VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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12536 research outputs found
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Modeling soil retention, erosion potential, and sedimentation risk using the InVEST SDR model
No full textThis study conducts an examination of the Ilam watershed, utilizing the InVEST and SDR models to assess soil retention, erosion, and transport. It incorporates factors like rainfall erosivity, soil erodibility, DEM, land use, vegetation, and conservation practices to explore the complex interplay between ecosystem services (ES) and disservices. The study found that the average soil retention in the watershed is 94.5 tons/ha/year, the average erosion potential is 62.8 tons/ha/year, and the average sediment transport is 10.5 tons/ha/year. Forest areas retain a significant portion of sediment (60%) with low discharge (13%), while agricultural and urban regions contribute more to erosion. This highlights the importance of integrating ES into land management strategies to mitigate environmental degradation. The study highlights the crucial role of ES in maintaining ecological balance and supporting human well-being. It advocates for innovative policies and customized solutions to mitigate land use impacts on soil conservation and sediment retention, thereby fostering awareness among managers and decision-makers for more sustainable land use planning
Factors influencing technical competencies in digital marketing of MSMEs in wholesale and retail sectors: the mediating role of core competencies
No full textWhile technology is essential for the survival of micro, small, and medium-sized businesses (MSMEs), their ability to use digital marketing effectively, especially in the wholesale and retail sectors, remains unclear. The study aims to investigate the current state of digital marketing capabilities and the influence of behavioral competencies on technical competencies through the core competencies in digital marketing. The research approach included surveying a sample size of 400 MSMEs in the wholesale and retail sectors. The collected data was analysed using a combination of descriptive statistics and structural equation modelling approaches. This study highlights the crucial role of core competencies in digital marketing. It suggests that strong behavioral skills positively influence technical skills by first impacting core competencies. The findings of this research show that improving behavioral and core competencies has a substantial and positive impact on the technical competencies in digital marketing. Including, the primary goal of developing digital marketing abilities should be to hone behavioral and core competencies since they will contribute to a demonstrable enhancement in the technical competencies of digital marketing
Parametric analysis of wing planforms to determine an optimal wing design
No full textIn designing of Unmanned Aerial Vehicle (UAV), selection of an optimal wing design is a crucial part of complete UAV design process. This research explores the different aerodynamic parameters and the comparison of different wing planforms to ascertain the optimal wing design and improve the overall efficiency of an UAV. The computational analysis using XFLR5 and Open-VSP software is studied to investigate the various aerodynamic parameters of wing. The impact of aspect ratio, taper ratio, wing reference area, coefficient of lift and drag, and stall angle of attack are examined using the Analytical Hierarchy Process (AHP). The results emphasize the importance of different wing planforms and create easier selection of planform for the UAV designers. The study does not only provide the values for operating parameters but also offers practical guidance for design optimization. The semi tapered, and moderate tapered (λ = 0.5) wings are the good choice to select at the initial phase of design. The highly tapered and elliptical wings provide higher lift but are not efficient in the stalling conditions. Furthermore, the rectangular wing provides elliptical lift distribution, but it is inefficient in the lift generation
Blade element – momentum aerodynamic model of a helicopter rotor operating at low-Reynolds numbers in ground effect
No full textThe general objective of this paper is to present the initial results obtained as an outcome of applying a coupled Experimentally Derived – Blade Element Momentum Theory (BEMT) model for evaluating the airflow-defining parameters of a hovering helicopter rotor close to obstacles and making performance predictions. Several empirical models are described, and proper comparison with the experimentally obtained data is conducted. In detail, the characterization of the rotor inflow ratio (λ), when operating at fixed rotational frequency (n), at different relative distances to the ground (H/R), varying the pitching angle (θ) is discussed. The dependencies show an increase in the rotor inflow ratio parameter (λ), when increasing the collective pitch angle (θ) in hovering regime at fixed constant relative distance to the ground surface (H/R). On the contrary, the inflow ratio (λ) is experiencing a decline once the helicopter rotor operates closer to the ground surface. Moreover, the inflow ratio characterization (λ) along the blade span can be applied to the total generated thrust (T). As a result, the corresponding thrust coefficients (CT) are calculated and graphically represented. The overall characterization of the thrust coefficients (CT) will allow the definition of the ground effect zone
Will peer-to-peer online lending affect the effectiveness of monetary policy?
No full textOnline lending is a product of digital transformation, which has had a profound impact on the traditional money market. This paper discusses the impact of peer-to-peer (P2P) online lending on the effectiveness of monetary policy. Through the bootstrap sub-sample rolling-window Granger causality tests show that P2P has both positive and negative impacts on the money supply (M2). The positive impact of P2P on M2 indicates that online loans increase the amount of money supply. The negative impact of P2P on M2 shows that it may cut the money supply, thus weakening the monetary policy effectiveness. The general equilibrium model is inconsistent with these results, which underlines a positive effect from P2P to M2. In turn, the negative impact points out that the adjustment of monetary policy will hinder the development of P2P. The negative impact of M2 on P2P indicates that through the regulation of money supply, the online lending market can be correctly guided to prevent financial market from getting out of control. Through the supervision of online lending industry, we can accurately grasp the development of the internet financial industry and reduce its impact on monetary policy.
First published online 3 September 202
Adapting to uncertainty: A quantitative investment decision model with investor sentiment and attention analysis
No full textIn the face of global uncertainties, including pandemics, economic fluctuations, disruptions in supply chains, major disasters, wars, and impending economic crises, the financial landscape and the impact of investor sentiment on the return of stock index futures can be significantly altered. Understanding the relationship between investor sentiment, attention, and stock index futures returns in the face of these diverse challenges has become particularly critical. However, existing research does not adequately consider the effect of these unexpected events on the market and the shifts in investor attention. Using the COVID-19 pandemic as a case study, this research proposes a dynamic quantitative investment decision-making model that considers the influence of investors’ attention and emotional characteristics, aiming to adapt to the financial market under these global changes and improve the accuracy of quantitative investment forecasting. Initially, the Bidirectional Encoder Representations from Transformers model is employed to analyze investor comment data, extract information on investor attention and emotional characteristics, and construct investor sentiment indicators. Subsequently, a stock index futures forecasting method based on Variational Mode Decomposition algorithm and Support Vector Regression (SVR) model is constructed, and the grey wolf optimization algorithm is introduced to optimize the parameters of the SVR model. Guided by investor sentiment indicators, different market states are further distinguished, and appropriate investment strategies are implemented to effectively enhance the returns of quantitative investment. When compared with models that neglect investor attention and emotional characteristics, the results show that considering investor sentiment indicators not only improves the predictive ability of the model, but also reduces cognitive bias and market risk.
First published online 6 December 202
Adaptive strategies and sustainable investments: navigating organizations through a VUCA environment in and after COVID-19
No full textThis study delves into the resilience and adaptability of employees within the volatile, uncertain, complex, and ambiguous (VUCA) business environment, examining their readiness to manage effectively and the organizational agility in navigating change, alongside the impact of sustainable investment practices. Employing quantitative methods, a survey was conducted among employees at two pivotal moments: during and after the COVID-19 pandemic restrictions. Factorial analysis revealed a strong preference for participatory work styles and highlighted the critical need for employee involvement in significant decision-making processes. Although the value of sustainable investments was recognized, a noticeable gap was found in employees’ understanding and adaptability towards these investments. The use of the Wilcoxon test illuminated the significant impact of external disruptions, such as the pandemic, on organizational operations and preparedness. The findings underscore the imperative for organizations to champion continuous learning and training, enabling strategic and innovative responses to the challenges unique to the VUCA world. By aligning adaptive interventions with the demands of the VUCA environment, organizations can define a clear trajectory towards sustainable growth and enhance their proactive stance against sudden shifts in the business landscape.
First published online 10 September 202
GDP per capita vs foreign direct investment: key drivers of a country\u27s technological leadership
No full textThis study aims to test the hypothesis that countries with high GDP per capita achieve technological leadership not primarily due to their domestic production capacity but through the inflow of foreign direct investment (FDI). The research covers 21 developed countries across Western Europe, the Americas, Asia, Africa, and Australia, for the period 2011 to 2022. The Bartlett test, Kaiser-Meyer-Olkin (KMO) criterion, and exploratory factor analysis (EFA) were employed to identify the most relevant indicators for the study. A true fixed-effects stochastic frontier model was applied to panel data, based on the Cobb-Doug- las production function and the translogarithmic function, to evaluate the determinants of technological development and identify technical efficiency. Fourteen indicators of techno- logical development were used as independent variables, while five key economic indicators were included as adjustment variables. Research and development expenditure served as the dependent variable. Three frontier models were constructed, incorporating adjustment variables such as GDP per capita, FDI net inflows, and FDI net outflows. The findings provide valuable insights for reviewing the key determinants of technological development management in economically advanced countries.
First published online 17 March 202
A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality
No full textTo solve the high-dimensional issue in credit risk assessment, a hybrid clustering and boosting tree feature selection method is proposed. In the hybrid methodology, an improved minimum spanning tree model is first used to remove redundant and irrelevant features. Then three embedded feature selection approaches (i.e., Random Forest, XGBoost, and AdaBoost) are used to further enhance the feature-ranking efficiency and obtain better prediction performance by applying the optimal features. For verification purpose, two real-world credit datasets are used to demonstrate the effectiveness of the proposed hybrid clustering and boosting tree feature selection (CBTFS) methodology. Experimental results demonstrated that the proposed method is superior to others classic feature selection methods. This indicates that the proposed hybrid clustering and boosting tree feature selection method can be used as a promising tool for solving high-dimensional issue in credit risk assessment.
First published online 12 February 202
Inflation and global supply chain pressure in Eurozone: a time-varying causal analysis
No full textThe aim of this paper is to analyze the causal relationship between inflation and global supply chain pressure in the Eurozone. In contrast to the full-sample causality method, this paper utilizes the bootstrap subsample rolling window causality method to account for structural changes. Initially, the computed vector autoregressive models demonstrate that the short-term relationship between inflation and global supply chain pressure is unstable. The dynamic causal relationship is reexamined in the subsample rolling window causality test using a time-varying method (RB bootstrap-based modified-LR causality test). The results indicate that inflation is influenced by the expansion of global supply chain pressure in a variety of sub-periods, with both positive and negative effects. Conversely, inflation fluctuations increase the uncertainty of the Global Supply Chain Pressure Index. The novelty of the findings is that they illustrate bidirectional causal relationships between the two variables, which is in contrast to the existing body of empirical research that does not support the direction of causality. The implications of these findings emphasize the need to implement appropriate monetary policy measures in order to mitigate the inflationary consequences of disruptions in the global supply chain and to ensure a more stable supply chain network