bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Biometric-Centric Encryption Framework for Secure Online Payments and Cloud Storage
Authors: S Vijayanand , S Nithya N. , P Shanmugasundaram , Natesan Thillaigovindan 
Published: February 2025

Abstract

The rapid expansion of digital payment systems and cloud-based storage has created an urgent need for advanced security measures to protect sensitive information. Conventional authentication techniques, such as passwords and one-time passcodes (OTPs), are increasingly vulnerable to modern cyber threats. This study introduces an innovative biometric-centric encryption framework that incorporates fingerprint authentication and multi-level encryption to strengthen the security of online payment platforms and cloud storage. The framework employs a three-tier authentication mechanism comprising user credentials, OTPs, and fingerprint verification to ensure robust identity validation. Upon authentication, data is encrypted using advanced algorithms before being transmitted or stored in the cloud, mitigating risks of unauthorized access. A decision-complementary approach enhances the matching accuracy of biometric systems, ensuring superior performance in user verification. Rigorous testing reveals the framework’s resilience against prevalent cyber threats, such as data breaches and hacking attempts, while maintaining seamless usability for end-users. This biometric-driven encryption model addresses critical security challenges in digital finance and cloud computing, delivering a scalable, efficient, and highly secure solution. The results emphasize the transformative potential of biometric-based systems in redefining security paradigms for future digital ecosystems.
Investigating the effects of a novel gamified cognitive training on adolescent mental health
Authors: Grunewald Karina , Minihan Savannah , Andrews Jack L. , Songco Annabel , Blakemore Sarah-Jayne , Chau Anson Kai Chun , Fischer Jaimee , Fox Elaine , Nelissen Alba Bruggeman , Raffe William , Richards Matthew , Werner-Seidler Aliza , Schweizer Susanne 
Published: February 2025

Abstract

Background
Adolescence is a time of increased emotional volatility, with emotion regulation still developing. Training the cognitive substrate of successful emotion regulation has been shown to benefit adolescents’ mental health. However, cognitive training interventions often have low adherence rates in this age group. The current study therefore trialled a novel gamified cognitive training program in adolescents.
Methods
A longitudinal study was conducted throughout 2023 where 144 culturally diverse adolescents (13–16 years, 48% female) completed 12 days of either a novel gamified affective control training program, the Social Brain Train (SBT), or a standard non-gamified affective control training program (AffeCT). Participants also completed mental health and mechanisms of change questionnaires at baseline, post-training, and 1-month follow-up, as well as behavioural affective control and interpretation bias measures at baseline and post-training.
Results
The total minutes spent training did not differ significantly across the two training groups. Participants assigned to SBT training, however, did engage in more training sessions than participants assigned to AffeCT training. Additionally, all participants showed improvements in affective control performance and a reduction in interpretation bias and rumination from baseline to post-training. The observed reduction in rumination persisted at 1-month follow-up.
Conclusions
As engagement is often the most difficult thing to achieve in cognitive training with adolescents, observing greater repeated engagement with the gamified cognitive training is promising, given training on these apps is entirely self-motivated. Observing benefits to affective and cognitive control performance and reduced interpretation bias and rumination tendencies after very limited training is also promising, as these factors have all been previously linked to improved mental health symptoms among adolescents. The present findings therefore suggest there may be merit in using gamification techniques to improve the design of future training programs, and employing these to improve affective, cognitive, and emotion regulation abilities in adolescents.
Thermophiles in Nanosized Biocalcification: a Novel Approach for Heavy metal Remediation
Authors: Negi Sujata , Sharma Shagun , Pant Deepak , Sharma Sonali , Chauhan Kalpana , Giri Anand , Kumar Manoj , Parida Kulamani 
Published: February 2025

Abstract

Biodeposition of minerals is a prevalent occurrence in the biological realm, facilitated by various organisms such as bacteria, protists, fungi, and plants. Calcium carbonate is one such mineral that precipitates naturally as a consequence of microbial metabolic processes. This study investigates an innovative approach for carbon dioxide (CO2) sequestration by utilizing thermophilic microorganisms (bacteria and enzymes) in the conversion of CO2 into CaCO3 and methanol. The thermophilic bacterial strains of Bacillus licheniformis and enzymes formaldehyde dehydrogenase (FalDH) and alcohol dehydrogenase (ADH) were screened for their ability of microbial-induced carbonate precipitation (MICP). The research focuses on identifying the various optimal conditions, such as CO2 concentration, bacterial concentration, pH, time, and temperature, for CaCO3 biosynthesis. Both strains of Bacillus licheniformis found to be the most efficient in biocalcification. Notably, these microbes also redirected their great potential towards the heavy metal remediation strategy. Heavy metals are non-biodegradable, and as they persist in the environment, they accumulate and magnify in ecosystems, intensifying the harmful effects on living organisms. Thermophilic bacterial strain excellently removes Pb (II) ions from aqueous solutions of MICP with 90% efficiency, and nanosized (35.85 nm, 38.58 nm) biominerals were formed. These comparative findings provide valuable insights for optimizing bio-based carbonate production in CO2 sequestration technology.
Disparities in Avoidance Behaviours to Air Quality Information Disclosure Exacerbate Environmental Inequality
Authors: Yan Haosheng , Xie Lunyu , Yu Dongli 
Published: February 2025

Abstract

Environmental inequality among people of different socioeconomic statuses are well documented in the literature. However, the effects of individual avoidance behaviours on the inequality are often overlooked. We address this question by examining how individuals of different socioeconomic statuses adapt their daily travel behaviors in response to air pollution information. We apply the regression discontinuity (RD) design using individual-level travel records from the Fifth Beijing Urban Transportation Comprehensive Survey. The results demonstrate that air pollution information disclosure significantly reduces the probability and duration of travel. This effect is weaker in the low-income group than in the high-income group, indicating it exacerbates environmental inequality. Moreover, the results reveal that lower avoidance behaviours in the low-income group, compared to the high-income group, stem from lower avoidance awareness and capability. These findings highlight the importance of considering avoidance behaviours when addressing environmental inequality.
Association of the visceral adiposity index with osteoarthritis among the U.S. adult females from NHANES 2011-2020: a cross-sectional study
Authors: Qiu Yijian , Deng Shuai , Xiang Wang , Guo Shifang , Yang Shanggeng , Li Enchen , Wu Qingsong 
Published: February 2025

Abstract

Background
The visceral adiposity index(VAI) is a marker of abdominal fat distribution and adipose tissue function. However, the association between VAI and osteoarthritis(OA) is unclear among the U.S. adult females.
Methods
Cross-sectional data for adults aged 20 years and older from 2011–2020 National Health and Nutrition Examination Survey(NHANES) were included. Weighted multivariate logistic regression analyses were used for assessing the correlation between VAI and OA. Subgroup analyses were performed to validate the finding. Moreover, mediation analysis was conducted to access the magnitude of the mediating effect of the BMI on the relationship between VAI and OA. Finally,the nonlinear relationship between VAI and OA was assessed by restricted cubic spline(RCS).
Result
There was a significant positive association between VAI and the OA, even after adjusting for multiple potential confounders(p for trend = 0.05). Subgroup analyses showed that the positive association between VAI and OA persisted in all populations with different characteristics, confirming its validity in all populations. Besides, the result of the mediation analysis indicated that the mediating effect of VAI on osteoarthritis through BMI is significant(p for trend < 0.01). Finally, RCS result showed a significant nonlinear relationship between VAI and OA(p-non-linear < 0.05).
Conclusions
Our findings indicated that there is a possible nonlinear positive correlation between VAI and OA. In addition, the nonlinear relationship between VAI and osteoarthritis warrants further study.
War and political motive for vaccine access suppression; Qualitative Explorations of Barriers to Child Vaccination Service in war-torn Tigray, north Ethiopia
Authors: Meles Abraham Gebrelibanos , Kahsay Znabu Hadush , Ayele Brhane , Hadgu Tsegay , Gebretnsae Hailay , Kahsay Hayelom , Alemayehu Mussie , Medhanyie Araya Abrha 
Published: February 2025

Abstract

Introduction:
Despite fast improvements in global vaccination service delivery networks, populations in conflict zones frequently have limited or no access to life-saving vaccines. The COVID-19 pandemic and the devastating war in Tigray significantly affected access to child vaccination. Timely, context-specific, and empirical evidence is crucial to designing recovery strategies to make vaccination accessible in conflict settings. The current study aimed to explore barriers to accessing child vaccination services in a war-torn Tigray region, in Northern Ethiopia.
Methods
A qualitative study was employed to explore the barriers to accessing child vaccination services among children aged 0–24 months. Three Focused Group Discussions, four in-depth interviews with women having children aged 0–24 months, and six Key Informant Interviews were included in the study. Discussions and interviews were facilitated using semi-structured guides, audio-taped, transcribed verbatim, and coded and analyzed using Atlas. ti version 9.0. Data were thematically analyzed.
Results
During the war in Tigray, there were barriers to accessing vaccination services for children aged 0–23 months, primarily due to vaccination service supply denial, security concerns to parents about visiting health facilities, damage and looting of health facilities, community and care providers displacement, and a gap in health system leadership communication and service monitoring. Participants frequently also described the consequences of service denial, such as a delay in vaccination initiation, an increase in the prevalence of vaccine-preventable diseases and child mortality, during a search for vaccine service increased security, and the economic burden of mothers and parents, traveling long distances to see traditional healers and holy water.
Conclusion & recommendations
: during the conflict in Ethiopia, Tigray region, mainly because of supply denial and security concern-related barriers, children aged 0–23 months old were denied their fundamental health right to get life-saving vaccines and other systematic approaches tested during the conflict were not tried in our study area. Because of the life-saving vaccination service discontinuation, many children were left unvaccinated; vaccine-preventable morbidities and mortalities existed, and people were returning to traditional medicinal practice. Addressing the barriers in conflict zone areas needs systematic strategic approaches, and advocacy for vaccination service access during a conflict unconditionally.
Artoo-Detoo: What imitating a Star Wars droid reveals on allospecific vocal imitation in parrots and starlings
Authors: Dam Nick , Honing Henkjan , Spierings Michelle 
Published: February 2025

Abstract

Vocal production learning is a remarkable ability, exclusively present in a few mammalian species and three bird clades. Besides learning conspecific vocalizations, vocal production can also lead to integration of allospecific sounds in an animals’ repertoire. While parrots and songbirds are well known for their allospecific imitation abilities, structural differences in imitation accuracy across species remain unclear. This study compared nine parrot species and European starlings on their ability to imitate monophonic and multiphonic sounds produced by the Star Wars droid R2-D2. These complex synthesizer sounds allow for a strong comparative analysis with the same spectral complexity imitated by multiple species. Our results show that starlings have a higher accuracy when imitating multiphonic sounds than parrots. However, both groups performed equally well when imitating monophonic sounds. Differences in imitation accuracy likely stem from syrinx anatomy rather than perceptual or cognitive disparities. Among parrots, species with smaller brains, such as budgerigars and cockatiels, outperformed larger-brained African greys and amazon parrots in monophonic sound imitation. Our study highlights the great potential of citizen science for expanding data collection beyond traditional studies and sheds light on the evolution of communication complexity in vocal learners.
Associations of onset time, severity, and persistence of hypothyroxinemia with pregnancy outcomes: A retrospective study
Authors: Xu Danchen , Wang Xiayi , Yang Xingyu , Cheng Weiwei 
Published: February 2025

Abstract

Background
Evidence regarding adverse pregnancy outcomes in pregnancies with hypothyroxinemia remains controversial, and the effect of onset time, severity, and persistence of hypothyroxinemia has not been thoroughly investigated. This study aims to evaluate the associations between hypothyroxinemia and adverse pregnancy outcomes with respect to the onset time, severity, and persistence of hypothyroxinemia.
Methods
This retrospective cohort study included 51, 305 ﻿singleton pregnancies ﻿who delivered at a tertiary hospital between January 2017 and December 2021. Thyroid function data and clinical information were obtained through digital medical records. Participants were categorized into euthyroid and hypothyroxinemia group according to free thyroxine (FT4) and thyrotropin (TSH) values in the first (gestational week 9-13) and third trimester (gestational week 32-36). Hypothyroxinemia was defined as FT4 < 10th gestational age-specific percentile in combination with TSH level within the 10th-90th percentile. Pregnancy outcomes were compared between women with hypothyroxinemia and euthyroidism with respect to the onset time, severity, and persistence of hypothyroxinemia. The associations between hypothyroxinemia and adverse outcomes were assessed using binary logistic regression in crude and adjusted models.
Results
Compared with the euthyroid group, hypothyroxinemia in the first trimester was associated with higher risk of Cesarean delivery (adjusted odds ratio [aOR], 1.20, 95% confidence interval [CI]: 1.11-1.29), macrosomia (aOR, 1.25, 95%CI: 1.08-1.44), and gestational diabetes mellitus (GDM) (aOR, 1.49, 95%CI: 1.36-1.63), and the risk increased as FT4 levels decreasing. Hypothyroxinemia in the third trimester was linked with heightened risk of Cesarean delivery (aOR, 1.52, 95%CI: 1.40-1.66), macrosomia (aOR, 1.44, 95%CI: 1.27-1.73), pre-eclampsia (aOR, 1.72, 95%CI: 1.38-2.14), and gestational hypertension (aOR, 1.44, 95%CI: 1.18-1.76), and the risk was negatively correlated with FT4 levels. Persistent hypothyroxinemia was linked with an increased risk of Cesarean delivery (aOR, 1.50, 95%CI: 1.28-1.76) and GDM (aOR, 1.45, 95%CI: 1.19-1.76). ﻿
Conclusion
This study revealed that both the first- and third-trimester hypothyroxinemia were associated with increased risk of Cesarean delivery and macrosomia, and the risk increased as the FT4 levels decreasing. However, the relations of hypothyroxinemia with pre-eclampsia, gestational hypertension, and GDM varied by the gestational age when hypothyroxinemia was diagnosed and the persistence of hypothyroxinemia.
Graph-Based Analysis of Chaotic Dynamics in the Double Pendulum System
Authors: Bakim Sumeyye , Ozalan Nurten Urlu , Gulbahce Erdi 
Published: February 2025

Abstract

Understanding the interplay between spatial and kinematic properties in chaotic systems is crucial for advancing nonlinear dynamics, yet remains a challenging problem. The double pendulum, as a classic example of deterministic chaos,
provides a rich platform for exploring these dynamics, making its study highly relevant to researchers in nonlinear systems. Previous works have extensively analyzed the trajectories of the double pendulum using traditional numerical and analytical methods, offering insights into its chaotic behavior. However, these approaches often fail to quantitatively link spatial relationships with physical motion under varying initial conditions, leaving gaps in our understanding of how geometry and dynamics interact. This study addresses these challenges by introducing a novel graph-theoretic framework to analyze the motion of the double pendulum. By representing trajectories as undirected graphs where nodes correspond to discrete positions of the pendulum bob and edges are defined based on a tunable threshold distance parameter ϵ we systematically explore the sensitivity of graph connectivity to ϵ. This methodology emphasizes the importance of careful parameter tuning to preserve trajectory continuity and ensure valid graph-based metrics. Through this framework, we hypothesize that graph representations will reveal meaningful correlations between spatial relationships (encoded as edge weights) and kinematic properties, providing a quantitative link between geometric and physical features. Additionally, sensitivity analysis is expected to uncover power-law relationships between ϵ and trajectory resolution, demonstrating the robustness of graph-based methods in capturing both regular and chaotic dynamics. These findings underscore the potential of graph theory as a versatile tool for analyzing the complex behavior of nonlinear systems, offering fresh insights into the intricate dynamics of chaotic phenomena such as those in the double pendulum.
The mTOR signaling pathway mediates the phenotypic switch between adipocytes and osteocytes in mice with obesity-related osteoporosis
Authors: Liang Chuanjie , Guo Yan , Liu Yongliang , Wang Yangjunjie , Xiao Jing , Li Haiying , Xiang Xinxin 
Published: February 2025

Abstract

Background: Obesity-related osteoporosis is a metabolic disorder of the adipose tissue and bone caused by energy accumulation. The role of adipogenic and osteogenic differentiation and phenotypic transformation in the pathogenesis of osteoporosis and the relevant signaling mechanism are still unclear.
Methods: Obesity-related osteoporotic mice were generated. Oil red and Von Kossa staining, RT-PCR , and western blotting were used to detect the differentiation capability of bone marrow mesenchymal stem cells (BMSCs) and the changes in related signaling pathways.
Results: High-energy states increased bone loss in ovariectomized mice, increased adipogenic differentiation, and inhibited the osteogenic differentiation of BMSCs from ovariectomized mice fed with a high-fat diet compared with those fed with a normal chow diet. The mTOR signaling pathway was activated during the adipogenic differentiation, while it did not change during the osteogenic differentiation of BMSCs from the ovariectomized group fed with a high-fat diet compared with the normal chow diet group. In vitro experiments showed that higher extracellular energy levels promoted adipogenic differentiation and inhibited osteogenic differentiation, while energy deficiency decreased adipogenic differentiation in mouse BMSCs through the mTOR signaling pathway.
Conclusion: The mTOR signaling pathway can mediate the phenotypic transformation of adipocytes and osteocytes in obesity-related osteoporotic mice.
Enhancing river discharge inversion for the midstream Jialing River (China) using the Google Earth Engine platform
Authors: Chen Dejing , He Yi 
Published: February 2025

ABSTRACT

This study focused on the Jinxi section of the Jialing River, utilizing data from Landsat 8, Sentinel-2, and MODIS accessed via the Google Earth Engine (GEE) platform. The objective was to estimate runoff by applying three models: the improved Manning's formula (Model 1), the relationship fitting method (Model 2), and the C/M signal method (Model 3). The models were evaluated based on their accuracy in runoff inversion, the influence of hydraulic parameters, and their suitability for medium-sized rivers. The results indicated that all three models performed well in simulating runoff, with Nash–Sutcliffe Efficiency coefficients exceeding 0.90. The root mean square error (RMSE) for the improved Manning's formula, the relationship fitting method, and the C/M signal method were 50.2, 117.1, and 69.5 m³/s, respectively, corresponding to relative RMSE (RRMSE) of 4.71, 16.15, and 5.88%. It was observed that both the improved Manning's formula and the C/M signal method generally underestimated flow, while the relationship fitting method tended to overestimate it. Overall, the improved Manning's formula and the C/M signal method outperformed the relationship fitting method in terms of accuracy and applicability.
Advanced machine learning models for robust prediction of water quality index and classification
Authors: Elmotawakkil Abdessamad , Enneya Nourddine , Bhagat Suraj Kumar , Ouda Mohamed Mohamed , Kumar Vikram 
Published: February 2025

ABSTRACT

This study presents an in-depth analysis of machine learning (ML) techniques for predicting water quality index and water quality classification using a dataset containing water quality metrics such as temperature, specific conductance, salinity, dissolved oxygen, depth, pH, and turbidity from multiple monitoring stations. Data preprocessing included imputation for missing values, feature scaling, and categorical encoding, ensuring balanced input features. This research evaluated artificial neural networks, decision trees, support vector machines, random forests, XGBoost, and long short-term memory (LSTM) networks. Results demonstrate that XGBoost and LSTM significantly outperformed other models, with XGBoost achieving an accuracy range of 99.07–99.99% and LSTM attaining an R2 of 0.9999. Compared with prior studies, our approach enhances predictive accuracy and robustness, showcasing advanced generalization capabilities. The proposed models exhibit significant improvements over traditional methods in handling complex, multivariate water quality data, positioning them as promising tools for water quality prediction and environmental management. These findings underscore the potential of ML for developing reliable, scalable water quality monitoring solutions, providing valuable insights for policymakers and environmental managers dedicated to sustainable water resource management.