Variance Inflation Research Articles

Analisis Faktor Penentu Ekspor Kopi di Indonesia

International trade includes export and import activities that take place globally between countries. Export is the act of selling goods abroad to generate more foreign exchange for a country. When a country has excess income, it can engage in export-related activities. Indonesia is the fourth largest coffee producer in the world, and is an important contributor to the country's economy. Indonesian coffee is also one of the most popular in the world, and is one of Indonesia's main agricultural export commodities, besides oil and gas. This study aims to see if there is a significant impact through production variables, land area, exchange rates, inflation, prices, consumption on the volume of coffee exports. For this study, the data collection used is secondary data, where secondary data is obtained through the central statistics agency and BI. The data analysis method is used to calculate all data that has been obtained using multiple linear regression analysis with the help of SPSS. The results of this study are that simultaneously production, land area, exchange rates, prices, consumption affect the volume of Indonesian coffee exports. While the inflation variable does not affect exports in Indonesia.

Preoperative OCT Characteristics Contributing to Prediction of Postoperative Visual Acuity in Eyes with Macular Hole.

Objectives: To develop a machine learning logistic regression algorithm that can classify patients with an idiopathic macular hole (IMH) into those with good or poor vison at 6 months after a vitrectomy. In addition, to determine its accuracy and the contribution of the preoperative OCT characteristics to the algorithm. Methods: This was a single-center, cohort study. The classifier was developed using preoperative clinical information and the optical coherence tomographic (OCT) findings of 43 eyes of 43 patients who had undergone a vitrectomy. The explanatory variables were selected using a filtering method based on statistical significance and variance inflation factor (VIF) values, and the objective variable was the best-corrected visual acuity (BCVA) at 6 months postoperation. The discrimination threshold of the BCVA was the 0.15 logarithm of the minimum angle of the resolution (logMAR) units. Results: The performance of the classifier was 0.92 for accuracy, 0.73 for recall, 0.60 for precision, 0.74 for F-score, and 0.84 for the area under the curve (AUC). In logistic regression, the standard regression coefficients were 0.28 for preoperative BCVA, 0.13 for outer nuclear layer defect length (ONL_DL), -0.21 for outer plexiform layer defect length (OPL_DL) - (ONL_DL), and -0.17 for (OPL_DL)/(ONL_DL). In the IMH form, a stenosis pattern with a narrowing from the OPL to the ONL of the MH had a significant effect on the postoperative BCVA at 6 months. Conclusions: Our results indicate that (OPL_DL) - (ONL_DL) had a similar contribution to preoperative visual acuity in predicting the postoperative visual acuity. This model had a strong performance, suggesting that the preoperative visual acuity and MH characteristics in the OCT images were crucial in forecasting the postoperative visual acuity in IMH patients. Thus, it can be used to classify MH patients into groups with good or poor postoperative visual acuity, and the classification was comparable to that of previous studies using deep learning.

The Influence of Inflation, Interest Rates, and Financial Performance on LQ45 Index Share Prices in 2019-2023

This research aims to understand and analyze the effect of inflation on stock prices, the effect of interest rates on stock prices, and the effect of financial performance on stock prices. This study uses a quantitative method. The population in this study consists of companies listed on the Indonesia Stock Exchange and included in the LQ45 index for the years 2019-2023. The sample selection technique used is purposive sampling. Based on predetermined criteria, 21 companies were selected as samples in this study. The data used are secondary data, including inflation data, interest rates, company financial reports, and stock prices. The data collection techniques include non-participatory observation with documentation study and literature review. The data analysis techniques used are descriptive tests, classical assumption tests, panel data regression analyses, and hypothesis testing, with the analytical tool used being EVIEWS 12. The results of the study show that simultaneously, the variables of Inflation, Interest Rates, and Financial Performance have a significant effect on stock prices. Partially, the inflation variable does not affect stock prices, while the interest rate variable has a significant negative effect on stock prices, and financial performance has a significant positive effect on stock prices.

The necessity of coupling the legacy effect with temperature response in crop phenology models

Global climate change has changed vegetation phenology substantially around the world. However, the necessity of coupling legacy effects with temperature responses in phenological models remains unclear. The objective of this study was to demonstrate that legacy effects [based on day of year (DOY) of phenology events] have substantial positive impacts on crop phenology. Data from 1883 crop×site combinations across Germany and China were analyzed. DOY was found to be a temperature-independent factor for both vegetative (VGP) and reproductive growth periods (RGP) (based on variance inflation factor values). Partial correlation analysis suggested that DOY explained almost the same variability in date of phenology events as temperature. Akaike information criterion showed the cost-effectiveness of coupling DOY with temperature in 71.2% and 59.1% of sites in VGP and RGP, respectively. A model that coupled a linear legacy effect and a temperature response mechanism (LETM) improved fitting efficiency by an average of 57%. LETM was observed to outperform the well-calibrated WOFOST and WE models in VGP and RGP for all crops. Averaged over all crops, root mean square errors for WOFOST, WE, and LETM were 4.0, 3.9, and 3.7 d in VGP, respectively, and are 5.3, 4.6, and 4.0 d in RGP, respectively. Our results verified the necessity of coupling the legacy effect with temperature responses in phenology models. Given that the results were consistent for all of the crops investigated, we believe that our conclusions can apply to other field crops. Results of this study expand the knowledge of crop phenology responses to environment, and are helpful for accurately predicting crop growth and development responses under future global climate change.

Creation of a predictive calculator to determine adequacy of occlusion of the woven endobridge (WEB) device in intracranial aneurysms-A retrospective analysis of the WorldWide WEB Consortium database.

Endovascular treatment with the woven endobridge (WEB) device has been widely utilized for managing intracranial aneurysms. However, predicting the probability of achieving adequate occlusion (Raymond-Roy classification 1 or 2) remains challenging. Our study sought to develop and validate a predictive calculator for adequate occlusion using the WEB device via data from a large multi-institutional retrospective cohort. We used data from the WorldWide WEB Consortium, encompassing 356 patients from 30 centers across North America, South America, and Europe. Bivariate and multivariate regression analyses were performed on a variety of demographic and clinical factors, from which predictive factors were selected. Calibration and validation were conducted, with variance inflation factor (VIF) parameters checked for collinearity. A total of 356 patients were included: 124 (34.8%) were male, 108 (30.3%) were elderly (≥65 years), and 118 (33.1%) were current smokers. Mean maximum aneurysm diameter was 7.09 mm (SD 2.71), with 112 (31.5%) having a daughter sac. In the multivariate regression, increasing aneurysm neck size (OR 0.706 [95% CI: 0.535-0.929], p = 0.13) and partial aneurysm thrombosis (OR 0.135 [95% CI: 0.024-0.681], p = 0.016) were found to be the only statistically significant variables associated with poorer likelihood of achieving occlusion. The predictive calculator shows a c-statistic of 0.744. Hosmer-Lemeshow goodness-of-fit test indicated a satisfactory model fit with a p-value of 0.431. The calculator is available at: https://neurodx.shinyapps.io/WEBDEVICE/. The predictive calculator offers a substantial contribution to the clinical toolkit for estimating the likelihood of adequate intracranial aneurysm occlusion by WEB device embolization.

Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region

The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (SQIw), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.

Inflation Disagreement Weakens the Power of Monetary Policy

Inflation Disagreement Weakens the Power of Monetary Policy

Does Macroeconomic Moderate The Effect of Risk and Diversification on Financial Stability? Evidence from Islamic Commercial Bank

<em><span>Islamic banking's financial stability can improve efficiency and health and be able to withstand shocks originating from internal and external factors. This study analyzes the impact of risk, diversification, and inflation on the financial stability of Islamic banking. By using monthly time series data from 2018–2022, the analysis method used is multiple linear regression and moderate regression analysis (MRA) using the Eviews version 10 application. The results showed that the credit risk variable (NPF) has a significant negative relationship to financial stability (Z-Score). As well as the liquidity risk variable (FDR), financing diversification has a significant positive effect on the financial stability of Islamic banking (Z-Score). Macroeconomic variables (inflation) can strengthen the effect of financing diversification and weaken the effect of liquidity risk (FDR), but inflation cannot moderate the effect of credit risk (NPF) on the financial stability of Islamic banking (Z-Score). This study introduces a new theoretical approach to the conceptual relationship between variables. The findings can serve as a reference for monetary authorities in considering the impact of inflation in setting financial stability policies.</span></em>

Robust Variance Inflation Factor: A Promising Approach for Collinearity Diagnostics in the Presence of Outliers

Robust Variance Inflation Factor: A Promising Approach for Collinearity Diagnostics in the Presence of Outliers

Analysis of Sustainable Food Sector Development in the Sumatra Island

The research aims to analyze the development of food security through indicators of the sustainable food sector on the island of Sumatra in 2018-2022. Food sector indicators include the number of poor people, food inflation, rice consumption per capita, insufficient food consumption, and food insecurity. Food security indicators use the food security index. The research method is quantitative through secondary data collection with panel data analysis from cross section results of 10 provinces on the island of Sumatra and time series data for 2018-2022. The approach used, namely CEM,FEM, REM. The research findings show that the food inflation variable has a positive and significant effect on the food security index, the poor population variable, rice consumption per capita, insufficient food consumption has a positive and insignificant effect on the food security index. Population with food insecurity has a negative and insignificant effect on the food security index.

Potential distribution prediction of Pomacea canaliculata in China based on the Biomod2.

Pomacea canaliculata is a globally significant invasive species that poses substantial threats to agricultural production, ecosystems, and human health in China. To evaluate its habitat suitability in China, we collected and collated 741 county-level occurrence points of P. canaliculata in China through database query, literature search and news reports, and obtained the five most important climatic factors by variance inflation factor and Pearson correlation coefficient from 19 bioclimatic factors. We investigated the potential suitable distribution areas of P. canaliculata under current and future climate scenarios using the Biomod2 ensemble model, and identified the primary environmental variables influencing their distributions. The results showed that under the current climate, the suitable habitat areas were concentrated in southern provinces or municipalities, such as Zhejiang, Shanghai, Fujian, Jiangxi, Guangdong, Guangxi, Hainan, and Yunnan. Under different climate scenarios in the future, the suitable habitat area would show large-scale expansion in the southern provinces or municipalities, and spread to the northern region. The most two important environmental factors affecting the distribution of suitable habitat areas were precipitation of wettest month and mean temperature of wettest quarter. A combination model to predict the potential distribution of P. canaliculata under current and future climate conditions would help manage the risk of its invasion and spread, and provide a reference for relevant regions and departments to take active measures in advance to prevent the spread, monitor and mitigate its invasion.

Differences Between Brachial-Ankle Pulse Wave Velocity and Lower Leg Circumference Ratio in Patients With and Without Type 2 Diabetes Mellitus.

Background Vascular endothelial dysfunction in type 2 diabetes mellitus (T2DM) patients causes atherosclerosis and microvascular damage. This study investigated the relationship between leg circumference and arterial stiffness in patients with T2DM compared to non-T2DM individuals. Methods Data from two studies were combined to form T2DM (T2DM group) and non-T2DM (N group) cohorts. The variables included age, sex, systolic blood pressure (SBP), brachial-ankle pulse wave velocity (ba-PWV), ankle-brachial index, height, weight, maximum leg circumference, lower leg circumference ratio, duration of T2DM, Achilles tendon reflex disorder, and the hemoglobin A1c level. Multiple regression analysis was performed with ba-PWV as the dependent variable and the interaction term between leg circumference ratio and T2DM as the independent variable. The control variables included leg circumference ratio, T2DM, SBP, Achilles tendon reflex disorder, age, and sex. IBM SPSS Statistics for Windows, Version 23.0 (Released 2015; IBM Corp., Armonk, NY, USA) was used for the statistical analysis, with the significance level set to p < 0.05. Results The interaction term between the lower leg circumference ratio and T2DM (β = -0.17, 95% CI: -46.11 to -10.92; p < 0.01) was significantly associated with ba-PWV (AdjR² = 0.51, variance inflation factor <4.12). Simple slope analysis indicated that a decreased lower leg circumference ratio was significantly associated with an increased ba-PWV (β = -0.20, p < 0.05) in the T2DM group. No significant relationship was found in the N group (β = -0.03, p = 0.69). Conclusion A significant interaction was found between the lower leg circumference ratio and T2DM presence, indicating an association between ba-PWV and the leg circumference ratio specific to patients with T2DM. This result suggests that the leg circumference ratio can be substituted for the ba-PWV to evaluate arterial stiffness in T2DM.

Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices.

Analisis Pengaruh Inflasi dan CAR terhadap Pembiayaan Murabahah pada PT Bank Aceh Syariah

This research aims to analyze the influence of Inflation and Capital Adequacy Ratio (CAR) on Murabahah Financing at PT Bank Aceh Syariah. The data used is Time Series data for the period September 2011 – June 2018 with a quantitative linear regression analysis method. The results of the discussion show that the Inflation variable is partially insignificant and has a negative relationship to distributed Murabahah Financing, while the CAR variable is partially significant and has a positive relationship to distributed Murabahah Financing. However, inflation and CAR simultaneously have a significant effect and are positively related to distributed Murabahah financing. These two independent variables are able to explain the dependent variable by 51.6% of the Adjusted R Square value and the remaining 48.4% is influenced by other variables.

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

What has inflation targeting done for household consumption?

Whether households’ consumption level in an Inflation-Targeting (IT) regime, which steers inflation and its expectations, differs from that of a non-IT regime remains empirically unexplored. Using IT as a treatment and acknowledging the potential for country heterogeneity and material confounding factors, we leverage several treatment methods to study the causal, dynamic IT effect on aggregate household consumption across developed and developing countries. We find significant heterogeneous IT effects on aggregate consumption levels in two-thirds of the IT sample. The heterogeneity does not support the notion of an isomorphic average causal IT effect on household consumption across countries. The government’s willingness to meet IT policy adoption preconditions and the country’s fiscal profile are critical drives of this parameter heterogeneity in developing and developed countries. However, the central bank’s appetite for restricting exchange rate movements contributes to this heterogeneity in only developing countries. We find that macroeconomic uncertainty proxied by the inflation level, inflation variability and output growth variability are essential channels in developing countries. In addition, financial development and savings are crucial channels across development status.

Effect of inflation and exchange rates on stock prices in banking sub-sector companies listed on The Indonesian Stock Exchange

Purpose: This study aims to determine the influence of inflation variables and exchange rates on share prices. Method: This study used quantitative methods. The population in this study was 47 banking subsector companies listed on the Indonesian Stock Exchange for the 2019-2023 period. The sample in this study comprised 15 companies with 75 financial reports. This research uses classical assumption tests such as the normality test, multicollinearity test, autocorrelation test and heteroscedasticity test, coefficient analysis, coefficient of determination analysis and hypothhesis testing consisting of the t test and f test. Results: The results of this research show that inflation has a partial and significant effect on share prices in banking subsector companies listed on the Indonesian Stock Exchange for the 2019-2023 period because it has a sig. 0.000 &lt; 0.05, and the exchange rate has a significant effect on share prices in banking subsector companies listed on the Indonesian Stock Exchange for the 2019-2023 period because it has a sig value. 0.000 &lt; 0.05. Simultaneously, the effect on share prices in banking subsector companies listed on the Indonesian Stock Exchange for the 2019-2023 period beccause they have sig. equal to 0.000 &lt; 0.05. Limitations: The object of the research is only banking sub-sector companies listed on the Indonesia Stock Exchange for the 2019–2023 period and only includes inflation, exchange rates, and stock prices Contribution: This research aims to contribute to science at the Faculty of Economics and Business, provide reading materials in the library of the University of PGRI Palembang about inflation and exchange rates on the stock prices of companies in the banking sub-sector on the Indonesia Stock Exchange for the 2019-2023 period, and increase the company's insight into the development of stock prices related to inflation and exchange rates.

A Study on the Impact of Inflation, Poverty, and Unemployment on Income Inequality at Cermin Beach, Serdang Bedagai Regency

One of the causes of income inequality is the large number of poor and unemployed people. The relationship between inflation, poverty, unemployment and income inequality shows a continuous interaction of these three factors. The aim is to determine the effect of inflation, poverty and unemployment on income inequality in the district. Mirror Beach, Kab. Serdang Bedagai. The data used is secondary data obtained from the Central Statistics Agency (BPS) for the 2019-2023 time period. The analytical method used is quantitative). The software used for analysis is spss 26.0. The research results show that the inflation variable has an effect on income inequality. The poverty variable has no effect on income inequality, the unemployment variable has an effect on income inequality and together the inflation, poverty and unemployment variables have a significant effect on income inequality in the district. Cermin Beach, Kab. Serdang Bedagai.

Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome.

Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.

Predictors of successful transition of adolescents and young adults living with HIV from pediatric to adult-oriented care in southern Ethiopia: a retrospective cohort study

BackgroundThe introduction of highly active antiretroviral therapy has significantly improved the life expectancies of children and adolescents living with HIV, leading to an increased number transitioning to adult care. However, there has been a lack of studies in Ethiopia focusing on factors influencing the success of this transition. Therefore, this study aimed to determine predictors of a successful transition from pediatric to adult HIV clinics among adolescents and young adults living with HIV in health facilities in southern Ethiopia.MethodsA retrospective cohort study included 337 adolescents and young adults who transitioned to adult-oriented HIV care. Successful transition was defined as having a viral load of less than 1000 copies/ml and maintaining care during the first year post-transition. Patients’ antiretroviral therapy (ART) cards and monitoring charts were reviewed. Secondary data analysis was conducted using a multivariable binary logistic regression model to identify predictors of a successful transition. Using the variance inflation factor, we checked for multi-collinearity between variables and assessed model fitness with the Hosmer and Lemeshow goodness-of-fit test. Adjusted Odds Ratio (AOR) with 95% confidence intervals (CI) and P-value ≤ 0.05 measured the strength of association and statistical significance.ResultsOf 337 participants, 230 (68.25%) successfully transitioned (95% CI = 63.25, 73.25). Transitioning at age 18 or older (AOR = 4.25; 95% CI = 2.29, 7.87), residing in an urban area (AOR = 1.78; 95% CI = 1.04, 3.02), and being on antiretroviral therapy for more than two years (AOR = 4.25; 95% CI = 1.17, 4.94; P < 0.017) were identified as positive predictors and opportunistic infection (AOR = 0.34; 95% CI = 0.15, 0.75; P < 0.008) was identified as a negative predictor for a successful transition from pediatric to adult ART clinic.ConclusionThis study sheds light on the challenges faced by HIV patients transitioning from pediatric to adult care, with less than 70% successfully navigating this critical phase. Factors such as age at transition, residence, duration of ART, and the presence of opportunistic infections were identified as key predictors of successful transition. The findings underscore the urgent need for tailored interventions, including standardized transition plans that address age and urban/rural disparities, to enhance transition outcomes for adolescents and young adults living with HIV in the region.