bluuebunny/crossref_metadata_2025 huggingface dataset:
Life Cycle Assessment of End-of-Life Tire Disposal Methods and Potential Integration of Recycled Crumb Rubber in Cement Composites
Authors: Kolendo Girts , Voronova Viktoria , Bumanis Girts , Korjakins Aleksandrs , Bajare Diana
Published: December 2024

Globally, 1.5 billion annual tire outputs generate a substantial volume of end-of-life tires (ELTs), creating significant environmental challenges. Despite increased recovery rates, ELT management costs in Europe underscore the need for proactive strategies to mitigate environmental and health risks. This study comprehensively evaluates the environmental impact of disposal methods, including landfilling, incineration, and crumb rubber production, using Life Cycle Assessment (LCA) via the OpenLCA software 2.0.2. While incineration is sometimes identified as a disposal method, unprocessed scrap tires have potential applications in civil engineering that can better align with sustainability goals. Detailed ELT composition analysis reveals significant recycling potential, with car and truck tires containing 10–20% steel fiber content, less than 1–8% textile fibers, and approximately 80% natural and synthetic rubber content. Recycling 1 ton of ELTs saves an estimated 1.4–1.6 tons of CO2 Eq. compared to incineration. Mechanical recycling and application of recycled crumb rubber in concrete show significant environmental advantages, reducing mass density by approximately 55% and enhancing ductility by up to 40%, according to material testing results. These properties make crumb rubber particularly suitable for acoustic and resilient applications. Additionally, its elasticity and durability offer effective solutions for shoreline reinforcement, mitigating erosion and providing stability during flooding events. When used as a replacement for river sand in cement composites, crumb rubber contributes to a 24.06% reduction in CO2 emissions, highlighting its potential for environmentally friendly construction.
An Analysis of Vaccine-Related Sentiments on Twitter (X) from Development to Deployment of COVID-19 Vaccines
Authors: Chandra Rohitash , Sonawane Jayesh , Lande Jahnavi
Published: December 2024

Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic caused fear and uncertainty about vaccines, which has been well expressed on social media platforms such as Twitter (X). We analyse sentiments from the beginning of the COVID-19 pandemic and study the public behaviour on X during the planning, development, and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. We provide visualisation and analysis of anti-vaccine sentiments throughout the COVID-19 pandemic. We review the nature of the sentiments expressed with the number of tweets and monthly COVID-19 infections. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19. We also find that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised, implying that the vaccine rollout impacted the nature of discussions on social media.
An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions
Authors: Vladov Serhii , Vysotska Victoria , Sokurenko Valerii , Muzychuk Oleksandr , Chyrun Lyubomyr
Published: December 2024

This article presents an intelligent self-validated sensor system developed for dynamic objects and based on the intelligent sensor concept, which ensures autonomous data collection and real-time analysis while adapting to changing conditions and compensating for errors. The research’s scientific merit is that an intelligent self-validated sensor for dynamic objects has been developed that integrates adaptive correction algorithms, fuzzy logic, and neural networks to improve the sensors’ accuracy and reliability under changing operating conditions. The proposed intelligent self-validated sensor system provides real-time error compensation, long-term stability, and effective fault diagnostics. Analytical equations are described, considering corrections related to influencing factors, temporal drift, and calibration characteristics, significantly enhancing measurement accuracy and reliability. The fuzzy logic application allows for refining the scaling coefficient that adjusts the relationship between the measured parameter and influencing factors, utilizing fuzzy inference algorithms. Additionally, monitoring and diagnostics implementation for sensor states through LSTM networks enable effective fault detection. Computational experiments on the TV3-117 engine demonstrated high data-restoring accuracy during forced interruptions, reaching 99.5%. A comparative analysis with alternative approaches confirmed the advantages of using LSTM (Long Short-Term Memory) neural networks in improving measurement quality.
Insights into the Biotic Factors Shaping Ectomycorrhizal Associations
Authors: El Amrani Belkacem
Published: December 2024

Ectomycorrhizal (EM) associations are essential symbiotic relationships that contribute significantly to the health and functioning of forest ecosystems. This review examines the biotic factors that influence EM associations, focusing on plant and fungal diversity, host specificity, and microbial interactions. Firstly, the diversity of host plants and ectomycorrhizal fungi (EMF) is discussed, highlighting how the richness of these organisms affects the formation and success of EM symbioses. Next, host specificity is explored, with a focus on the complex relationships between EMF and their host plants. Microbial interactions are examined in depth, with sections on both positive and negative influences of bacteria and different fungal groups on EM formation. Overall, this review provides a comprehensive overview of the biotic factors that shape EM associations, offering insights into the mechanisms that underpin these critical ecological interactions and their broader implications for ecosystem management and restoration.
Pathophysiological, Neuropsychological, and Psychosocial Influences on Neurological and Neuropsychiatric Symptoms of Post-Acute COVID-19 Syndrome: Impacts on Recovery and Symptom Persistence
Authors: Malioukis Alex , Snead R Sterling , Marczika Julia , Ambalavanan Radha
Published: December 2024

Although the impact of post-acute COVID-19 syndrome (PACS) on patients and public health is undeniably significant, its etiology remains largely unclear. Much research has been conducted on the pathophysiology, shedding light on various aspects; however, due to the multitude of symptoms and clinical conditions that directly or indirectly define PACS, it is challenging to establish definitive causations. In this exploration, through systematically reviewing the latest pathophysiological findings related to the neurological symptoms of the syndrome, we aim to examine how psychosocial and neuropsychological symptoms may overlap with neurological ones, and how they may not only serve as risk factors but also contribute to the persistence of some primary symptoms of the disorder. Findings from our synthesis suggest that psychological and psychosocial factors, such as anxiety, depression, and loneliness, may interact with neurological symptoms in a self-reinforcing feedback loop. This cycle seems to be affecting both physical and psychological distress, potentially increasing the persistence and severity of PACS symptoms. By pointing out this interaction, in this review study, we attempt to offer a new perspective on the interconnected nature of psychological, psychosocial, and neurological factors, emphasizing the importance of integrated treatment approaches to disrupt this cycle and improve outcomes when possible.
The Chicken Embryo: An Old but Promising Model for In Vivo Preclinical Research
Authors: Sarnella Annachiara , Ferrara Ylenia , Terlizzi Cristina , Albanese Sandra , Monti Serena , Licenziato Luca , Mancini Marcello
Published: December 2024

The chicken embryo has emerged as a valuable model for preclinical studies due to its unique combination of accessibility, affordability, and relevance to human biology. Its rapid development, external growth environment, and clear structural visibility offer distinct advantages over traditional mammalian models. These features facilitate the study of real-time biological processes, including tissue development, tumor growth, angiogenesis, and drug delivery, using various imaging modalities, such as optical imaging, magnetic resonance imaging, positron emission tomography, computed tomography, and ultrasound. The chicken embryo model also minimizes ethical concerns compared to mammalian models, as it allows for early-stage research without the complexity of a fully developed animal. Moreover, its ability to integrate human tumor cells into xenograft models provides a reliable platform for cancer research, enabling high-throughput screening of therapeutic interventions and tracking molecular dynamics in vivo. Advances in molecular imaging techniques further enhance the resolution and depth of data obtained from these studies, offering insights into cellular and molecular mechanisms underlying disease. Given its versatility, cost-effectiveness, and translational potential, the chicken embryo represents a promising tool for advancing preclinical research, particularly in drug development, cancer biology, and regenerative medicine.
The Contribution of the Limbus and Collagen Fibrils to Corneal Biomechanical Properties: Estimation of the Low-Strain In Vivo Elastic Modulus and Tissue Strain
Authors: Silver Frederick H. , Deshmukh Tanmay , Benedetto Dominick , Asfaw Mickael , Doyle Olivia , Kozachuk Nicholas , Li Kamryn
Published: December 2024

We have compared the biomechanical properties of human and porcine corneas using vibrational optical coherence tomography (VOCT). The elastic modulus of the cornea has been previously reported in the literature to vary from about several kPa to more than several GPa based on the results of different techniques. In addition, the formation of corneal cones near the central cornea in keratoconus has been observed in the clinic. Measurements of the resonant frequency and morphology of human and porcine corneas were used to evaluate the role of the limbus in corneal stabilization, the effect of Bowman’s layer, and the effect of collagen content on the low-strain corneal biomechanics. The results of these studies indicate that limbus stability plays an important anatomic role in preventing folding, corneal slippage, and cone formation. Machine learning studies of both human and porcine corneas indicate that Bowman’s membrane, like that of the collagen fibrils found in the anterior corneal stroma, contributes to the 110–120 Hz resonant frequency peak. Finite element and SOLIDWORKS models of normal and keratoconus corneas suggest that the deformation of the cornea is the highest at the central zone and is higher in keratoconus corneas compared to normal controls. VOCT results suggest that although collagen fibril slippage occurs first at the limbus, cone formation in keratoconus occurs centrally/paracentrally, where stress concentration and deformation due to intraocular forces are the highest. Cone formation occurs at the points of maximum curvature. Results of these studies indicate the elastic modulus of cornea fibrillar collagen dictates the corneal elastic modulus at low strains. These results suggest that tension in the cornea at the limbus results in deformation into the low modulus region of the J-shaped stress–strain curve, resulting in an in vivo strain of less than about 10%. We propose that tension in the cornea provides a baseline force that regulates corneal epithelial regeneration as well as corneal lamellae composition and matrix turnover.
Efficacy of the reGENER@r Program on Socio-Emotional Skills and Sexist Beliefs in Perpetrators of Gender-Based Violence: A Pilot Study in Spain
Authors: Sánchez Ana Isabel , Maroto Laura , Rubiano Sara , Berzosa Sáez Clotilde , Quevedo-Blasco Raúl , Astudillo-Reyes Karla , Martínez María Pilar
Published: December 2024

Background: Gender-based violence (GBV) is one of the most pronounced expressions of the unequal power relations between women and men. As a tool for action against this phenomenon, psychological intervention programs for perpetrators of GVB are offered. This is how reGENER@r was born; it is a two-month program based on psychoeducational and cognitive-behavioral strategies that is part of the alternative measures to GBV-related prison sentences. The purpose of this study is to assess the efficacy of the reGENER@r program on the variables of emotional intelligence, empathy, coping responses, emotional dependency, gender role conflict, and sexist beliefs. Method: To this end, a sample of 37 subjects convicted of crimes of GBV was collected, and a pre- and post-evaluation by means of self-report was carried out. Changes were examined for statistical significance and clinical significance. Results: Significant improvements were observed in the variables of cognitive avoidance, emotional attention, hostile sexism, and distorted thoughts about women and the use of violence. Conclusions: Limitations and implications of these findings are discussed, and some modifications are suggested such as making interventions longer, with a greater gender focus, adapted to the individual characteristics of the participants, and complemented with individual sessions.
Research on the Mechanical Properties of Elliptical Negative Poisson’s Ratio Structures
Authors: Xie Ming , Wu Xiangdong , Xu Fangbo , Wang Zhangdong , Yin Li’e , Xu Mengqi
Published: December 2024

A negative Poisson’s ratio structure has special deformation behavior and energy absorption characteristics, and is a new structure with broad application prospects. However, most of the current research is still at the micro theoretical level, and there is less research on the macro mechanical properties. Therefore, this paper proposes a polyurethane elliptical negative Poisson’s ratio structure (PES), uses the methods of experimental simulation and numerical simulation to carry out a mechanical comparison with a concave negative Poisson’s ratio structure (PCS) and analyze the influence of length–width ratio on the structure, highlighting the advantages of the PES in energy absorption, and using parameter analysis to study the influence of the structural form and material properties on the Poisson’s ratio and elastic modulus of the PES, so as to provide a scientific basis and technical support for the application of the structure in high-end equipment manufacturing, seismic isolation and other fields.
Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants
Authors: Agiomavriti Aikaterini-Artemis , Nikolopoulou Maria P. , Bartzanas Thomas , Chorianopoulos Nikos , Demestichas Konstantinos , Gelasakis Athanasios I.
Published: December 2024

Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.
Subjective and Objective Assessment of the Preferred Rotational Cervical Spine Position in Infants with an Upper Cervical Spine Dysfunction: A Cross-Sectional Study
Authors: Langenfeld Anke , Paravicini Inga , Hobaek Siegenthaler Mette , Wehrli Martina , Häusler Melanie , Bergander Torsten , Schweinhardt Petra
Published: December 2024

Background: We aimed to assess (1) the awareness of parents regarding the cervical rotation preference of their infant and the agreement of the parent, clinician and objective assessments, and (2) the test–retest reliability for objective (measured) rotation, lateral flexion and combined flexion–rotation. Methods: This was a cross-sectional study including 69 infants aged three to six months with upper cervical spine dysfunction, without general health issues or specific cervical spine impairments. No treatment was applied. The primary outcomes were parent and clinician assessments of cervical spine rotation preference. The secondary outcome was the cervical range of motion measured by inertial measurement units (IMUs) at two different timepoints. Spearman correlation was performed for the parent, clinician and objective assessments. IMU data were dichotomized into the preferred and unpreferred sides, and test–retest reliability was assessed (ICC). Results: The mean age of infants was 145 days ± 29.1 days, birth length 49.40 cm ± 2.7 cm, birth weight 3328 g ± 530.9 g and 24 were female. In total, 33 infants were assessed by their parents as right-preferred, 30 as left-preferred and 6 as having no preference. The clinician assessed 38 infants as right-preferred and 31 as left-preferred. The correlation between parents and the clinician was rs = 0.687 (p < 0.001), the clinician and the IMU rs = 0.408 (p = 0.005) and parents and the IMU rs = 0.301 (p = 0.044). The ICC of cervical range of motion measurements ranged from poor to moderate. Conclusions: Clinicians can use the parents’ assessment of cervical spine rotation preference as a foundation for their clinical examination. IMU measurements are difficult in infants, possibly due to their lack of cooperation during measurements. Clinical Trial Registration Number: clinicaltrails.gov (NCT04981782).
A Comprehensive AI Approach for Monitoring and Forecasting Medicanes Development
Authors: Martinez-Amaya Javier , Nieves Veronica , Muñoz-Mari Jordi
Published: December 2024

Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In response to this issue, we present an AI-based system for tracking and forecasting Medicanes, employing machine learning techniques to identify cyclone positions and key evolving spatio-temporal structural features of the cloud system that are associated with their intensification and potential extreme development. While the forecasting model currently operates with limited training data, it can predict extreme Medicane events up to two days in advance, with precision rates ranging from 65% to 80%. These innovative data-driven methods for tracking and forecasting provide a foundation for refining AI models and enhancing our ability to respond effectively to such events.
The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors
Authors: Chivangulula Fernando Maliti , Amraoui Malik , Pereira Mário Gonzalez
Published: December 2024

Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based on vegetation and meteorological indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971–2020 period. The results revealed the following: (i) droughts of various classes at different timescales occurred throughout the study period and region; (ii) a greater Sum of Drought Intensity and Number, in all classes, but lower duration and severity of droughts with the SPI than with the SPEI; (iii) drought frequency varies from 1.3 droughts/decade to 4.5 droughts/decade, for the SPI at 12- to 3-month timescales; (iv) the number, duration, severity and intensity of drought present high spatial variability, which tends to decrease with the increasing timescale; (v) the area affected by drought increased, on average, 6.6%/decade with the SPI and 9.1%/decade with the SPEI; and (vi) a high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers and managers in defining legislation and strategies to manage drought and water resources.
Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods
Authors: Gul Ates Eylem , Coban Gokcen , Karakaya Jale
Published: December 2024

Backgrounds: Although COVID-19 is primarily known as a respiratory disease, there is growing evidence of neurological complications, such as ischemic stroke, in infected individuals. This study aims to evaluate the impact of COVID-19 on acute ischemic stroke (AIS) using radiomic features extracted from brain MR images and machine learning methods. Methods: This retrospective study included MRI data from 57 patients diagnosed with AIS who presented to the Department of Radiology at Hacettepe University Hospital between March 2020 and September 2021. Patients were stratified into COVID-19-positive (n = 30) and COVID-19-negative (n = 27) groups based on PCR results. Radiomic features were extracted from brain MR images following image processing steps. Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. Model performance was evaluated using a range of classification metrics, including measures of predictive accuracy and diagnostic reliability, with 95% confidence intervals provided to enhance reliability. Results: This study assessed the performance of dimensionality reduction and classification algorithms in distinguishing COVID-19-negative and COVID-19-positive cases using radiomics data from brain MR scans. Without feature selection, ANN achieved the highest AUC of 0.857 (95% CI: 0.806–0.900), demonstrating strong discriminative power. Using the Boruta method for feature selection, the k-NN classifier attained the best performance, with an AUC of 0.863 (95% CI: 0.816–0.904). LASSO-based feature selection showed comparable results across k-NN, RF, and ANN classifiers, while SVM exhibited excellent specificity and high PPV. The RFE method yielded the highest overall performance, with the k-NN classifier achieving an AUC of 0.882 (95% CI: 0.838–0.924) and an accuracy of 79.1% (95% CI: 73.6–83.8). Among the methods, RFE provided the most consistent results, with k-NN and the ANN identified as the most effective classifiers for COVID-19 detection. Conclusions: The proposed radiomics-based classification model effectively distinguishes AIS associated with COVID-19 from brain MRI. These findings demonstrate the potential of AI-driven diagnostic tools to identify high-risk patients, support optimized treatment strategies, and ultimately improve clinical implications.
Prevalence of Diabetes, Hypertension, and Associated of Cardiovascular Diseases: A Comparative Pre- and Post-COVID Study
Authors: Chiavarini Manuela , Dolcini Jacopo , Firmani Giorgio , Ponzio Elisa , Barbadoro Pamela
Published: December 2024

Background: Diabetes and hypertension are major global health challenges aggravated by COVID-19’s impact on healthcare and lifestyle factors. This study aims to compare the prevalence and associated socio-demographic factors of these conditions before and after the pandemic (2019 vs. 2022). Materials and Methods: We used data from Italy’s “Aspects of Daily Life” survey; 74,294 adults were included. Results: Results show a rise in diabetes prevalence from 7.76% in 2019 to 8.49% in 2022 (p < 0.05), while hypertension did not show this. Logistic regression analysis for the years 2019 and 2022 revealed a statistically significant association between the year 2022 and increased odds of diabetes (OR = 1.08, p = 0.008). BMI’s role as a risk factor intensified, with higher odds ratios (ORs) for both conditions in overweight and obese individuals in 2022. For example, obesity-related ORs for diabetes increased from 2.45 (95%CI 1.73–3.47) in 2019 to 3.02 (95%CI 2.09–4.35) in 2022, and for hypertension from 2.86 (95%CI 2.28–3.58) to 3.64 (95%CI 2.87–4.61). Lower education levels also showed a greater association with hypertension risk in 2022; subjects with only middle or high school diplomas had significantly higher ORs than individuals with higher education; there was a non-significant trend in 2019. However, diabetes risk associated with lower education remained stable and significant in both years. Conclusions: These findings suggest that the pandemic may have increased risk factors for diabetes and hypertension, particularly BMI and educational level, compared with the literature on the increased burden of chronic diseases during COVID-19.
Fixed Full-Arch Implant-Supported Restorations: Techniques Review and Proposal for Improvement
Authors: Froimovici Florin-Octavian , Butnărașu Cristian Corneliu , Montanari Marco , Săndulescu Mihai
Published: December 2024

Full-arch zirconia restorations on implants have gained popularity due to zirconia’s strength and aesthetics, yet they are still associated with challenges like structural fractures, peri-implant complications, and design misfits. Advances in CAD/CAM and digital workflows offer potential improvements, but a technique that consistently addresses these issues in fixed, full-arch, implant-supported prostheses is needed. This novel technique integrates a facially and prosthetically driven treatment approach, which is divided into three phases: data acquisition, restoration design, and manufacturing/delivery. Digital tools, including intraoral scanning and photogrammetry, facilitate accurate implant positioning, while 3D design software enables functional and aesthetic validation before final milling. A dual software approach is used to reverse engineer a titanium bar from the final restoration design, ensuring a superior outcome to other protocols. The restoration incorporates a zirconia–titanium hybrid structure, optimizing strength, flexibility, and weight. The proposed workflow enhances restoration precision and predictability through a prosthetically driven treatment plan, by ensuring passivity and aligning with biological and mechanical principles to promote long-term stability. By starting with the proposed restoration design and reverse engineering the bar, while also allowing for flexibility in material and component choices, this technique accommodates both patient needs and financial considerations. This approach demonstrates potential for improving patient outcomes in full-arch implant restorations by minimizing complications associated with traditional methods. Further research is recommended to validate the technique’s efficacy and broaden its clinical applications.
Rapid Integrated Design Verification of Vertical Take-Off and Landing UAVs Based on Modified Model-Based Systems Engineering
Authors: Bai Zhuo , Zhang Bangchu , Song Mingli , Tian Zhong
Published: December 2024

Unmanned Aerial Vehicle (UAV) development has garnered significant attention, yet one of the major challenges in the field is how to rapidly iterate the overall design scheme of UAVs to meet actual needs, thereby shortening development cycles and reducing costs. This study integrates a “Decision Support System” and “Live Virtual Construct (LVC) environment” into the existing Model-Based Systems Engineering framework, proposing a Modified Model-Based Systems Engineering methodology for the full-process development of UAVs. By constructing a decision support system and a hybrid reality space—which includes pure digital modeling and simulation analysis software, semi-physical simulation platforms, real flight environments, and virtual UAVs—we demonstrate this method through the development of the electric vertical take-off and landing fixed-wing UAV DB1. This method allows for rapid, on-demand iteration in a fully digital environment, with feasibility validated by comparing actual flight test results with mission indicators. The study results show that this approach significantly accelerates UAV development while reducing costs, achieving rapid development from “demand side to design side” under the “0 loss” background. The DB1 platform can carry a 2.5 kg payload, achieve over 40 min of flight time, and cover a range of more than 70 km. This work provides valuable references for UAV enterprises aiming to reduce costs and increase efficiency in the rapid commercialization of UAV applications.
Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices
Authors: Alm Antonie
Published: December 2024

The emergence of Generative Artificial Intelligence (GenAI) raises critical questions about learner autonomy and agency. This exploratory case study examines how four university-level German language learners with diverse backgrounds developed autonomy in their learning process through engagement with AI tools. The study was conducted in early 2023 when most learners were first discovering ChatGPT’s potential for language learning. Data were collected through reflective journals, digital portfolios, and interviews during a semester-long course that scaffolded self-directed learning with AI integration. The findings reveal emerging patterns of shared agency between learners and AI tools. Learners developed distinct strategies for AI integration based on their language learning backgrounds, with heritage speakers focusing on accuracy improvement while classroom learners emphasized communication practice. Cross-case analyses identified key dimensions of autonomy development: a critical evaluation of AI output, evolving learner–AI relationships, maintaining and developing a second language (L2) voice, and the strategic integration of AI tools while preserving learner agency. These patterns suggest that autonomy in AI-mediated environments manifests through learners’ capacity to engage productively with AI while maintaining critical awareness and personal agency in their learning process.
A Meta-Analysis of Technology-Based Word-Problem Interventions for Students with Disabilities
Authors: Kim Soo Jung , Xin Yan Ping
Published: December 2024

The aim of this meta-analysis was to investigate the effect of technology-based interventions on mathematical word problem solving for students with disabilities. This study also assessed the quality of research based on What Works Clearinghouse (WWC) design standards. A total of 21 studies were included in the analysis. This study computed an overall effect and analyzed moderators of group design studies (Hedges’ g) and single-case design studies (between-case standardized mean difference) in the same analyses. The results show that the overall effect of technology-based interventions on word problems across 21 studies was large (Hedges’ g = 1.18, 95% CI = 0.84, 1.52). Additionally, the studies meeting WWC design standards demonstrated significantly greater effects. The moderator analyses indicated that certain study characteristics, such as publication years, settings, intervention agents, technology devices and types, and mathematics topics moderated the effects of interventions. The four studies that evaluated the generalization effect of the intervention using distal standardized assessments showed a near moderate effect (Hedges’ g = 0.49, 95% CI = 0.09, 0.9). Implications for future research and practice are discussed.
Gross Domestic Product Forecasting: Harnessing Machine Learning for Accurate Economic Predictions in a Univariate Setting
Authors: Oancea Bogdan , Simionescu Mihaela
Published: December 2024

In recent years, precise economic forecasting has primarily relied on econometric models, which often assume linearity and stationarity in time series data. However, the nonlinear and dynamic nature of economic data calls for more innovative approaches. Machine learning (ML) techniques offer significant advantages over traditional methods by capturing complex, nonlinear patterns without predefined specifications. This study investigates the effectiveness of Long Short-Term Memory (LSTM) networks for forecasting Gross Domestic Product (GDP) in a univariate setting using quarterly Romanian GDP data spanning from 1995 to 2023. The dataset encompasses significant economic events, including the 2008 financial crisis and the COVID-19 pandemic, highlighting its relevance for broader economic forecasting applications. While the univariate approach simplifies model development, it also limits the incorporation of additional economic indicators, potentially affecting generalizability. Furthermore, computational challenges, such as time-intensive hyperparameter tuning, emerged during model optimization. We implemented LSTM networks with input data based on four and six lags to predict GDP and compared their performance with Seasonal Autoregressive Integrated Moving Average (SARIMA), a classical econometric method. Our results reveal that LSTM networks consistently outperformed SARIMA in predictive accuracy, demonstrating their robustness in capturing economic trends. These findings underscore the potential of ML in enhancing economic forecasting methodologies.
A Blockchain Multi-Chain Federated Learning Framework for Enhancing Security and Efficiency in Intelligent Unmanned Ports
Authors: Xie Zeqiang , Li Zijian
Published: December 2024

The integration of blockchain and federated learning (FL) has emerged as a promising solution to address data privacy and security challenges in Intelligent Unmanned Ports (IUPs). However, existing blockchain federated learning (BFL) frameworks encounter significant limitations, including high latency, inefficient data processing, and limited scalability, particularly in scenarios with sparse and distributed data. This paper introduces a novel multi-chain federated learning (MFL) framework to overcome these challenges. The proposed MFL architecture interconnects multiple BFL chains to facilitate the secure and efficient aggregation of data across distributed devices. The framework enhances privacy and efficiency by transmitting aggregated model updates rather than raw data. A low-frequency consensus mechanism is employed to improve performance, leveraging game theory for representative selection to optimize model aggregation while reducing inter-chain communication overhead. The experimental results demonstrate that the MFL framework significantly outperforms traditional BFL in terms of accuracy, latency, and system efficiency, particularly under the conditions of high data sparsity and network latency. These findings highlight the potential of MFL to provide a scalable and secure solution for decentralized learning in IUP environments, with broader applicability to other distributed systems such as the Industrial Internet of Things (IIoT).
Novel Indoor Educational I-V Tracer for Photovoltaic Modules
Authors: Muñoz Jose Vicente , Nieto Luis Miguel , Canalejo Juan Francisco , Montes-Romero Jesus , Gonzalez-Rodriguez Angel Gaspar , Gulkowski Slawomir
Published: December 2024

The renewable energy market, particularly the photovoltaic sector, has experienced significant growth over the past decade. Higher education institutions must play a vital role in the training of professionals, which the sector is currently demanding and will continue to require in the future. A pivotal resource for understanding the performance of PV modules is the experimental extraction of the characteristic I-V curve in laboratory practices. This paper presents an innovative and low-cost I-V curve tracer which can be used in indoor laboratories for teaching purposes. The described measurement system presents the novelty of helping form an energy-harvesting IC to force a sweep of the voltage from values close to zero to the open voltage circuit (Voc). An Arduino Micro board interfaces the implemented electronics and a LabVIEW-based monitoring and control program. The system proved its reliability and accuracy when it was compared to a calibrated commercial I-V tracer. The experimental results show that for a low-power PV module illuminated by a lamp, the proposed I-V tracer only deviated 1.3% from the commercial one in measurements of the maximum power.
Dynamic Artificial Bee Colony Algorithm Based on Permutation Solution
Authors: Gong Yongkang , Zhu Donglin , Ouyang Chengtian , Guo Hongjie , Zhou Changjun
Published: December 2024

The artificial bee colony algorithm (ABC), as a classic swarm intelligence algorithm, has advantages such as fewer parameters and clear logic. However, ABC cannot balance the exploration and development stages well in the iterative process, and is easily affected by local optimal solutions in the final optimization stage, which affects the final optimal solution. To effectively compensate for the shortcomings of the algorithm, a neighbor learning artificial bee colony algorithm based on permutation solutions (CNABC) is proposed. In CNABC, a dynamic neighbor learning strategy is proposed to improve the search ability and optimal selection ability of the algorithm in the exploration phase. To solve the problem of lack of balance between exploration and development, the local optimal solution is used to guide the update of the surrounding difference. After the three stages of the algorithm are completed, a substitution mechanism is introduced, which replaces the worst solution by introducing external candidate solutions as feasible solutions, thereby improving the algorithm’s ability to escape from local optima. Finally, comparative algorithm experiments are conducted on the CEC2022 test set, and compared with the TOP algorithm in the CEC competition on the CEC2022 test set. According to the experimental results, CNABC has good competitiveness in the comparative algorithm, which verifies the novelty and optimization ability of CNABC.
Study on the Broadband Measurement Method of Electric Field Sensing Voltage Based on the Add-Window Technique
Authors: Xu Minrui , Li Zhixin , Lu Shufeng , Chen Gang , Quan Xiangjun , Mo Xiaoliang
Published: December 2024

With the popularization of power electronic equipment, the problem of harmonic pollution has become more and more prominent, posing a serious threat to the safe operation of power systems. In order to improve the stability and efficiency of the power system, the broadband measurement of harmonics has become a key direction of research. In this paper, a novel broadband harmonic measurement method is proposed, which combines the electric field sensing technique and the TCW add-window discrete Fourier transform technique. The amplification gain coefficient deviation of the measurement system is less than 3 dB in both steady-state and transient excitation signal test experiments. The measurement system can maintain a stable and continuous output in a wide frequency band within the range of MHz, which can effectively detect the harmonic components and provide an effective technical means for the monitoring of harmonics in the power system.
Optimal Design of a Liquid Hydrogen Centrifugal Pump Impeller
Authors: Harsito Catur , Yun Jeong-Eui , Shin Joon-Young , Kim Jae-Min
Published: December 2024

Global energy consumption has continued to increase in recent years with economic development. Fossil energy sources are now being replaced with renewable energy, and hydrogen is one of such alternatives. Pumps are used for storage, transportation, and distribution. One such pump is the liquefied hydrogen centrifugal pump. In this study, optimisation design of a liquefied hydrogen centrifugal pump was performed using the response surface method, which is the optimisation method of the DesignXplorer provided by ANSYS, based on the flow analysis results of the impeller of the centrifugal pump. The design variables used in the optimisation process are the outlet width b2, % of the blade thickness Su2, leading edge inclination angle α, hub inclination angle δ, wrap angle θ, and outlet blade angle β2. The optimisation analysis results obtained confirmed that all the selected design variables are semi-galactic and are sensitive to pump efficiency and head. It was confirmed that the efficiency of the centrifugal pump achieved using liquefied hydrogen as the working fluid is approximately 82.4%, which is significantly higher than that achieved by a centrifugal pump using water as the working fluid under the same operating conditions.
Collapse of Silicon Valley Bank and USDC Depegging: A Machine Learning Experiment
Authors: Diop Papa Ousseynou , Chevallier Julien , Sanhaji Bilel
Published: December 2024

The collapse of Silicon Valley Bank (SVB) on 11 March 2023, and the subsequent depegging of the USDC stablecoin highlighted vulnerabilities in the interconnected financial ecosystem. While prior research has explored the systemic risks of stablecoins and their reliance on traditional banking, there has been limited focus on how banking sector shocks affect digital asset markets. This study addresses this gap by analyzing the impact of SVB’s collapse on the stability of major stablecoins—USDC, DAI, FRAX, and USDD—and their relationships with Bitcoin and Tether. Using daily data from October 2022 to November 2023, we found that the SVB incident triggered a series of depegging events, with varying effects across stablecoins. Our results indicate that USDC, often viewed as one of the safer stablecoins, was particularly vulnerable due to its reliance on SVB reserves. Other stablecoins experienced different impacts based on their collateral structures. These findings challenge the notion of stablecoins as inherently safe assets and underscore the need for improved risk management and regulatory oversight. Additionally, this study illustrates how machine learning models, including gradient boosting and random forests, can enhance our understanding of financial contagion and market stability.
Does Nutrition Knowledge Help? Heterogeneity Analysis of Consumers’ Willingness to Pay for Pre-Packed Mooncakes Labeled with the Smart Choice Logo
Authors: Huang Zeying
Published: December 2024

The Smart Choice logo (SCL), as an encouraging form of front-of-package nutrition labeling (FOPNL), helps consumers to choose low-oil, -salt, and -sugar mooncakes during the Mid-Autumn Festival. It is widely acknowledged that nutrition knowledge contributes to nutrition label use, but there has been little research on whether it helps enhance consumers’ willingness to pay (WTP). Our study aims to fill this gap by investigating 630 randomly selected Chinese adults from Jilin, Inner Mongolia, Shaanxi, Shandong, Henan, Sichuan, and Guangdong. The semi-double-bounded dichotomous choice contingent value method was selected to measure their WTP for pre-packed mooncakes with the SCL at 20 different premium levels, ranging from 0% to 95% of the price per unit. It was found that the respondents’ WTP decreased by 0.7% as the premium level increased by 1%, and the WTP of people from South China, those who were obese, and those with a high income was not sensitive to changes in premium. Nutrition knowledge played a negative moderating role, and the probability of the premium levels affecting WTP decreased by 1.0% for each 1 point increase in the nutrition knowledge level. These findings highlight the potential implications associated with SCL promotion and differentiated mooncake pricing, as well as the supply of healthier Chinese holiday foods.
Brazilian Food Waste as a Substrate for Bioethanol Production
Authors: de Abreu Íthalo Barbosa Silva , Silva Rayssa Karla , Siqueira Joyce Gueiros Wanderley , Silva Paula Katharina Nogueira da , Sonego Jorge Luiz Silveira , de Souza Rafael Barros , Antonino Antonio Celso Dantas , Menezes Rômulo Simões Cezar , Dutra Emmanuel Damilano
Published: December 2024

Food waste (FW) is a common source of contamination, contaminating both soils and water bodies by releasing greenhouse gases. FW holds great potential for biofuel and bioproduct production, which can mitigate its environmental impact and become a valuable addition to the circular bioeconomy. Therefore, this work aimed to investigate the use of food waste as a substrate to produce fermentable sugars and bioethanol. FW was pretreated by lipid removal. Raw and treated FW was hydrolyzed by amylases. Also, FW was hydrolyzed using sulfuric acid under different residence times (20, 40, and 60 min), sulfuric acid concentrations (0.5, 1.0, and 1.5% v·v−1), solid loads (5, 10, and 15% m·v−1), and temperatures (111, 120, and 127 °C). The best reducing sugar concentration was obtained at a 1.5% concentration of sulfuric acid and a 15% solid load applied for 1 h at 127 °C. The acid hydrolysis process was more efficient (76.26% efficiency) than the enzymatic one (72.7%). Bioethanol production was carried out as static submerged fermentation, with Saccharomyces cerevisiae at 10% (humidity m·v−1) being used as the producer and the acid and enzymatic hydrolysates being used as carbon sources. Lipid removal from FW did not influence the acid or enzymatic hydrolytic processes. For fermentation, the highest bioethanol yield was obtained from the acid hydrolysate of raw FW (0.49 kg·kg glicose−1). Thus, the processes used were efficient for bioethanol production, presenting alternatives for sustainable food waste destinations and low-cost biofuel production.
The Effect of Ice-Binding Protein from Leucosporidium sp. AY30 (LeIBP) on the Physicochemical Quality and Microstructure of Largemouth Bass During Freeze–Thaw Cycles
Authors: Ren Junde , Meenu Maninder , Hu Lihui , Song Tao , Liu Ying , Ramaswamy Hosahalli S. , Yu Yong
Published: December 2024

This study investigated the effect of various concentrations (0.01%, 0.05%, 0.1%, 0.2%, 0.5%) of ice-binding protein from Leucosporidium sp. AY30 (LeIBP) on the freezing efficiency, microstructure, and physicochemical quality of largemouth bass during freeze–thaw cycles and demonstrated the optimal addition conditions of LeIBP. This study found that LeIBP could effectively lower the freezing point of fish without altering the phase transition time significantly. LeIBP can significantly reduce the cross-sectional area and diameter of ice crystals and inhibit recrystallization. LeIBP was found to maintain the stability of protein secondary structure and prevented protein denaturation by increasing the proportion of α-helix. The inclusion of LeIBP retained the water-holding capacity of fish effectively. Furthermore, LeIBP treatment could partially prevent the degradation of fish meat texture. The lightness and whiteness values of fish treated with LeIBP were increased, while the redness and yellowness values were decreased. At the end of freeze–thaw cycle, the LeIBP-treated group presented pH values similar to fresh fish. Overall, 0.05% LeIBP was observed to be the most effective concentration to inhibit ice crystal growth, thereby maintaining the quality of the fish.