Search Results (54)

Current practices in the construction industry could negatively affect the long lifecycle of building management due to the lack of information and stakeholder management. The purpose of this paper is to identify the critical success factors (CSFs) of integrating BIM models into facility management systems (FMS). This paper conducted a series of semi-structured interviews with industry experts in the FM sector. It used a structured questionnaire to identify the hierarchy arrangement of the identified CSFs using statistical analogies. The findings demonstrated a robust consistency with significant correlation, alongside a strong correlation established using Spearman’s rank correlation coefficient and strong agreement using Kendall coefficient. Additionally, the Relative Importance Index (RII) was employed to prioritize factors according to the professionals’ assessments, yielding the subsequent impact ranking: (1) define the OIR, AIR, and FM information requirements; (2) acquire correct files, data, and formats; and (3) update of information requirements during the defect liability period (DLP). These findings would help in assisting the management of information during FM operations by establishing clear guidelines to be added into the EIR in the early project initiation stages for a successful integration of BIM-FMS for more efficient life cycle management, operation, and maintenance by the FM. Full article

Each layer of the constructed asphalt pavement is evaluated by measuring its quality indicators, as specified in the construction regulations ĮT ASFALTAS 08, and comparing the obtained values with the corresponding design or threshold values. Due to inherent variability in material properties and systematic or random errors during the production, transport, and installation of the asphalt mixture, the quality indicators of the asphalt layers often deviate from their optimal values. When deviations exceed permissible deviations (PD) or limit values (LV), monetary deductions (MDs) are applied. This study presents normalised values and variation dynamics for 10 quality indicators of the asphalt layer subject to MDs in Lithuania. Using the expertise of 71 road construction professionals and multi-criteria decision-making (MCDM) methods, the influence of these deviations on road quality was assessed. The experts ranked all indicators using percentage weights and the Analytic Hierarchy Process (AHP) method. Expert consensus was verified using concordance coefficients and consistency ratios. After eight statistical outliers were excluded, adjusted weights were calculated based on responses from 63 experts. The proposed method, termed CIBRO (Criteria Importance But Rejected Outliers), enables the objective prioritisation of asphalt quality indicators. The CIBRO method enhances expert concordance and results reliability by aligning criterion ranks with the normal distribution, complementing the Kendall rank correlation approach. The findings highlight that insufficient compaction, inadequate layer thickness, and binder content deviations are the most influential factors that affect layer quality. In contrast, deviations in pavement width, friction coefficient, and surface evenness (measured with a 3 m straight edge) were found to have a lesser impact. The CIBRO method offers a robust approach to assessing the importance of the quality of the asphalt layer, supporting improvements in construction standards and pavement assessment systems. Full article

In this paper, we generate random graphs for a specific area, namely, models of real communication networks. We propose a method that determines the “best” invariant; the corresponding basic algorithm is as follows. For the generated set of graphs, we calculate the numerical values of each of the pre-selected invariants (i.e., indexes of Graovac–Ghorbani, Randic̀, Wiener, global clustering coefficients and the vector of second-order degrees). For all graphs, we arrange these numerical values in descending order, after which, for each of the 10 pairs of invariants, we calculate the rank correlation of these orders; for such calculations, we use 5 different variants of rank correlation algorithms (i.e., usual pair correlation, Spearman’s algorithm, Kendall’s algorithm and its improved version, and the algorithm proposed by the authors). In such a way, we get 10 pairs of rank correlation values, then we arrange them as the values of 10 independent elements of the 5 × 5 table (rows and columns of this table correspond to the 5 invariants under consideration). If the rank correlation values are negative, we record the absolute value of this value in the table. The basic idea is that the “most independent” invariant of the graph gets the minimum sum when summing 4 values of its row, i.e., less than for other invariants (other rows). For our subject area, we obtained the same result for 5 different variants of calculating the rank correlation: the value obtained for the vector of second-order degrees is significantly better than all the others, and among the usual invariants, the global clustering coefficients invariant is significantly better than others ones. This fact corresponds to our previous calculations, in which we ordered the graph invariants according to completely different algorithms. Full article

Coordinate registration (CR) is the key technology for improving the target positioning accuracy of sky-wave over-the-horizon radar (OTHR). The CR parameters are derived by matching the sea–land clutter classification (SLCC) results with prior geographic information. However, the SLCC results often contain mixed clutter, leading to discrepancies between land and island contours and prior geographic information, which makes it challenging to calculate accurate CR parameters for OTHR. To address these challenges, we transform the sea–land clutter data from Euclidean space into graph data in non-Euclidean space, and the CR parameters are obtained by calculating the similarity between graph pairs. And then, we propose a similarity calculation via a graph neural network (SC-GNN) method for calculating the similarity between graph pairs, which involves subgraph-level interactions and node-level comparisons. By partitioning the graph into subgraphs, SC-GNN effectively captures the local features within the SLCC results, enhancing the model’s flexibility and improving its performance. For validation, we construct three datasets: an original sea–land clutter dataset, a sea–land clutter cluster dataset, and a sea–land clutter registration dataset, with the samples drawn from various seasons, times, and detection areas. Compared with the existing graph matching methods, the proposed SC-GNN achieves a Spearman’s rank correlation coefficient of at least 0.800, a Kendall’s rank correlation coefficient of at least 0.639, a p@10 of at least 0.706, and a p@20 of at least 0.845. Full article

The digital economy, driven by innovative technologies and artificial intelligence (AI), is transforming economic systems and increasing the demand for accurate assessments of digital competitiveness. This study addresses the inconsistencies in country rankings derived from global digital indices and aims to determine whether these rankings differ due to methodological variations. It also examines whether the rankings correlate significantly across different evaluation frameworks. The research focuses on 29 European countries and analyzes rankings from four widely recognized indices: the World Digital Competitiveness Ranking (WDCR), Network Readiness Index (NRI), AI Readiness Index (AIRI), and Digital Quality of Life Index (DQLI). To assess the consistency and variability in rankings from 2019 to 2024, the study applies Friedman’s ANOVA and Kendall’s coefficient of concordance. The results demonstrate strong correlations at the level of country rankings, indicating a high degree of consistency, but also confirm statistically significant differences in rankings among the indices, which reflect the diversity of their conceptual foundations. Countries such as Finland, the Netherlands, and Denmark consistently achieve top rankings, indicating convergence, while more variability is observed in indices like the DQLI. These findings highlight the importance of rank-based, multidimensional assessments in evaluating digital competitiveness. They support the use of such assessments as policy tools for monitoring progress, identifying gaps, and promoting inclusive digital development. Full article

The article aims to identify the effects and determinants of implementing digital customer service tools in Polish SMEs in terms of digital customer requirements. Quantitative research was conducted among Polish SMEs using a survey. The following statistical methods were used to analyze the survey data: Dunn’s post hoc tests, ANOVA Kruskal–Wallis test, Kendall’s rank correlation coefficient, and Multivariate Adaptive Regression Splines (MARSplines). Research results showed that Polish SMEs demonstrating better preparedness to serve digital customers achieve higher financial results, an increase in the rapidity and agility of customer service, increased customer satisfaction, and improved image. In addition, they gain sustainability benefits in the form of reduced emissions of hazardous substances or waste, recycling of waste, and reduced consumption of water, electricity, and other raw materials. The main determinants of digital transformation in customer service are the type of business (Polish Classification of Activities—PKD), the age of the company, and the educational level of its manager. The article contributes to promoting digitization among SME managers and motivates them to support customer service with digital tools. The identified effects and determinants provide practical guidance and encourage the implementation of digital technologies to meet the demands of digital customers. Using this approach, SMEs can increase their satisfaction and loyalty, resulting in better financial performance and improved competitiveness. This article identifies the economic and sustainability effects and determinants of implementing digital customer service tools in Polish SMEs in the context of digital customer requirements. This study has an original approach to the issue of digital transformation in the SME sector in Poland. Full article

Understanding the hydrometeorological processes of the Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau, is crucial for effective water resource management and climate change adaptation. This study provides a comprehensive analysis of the basin’s hydrometeorological characteristics using long-term observational data from six representative stations across the upper, middle, and lower reaches. We examined trends, periodicity, variability, and correlations of key elements—precipitation, temperature, evaporation, and discharge—employing methods such as linear regression, Mann–Kendall tests, wavelet analysis, and Kendall rank correlation coefficient tests. The results indicated that precipitation and discharge exhibited non-significant upward trends, with fluctuations across decades, while temperature showed a significant increase of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, and a negative correlation between evaporation and discharge. The hydrometeorological characteristics are significantly influenced by climatic factors, especially precipitation and temperature, with the warming trend potentially affecting water’s availability and distribution. These findings offer valuable insights for water resource management and highlight the need for continuous monitoring to understand hydrological responses to climatic and anthropogenic changes in this critical region. Full article

The changing demographics of the global population represent a significant challenge for humanity. Such changes have an impact on the functioning of the economy, including the housing market, and necessitate constant monitoring. This study evaluated the spatial diversity of all the large and medium-sized cities in the West Pomeranian Voivodeship, situated in the north-west of Poland, in terms of three key factors: demographic potential, housing stock and their price levels. Furthermore, the interactions between the cities’ positions in the rankings, which were created on the basis of the aforementioned phenomena, were identified. In order to achieve the objectives of the study, the linear object ordering method, the Hellwig pattern method and Kendall’s tau rank correlation coefficient were employed. The research was conducted using data from the years 2018 to 2022, sourced from the databases of the Polish Statistical Office and the Analysis and Monitoring System of the Real Estate Market. The study observed a relatively strong positive correlation between the positions of cities in the ranking created for demographic potential and the level of residential property prices for the year 2020. The correlation between the positions of cities in the rankings for demographic potential and housing real estate stock was found to be very weak. The case of Koszalin was identified as an optimal location for residence due to the existing residential property stock and its prices. This was corroborated by the city’s residents, who also enabled the city to be ranked at the top of a ranking created for this phenomenon through the diagnostic variables for demographic potential. This article addresses a research gap, as, to the best of our knowledge, the indicated relationships have not yet been analysed in the contexts presented in the article. Full article

U-statistics are fundamental in modeling statistical measures that involve responses from multiple subjects. They generalize the concept of the empirical mean of a random variable X to include summations over each m-tuple of distinct observations of X. W. Stute introduced conditional U-statistics, extending the Nadaraya–Watson estimates for regression functions. Stute demonstrated their strong pointwise consistency with the conditional expectation $r^{\left(m\right)}(\phi ,\mathbf{t})$, defined as $\mathbb{E}\left[\phi (Y_1,\dots ,Y_m)\right|(X_1,\dots ,X_m)=\mathbf{t}]$ for $\mathbf{t}\in {\mathcal{X}}^m$. This paper focuses on estimating functional single index (FSI) conditional U-processes for regular time series data. We propose a novel, automatic, and location-adaptive procedure for estimating these processes based on k-Nearest Neighbor (kNN) principles. Our asymptotic analysis includes data-driven neighbor selection, making the method highly practical. The local nature of the kNN approach improves predictive power compared to traditional kernel estimates. Additionally, we establish new uniform results in bandwidth selection for kernel estimates in FSI conditional U-processes, including almost complete convergence rates and weak convergence under general conditions. These results apply to both bounded and unbounded function classes, satisfying certain moment conditions, and are proven under standard Vapnik–Chervonenkis structural conditions and mild model assumptions. Furthermore, we demonstrate uniform consistency for the nonparametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship. This result is independently valuable and has potential applications in areas such as set-indexed conditional U-statistics, the Kendall rank correlation coefficient, and discrimination problems. Full article

The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an $R^2$ of 0.88 and an $RMSE$ of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the $R^2$ increased by 0.19 and 0.26, respectively, and the $RMSE$ decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums. Full article

In his work published in (Ann. Probab. 19, No. 2 (1991), 812–825), W. Stute introduced the notion of conditional U-statistics, expanding upon the Nadaraya–Watson estimates used for regression functions. Stute illustrated the pointwise consistency and asymptotic normality of these statistics. Our research extends these concepts to a broader scope, establishing, for the first time, an asymptotic framework for single-index conditional U-statistics applicable to locally stationary random fields $\{{\mathbf{X}}_{s,A_n}:sin{R}_n\}$ observed at irregularly spaced locations in $R_n$, a subset of ${\mathbb{R}}^d$. We introduce an estimator for the single-index conditional U-statistics operator that accommodates the nonstationary nature of the data-generating process. Our method employs a stochastic sampling approach that allows for the flexible creation of irregularly spaced sampling sites, covering both pure and mixed increasing domain frameworks. We establish the uniform convergence rate and weak convergence of the single conditional U-processes. Specifically, we examine weak convergence under bounded or unbounded function classes that satisfy specific moment conditions. These findings are established under general structural conditions on the function classes and underlying models. The theoretical advancements outlined in this paper form essential foundations for potential breakthroughs in functional data analysis, laying the groundwork for future research in this field. Moreover, in the same context, we show the uniform consistency for the nonparametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship, which is of its own interest. Potential applications of our findings encompass, among many others, the set-indexed conditional U-statistics, the Kendall rank correlation coefficient, and the discrimination problems. Full article

Background: Antibiotic residue in food products and the resulting antibiotic-resistant bacteria represent a significant global public health threat. The misuse of antibiotics is a primary contributor to this issue. This study investigated the knowledge, attitudes, and practices (KAP) regarding antibiotic use among cage fish farmers on Ghana’s Volta Lake. Method: We conducted a cross-sectional survey with 91 cage fish farmers across three scales: small, medium, and large. A semi-structured questionnaire complemented by personal observations provided comprehensive data. We used several statistical methods for analysis: Pearson Chi-Square and Spearman correlation tests to examine relationships and trends among variables, logistic regression to analyze variable interactions, and Cronbach’s alpha to check internal consistency. Additionally, Kendall’s coefficient was used to rank challenges, utilizing STATA and SPSS for these calculations. Results: The survey revealed that 58.55% of cage fish farmers earn an average of 10,000 USD annually, with 35.16% having over 16 years of experience. From the survey, all sampled populations admitted to antibiotic applications in their farming operation. Knowledge of antibiotic types was mainly influenced by peers (46.15%), with tetracycline being the most recognized and used. There was a significant reliance on the empirical use of antibiotics, with 52.75% of farmers using them based on personal experience and 40.66% without a prescription. When initial treatments failed, 41.76% of the farmers would change or combine drugs. Older farmers (over 51 years) and those with tertiary education demonstrated significantly better KAP scores regarding antibiotic use. Strong correlations were also found among knowledge, attitudes, and practices in antibiotic usage. Conclusions: The findings indicate a need for improved education on antibiotic use among fish farmers to reduce misuse and enhance awareness of the potential consequences. This study provides foundational data for designing interventions to address these issues in the context of cage fish farming on Volta Lake. Full article

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson’s linear correlation coefficient, Spearman’s rank correlation coefficient, Kendall’s coefficient, partial correlation, dynamic time warping measure, and Kullback–Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia’s invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries’ geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries. Full article

Four measures of association, namely, Spearman’s $\rho$, Kendall’s $\tau$, Blomqvist’s $\beta$ and Hoeffding’s ${\mathsf{\Phi }}^2$, are expressed in terms of copulas. Conveniently, this article also includes explicit expressions for their empirical counterparts. Moreover, copula representations of the four coefficients are provided for the multivariate case, and several specific applications are pointed out. Additionally, a numerical study is presented with a view to illustrating the types of relationships that each of the measures of association can detect. Full article

Complex tax systems can result in tax evasion, which further impacts the revenues necessary to achieve sustainable development goals. Enhancing taxpayer education, tax knowledge, and tax fairness perception is essential for boosting revenues to support societal sustainability. The aim of this study was to assess the levels of tax knowledge and tax fairness perception within the Slovene taxpayer population, with a specific focus on the differences related to gender and settlement size. Further, the connections between tax knowledge and various aspects of tax fairness were explored. The Kruskal–Wallis test was used to assess the statistical significance of gender and settlement size differences and the Kendall’s coefficient of rank to determine the association between the tax knowledge and fairness perception dimensions. The results provide evidence that highlights disparities in tax knowledge between male and female taxpayers (p-value = 0.0116). Additionally, this study demonstrates that settlement size does not significantly impact tax knowledge perception among Slovene taxpayers (p-value = 0.2067). However, tax fairness encompasses various dimensions, and our research reveals no disparities based on gender (p-value = 0.7263) or settlement size (p-value = 0.2786). When assessing the correlation between tax knowledge and tax fairness perception, the results indicate statistically significant but weak correlations in both directions, depending on the specific fairness dimension. Full article