bluuebunny/crossref_metadata_2025 huggingface dataset:
The Economic and Financial Health of Lithuanian Logistics Companies
Authors: Bužinskienė Rita , Gelashvili Vera
Published: June 2024

In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.
Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy
Authors: Karimov Artur , Strelnikov Maksim , Mazin Sergei , Goryunov Dmitriy , Leonov Sergey , Butusov Denis
Published: June 2024

Robot artistic painting and robot calligraphy do require brush models for brushstroke simulation and painting robot control. One of the main features of the brush is its compliance, which describes the relationship between the brush footprint shape and the pressure applied to the brush. In addition, during motion, the brush footprint position lags from the brush handle position in a complicated manner. To date, the question of creating a physically correct model of these effects and choosing the best method for the model parameter calibration has not been presented in the literature. In the current paper, we derive equations of the brush contact patch motion, give their closed-form solutions, and investigate three methods for the brush model calibration: capturing brush footprints on a matte glass with a camera, painting calibration brushstrokes, and capturing a brush shape side projection with a camera. As we show, calibration brushstrokes give us primary information on brush contact patch displacement during painting, and capturing the brush side projection allows the accurate estimation of the gap from the brush tip to the center of the contact patch. Capturing brush footprints is useful for creating a brushstroke executable model. As an example, a model for a round artistic brush was created and verified in three tests, including measuring the coordinates of an angular brushstroke center line, simulating an angular brushstroke, and writing a signature using a robotic setup.
iblueCulture: Data Streaming and Object Detection in a Real-Time Video Streaming Underwater System
Authors: Vlachos Apostolos , Bargiota Eleftheria , Krinidis Stelios , Papadimitriou Kimon , Manglis Angelos , Fourkiotou Anastasia , Tzovaras Dimitrios
Published: June 2024

The rich and valuable underwater cultural heritage present in the Mediterranean is often overlooked, if not completely unknown, due to the inherent difficulties in using physical approaches. The iblueCulture project was created to bridge that gap by introducing a real-time texturing and streaming system. The system captures video streams from eight underwater cameras and manipulates it to texture and colorize the underwater cultural heritage site and its immediate surroundings in a virtual reality environment. The system can analyze incoming data and, by detecting newly introduced objects in sight, use them to enhance the user experience (such as displaying a school of fish as they pass by) or for site security. This system has been installed in some modern and ancient shipwrecks in Greece and was used for in situ viewing. It can also be modified to work remotely, for example, in museums or educational institutions, to make the sites more accessible and raise public awareness. It can potentially be used in any underwater site, both for presentation and education, as well as for monitoring and security purposes.
Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds
Authors: Wagner Nike , Franke Gunnar , Schmieder Klaus , Mandlburger Gottfried
Published: June 2024

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future.
Estimation of the Wind Field with a Single High-Frequency Radar
Authors: Dussol Abïgaëlle , Chavanne Cédric
Published: June 2024

Over several decades, high-frequency (HF) radars have been employed for remotely measuring various ocean surface parameters, encompassing surface currents, waves, and winds. Wind direction and speed are usually estimated from both first-order and second-order Bragg-resonant scatter from two or more HF radars monitoring the same area of the ocean surface. This limits the observational domain to the common area where second-order scatter is available from at least two radars. Here, we propose to estimate wind direction and speed from the first-order scatter of a single HF radar, yielding the same spatial coverage as for surface radial currents. Wind direction is estimated using the ratio of the positive and negative first-order Bragg peaks intensity, with a new simple algorithm to remove the left/right directional ambiguity from a single HF radar. Wind speed is estimated from wind direction and de-tided surface radial currents using an artificial neural network which has been trained with in situ wind speed observations. Radar-derived wind estimations are compared with in situ observations in the Lower Saint-Lawrence Estuary (Quebec, Canada). The correlation coefficients between radar-estimated and in situ wind directions range from 0.84 to 0.95 for Wellen Radars (WERAs) and from 0.79 to 0.97 for Coastal Ocean Dynamics Applications Radars (CODARs), while the root mean square differences range from 8° to 12° for WERAs and from 10° to 19° for CODARs. Correlation coefficients between the radar-estimated and the in situ wind speeds range from 0.89 to 0.93 for WERAs and from 0.81 to 0.93 for CODARs, while the root mean square differences range from 1.3 m.s−1 to 2.3 m.s−1 for WERAs and from 1.6 m.s−1 to 3.9 m.s−1 for CODARs.
Assessing Vertical Accuracy and Spatial Coverage of ICESat-2 and GEDI Spaceborne Lidar for Creating Global Terrain Models
Authors: Pronk Maarten , Eleveld Marieke , Ledoux Hugo
Published: June 2024

Digital Elevation Models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of data from two spaceborne lidar altimetry missions, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric data collection patterns—as sources for rasterisation towards creating global DEMs. We validate the terrain measurements of both missions against airborne lidar datasets over three areas in the Netherlands, Switzerland, and New Zealand and differentiate them using land-cover classes. For our experiments, we use five years of ICESat-2 ATL03 data and four years of GEDI L2A data for a total of 252 million measurements. The datasets are filtered using parameter flags provided by the higher-level products ICESat-2 ATL08 and GEDI L3A. For all areas and land-cover classes combined, ICESat-2 achieves a bias of −0.11 m, an MAE of 0.43 m, and an RMSE of 0.93 m. From our experiments, we find that GEDI is less accurate, with a bias of 0.09 m, an MAE of 0.98 m, and an RMSE of 2.96 m. Measurements in open land-cover classes, such as “Cropland” and “Grassland”, result in the best accuracy for both missions. We also find that the slope of the terrain has a major influence on vertical accuracy, more so for GEDI than ICESat-2 because of its larger horizontal geolocation error. In contrast, we find little effect of either beam power or background solar radiation, nor do we find noticeable seasonal effects on accuracy. Furthermore, we investigate the spatial coverage of ICESat-2 and GEDI by deriving a DEM at different horizontal resolutions and latitudes. GEDI has higher spatial coverage than ICESat-2 at lower latitudes due to its beam pattern and lower inclination angle, and a derived DEM can achieve a resolution of 500 m. ICESat-2 only reaches a DEM resolution of 700 m at the equator, but it increases to almost 200 m at higher latitudes. When combined, a 500 m resolution lidar-based DEM can be achieved globally. Our results indicate that both ICESat-2 and GEDI enable accurate terrain measurements anywhere in the world. Especially in data-poor areas—such as the tropics—this has potential for new applications and insights.
Voxel- and Bird’s-Eye-View-Based Semantic Scene Completion for LiDAR Point Clouds
Authors: Liang Li , Akhtar Naveed , Vice Jordan , Mian Ajmal
Published: June 2024

Semantic scene completion is a crucial outdoor scene understanding task that has direct implications for technologies like autonomous driving and robotics. It compensates for unavoidable occlusions and partial measurements in LiDAR scans, which may otherwise cause catastrophic failures. Due to the inherent complexity of this task, existing methods generally rely on complex and computationally demanding scene completion models, which limits their practicality in downstream applications. Addressing this, we propose a novel integrated network that combines the strengths of 3D and 2D semantic scene completion techniques for efficient LiDAR point cloud scene completion. Our network leverages a newly devised lightweight multi-scale convolutional block (MSB) to efficiently aggregate multi-scale features, thereby improving the identification of small and distant objects. It further utilizes a layout-aware semantic block (LSB), developed to grasp the overall layout of the scene to precisely guide the reconstruction and recognition of features. Moreover, we also develop a feature fusion module (FFM) for effective interaction between the data derived from two disparate streams in our network, ensuring a robust and cohesive scene completion process. Extensive experiments with the popular SemanticKITTI dataset demonstrate that our method achieves highly competitive performance, with an mIoU of 35.7 and an IoU of 51.4. Notably, the proposed method achieves an mIoU improvement of 2.6 % compared to previous methods.
Massive Sea-Level-Driven Marsh Migration and Implications for Coastal Resilience along the Texas Coast
Authors: Jung Nathalie W. , Doe Thomas A. , Jung Yoonho , Dellapenna Timothy M.
Published: June 2024

Tidal salt marshes offer crucial ecosystem services in the form of carbon sequestration, fisheries, property and recreational values, and protection from storm surges, and are therefore considered one of the most valuable and fragile ecosystems worldwide, where sea-level rise and direct human modifications resulted in the loss of vast regions of today’s marshland. The extent of salt marshes therefore relies heavily on the interplay between upland migration and edge erosion. We measured changes in marsh size based on historical topographic sheets from the 1850s and 2019 satellite imagery along the Texas coast, which is home to three of the largest estuaries in North America (e.g., Galveston, Corpus Christi, and Matagorda Bays). We further distinguished between changes in high and low marsh based on local elevation data in an effort to estimate changes in local ecosystem services. Our results showed that approximately 410 km2 (58%) of salt marshes were lost due to coastal erosion and marsh ponding and nearly 510 km2 (72%) of salt marshes were created, likely due to upland submergence. Statistical analyses showed a significant relationship between marsh migration and upland slope, suggesting that today’s marshland formed as a result of submergence of barren uplands along gently sloping coastal plains. Although the overall areal extent of Texas marshes increased throughout the last century (~100 km2 or 14%), economic gains through upland migration of high marshes (mostly in the form of property value (USD 0.7–1.0 trillion)) were too small to offset sea-level-driven losses of crucial ecosystem services of Texan low marshes (in the form of storm protection and fisheries (USD 2.1–2.7 trillion)). Together, our results suggest that despite significant increases in marsh area, the loss of crucial ecosystem services underscores the complexity and importance of considering not only quantity but also quality in marshland conservation efforts.
Ionosphere-Weighted Network Real-Time Kinematic Server-Side Approach Combined with Single-Differenced Observations of GPS, GAL, and BDS
Authors: Ma Yi , Xu Hongjin , Wang Yifan , Yuan Yunbin , Chen Xingyu , Dai Zelin , Ai Qingsong
Published: June 2024

Currently, network real-time kinematic (NRTK) technology is one of the primary approaches used to achieve real-time dynamic high-precision positioning, and virtual reference station (VRS) technology, with its high accuracy and compatibility, has become the most important type of network RTK solution. The key to its successful implementation lies in correctly fixing integer ambiguities and extracting spatially correlated errors. This paper first introduces real-time data processing flow on the VRS server side. Subsequently, an improved ionosphere-weighted VRS approach is proposed based on single-differenced observations of GPS, GAL, and BDS. With the prerequisite of ensuring estimable integer properties of ambiguities, it directly estimates the single-differenced ionospheric delay and tropospheric delay between reference stations, reducing the double-differenced (DD) observation noise introduced by conventional models and accelerating the system initialization speed. Based on this, we provide an equation for generating virtual observations directly based on single-differenced atmospheric corrections without specifying the pivot satellite. This further simplifies the calculation process and enhances the efficiency of the solution. Using Australian CORS data for testing and analysis, and employing the approach proposed in this paper, the average initialization time on the server side was 40 epochs, and the average number of available satellites reached 23 (with an elevation greater than 20°). Two positioning modes, ‘Continuous’ (CONT) and ‘Instantaneous’ (INST), were employed to evaluate VRS user positioning accuracy, and the distance covered between the user and the master station was between 20 and 50 km. In CONT mode, the average positioning errors in the E/N/U directions were 0.67/0.82/1.98 cm, respectively, with an average success fixed rate of 98.76% (errors in all three directions were within 10 cm). In INST mode, the average positioning errors in the E/N/U directions were 1.29/1.29/2.13 cm, respectively, with an average success fixed rate of 89.56%. The experiments in this study demonstrate that the proposed approach facilitates efficient ambiguity resolution (AR) and atmospheric parameter extraction on the server side, thus enabling users to achieve centimeter-level positioning accuracy instantly.
Spatially Explicit Individual Tree Height Growth Models from Bi-Temporal Aerial Laser Scanning
Authors: Salekin Serajis , Pont David , Dickinson Yvette , Amarasena Sumedha
Published: June 2024

Individual-tree-based models (IBMs) have emerged to provide finer-scale operational simulations of stand dynamics by accommodating and/or representing tree-to-tree interactions and competition. Like stand-level growth model development, IBMs need an array of detailed data from individual trees in any stand through repeated measurement. Conventionally, these data have been collected through forest mensuration by establishing permanent sample plots or temporary measurement plots. With the evolution of remote sensing technology, it is now possible to efficiently collect more detailed information reflecting the heterogeneity of the whole forest stand than before. Among many techniques, airborne laser scanning (ALS) has proved to be reliable and has been reported to have potential to provide unparallel input data for growth models. This study utilized repeated ALS data to develop a model to project the annualized individual tree height increment (ΔHT) in a conifer plantation by considering spatially explicit competition through a mixed-effects modelling approach. The ALS data acquisition showed statistical and biological consistency over time in terms of both response and important explanatory variables, with correlation coefficients ranging from 0.65 to 0.80. The height increment model had high precision (RMSE = 0.92) and minimal bias (0.03), respectively, for model fitting. Overall, the model showed high integrity with the current biological understanding of individual tree growth in a monospecific Pinus radiata plantation. The approach used in this study provided a robust model of annualized individual tree height growth, suggesting such an approach to modelling will be useful for future forest management.
Task-Sensitive Efficient Feature Extraction Network for Oriented Object Detection in Remote Sensing Images
Authors: Liu Zhe , He Guiqing , Dong Liheng , Jing Donglin , Zhang Haixi
Published: June 2024

The widespread application of convolutional neural networks (CNNs) has led to significant advancements in object detection. However, challenges remain in achieving efficient and precise extraction of critical features when applying typical CNN-based methods to remote sensing detection tasks: (1) The convolutional kernels sliding horizontally in the backbone are misaligned with the features of arbitrarily oriented objects. Additionally, the detector shares the features extracted from the backbone, but the classification task requires orientation-invariant features while the regression task requires orientation-sensitive features. The inconsistency in feature requirements makes it difficult for the detector to extract the critical features required for each task. (2) The use of deeper convolutional structures can improve the detection accuracy, but it also results in substantial convolutional computations and feature redundancy, leading to inefficient feature extraction. To address this issue, we propose a Task-Sensitive Efficient Feature Extraction Network (TFE-Net). Specifically, we propose a special mixed fast convolution module for constructing an efficient network architecture that employs cheap transform operations to replace some of the convolution operations, generating more features with fewer parameters and computation resources. Next, we introduce the task-sensitive detection module, which first aligns the convolutional features with the targets using adaptive dynamic convolution based on the orientation of the targets. The task-sensitive feature decoupling mechanism is further designed to extract orientation-sensitive features and orientation-invariant features from the aligned features and feed them into the regression and classification branches, respectively, which provide the critical features needed for different tasks, thus improving the detection performance comprehensively. In addition, in order to make the training process more stable, we propose a balanced loss function to balance the gradients generated by different samples. Extensive experiments demonstrate that our proposed TFE-Net can achieve superior performance and obtain an effective balance between detection speed and accuracy on DOTA, UCAS-AOD, and HRSC2016.
Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images
Authors: Perregrini Dario , Casella Vittorio
Published: June 2024

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%.
Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation
Authors: Kim Sin-Young , Lee Jong-Seok , Jeong Youchul , Jo Young-Heon
Published: June 2024

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (Kx, Ky) ranging between 0.01–0.1 radm−1 and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R2) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms−1. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.
A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
Authors: Tian Yijun , Xiao Guorui , Guo Rui , Zhao Dongqing , Zhang Lu , Xin Jie , Guo Jinglei , Han Yuechao , Du Xuefan , He Donghan , Qin Zheng
Published: June 2024

With the modernization of global navigation satellite systems (GNSS), especially the rapid development of the BeiDou Navigation Satellite System (BDS), more observations of satellites and signals have become available. Using data of the globally distributed MGEX stations, a systematic and comprehensive evaluation of signal characteristics for BDS-3, BDS-2, GPS, and Galileo is conducted in terms of carrier-to-noise ratio (C/N0), code noise, and multipath in the contribution. First, a comprehensive signal quality assessment method for BDS/Galileo/GPS satellites and signals is proposed, including C/N0 modeling and MP modeling. For BDS, the BDS-3 satellites apparently have higher signal power than the BDS-2 satellites at the same frequency such as B1I and B3I, and the signal B2a of BDS-3 is superior to other signals in regard to signal power, which is comparable with the superior Galileo E5 signals and GPS L5. Among all the signals, the observation accuracy of E5 is the highest regardless of receiver types, and next highest are BDS-3 B2a and GPS L5. Due to not being affected by the systematic code errors of BDS-2, the observations of BDS-3 satellites contain smaller multipath errors than that of BDS-2 satellites. As for the multipath suppression performance, the BDS-3 signal B2a, GPS L5, and Galileo E5 and E5b perform better than the other signals, which may be related to their wide signal bandwidths.
Attitudes towards Homosexuality in Europe: Analysis of the European Values Study
Authors: Silvestre Cabrera María , Royo Prieto Raquel , Aristegui Fradua Iratxe
Published: June 2024

The aim of the article is to determine which countries show the most homophobic attitudes and understand the profile of homophobia in Europe. This article analyses data from the survey “European Values Study” and focuses on three of its questions: “Do you think homosexual people can be good parents”; “the degree of justification of homosexuality”, and “the attitude towards the possibility of having a homosexual neighbour”. For this, we have used a quantitative methodology (mean comparison analysis and multiple correspondences. The sample consists of 56,451 people from 34 European countries. One of the main conclusions is that homophobic attitudes are highest in the eastern countries, former Soviet republics, and, on the contrary, residents in Nordic countries have more inclusive attitudes.
Digital Signal Processing (DSP)-Oriented Reduced-Complexity Algorithms for Calculating Matrix–Vector Products with Small-Order Toeplitz Matrices
Authors: Papliński Janusz P. , Cariow Aleksandr , Strzelec Paweł , Makowska Marta
Published: June 2024

Toeplitz matrix–vector products are used in many digital signal processing applications. Direct methods for calculating such products require N2 multiplications and N(N−1) additions, where N denotes the order of the Toeplitz matrix. In the case of large matrices, this operation becomes especially time intensive. However, matrix–vector products with small-order Toeplitz matrices are of particular interest because small matrices often serve as kernels in modern digital signal processing algorithms. Perhaps reducing the number of arithmetic operations when calculating matrix–vector products in the case of small Toeplitz matrices gives less effect than of large ones, but this problem exists, and it needs to be solved. The traditional way to calculate such products is to use the fast Fourier transform algorithm. However, in the case of small-order matrices, it is advisable to use direct factorization of Toeplitz matrices, which leads to a reduction in arithmetic complexity. In this paper, we propose a set of reduced-complexity algorithms for calculating matrix–vector products with Toeplitz matrices of order N=3,4,5,6,7,8,9. The main emphasis will be on reducing multiplicative complexity since multiplication in most cases is more time-consuming than addition. This paper also provides assessments of the implementation of the developed algorithms on FPGAs.
Evolution of the Olympic Movement: Adapting to Contemporary Global Challenges
Authors: Theodorakis Yannis , Georgiadis Konstantinos , Hassandra Mary
Published: June 2024

This paper explores the diverse impact of the Olympic Movement on society, emphasizing core values like ‘excellence’, ‘friendship’, and ‘respect’. Traditionally, the Olympic Movement actively promotes global sport through initiatives such as Olympic education programs, instilling moral dimensions, cultural values, and essential life skills. Recent Olympic Games are scrutinized for their organizers’ focus on safety, pandemic management, environmental sustainability, and gender equality. This paper addresses crucial policy options, spanning human rights, social inclusion through sport, and the pervasive issue of inactivity affecting public health. Noteworthy successes in leveraging sports for refugees and combating substance use disorders are discussed, alongside joint efforts by the World Health Organization and the International Olympic Committee, to combat inactivity and promote health through sports. Exploration of gender equality in the Olympic Movement recognizes challenges and suggests actions, including increasing female participation and addressing sexual harassment. The intersection of sports, climate change, and environmental responsibility is examined, with a focus on the ambitious ‘climate-positive’ goals of the Paris 2024 Olympics. However, since most of the IOC actions are rather symbolic and not substantial, many organizations are called upon to take active initiatives. Actionable recommendations urge countries to prioritize physical activity policies, organize exercise programs, and collaborate across sectors for health and environmental sustainability. The Olympic Games should focus on promoting mass sports participation, fostering positive attitudes, enhancing public health through sports, education, peace, and societal values, advocating for a holistic approach that champions ethical values, and promoting Olympic education to build a better world through sports.
Crises, Opportunities, and the Evolution of Greece’s Growth Model in the EMU
Authors: Katsikas Dimitris
Published: June 2024

Greece’s entry into the Eurozone was regarded as a unique opportunity to reform the country’s inefficient growth model. These hopes were dashed as the decade-long crisis of the 2010s wiped out a substantial part of the wealth accumulated during the previous decades and threatened the stability of Greece’s political system. The crisis highlighted the weaknesses of the Economic and Monetary Union (EMU); the economic governance agreed upon at Maastricht was inadequate to support a monetary union comprising many, widely diverse economies. On the other hand, given the EMU’s economic and institutional architecture, Greece was ill-prepared and unwilling to undertake the necessary adjustments to survive in the Eurozone. Understanding the economic and political aspects of this dual and mutually reinforcing failure is crucial for analyzing the challenges facing the Greek economy. In this article, we take a long-term view of the evolution of Greece’s political economy, adopting a framework inspired by the recent literature on comparative political economy. The aim is to determine whether Greece’s EMU membership affected its growth model and, if so, in what ways. The analysis shows that EMU membership has been crucial for the performance of the Greek economy during different periods but less so for the transformation of its demand-led growth model, which exhibits remarkable stability. This is because neither the ex ante nor the ex post conditionality imposed on Greece in the run-up to EMU entry and during the 2010s crisis, respectively, focused on the structural features of the Greek growth model.
A Comparison of Limited Information Estimation Methods for the Two-Parameter Normal-Ogive Model with Locally Dependent Items
Authors: Robitzsch Alexander
Published: June 2024

The two-parameter normal-ogive (2PNO) model is one of the most popular item response theory (IRT) models for analyzing dichotomous items. Consistent parameter estimation of the 2PNO model using marginal maximum likelihood estimation relies on the local independence assumption. However, the assumption of local independence might be violated in practice. Likelihood-based estimation of the local dependence structure is often computationally demanding. Moreover, many IRT models that model local dependence do not have a marginal interpretation of item parameters. In this article, limited information estimation methods are reviewed that allow the convenient and straightforward handling of local dependence in estimating the 2PNO model. In detail, pairwise likelihood, weighted least squares, and normal-ogive harmonic analysis robust method (NOHARM) estimation are compared with marginal maximum likelihood estimation that ignores local dependence. A simulation study revealed that item parameters can be consistently estimated with limited information methods. At the same time, marginal maximum likelihood estimation resulted in biased item parameter estimates in the presence of local dependence. From a practical perspective, there were only minor differences regarding the statistical quality of item parameter estimates of the different estimation methods. Differences between the estimation methods are also compared for two empirical datasets.
Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study
Authors: Liu Amy , Dorsey Kennedy , Granger Almetra , Bryant Ty-Runet , Tseng Tung-Sung , Celestin Michael , Yu Qingzhao
Published: June 2024

Background: Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the racial differences. Objective: This study sought to identify racial disparities in e-cigarette use and to determine risk factors that help explain these differences. Methods: We used Wave 5 (2018–2019) of the Adult Population Assessment of Tobacco and Health (PATH) Study. First, we conducted descriptive statistics of e-smoking across our risk factor variables. Next, we used multiple logistic regression to check the risk effects by adjusting all covariates. Finally, we conducted a mediation analysis to determine whether identified factors showed evidence of influencing the association between race and e-cigarette use. All analyses were performed in R or SAS. The R package mma was used for the mediation analysis. Results: Between Hispanic and non-Hispanic White populations, our potential risk factors collectively explain 17.5% of the racial difference, former cigarette smoking explains 7.6%, receiving e-cigarette advertising 2.6%, and perception of e-cigarette harm explains 27.8% of the racial difference. Between non-Hispanic Black and non-Hispanic White populations, former cigarette smoking, receiving e-cigarette advertising, and perception of e-cigarette harm explain 5.2%, 1.8%, and 6.8% of the racial difference, respectively. E-cigarette use is most prevalent in the non-Hispanic White population compared to non-Hispanic Black and Hispanic populations, which may be explained by former cigarette smoking, exposure to e-cigarette advertising, and e-cigarette harm perception. Conclusions: These findings suggest that racial differences in e-cigarette use may be reduced by increasing knowledge of the dangers associated with e-cigarette use and reducing exposure to e-cigarette advertisements. This comprehensive analysis of risk factors can be used to significantly guide smoking cessation efforts and address potential health burden disparities arising from differences in e-cigarette usage.
Unleashing the Power of Fungi: Utilizing the Arbuscular Mycorrhizal Fungi Rhizophagus clarus to Mitigate Salinity Stress and Boost Cowpea Bean Productivity for Food Security
Authors: Tavares Davi Santos , Sant’Anna-Santos Bruno Francisco , Gomes Marcelo Pedrosa
Published: June 2024

The increasing demands for food, driven by shrinking arable land areas and a growing population, underscore the need for innovative agricultural practices to mitigate the effects of soil degradation due to salinity and promote food security, particularly in regions heavily impacted by salinity. In this study, we investigated the effects of inoculating the arbuscular mycorrhizal fungus (AMF) Rhizophagus clarus on the productivity of Vigna unguiculata cv. BRS Imponente plants in response to salinity (0, 25, 50, 75, and 100 mM). We found that NaCl concentrations ≥ 50 mM were phytotoxic, reducing plant growth and productivity. However, inoculation with AMF reduced plant oxidative stress (hydrogen peroxide concentration and lipid peroxidation) and ionic stress (Na+/K+ ratio). Inoculated plants exhibited increased antioxidant enzyme activity (ascorbate peroxidase and catalase), higher P and K concentrations, and lower Na concentrations in their leaves. As a result, salt did not interfere with grain production in the AMF-inoculated plants. For the first time, we demonstrate that inoculation with R. clarus can counteract the harmful effects of NaCl in V. unguiculata plants, ensuring their grain yields. Therefore, amid the escalating soil salinization globally, the AMF R. clarus emerges as a practical approach to ensure cowpea yields and enhance production in deteriorating agricultural lands, especially in saline areas. This can significantly contribute to promoting food security.
Analysis of Commuting Habits and Perceived Risks: An Empirical Case Study in a Large Spanish Company
Authors: Fernández Cosme , Alonso Francisco , Tortosa Francisco , Faus Mireia
Published: June 2024

Commuting road accidents have a direct impact on workers as well as companies. Therefore, analyzing the characteristics of commuting to and from work and workers’ perceptions of their potential risk is necessary. This study analyzes these factors in a sample of employees in a large Spanish company. A questionnaire was distributed to a total of 665 workers, assessing variables such as means of transport used and preferred in this type of commuting, perceived risks, assessment of preventive measures, involvement in a road accident during commuting, and road safety training received by the company, among others. There is a difference between preferred and used modes of transport, with factors limiting the use of soft modes. People who have experienced an accident on the way to work have a higher risk perception of this type of journey. The time and distance of the journey also have a significant influence on the perceived risk. The most highly rated preventive actions are mainly related to the accessibility and comfort of the workers’ daily commute, which helps minimize the time spent on journeys. This analysis provides relevant information on the social acceptability of different measures for the development and implementation of future actions of the prevention services that contribute to reducing this type of accident and designing strategies to promote more sustainable commuting mobility.
Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis
Authors: Ciucu-Durnoi Alexandra Nicoleta , Delcea Camelia , Stănescu Aurelia , Teodorescu Cosmin Alexandru , Vargas Vanesa Mădălina
Published: June 2024

The rapid advancement of technology has led to significant milestones in industrial progress, surpassing previous capabilities and presenting new challenges for adaptation. The current phase of industrial revolutions is characterized by accelerated technological development, particularly in automation and digitalization. For instance, the global industrial robotics market was valued at approximately USD 43.0 billion in 2022 and is projected to reach USD 70.6 billion by 2028. The integration of human labor alongside robotic machinery, though a tangible reality, may still seem abstract in certain regions. Despite the recent announcement of the fourth industrial revolution, Industry 5.0 has quickly emerged as the new standard toward which industries aspire. This study performs a bibliometric analysis of articles published between 2020 and 2023 that explores the implications of these two industrial revolutions and the transition between them. Using the Clarivate Analytics’ Web of Science Core Collection, the study identifies 154 articles using the Biblioshiny package in R, which simultaneously discuss Industry 4.0 and Industry 5.0 within their titles, abstracts, or keywords. An impressive annual growth rate of 119.47% among the published papers included in the dataset underlines the interest of the research community in this field. Additionally, key findings include the identification of prominent sources, prolific authors, highly cited articles and their content, as well as common research themes explored across the analyzed papers. Among the most relevant sources in terms of the number of publications, the journal Sustainability plays a key role, holding the first position, followed by Applied Sciences, and Sensors. In terms of motor themes, digital transformation, artificial intelligence, the Internet of Things, and smart manufacturing have been found to play a key role. As a result, the present research contributes to understanding the rapid evolution from Industry 4.0 to Industry 5.0, highlighting key trends, influential research, and emerging themes that are shaping the future of industrial advancements.
The Pathway to Sustainability in a Mass Tourism Destination: The Case of Lanzarote
Authors: Fernández Héctor , Picazo Patricia , Moreno Gil Sergio
Published: June 2024

This article delves into the unique evolution of Lanzarote as a benchmark for sustainable tourism. It examines how the island’s cultural values and political influences have shaped its sustainable tourism model over the last 50 years. This study utilizes a review of academic literature on tourism sustainability and case studies, emphasising the need for a conceptual and analytical framework. By analysing Lanzarote’s sustainable practices and challenges, this article highlights the island’s paradoxical status as both a mass tourism hub and a sustainable destination, led by the artist César Manrique’s visionary approach, which seamlessly integrates art and nature while providing a blueprint for sustainable tourism. Key milestones and achievements are identified, such as UNESCO’s designation of the entire island as a Biosphere Reserve in 1993, showcasing Lanzarote’s governance approach to balancing economic, socio-cultural, and environmental sustainability. The findings underscore Lanzarote’s role as an international benchmark in sustainable tourism, offering insights and lessons applicable to other destinations. The article concludes that Lanzarote’s sustained commitment to social awareness and natural resource protection continues to address global challenges, serving as a reference for sustainable development in island tourism destinations.
Response of Sustainable Solar Photovoltaic Power Output to Summer Heatwave Events in Northern China
Authors: Huang Zifan , Duan Zexia , Zhang Yichi , Ji Tianbo
Published: June 2024

Understanding the resilience of photovoltaic (PV) systems to extreme weather, such as heatwaves, is crucial for advancing sustainable energy solutions. Although previous studies have often focused on forecasting PV power output or assessing the impact of geographical variations, the dynamic response of PV power outputs to extreme climate events still remains highly uncertain. Utilizing the PV power data and meteorological parameters recorded at 15 min intervals from 1 July 2018 to 13 June 2019 in Hebei Province, this study investigates the spatiotemporal characteristics of the PV power output and its response to heatwaves. Solar radiation and air temperature are pivotal in enhancing PV power output by approximately 30% during heatwave episodes, highlighting the significant contribution of PV systems to energy supplies under extreme climate conditions. Furthermore, this study systematically evaluates the performance of Random Forest (RF), Decision Tree Regression (DTR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Deep Belief Network (DBN), and Multilayer Perceptron (MLP) models under both summer heatwave and non-heatwave conditions. The findings indicate that the RF and LightGBM models exhibit higher predictive accuracy and relative stability under heatwave conditions, with an R2 exceeding 0.98, with both an RMSE and MAE below 0.47 MW and 0.24 MW, respectively. This work not only reveals the potential of machine learning to enhance our understanding of climate–energy interplay but also contributes valuable insights for the formulation of adaptive strategies, which are critical for advancing sustainable energy solutions in the face of climate change.
Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model
Authors: Han Mengmeng , Zhang Chunxiao , Yin Sihui , Jia Jingjing , Kim Chulsoo
Published: June 2024

As the growth of society and the continuous upgrading of people’s living standards increase, people’s requirements for indoor environment are also increasing. To optimize the ventilation methods inside buildings, a numerical simulation method was used to construct numerical simulations of airflow organization and aerosol diffusion, and based on this model, better ventilation methods were determined. To optimize the determined better ventilation method, a multi-constraint optimization model was constructed using infection probability, thermal comfort, energy utilization coefficient, and velocity non-uniformity coefficient. The ventilation method was optimized through multi-objective constraints. To solve the optimization model, an optimized particle swarm algorithm was studied and designed. The results showed that under the “air rain” flow field, the maximum values of aerosol particles at the human body, bed surface, and outlet were 171, 769, and 19,973, respectively, while the minimum values were 4, 169, and 2197, respectively. The “air rain” flow field is a better ventilation method. The maximum and minimum values of the original non-uniformity coefficient were 0.44 and 0.08, respectively. After optimization by the particle swarm optimization algorithm, the maximum and minimum predicted non-uniformity coefficients were 0.457 and 0.08, respectively. The original value and predicted value are very close. The numerical model and algorithm constructed by the research institute are effective. The algorithm designed by the research institute can provide technical support for multi-objective optimization of indoor ventilation methods.