Search Results (1,357)

Background: Endometriosis is a chronic condition associated with substantial healthcare costs, diagnostic delays and long-term impairment in quality of life. Despite the recognized economic burden, patient-reported financial distress remains insufficiently studied. The aim of this study was to adapt and validate the Polish version of the Comprehensive Score for Financial Toxicity (COST-FACIT) for use in women with endometriosis, as well as to examine demographic and clinical factors associated with financial toxicity. Methods: A cross-sectional study was conducted among Polish women with endometriosis using an online survey. The COST-FACIT was adapted following standard forward–backward translation procedures, with FACIT approval. Psychometric evaluation included internal consistency, construct validity, convergent validity with the Financial Well-Being Scale, and test–retest reliability. Exploratory and confirmatory factor analyses were performed, and multivariable models were used to identify factors associated with financial toxicity. Results: The adapted scale demonstrated good psychometric properties, with excellent internal consistency (Cronbach’s α = 0.92; McDonald’s ω = 0.92) and strong test–retest reliability (r = 0.87). Exploratory factor analysis supported a two-factor structure of the instrument. COST-FACIT scores were strongly correlated with financial well-being (r = 0.78). Higher education, stable employment and higher income were associated with better financial well-being, whereas longer symptom duration, greater distance to care and higher healthcare expenditures were associated with worse scores. Conclusions: The Polish COST-FACIT demonstrated good psychometric properties and may serve as a useful instrument for assessing financial toxicity in women with endometriosis. The results highlight the financial burden of the disease and support the use of patient-reported measures to identify individuals at risk of financial distress and reduced quality of life. This tool may facilitate clinical research and improve patient-centered care. Full article

This study examined rural pre-service teachers’ preferences for online learning platforms, Telegram, WhatsApp, and Moodle discussion forums in the Open Distance e-Learning environment. This group of students experiences digital illiteracy, limited access to assistive technologies, and network challenges, which may prevent them from optimally utilising formal learning platforms such as Moodle. They can, however, use Telegram and WhatsApp, as they regularly engage informally on these platforms. Against this backdrop, this study explored rural pre-service teachers’ experiences with Moodle and these social media platforms in an Open-Distance e-Learning space. This study employed a descriptive, qualitative case study with semi-structured interviews, guided by Siemens’ Connectivism theory. Fifteen student teachers from the College of Education in an ODeL institution were purposively sampled to provide in-depth insights into their lived experiences of platform use. The findings revealed that, although each platform served a unique instructional function, their perceived professionalism, safety, and interactivity differed substantially. Social media platforms such as Telegram and WhatsApp were lauded for their immediacy, accessibility, and low bandwidth usage, chiefly among rural pre-service teachers from economically disadvantaged communities. However, participants perceived these platforms as unprofessional, disruptive, and unsafe. Conversely, Moodle’s discussion forum was viewed as a credible, structured space that fostered academic discipline through the presence and guidance of lecturers. These contrasting perceptions highlight tensions between accessibility and academic regulation within ODeL environments. Although prior studies support incorporating social media platforms into LMSs, this research extends this discourse by emphasising the need to balance accessibility, interaction, and academic integrity within resource-constrained contexts. The study concludes that social media platforms and discussion forums can complement each other in ODeL, encouraging student interaction and inclusion, while discussion forums ensure educational rigour, safety, and institutional integrity. Full article

Abnormal circulating current induced by sheath grounding faults in cross-bonded high-voltage cables is a major cause of single-phase grounding faults. For the early detection and localization of sheath grounding faults, this paper constructs an equivalent circuit model for three-phase nine-section cross-bonded cables. Circuit model parameters are estimated via online monitoring data. The relational equation between sheath electrical quantities, fault impedance, and distance is derived for typical sheath grounding faults. Using the Adam algorithm, the solution of fault impedance and location is converted into the minimization of an optimization objective function. Simulation results show that under the influences of phase current imbalance, measurement error, and fault impedance fluctuation, the Adam algorithm exhibits superior optimization accuracy and computational efficiency in comparison with the ED and GA algorithms. Experimental results show that for low-resistance sheath grounding, the proposed method has a fault impedance calculation error ≤ 0.59% and a fault positioning error ≤ 1.89%. For metallic sheath grounding with zero resistance, the positioning error is ≤1.37%. Field test results demonstrate that the proposed method performs similarly to the time-domain reflectometry method, with a positioning deviation ≤ 0.15 m, and can meet online monitoring requirements. Full article

Background: Cancer is a chronic disease with significant health impacts and is a leading cause of mortality worldwide. Cancer patients often require frequent hospital visits to manage their condition effectively. Therefore, understanding the factors that influence their ability to access healthcare services—such as age, gender, citizenship, region of residence, educational level, and income—is crucial, as these factors can impact continuity of care and overall quality of life. Purpose: This study aims to identify the factors determining cancer patients’ healthcare access and to propose alternative solutions that will enhance their ability to access services. Methods: This cross-sectional quantitative study utilized the health belief model for data analysis. Data were collected randomly through an online questionnaire targeting cancer patients across Saudi Arabia. Results: The findings indicated that payment method, distance to healthcare facilities, tumor type, and willingness to use virtual appointments were significantly associated with access to healthcare services. A total of 391 participants were included, the majority of whom were female (n = 291), aged 39 to 48 (n = 111), Saudi citizens (n = 376), residing in the Eastern region (n = 210), holding a bachelor’s degree (n = 193), and reporting no monthly income (n = 110). Conclusions: Access to healthcare services for cancer patients in Saudi Arabia is challenged by several factors, including payment methods, travel distance, cancer type, and the acceptance of health applications. Promoting digital health tools and virtual appointments can significantly improve access to care and facilitate ongoing management for cancer patients. Full article

The change in learning methods to online/distance learning, catalyzed by recent health pandemics/social distancing requirements, has significantly changed how teaching occurs and what students experience in their learning spaces in regard to interference. New forms of interference exist, and they are related to the domestic setting of the student’s life. This study examined how factors of domestic life influence what students find in regard to interference in their online learning spaces through a Likert-scale defined answer process to a 29-question predictor variable inventory that also includes two outcome variables that address the amount of acoustic interference experienced in learning spaces. Moreover, through regression models and various applications of machine learning science, this research aims to reveal crucial indicators that influence student experiences regarding disturbances. In this respect, these findings highlight crucial roles that housing density and internal interactive actions within residential contexts have on disturbances. Furthermore, this research reveals critical understandings of perceptual inequalities present within distance learning student populations and indicates significant cultural and social consequences related to digital technologies. This is crucial, understood within foundational perspectives that are necessary to address psychosocial challenges and human–building interaction present within distance learning science and policies aimed at reducing noise. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

Network intrusion detection systems play a vital role in protecting networks by detecting malicious network traffic which can then be investigated by a cybersecurity operations centre. State-of-the-art approaches utilise supervised machine learning methods to train a classification model to recognise known cyberattacks; however, these models require a large labelled dataset to train and show poor performance when trained on smaller datasets. In an attempt to address this shortcoming, anomaly detection models learn the distribution of benign traffic and flag non-conforming traffic as malicious. While these methods do not require malicious examples to train, they suffer from high false-positive rates rendering them impractical. As a result, networks may be particularly vulnerable when there are insufficient labelled instances of a specific attack class to train an effective classifier. This often occurs in newly established networks or when previously unseen types of attacks emerge. To address this challenge, this work proposes the use of a triplet network, utilising online triplet mining and a KNN classifier, which is able to perform few-shot classification, enabling effective intrusion detection after being trained on a limited number of malicious examples. Various online triplet mining algorithms were explored and model design choices, such as the inference algorithm and optimised distance metrics, were compared and evaluated through a series of ablation studies. The final model was compared against other state-of-the-art approaches in few-shot binary and multiclass classification, where the proposed approach was found to be competitive with existing methods when trained on as little as 10 malicious samples of each class. Full article

This study examines how individuals adapt to situations of danger and change arising from the COVID-19 pandemic by testing a model based on self-regulatory mechanisms. The model includes three key elements: (1) the intensity of negative emotions as a manifestation of affective reaction to situations of danger and change and, at the same time, as a signal activating self-regulatory processes; (2) protective mechanisms in change, that is, the perception of goal congruence and a positive future self; and (3) the individual sense of danger and the overall sense of danger at various physical distances to the self. The study was conducted online. Participants completed a set of standardized questionnaires assessing negative emotions, perceived danger, and protective mechanisms across three measurement waves corresponding to successive stages of the COVID-19 pandemic. The model was validated in three waves of the pandemic in Poland and one in Ukraine. To verify the relationships between the measured variables, a structural equation model was constructed using the RStudio software (version 1.2, 2019) with the Lavaan package. The results were consistent with the prediction model, which considers the relationships between the intensity of negative emotions and the intensity of protective mechanisms in change and perceived danger. Two factors within negative emotions were identified, each interacting differently with self-regulation. Negative emotions were related to the intensity of perceived danger, while protective mechanisms were linked to the reduction in danger. The results confirm the complex nature of self-regulation mechanisms both affectively and cognitively and also their orientation toward the future, which constitutes a protective resource. The study’s limitations include the online method, which limits standardization, control, and the sample’s limited randomness and inequivalence of measurements and sampling in Ukraine and Poland. Full article

Maglev trains, as an emerging transportation modality, have attracted significant attention with respect to their safety and ride comfort. In this study, the improved R index and τ-distance index are incorporated into the evaluation framework, and a data-driven comprehensive evaluation method for the suspension system of medium- and low-speed maglev trains is developed based on a Gaussian mixture model, enabling a comprehensive assessment of suspension gap stability and operational smoothness. Experimental results demonstrate that the proposed method can accurately identify various motion modes of the suspension system and provide effective early warnings of abnormal operational states. Compared with conventional error integral performance indices, this method exhibits superior anomaly detection sensitivity and enhanced interpretability of the results. Computational efficiency analysis indicates that the proposed method meets the requirements for online real-time monitoring. Under different operating conditions, the GMM trained on normal operational data maintains stable evaluation performance, demonstrating favorable robustness. Full article

Despite significant advances in AI language models, Modern Standard Arabic (MSA) remains a linguistically complex domain in which apparent fluency often masks deeper grammatical instability. This study investigates recurrent grammatical error patterns in ChatGPT-generated Arabic texts, focusing on how these patterns reflect underlying morpho-syntactic challenges and the constraints of probabilistic language generation. Adopting a qualitative, pattern-oriented analytical framework, the study draws on online focus group discussions with secondary-level Arabic teachers, who served as expert linguistic evaluators. Participants collaboratively examined a set of AI-generated texts to identify and interpret systematic grammatical deviations across five key domains: agreement, inflection and case marking, sentence structure, prepositions and transitivity, and cross-linguistic influence. The findings indicate that grammatical errors in AI-generated Arabic are not random but occur as recurring, structured patterns, particularly in contexts involving long-distance dependencies and morphologically complex constructions. These patterns suggest a reliance on surface-level fluency at the expense of deeper grammatical coherence, reflecting limitations in maintaining consistent morpho-syntactic relationships. This study contributes by identifying and characterizing systematic grammatical patterns in AI-generated MSA as interpreted through expert linguistic judgment, offering a qualitative perspective that complements existing quantitative approaches and advances understanding of how large language models engage with morphologically rich languages. Full article

Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing. Full article

Active sonar tracking of multiple underwater targets is frequently challenged by intense reverberation, which leads to sonar returns that are both non-stationary and non-Gaussian. In such scenarios, the generalized labeled multi-Bernoulli (GLMB) filter, which relies on a Gaussian assumption, often experiences a rise in an Optimal Subpattern Assignment (OSPA) distance, along with recurrent label switching. To mitigate this problem, a robust delta-generalized labeled multi-Bernoulli technique (ST-δ-GLMB) is introduced; it characterizes noise using a Student’s t-distribution and employs variational Bayes to estimate the corresponding parameters. More precisely, the Student’s t-distribution is utilized to represent measurement non-stationarity, and an online variational Bayesian estimation of the noise parameters is conducted within a multi-target framework based on the Student’s t-model. Moreover, without altering the GLMB data-association and label-management machinery, we derive closed-form updates and propagation for the Student’s t-parameters, thereby keeping the recursive computational burden and practical implementability under control. Finally, Monte Carlo simulations and lake-trial data demonstrate that, under non-stationary and heavy-clutter conditions, ST-δ-GLMB maintains stable track continuity and accurate target-number (cardinality) estimates in the presence of non-stationary measurements. Full article

Online courses and distance learning are playing a significant role nowadays. These open, massive online courses (MOOCs) have garnered significant attention from academics, scholars, and policymakers; however, the literature offers limited empirical evidence, especially from a Saudi Arabian perspective. MOOCs help educators gain not only knowledge but also promote sustainability. The objective of this study was to investigate the impact of self-determination theory on the behavioral intention and actual use of MOOCs through the mediation of behavioral intention and media richness. For this purpose, convenience sampling was used, and data were collected from 145 respondents, including faculty members and students, across public and private sector universities. Smart PLS-SEM and CB-SEM were used to investigate the reliability, convergent validity, and discriminant validity by developing and testing measurement models using a confirmatory factor analysis. The hypotheses were tested using bootstrapping by developing structural models. The findings indicate that all the scales are reliable and valid, meeting the required threshold levels. Furthermore, all hypothesized relationships are positive and significant, except for the effect of perceived relatedness on the behavioral intention and actual use of MOOCs. Behavioral intention does not mediate the relationship involving perceived relatedness; however, it does mediate the relationships among perceived autonomy, competence, and actual use. Media richness also mediates the relationship between behavioral intention and actual use of MOOCs. The results suggest that MOOC providers should offer courses through renowned universities and adopt self-paced learning formats rather than fixed schedules. Additionally, learners should receive credits upon course completion, and these credits should be recognized by employers to enhance motivation for the continued use of MOOCs. Full article

Private tutoring has become an increasingly popular approach for improving academic performance by providing individual or group support outside regular school hours to enhance student outcomes. In the context of mathematics tutoring, we introduce the eMath4All platform, designed to replicate traditional teaching methods through virtual tools for distance learning. Despite the growing prevalence of private tutoring, research on online tutoring platforms and their use in practice remains limited. Accordingly, this study explores the application of the eMath4All platform in two different private tutoring scenarios involving secondary school students from Romania. Study A examines group tutoring with five eighth-grade students preparing for a national examination over a three-month period, while Study B explores individual tutoring with ten students from various secondary education levels over a 12-month period. The paper analyzes how the key components of the eMath4All platform (such as the virtual whiteboard, mathematical editor, real-time audio–video communication, virtual library, assessment tool, and personal student profile) support tutoring activities. The platform is examined through a combination of platform usage data, descriptive analysis of student progression, and student-reported experience collected via questionnaires. The results of the exploratory study indicate consistent usage patterns, high engagement with platform features, and high usability ratings, highlighting the platform’s potential for supporting both individual and group mathematics tutoring. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article