate and exchange rates on Net Asset Value (NAV) of equity funds. The method used in this study is the Error Correction Model. The results show that in the long run the BI-7 day repo rate variable has a significant positive effect on the NAV of equity funds. However, the exchange rate variable has a significant negative effect. Furthermore, in the short-term estimation, BI-7 day repo rate has no effect on the NAV of equity funds. Then the exchange rate has a significant negative effect. Simultaneously (together) in the long run has a significant influence on the NAV of equity funds. This is inversely proportional to the short-term estimation results which state that simultaneous BI-7 day repo rate and exchange rates have no effect on the NAV of equity funds. The data used in this study is the period 2018 – 2022.

Short-term damping estimation for time-varying vibrating structures in nonstationary operating conditions

Various structures exhibit time-varying modal properties driven by short-term variability of environmental and operational conditions, which can cause considerable short-term variations in the modal characteristics, and modal damping, in particular. This study focuses on short-term damping estimation based on operational response measurements, for which a Gaussian process time-dependent auto-regressive moving average time series model is proposed. The model coefficients are represented by basis functions which express dependence on influencing environmental and operational variables, to account for various phenomena driving the time-varying nature of the modal characteristics. The method is verified by estimating short-term damping from the response of a simulated two-mode model. The applicability of the method for more complex cases is demonstrated by estimating the short-term damping of a multi-megawatt wind turbine in operation (and during an extreme wind gust) by means of its simulated edgewise blade response. The results demonstrate that the proposed method offers an enabling approach towards short-term damping and natural frequency estimation based on response measurements.

The Impact of Exchange Rate Changes on Indonesian Exports

This study delves into how Exchange Rates and Gross Domestic Product (GDP) have shaped Indonesian exports over the past 17 years, from 2006 to 2022. On the whole, Indonesian exports have generally shown a positive trajectory during this period, although there have been occasional drops in specific years. The main countries that receive Indonesian exports are China, the United States, Japan, India, and Malaysia, and the primary export goods include coffee, animal and vegetable fats and oils, Crude Palm Oil (CPO), and coal. When we look at long-term model analyses, it becomes evident that exchange rates play a significant role in negatively affecting Indonesian exports. This means that, over an extended period, an increase in the value of the Indonesian currency can lead to a reduction in export volumes. Furthermore, GDP also exerts a notable negative influence on Indonesian exports, suggesting that the overall economic growth of the country can impact export levels. In the context of short-term model estimations, it is apparent that fluctuations in currency exchange rates have a substantial negative impact on Indonesian exports in the immediate term, assuming all other factors remain constant. The results of these estimations also indicate that GDP has a considerable negative effect on Indonesian export performance in the short run.

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.

Long-Term Traffic Prediction Based on Stacked GCN Model

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to error propagation within the model. This model effectively combines Graph Convolutional Network (GCN) capacity to extract spatial characteristics from the road network with the stacked GCN aptitude for capturing temporal context. Our developed model is subsequently employed for traffic flow forecasting within urban road networks. We rigorously compare our method against baseline techniques using two real-world datasets. Our approach significantly reduces prediction errors by 40% to 60% compared to other methods. The experimental results underscore our model's ability to uncover spatiotemporal dependencies within traffic data and its superior predictive performance over baseline models using real-world traffic datasets.

Comparisons of land-use regression with machine learning and microenvironmental exposure models for short-term exposure estimates of PM2.5

Comparisons of land-use regression with machine learning and microenvironmental exposure models for short-term exposure estimates of PM2.5

Comparative Evaluation of Intermountain Risk Score With Mehran Risk Score for Risk Estimation of Contrast-Induced Nephropathy and Short-Term Mortality in ST-Segment Elevation Myocardial Infarction Patients.

Contrast-induced nephropathy (CIN) has become one of the most important causes of in-hospital acute renal failure with the increasing use of contrast-mediated imaging tools. This significantly increases the morbidity and mortality of the affected subjects and causes a financial burden on the health system. In this context, prediction of CIN is important and some risk scores have been developed to predict CIN. The most frequently used and popular among these is the Mehran Score (MS), which is based on a number of hemodynamic and metabolic parameters. The Intermountain Risk Score (IMRS) is a recently developed risk score that highly predicts short-term mortality based on common laboratory parameters, and many parameters of this risk score have been found to be closely associated with CIN. In this context, we aimed to compare MS and IMRS in terms of CIN and short-term mortality estimation. The study included 931 patients who underwent percutaneous coronary intervention. CIN developed in 21.5% of patients. Both MS and IMRS independently predicted CIN. In receiver operating characteristic analysis, IMRS was found to be non-inferior to MS in predicting CIN and IMRS was superior to MS in predicting short-term mortality. IMRS and MS were independently associated with short-term mortality.

Does higher education promote sustainable development? Role of green technology and financial performance.

How do digitalizing businesses help them achieve sustainable growth? This research examines the mediating function of green technology innovation in answering this question by defining sustainable development performance in terms of corporations' financial and environmental success. The educational system in China is examined to see how much of an impact it has on eco-innovation, as well as the relationship between green technology and innovation. The IFE test was utilized to determine whether or not the associations between variables such as GDP per capita, urbanization, green technology, higher education, and carbon dioxide emissions will continue to exist between 2004 and 2020 in China. The data for this analysis came from 30 of China's provinces. The findings of both the short-term and long-term CS-ARDL estimations demonstrated a positive link between eco-innovation and GDP per capita, green technology, higher education, and CO2 emissions. On the other hand, a negative correlation was found between urbanization and eco-innovation. The next topic covered in the research was how the effects of green technology might be seen in areas such as GDP per capita, higher education, and carbon dioxide emissions. The findings might provide valuable knowledge that developing economies can use to construct a feasible, sustainable path.

SEDIMENT TRANSPORT ESTIMATION FROM HYDROLOGICAL AND AUTOREGRESSIVE MODELS

Hydrologic modeling allows the simulation of runoff and sediment processes, which are applied in integrated watershed management, soil and water nutrients, among others. However, these models require considerable amounts of input data. Sediment data is often lacking in quantity and quality, which leads to uncertainty in hydrological models. The objective of the present study was to propose a methodological alternative based on sediment time series in the Santa Cruz de Aquismón sub-basin, San Luis Potosí, Mexico, by means of autoregressive integrated moving average models (ARIMA) and the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated with measured flows from the National Surface Water Data Bank (BANDAS) of station 26 241 (Ballesmi). Model calibration and validation performance was assessed with Nash-Sutcliffe Coefficient (NSE), percent bias (PBIAS), and the root mean squared error (RMSE). The SWAT model fit was rated as very good. The hydrologic model results were compared to the daily sediment estimates from three months in 1985 (June, September, and November) obtained from ARIMA models. The mean absolute percent error (MAPE) was 0.571, 0.168, and 0.029, respectively. The results indicated that the use of the ARIMA model for sediment estimation is useful when there are short time series with limited information, since it allows the completion of missing data series or short-term estimates.

Analysis of the Influence of Inflation Fluctuations, BI Rate, Money Supply, and Exchange Rate on the Composite Stock Price Index of the Indonesian Stock Exchange in the Period 2014-2019

Economic growth is one of the indicators of successful development in an economy. One indicator of economic growth is the stock market. The stock market is a place where people with excess funds (investors) meet companies that need funds (issuers). The purpose of this research is: 1) To determine whether the inflation variable has a significant influence on the Composite Stock Price Index in 2014-2019. 2) To determine whether the BI Rate variable has a significant influence on the Composite Stock Price Index in 2014-2019. 3) To determine whether the Money Supply variable has an influence on the Composite Stock Price Index in 2014-2019. 4) To determine whether the exchange rate variable has a significant influence on the Composite Stock Price Index in 2014-2019. 5) To describe the magnitude of the shock generated by the variables of inflation, BI Rate, Money Supply, and exchange rate on the Composite Stock Price Index in 2014-2019. To identify these issues, this study is a quantitative research and uses the Vector Error Correction Model (VECM) method to examine short-term relationships and employ cointegration tests to identify indications of long-term relationships. Based on the research results, it can be concluded that: 1) Based on the short-term VECM test, the estimated inflation variable has a positive but insignificant effect. Then, the long-term estimation results show that the inflation variable has a negative but insignificant effect on the IHSG variable. 2) The short-term estimation results for the BI Rate variable have a negative but insignificant effect on the IHSG. Then, the long-term estimation results show that the BI Rate variable has a negative but insignificant effect on the IHSG variable. 3) The Money Supply variable has a significant negative effect on the IHSG in the short term. The long-term estimation results show that the Jumlah Uang Beredar variable has a significant negative effect on the IHSG. 4) The short-term estimation results for the nilai tukar variable have a positive but insignificant effect on the IHSG. Then, the long-term estimation results show that the nilai tukar variable has a positive and significant effect on the IHSG. 5) Based on the IRF test to determine shocks on each variable, the BI Rate variable shows a tendency for thin shocks above the horizontal line, indicating that the BI Rate variable has a permanent positive impact.

An adaptive operational strategy for enhanced provision of frequency containment reserve by Wind Turbines: Data-driven based power reserve adjustment

Due to the growing penetration of renewables, Wind Turbines (WT) are becoming increasingly crucial for grid balancing services, such as Frequency Containment Reserve (FCR). This study proposes an adaptive operational strategy that optimally accommodates the power reserve and controls the active power based on grid frequency uncertainties and stochastic wind variations. The proposed approach includes an end-to-end solution, considering fixed and percentage reserve methods, from estimating an appropriate reserve margin to the real-time computation of generator torque and pitch control setpoints in response to grid frequency variations. A real-time look-up table is incorporated to actively adjust the reserve and adapt the deloading rotor speed-power curve based on a short-term estimation of the grid frequency using a deep-learning technique. Applying the proposed strategy improves WTs’ FCR contribution by at least 3.3 times reserve in MW. Moreover, adaptive fuzzy-PI pitch-torque controllers are suggested to enhance the WT dynamic response and ensure smooth provision of FCR. Simulation results of a 5 MW-NREL offshore model show the improvement of the fuzzy-PI in power reference tracking, rotor speed regulation, and average studied mechanical load parameters in the range of 2.14–11.69%, 11.1%, and 8.81%, respectively, for an average of 250 kW reserve, confirming an overall improvement.

Transcriptomic forecasting with neural ordinary differential equations

Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.

Population aging and eco-tourism efficiency: Ways to promote green recovery

The eco-tourism development has been addressed as a major driver of green recovery in countries. The primary purpose of this research is to determine the impacts of population aging on tourism market size and eco-tourism efficiency in six Chinese regions from 2000 to 2021. The findings confirm that population aging does not significantly impact eco-tourism efficiency, while it reduces the tourism market size in Chinese regions. Regarding short-term estimations, societal aging has only a negative and significant impact on the tourism market size in Chinese regions. In addition, the impacts of population aging on eco-tourism efficiency are only significant for Northeast China and Southwest China in the long and short term. The Chinese government can pay more attention to senior tourism, virtual tourism services, support of SMEs that deal with the tourism industry, and providing stimulating packages for aged people to use tourism services

Comparing predictive performance of pulmonary embolism risk stratification tools for acute clinical deterioration.

Existing pulmonary embolism (PE) risk scores were developed to predict death within weeks, but not more proximate adverse events. We determined the ability of 3 PE risk stratification tools (simplified pulmonary embolism severity index [sPESI], 2019 European Society of Cardiology guidelines [ESC], and PE short-term clinical outcomes risk estimation [PE-SCORE]) to predict 5-day clinical deterioration after emergency department (ED) diagnosis of PE. We analyzed data from six EDs on ED patients with confirmed PE. Clinical deterioration was defined as death, respiratory failure, cardiac arrest, new dysrhythmia, sustained hypotension requiring vasopressors or volume resuscitation, or escalated intervention within 5 days of PE diagnosis. We determined sensitivity and specificity of sPESI, ESC, and PE-SCORE for predicting clinical deterioration. Of 1569 patients, 24.5% had clinical deterioration within 5 days. sPESI, ESC, and PE-SCORE classifications were low-risk in 558 (35.6%), 167 (10.6%), and 309 (19.6%), respectively. Sensitivities of sPESI, ESC, and PE-SCORE for clinical deterioration were 81.8 (78, 85.7), 98.7 (97.6, 99.8), and 96.1 (94.2, 98), respectively. Specificities of sPESI, ESC, and PE-SCORE for clinical deterioration were 41.2 (38.4, 44), 13.7 (11.7, 15.6), and 24.8 (22.4, 27.3). Areas under the curve were 61.5 (59.1, 63.9), 56.2 (55.1, 57.3), and 60.5 (58.9, 62.0). Negative predictive values were 87.5 (84.7, 90.2), 97 (94.4, 99.6), and 95.1 (92.7, 97.5). ESC and PE-SCORE were better than sPESI for detecting clinical deterioration within 5 days after PE diagnosis.

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

BackgroundKinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (18F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance.MethodsThis study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term 18F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation.ResultsThere is a strong correlation between the kinetic parameters K1 ~ k3, HPI and {{varvec{V}}}_{{varvec{b}}} in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k2, HPI and k3 values than background liver tissues, while K1, k4 and {{varvec{V}}}_{{varvec{b}}} values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K1 and k2, k3 and {{varvec{V}}}_{{varvec{b}}} values than background liver tissues; however, the k4 value was not significantly different between HCCs and the background liver tissues.ConclusionsShort-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation.Clinical relevance statementShort-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters.Graphical

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.

Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models

Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to predict subsequent adverse cardiovascular events/treatments for patients within 90 days after their first diagnostic catheterizations. Patients (6539) without previously diagnosed CVD were selected from the DukeCath dataset. Ten ML methods were used. Three medical outcomes, varied cardiovascular-related scenarios, percutaneous coronary intervention (PCI) treatments, and coronary artery bypass graft (CABG) treatments, were targeted individually. With patient medical history, vital measurements, laboratory results, and the number of diseased vessels, the random forest classifier (RFC) performed best in predicting combined cardiovascular scenarios, including CABG, PCI, valve surgery (VS), stroke, and myocardial infarction (MI), achieving accuracy: 88.17%, sensitivity: 89.72%, specificity: 86.98%, area under receiver operating characteristic (AUROC): 91.68%. The gradient boosting classifier (GBC) performed best in predicting the PCI and CABG treatments (PCI treatments: accuracy: 89.21%, sensitivity: 90.20%, specificity: 88.74%, AUROC: 94.16%; CABG treatments: accuracy: 93.86%, sensitivity: 77.57%, specificity: 96.23%, AUROC: 96.47%). Our results show that the ML applications effectively identify high-risk patients, can provide diagnostic assistance in cardiovascular treatment planning, and improve outcomes in cardiovascular medicine.

The Effect of Eco-Preneurship and Green Technology Management on Greenhouse Gas Discharge: An Analysis on East Asian Economies

This study finds that eco-preneurship and green technology initiatives have the potential to improve the environmental quality of selected East Asian economies in the short term, but in the long term, the impact is negative, and there is a need for a better understanding of their effects on greenhouse gas emissions. The pressing need to address the negative impact of human activity on the environment has given rise these initiatives worldwide. However, there is a lack of research on the effectiveness of these initiatives in East Asian economies. In this study, we examine the short-term and long-term effects of eco-preneurship and green technology on greenhouse gas emissions in China and Japan. The results show that in the short term, both linear and non-linear estimations of eco-preneurship have a significant impact on reducing greenhouse gas emissions in China and Japan. Similarly, short-term linear estimations are significant in both China and Japan, but non-linear estimates are only significant in Japan. The same is true for positive shocks related to green technology in China and Japan, although the estimated coefficient for negative shocks is negative only in Japan. Taken together, these results indicate that eco-preneurship and green technology can improve the environmental quality of these selected economies in East Asia to some extent.