Search in Publications
Found 6383 publications
Gender Differences in Environmental Attitudes: An Analysis Using the NEP Scale
Publication Name: Gender Issues
Publication Date: 2025-03-01
Volume: 42
Issue: 1
Page Range: Unknown
Description:
This study initially conducted a systematic literature review to examine gender differences in environmental orientation (EO) using the New Ecological Paradigm (NEP) scale. Following PRISMA guidelines, 38 studies were selected from a total of 168, providing a comprehensive overview of the existing research on the topic. Based on the insights gained from the review, a large sample survey was then conducted to explore further the differences in EO between male and female consumers. The results were compared with previous studies employing similar methodologies. The central research question is: Does gender identity influence EO as measured by the New Ecological Paradigm (NEP) scale? The NEP scale is a well-established instrument for assessing environmental attitudes. The significance of this topic lies in the potential impact of differing attitudes among gender groups on the effectiveness of communication and comprehension. Understanding these differences is crucial for developing strategies for sustainable development through targeted and effective messaging. Our findings indicate that women exhibit a higher NEP orientation, suggesting they are more aware of environmental concerns than men. The systematic review also confirmed this trend, with the majority of studies reporting higher environmental awareness among women. Additionally, women demonstrate greater awareness of related problems and a higher willingness to change their behavior to reduce their environmental impact. In contrast, men’s attitudes are more varied, reflecting a broader diversity of opinions within this group.
Open Access: Yes
The Use of Earth Observation Data for Railway Infrastructure Monitoring—A Review
Publication Name: Infrastructures
Publication Date: 2025-03-01
Volume: 10
Issue: 3
Page Range: Unknown
Description:
Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices.
Open Access: Yes
Quasi-Viral Technologies as the Drivers of the Economy Digital Transformation Towards sustainability
Publication Name: Hightech and Innovation Journal
Publication Date: 2025-03-01
Volume: 6
Issue: 1
Page Range: 183-200
Description:
The relevance of the article is related to the phenomenon of quasi-viral technologies, which are the drivers of the phase transition to sustainable development. The study is aimed at defining the category “quasi-viral emerging technology”, as well as the disclosure of their content and form, and the analysis of the features in the conditions of digital transformations. The research method is based on the analysis of transformational changes in the components of the trialectic mechanism of the reproduction of socio-economic systems, which occur under the influence of quasi-viral sustainable technologies. The article defines the quasi-viral process of spreading emerging technologies as a transformational process of the informational component replacement within the technological base by methods imitating the course of viral infection. The signs of quasi-viral processes are formulated on several levels: “infection” due to a change in the information algorithm; substantial user preferences; lack of sufficient barriers; significant potential to increase users; and disruptive efficiency. Signs of quasi-viral technologies have the following types of innovations: renewable energy, 3D printing, electric transport, energy storage, IT technologies, digital recording of information, cloud technologies, etc. The authors hypothesize the possibility of using entropy estimates as the only measure of approximating the results of the implementation of quasi-viral technologies to the state of sustainability in society and nature. The expected results of the spread of quasi-viral technologies can be significant dematerialization of industrial metabolism, provision of functions of self-organization and self-improvement of social systems, preservation of biodiversity and ecosystems of the planet, and formation of the foundations of sustainable development.
Open Access: Yes
Symptomatology of hypoglycemia in diabetes: A bibliometric analysis (2000-2022) of bayesian approaches
Publication Name: Multidisciplinary Reviews
Publication Date: 2025-03-01
Volume: 8
Issue: 3
Page Range: Unknown
Description:
Hypoglycemia poses a critical challenge in managing diabetes. Existing literature, while extensive, lacks a holistic perspective. This study aims to bridge this gap by combining bibliometric analysis and a comprehensive review of Bayesian analysis-related hypoglycemic issues. This study employed data from the SCI-EXPANDED database for bibliometric analysis. The keywords "symptom" or "symptoms," "hypoglycemic" or "hypoglycemia," or "hypoglycaemia" or "hypoglycaemic," and "Diabetes" or "Diabetic" or "Diabetics" were used to locate 1,596 documents from 2000 to 2022. Document types, authorship patterns, and citation metrics were examined. Bayesian methodologies were systematically reviewed across various diabetes types and evaluated using specific assessment tools. Most of the articles published in "Endocrinology & Metabolism" contributed 37.2% of total articles, with a notable CPP2022 (Citations Per Publication (CPP)) of 35, and the main publication type were articles with an average of about six authors and over 32,000 citations in 2022. The United States (US) consistently leads in the number of published articles, followed by China, Japan, and India. Novo Nordisk led institutions with 36 publications and a substantial CPP2022 of 60.9. The comprehensive review emphasized that Bayesian statistical modeling is widely used for adult Type 1 and Type 2 diabetes but is limited in child Type 1 and absent in Gestational Diabetes (GAD) research. In contrast, Bayesian Networks (BNs) are mainly applied to adult Type 2 diabetes, with gaps in other types. Furthermore, Bayesian Neural Networks (BNNs) are prevalent in adult and child Type 1 studies but not applied to Type 2 or GAD. Since 2010, Total Publications (TP) have increased rapidly, indicating increased interest in researching hypoglycemia. Outlining potential research directions and emphasizing the transformative impact of Bayesian methodologies provides valuable insights for clinicians, researchers, and healthcare stakeholders.
Open Access: Yes
How realistic a bicycle simulator can be? - A validation study
Publication Name: Multimodal Transportation
Publication Date: 2025-03-01
Volume: 4
Issue: 1
Page Range: Unknown
Description:
The aim of this research is to objectively and subjectively validate the virtual reality Bicycle Simulator (BS) developed using off-the-shelf components at the University of Győr, Hungary. To this end, this research compares the performance of 32 participants in two real-world environments (Site 1: separated bicycle-pedestrian path and Site 2: advisory bicycle lane) and in their replication in virtual reality (VR). The objective measures collected for the comparison include speed and Cumulative Lateral Position (CLP), whereas subjective measures include the Perceived Level of Realism (PLR) based on participants’ self-reported perceptions in a post-experiment questionnaire. PLR is a new indicator that we propose using subjects' perceptions of speed, BS control, and VR representation. The combination of these subjective and objective measures is proposed as the Degree of Realism (DR) to standardise the classification of the realism level of a bicycle simulator. Subjectively, the results indicate that the BS provides a high level of safety and comfort for conducting such research. Subject characteristics have no significant influence on VR sickness scores or Perceived Level of Realism. Objectively, for both speed and CLP, we found no significant difference between on-site and the simulation measurements in the case of Site 1, but otherwise for Site 2. However, subjects were not able to accurately perceive either the actual or the relative differences. In conclusion, our bicycle simulator is a safe and comfortable traffic safety research tool that needs further improvement. The proposed preliminary concept of the degree of realism requires further investigation.
Open Access: Yes
A robust fingerprint identification approach using a fuzzy system and novel rotation method
Publication Name: Pattern Recognition
Publication Date: 2025-03-01
Volume: 159
Issue: Unknown
Page Range: Unknown
Description:
Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.
Open Access: Yes
Comparison of Five Rehabilitation Interventions for Acute Ischemic Stroke: A Randomized Trial
Publication Name: Journal of Clinical Medicine
Publication Date: 2025-03-01
Volume: 14
Issue: 5
Page Range: Unknown
Description:
Background: Comparative efficacy of rehabilitation interventions in persons with acute ischemic stroke (PwS) is limited. This randomized trial assessed the immediate and lasting effects of five interventions on clinical and mobility outcomes in 75 PwS. Methods: Five days after stroke, 75 PwS were randomized into five groups: physical therapy (CON, standard care, once daily); walking with a soft robotic exoskeleton (ROB, once daily); agility exergaming once (EXE1, once daily) or twice daily (EXE2, twice daily); and combined EXE1+ROB in two daily sessions. Interventions were performed 5 days per week for 3 weeks. Outcomes were assessed at baseline, post-intervention, and after 5 weeks of detraining. Results: Modified Rankin Scale (primary outcome) and Barthel Index showed no changes. EXE1, EXE2, ROB, and EXE1+ROB outperformed standard care (CON) in five secondary outcomes (Berg balance scale, 10m walking speed, 6-min walk test with/without robot, standing balance), with effects sustained after 5 weeks. Dose effects (EXE1 vs. EXE2) were minimal, while EXE1+ROB showed additive effects in 6-min walk tests. Conclusions: These novel comparative data expand evidence-based options for therapists to design individualized rehabilitation plans for PwS. Further confirmation is needed.
Open Access: Yes
DOI: 10.3390/jcm14051648
Exploring the impact of China's low carbon energy technology trade on alleviating energy poverty in Belt and Road Initiative countries
Publication Name: Energy
Publication Date: 2025-03-01
Volume: 318
Issue: Unknown
Page Range: Unknown
Description:
The objective of this study is to analyze how low-carbon technology imports such as wind turbines, solar panels, carbon capture equipment, and biomass systems from China affect Belt and Road Initiative (BRI) countries’ energy poverty. Additionally, we analyze the role of financial development, deliberative democracy, economic complexity, human development, and telecommunications infrastructure on energy poverty in BRI countries. We use 69 countries from Belt and Road initiative countries and a sample period from 2000 to 2019. We classify these countries according to the IMF classification of advanced, emerging and low-income developing countries. We employ the instrumental variable generalized method of moments (IV-GMM) approach as the main technique to take care of the endogeneity concerns inherent in the model, as well as a robust quantile-based technique called the method of moments quantile regression estimator (MMQREG). Our results reveal that low-carbon technology trade from China does not significantly alleviate energy poverty in the BRI countries. Financial development increases energy poverty while deliberative democracy decreases it. Economic complexity, as well as human development, negatively affects energy poverty, while telecommunications infrastructure does not affect energy poverty significantly. Based on the results, policy implications are provided.
Open Access: Yes
A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-03-01
Volume: 15
Issue: 6
Page Range: Unknown
Description:
Featured Application: The proposed framework introduces a new feature matching approach for corroded pipelines based on in-line inspections and data clustering, contributing to the broader field of pipeline integrity management. The effectiveness of this framework suggests potential for application in other domains that benefit from spatial feature matching. Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management.
Open Access: Yes
DOI: 10.3390/app15062943
