VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Developing standards for surveying, measuring, and mapping underground objects using the GPR method for Vietnam
No full textIn the context of rapid urbanization, detailed investigation of underground objects in the shallow layer is very important for safe and efficient urban planning, construction, and operation. With many outstanding advantages, Ground Penetrating Radar (GPR) technology has been increasingly applied worldwide in shallow geophysical surveys. However, in Vietnam, applying GPR for surveying and measuring objects in the above spatial range is only in its early stages, and there are no official standards or regulations. This paper uses the GPR method to develop technical standards for investigating and mapping underground objects within 0–6 m depth. The research methodology includes synthesis analysis of international standards, expert consultation, and field experiments. The main results of the study include: (1) Proposing a 9-step detailed GPR survey process; (2) Specific technical regulations for GPR survey work; (3) Classification of 4 quality levels of products (QLB1-QLB4) with specific requirements; (4) Guidelines for representing and presenting maps of underground objects. This standard will be the first document to standardize the application of GPR in measuring and acquiring information on underground objects in Vietnam, contributing to promoting the widespread application of GPR technology in practice
Property price and anti-social behaviour: An empirical study of Northern Ireland
No full textThis study examines the impact of anti-social behaviour (ASB) on property prices. By analysing over 14,500 market transactions in Northern Ireland, we find that the prevalence of ASB within a neighbourhood exerts a direct and negative influence on house prices, albeit with diminishing effect at the margin. Furthermore, the dampening effects of ASB are more pronounced in districts characterised by higher population density, proximity to the capital city (Belfast), and lower property values. A thorough analysis of district-level data across a wide range of statistical indicators further indicates that the adverse impact of ASB on property prices is most acute in areas marked by social and economic deprivation, including factors such as income, employment, education, and access to services. Lastly, our submarket analysis suggests that the apartment sector and public housing are disproportionately affected by ASB in terms of price depreciation compared to other property types
Housing rental availability index: A tool for addressing supply-demand challenges in rental markets
No full textWe develop a Housing Rental Availability Index (HRAI) to measure rental housing availability across Poland by incorporating household income, rental prices, and supply-side factors. The HRAI is constructed using the structured parametric approach, offering a comprehensive and adaptable framework for analysing rental housing dynamics in Poland. Based on the Polish rental market data from 2021 to 2024, the HRAI reveals that supply constraints and rent fluctuations have a greater impact on rental availability than household income. This result challenges traditional affordability metrics. Sensitivity analysis confirms that rental availability emerges from the interaction of supply and demand rather than from either factor alone. This integrated approach positions HRAI as an “anti-separatist” indicator and presents an original approach to examining rental housing availability. The index can help local and national policymakers design targeted rent subsidies, address supply-demand imbalances, and promote spatial equity. Our findings highlight the value of combining economic measures into a single availability index and provide a framework for applying the HRAI in both academic research and housing policy decisions
Urban distances, individual resources, and migrant entrepreneurship: a configurational analysis in China
No full textGiven its significance for economic and social sustainability, migrant entrepreneurship (ME) has attracted increasing attention from scholars and policymakers. However, existing research provides limited insights into how various antecedents jointly affect ME. To address this gap, this study develops a theoretical model that integrates mixed embeddedness theory and entrepreneurial opportunity construction theory to explain the processes of opportunity construction and exploitation in ME. Using 130 cross-city migration cases in China – each comprising individuals from the same origin and destination cities – we examine how urban distances and individual resources jointly shape ME. The analysis identifies three pathways to high ME: the opportunity-resource endowed path, the resource bricolage path, and the opportunity-resource matching path. Although no single factor is necessary for high ME, greater geographic distance consistently promotes it. This study advances our understanding of the interplay between urban conditions, individual resources, and ME, and further enriches the mixed embeddedness theory by integrating the opportunity construction perspective
Prediction of cost contingency in construction projects by introducing machine learning algorithms
No full textConstruction projects are bound by uncertainties and changes by its nature. Thus, cost contingency needs to be allocated to construction project budget to cope with any deviation of actual costs from planned ones. However, existing methods for predicting cost contingencies, as studied and practiced, still present limitations in reliability and accuracy. Machine learning (ML) has gained popularity for enhancing prediction power in various fields. The paper aims to examine various ML algorithms to implement a cost contingency prediction model, employing both continuous and categorical predictor variables. To develop the model, construction transportation project datasets, which were bid between 2013‒2017, were collected from the Florida Department of Transportation (FDOT) website. To address imbalanced regression dataset issues, the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) algorithm is introduced. ML random forest (RF) regression associated with random search hyperparameter optimization, achieved remarkably accurate predictions compared to extreme gradient boosting (XGBoost) regression and artificial neural network (ANN) models. The results also demonstrate that four parameters are significant factors in predicting construction cost contingency: project amount, project duration, and latitude and longitude factors. These findings provide new insights for researchers in developing models and for practitioners seeking more advanced method
Heavy rainfall resilience: adoption of climate smart agriculture among marginal farmers in a sub-basin of India
No full textHeavy rainfall is a significant challenge for marginal farmers in the aspect of sustainable agriculture. This research analyzed data from eight grid points over 42 years to determine rainfall criteria: 50.91 mm to 79.65 mm for heavy rain, 76.95 mm to 101.21 mm for extreme rain, and 101.21 mm for rare 24-hour occurrences. The vulnerability mapping found 58 agriculturally susceptible communities. Research shows cashew nuts, coriander, sugarcane, sweet potatoes, and turmeric are the five main crops in the 58 most susceptible villages to heavy rainfall. These villages contain a greater number of marginal farmers. The DELPHI method revealed that coriander is the most susceptible crop. In this study, climate-smart agricultural practices such as Integrated Pest Management methods, shifting crop seasons, and Meghdoot application projections are used to minimize the damages caused by heavy rainfall. This includes protecting crops before heavy rainfall and monitoring them after heavy rainfall. For emission reduction as one of the pillars of Climate Smart Agriculture, biochar from biomass breakdown without oxygen is suggested. A poll found that 44% of respondents would use social entrepreneurship for biochar kilns. As, a result 33 farmers from 7 villages used the suggested Integrated Pest Management Technique and Meghdoot to harvest their second season with minimum losses
Estimation on the attractiveness of public transport: Vilnius city case
No full textThe paper analyses the attractiveness problems of urban public transportation in the context of promoting sustainable mobility and multimodality. The article is based on the methods of scientific literature and analysis and qualitative research analysis. The analytical-methodical section of the article discusses the principles of sustainable mobility and multimodality, assess the possibilities of their application. In the research section, the expert analysis that evaluates Vilnius public transport attractiveness is presented, and affecting problems are assessed and considered. Re-search results have shown that experts agree on the issues (factors) that affect the attractiveness of public transport. These factors have been ranked in order of priority and relevance from 1 (big-gest problem) to 10 (smallest problem). In conclusion, the paper presents a structural model which aims to promote and increase the attractiveness of public transport system
Enhancing land use classification with hybrid machine learning and satellite imagery
No full textThe growing accessibility of satellite imagery and the rapid evolution of machine learning (ML) techniques have significantly advanced land use classification for environmental monitoring. However, challenges such as cloud coverage, varying image resolutions, and seasonal changes continue to hinder classification accuracy and consistency. This study aims to improve land use classification by proposing an integrated cloud interpolation, vegetation indices and ML based approach for classification of Sentinel-2 (S2) satellite data across the Baltic States. Specifically, a spatiotemporal interpolation module is introduced that reconstructs cloud-obscured pixels using multi-temporal coherence and derives optimized vegetation-index composites to enhance class separability under varying seasonal conditions. In order to achieve this aim and to choose the best ML algorithm for land use classification, we compare the performance of three classification algorithms, i.e., Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and evaluate their effectiveness in handling noisy and incomplete data. Our experimental results show that all three methods achieve strong classification accuracy, with RF exceeding 90%, while KNN and SVM also demonstrate competitive results. These methodological enhancements have been demonstrated to reduce cloud-induced misclassification and provide a scalable, transferable framework for operational land-use mapping in challenging atmospheric and seasonal contexts. These findings highlight the robustness of the proposed approach and provide valuable insights for future applications of ML in land use classification and environmental analysis.
Optimizing chaotic systems by orbit counting and Fourier spectrum: FPGA implementation and image encryption application
No full textThe optimization of chaotic systems has been performed by considering dynamical characteristics of the mathematical models. The proposed work shows the application of genetic algorithms (GAs) to optimize the chaotic behavior of three well-known systems, namely: Lorenz, Chen and Lü. The parameters of the chaotic systems are varied in a specific range of values considered as the search space, and the evaluation of the mathematical model is performed by applying the Forward Euler method. The contribution presented herein is that the chaotic behavior is evaluated by counting the orbits in an attractor and the sparsity of them. In addition, the chaotic behavior is guaranteed by evaluating the Fourier spectrum of the time series. The solutions provided by the GA, are then implemented on a field-programmable gate array (FPGA) to verify the experimental generation of chaotic attractors. Finally, two optimized chaotic systems are synchronized and used to encrypt an image, thus confirming the appropriateness of optimizing the chaotic behavior by orbit counting and Fourier spectrum analysis
Unveiling the impact of improvement methodologies on employee engagement: Insights from central European companies
No full textImprovement methodologies (IMs) consist of many components; however, employee engagement (EE) is particularly important in bottom-up initiated process improvement. This study aims to investigate EE with reference to IMs used by companies. EE measurement constructs focused on process improvement has been developed and verified as reliable. This study examines a sample of 380 medium- and large-sized companies. The ANOVA procedure proves that: (1) IMs support EE in companies, and (2) the absence of IMs leads to lower EE. However, support for EE is specific and does not primarily concern issues directly related to process improvement. This study also discovers the multi-use of IMs by companies. IMs such as Lean, strongly focused on EE by their assumptions, only moderately support EE in companies. The study found no exceptionally effective IM which allows for the easy gain of EE in the investigated companies’ current state of implementation. The results imply that companies are approximately halfway towards the effective use of IMs. They should rethink the use of IMs, transform their leadership style, and better motivate employees to engage in bottom-up process improvement, which is indispensable.
First published online 18 November 202