Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes Mellitus Mediates the Relationship Between Atherogenic Index of Plasma and Gallstones: A Population-Based Cross-Sectional Study

Purpose Previous studies have shown a correlation between diabetes mellitus and gallstone formation. The atherogenic index of plasma (AIP) is associated with many metabolic diseases. However, insufficient evidence still exists to elucidate the association between AIP and gallstones. The primary objective of this study was to investigate the correlation between AIP and gallstones in US adults, and the secondary objective was to analyze whether diabetes plays a mediating role in the association. Patients and Methods Using data from the National Health and Nutrition Survey (NHANES) conducted between 2017 and March 2020, this study investigated the association between AIP and gallstone incidence in US adults. A variety of statistical methods were used to analyze the data in this study, including multivariate logistic regression, subgroup analyses, restricted cubic spline curves (RCS), and mediation effects analysis. In addition, two-stage linear regression was used to detect possible threshold and saturation effects. Results A total of 6952 subjects were enrolled in the trial, of which 748 patients were diagnosed with gallstones. A significant positive association between AIP and gallstones was observed by fully adjusted multivariate logistic regression analysis, with an odds ratio (OR) of 1.45 and a 95% confidence interval (CI) of (1.09, 1.93). In addition, a non-linear positive association and saturation effect between AIP and gallstones were found, with an inflection point of 0.2246. Mediation analysis showed that diabetes had a mediating effect of 16.9% in the association between AIP and gallstones. Conclusion This study suggests that elevated levels of AIP are linked to an augmented vulnerability to gallstone development, with diabetes serving as a mediating factor. These findings present a novel perspective on clinical approaches to prevent and manage gallstones.
Liberalization in the Context of Democratization: Assessment of the January 2012 Fuel Subsidy Removal Protests in Nigeria

The decision by the Good Luck Jonathan-led regime to effect the removal of fuel subsidies on January 1st, 2012, prompted the most coordinated popular protests in Nigeria’s Fourth Republic. The subsidy removal and social protests it triggered had numerous socio-political implications. This paper examines the implications of the social protest on civic activism and political participation. The study adopts the qualitative research method. Data was derived from a combination of primary and secondary sources, which include news articles, opinion pieces, commentaries, editorials and press interviews published by Nigerian newspapers and sourced from their archives. It also utilized information sourced from official documents and reports published by governmental and non-governmental bodies. These materials were analyzed using the content analysis method. The study finds that it is essential for the State to engender citizen trust, given that trust is a core requirement for securing the legitimacy of the governed. The study also finds that the protests helped to rekindle the spirit of civic activism among Nigerians, especially the youth segment. This activism was largely aided by the deployment of digital tools as instruments for mobilization, coordination and communication in the protest movement. The article concludes that the subsidy removal protests had profound impacts on civic political activism, long thought to be dead among Nigerians.
Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.
Psychologically-Valid Generative Agents: A Novel Approach to Agent-Based Modeling in Social Sciences

Incorporating dynamic realistic human behaviors in population-scale computational models has been challenging. While some efforts have leveraged behavioral theories from social science, validated theories specifically applicable to Agent-based modeling remain limited. Existing approaches lack a comprehensive framework to model the situated, adaptive nature of human cognition and choice. To address these challenges, this paper proposes a novel framework, Psychologically-Valid Generative Agents. These agents consist of a Cognitive Architecture that provides data-driven and cognitively-constrained decision-making functionality, and a Large Language Model that generates human-like linguistic data. In addition, our framework benefits from Stance Detection, a Natural Language Processing technique, that allows highly personalize initialization of the agents, based on real-world data, within Agent-based modeling simulations. This combination provides a flexible yet structured approach to endogenously represent how people perceive, deliberate, and respond to social or other types of complex decision-making dynamics. Previous work has demonstrated promising results by using a subset of the components of our proposed architecture. Our approach has the potential to exhibit highly-realistic human behavior and can be used across a variety of domains (e.g., public health, group dynamics, social and psychological sciences, and financial markets).
Synthetic Proteins in Dental Applications

Biotechnology and artificial intelligence have sparked a revolution in dentistry, with a focus on restoring natural tissue functions. This transformation has given rise to bioactive materials, inspired by biomimetics, aimed at replicating the processes found in nature. As synthetic biology advances, there is a heightened focus on signaling systems crucial for bio-based diagnostics and therapeutics. Dentistry now harnesses synthetic proteins for tissue regeneration and dental material enhancement. A current research priority is bacterial biofilm inhibition, vital for dental health. Given the role of Streptococcus mutans in dental caries, the development of synthetic antimicrobial peptides targeting this bacterium is underway. The balance of dental enamel between demineralization and remineralization impacts caries formation. Factors such as the presence of hydroxyapatite and salivary peptides influence enamel health. Recent studies have spotlighted salivary protein-inspired peptides for enhanced remineralization. In the realm of bone regeneration, synthetic proteins like bone morphogenetic proteins (BMP) have been spotlighted, earning FDA approval. Research is currently delving into peptides such as cementum protein 1 peptide (CEMP-1-p1) and parathyroid hormone variants like PTH (1-34), underscoring their potential in advancing dental and bone health.
Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image

This paper proposes a method for demosaicing raw images captured by multispectral cameras. The proposed method estimates a pseudo-panchromatic image (PPI) via an iterative-linear-regression model and utilizes the estimated PPI for multispectral demosaicing. The PPI is estimated through horizontal and vertical guided filtering, with the subsampled multispectral-filter-array-(MSFA) image and low-pass-filtered MSFA as the guide image and filtering input, respectively. The number of iterations is automatically determined according to a predetermined criterion. Spectral differences between the estimated PPI and MSFA are calculated for each channel, and each spectral difference is interpolated using directional interpolation. The weights are calculated from the estimated PPI, and each interpolated spectral difference is combined using the weighted sum. The experimental results indicate that the proposed method outperforms the State-of-the-Art methods with regard to spatial and spectral fidelity for both synthetic and real-world images.
BGP Link State Extensions for SRv6

Segment Routing IPv6 (SRv6) allows for a flexible definition of end- to-end paths within various topologies by encoding paths as sequences of topological or functional sub-paths, called "segments". These segments are advertised by the various protocols such as BGP, ISIS and OSPFv3. BGP Link-state (BGP-LS) address-family solution for SRv6 is similar to BGP-LS for SR for MPLS dataplane. This draft defines extensions to the BGP-LS to advertise SRv6 Segments along with their functions and other attributes via BGP.
Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.
Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Estimating musical vogue patterns is critical for the Chinese music industry, as it assists artists, producers, record labels, and investors in discerning public musical preferences. This knowledge facilitates market strategy optimization, recommendation system improvement, inventory management, and provides informed support for advertising and sponsorships. The benefits extend beyond enhancing user experience and contributing to cultural studies, as they are also invaluable for the discovery of emerging artists and the sustainable growth of the entire Chinese music ecosystem. Nonetheless, existing models for prolonged musical vogue pattern estimation have been challenged by the severe decay of past information. Inspired by the concept of incremental learning, we propose the Dynamic Incremental Network (DINet). DINet capitalizes on high-dimensional sequential information embedded in the memory states of trained sequence models. It dynamically integrates information from both previous and current time steps when constructing the model to estimate the next time step’s input sequence, enabling the incremental transmission of past information and addressing the decay problem in prolonged musical vogue pattern estimation. Leveraging an open-source dataset from a musical pattern estimation contest, ablation experiments and comparative analyses have shown that DINet not only surpasses traditional and baseline models but also outperforms existing musical vogue pattern estimation models, demonstrating its superior performance. In the future, we intend to employ the proposed Dynamic Incremental Method (DIM)—an Occam’s razor (simple yet effective) mechanism—to further enhance the performance of state-of-the-art musical and other sequential estimation tasks.
Graceful gait: virtual ballet classes improve mobility and reduce falls more than wellness classes for older women

Introduction Dance is an effective and motivating form of exercise for older women, but few studies have quantified the benefits of virtual dance classes nor, specifically, ballet. This study tested the effectiveness of virtual ballet compared to virtual wellness classes, with the goal of reaching underserved populations. It is among the first to explore the effects of virtual classical ballet on functional gait mobility, balance, and quality of life measures in older women. Methods Older women were recruited in two waves and randomized to two groups: a ballet class modified for older adults and a wellness-based control class. Both groups received 12 weeks of online classes, meeting twice per week for 45-min sessions. Classes were taught by a local company that offers community-based ballet classes. The same instructor led both the ballet and the wellness classes. Pre- and post-intervention assessments include gait and balance testing using wearable inertial sensors and self-report outcomes including quality of life and mood questionnaires. Results Forty-four older women completed the study: Ballet group (n = 21, 67.81 ± 7.3 years); Wellness group (n = 23, 69.96 ± 6.7 years). Pre- to post-intervention, both groups increased velocity on the two-minute walk test (F1,42 = 25.36, p < 0.001) and improved their time on the Timed Up and Go (F1,42 = 4.744, p = 0.035). Both groups improved balance on the Mini-BESTest (F1,42 = 38.154, p < 0.001), increased their scores on the Activities-Specific Balance Confidence Scale (F1,42 = 10.688, p < 0.001), and increased quality of life via the Short Form Health Survey (F1,42 = 7.663, p = 0.008). The ballet group improved gait variability in the backward direction (F1,42 = 14.577, p < 0.001) and reduced fall rates more than the wellness group [χ2(1) = 5.096, p = 0.024]. Discussion Both virtual ballet and wellness classes improve select measures of gait, balance, and quality of life. The benefits seen in both groups highlight the importance of considering social interaction as a key component when developing future interventions to target mobility in older women.
Inter-annual variability of air mass and acidified pollutants transboundary exchange in the north-eastern part of the EANET region

Anthropogenic emissions, be it exhaust gases or aerosols, stem from multitude of sources and may survive longrange transport within the air masses they were emitted into. So they follow regional and global transport pathways varying under different climatological regimes. Transboundary transfer of pollutants occurs this way and has a significant impact on the ecological situation of the territories neighbouring those of emission sources, as found in a few earlier studies examining the environmental monitoring data [1]. In this study, we employ a relatively facile though robust technique for estimating the transboundary air and concomitant pollutant fluxes using actual or climatological meteorological and air pollution monitoring data. Practically, we assume pollutant transfer being proportional to the horizontal transport of air enclosed in the lower troposphere and to the concentration of the pollutant of interest. The horizontal transport, in turn, is estimated using the mean layer wind direction and strength, or their descriptive statistics at the individual transects of the boundary of interest.
THE DETERMINANTS OF SERVICE QUALITY IN SKI TOURISM

Regarding the fact that skiing is the most popular winter sport in Europe, the aim of the paper was to analyse the main determinants of service quality in ski tourism. When defining the research process, it was noted that literature review has recognised significant studies dealing with the concept of ski tourism from different aspects such as tourist motivation to travel, ski tourism sustainability and clime change, marketing strategies or ski tourism infrastructure. However, there is a certain research gap in exploring the determinants of service quality in winter sports. Therefore, the main research question in this paper is to identify the significant factors that influence the service quality in ski tourism and affect tourist satisfaction levels. The research is based on a sample of 157 recreational skiers who visited the Republic of Serbia during winter seasons in years 2022 and 2023. In the interpretation and processing of data, descriptive statistics and t test were used. The findings indicated the diversity of ski tracks, landscape, nature and hospitality as the most significant determinants of service quality in ski tourism.
Development of a method and an assessment construct for person-centered translation of dementia public stigma scales

Background With the number of people with dementia dramatically increasing over time and dementia becoming a major health concern worldwide, scales have been developed to assess the stigma socially attached to this neurodegenerative disorder. There are, however, almost no available methods and assessment constructs for person-centered translation of dementia public stigma scales. Objective To develop such a method and such an assessment construct by translating the Dementia Public Stigma Scale (DPSS) into standard written Chinese. Methods We translated the DPSS following three major steps: (1) literal translation and mistranslation identification; (2) panel discussions of items with problematic translations; and (3) the final checking of the translated scale. Informed by the translation and adaptation process, we then developed a method for person-centered translation of dementia public stigma scales. Based on this method and our panel discussions, we finally proposed a tripartite assessment construct for quality evaluation of the translation of dementia public stigma scales. Results Forward and backward translation did not work sufficiently in dementia public stigma scale translation. Mistranslations were induced by three major causes, including confusion caused by multiple Chinese meanings of the immediate Chinese direct translation, the lack of immediate Chinese direct translation because of varying positive/negative emotions attached to multiple translations, and the lack of culture-specific idioms in Chinese. Based on these factors, we proposed a tripartite dementia translation assessment construct. Following this assessment tool, we determined the best Chinese version that could further be tested for its psychometric properties among the public. Conclusion A method and an assessment construct for person-centered translation of dementia public stigma scales were developed. Such a method and such an assessment construct could be followed in the translation of dementia public stigma scales and the translation evaluation of such scales.
Exploring the Perspectives of Tribhuvan University Undergraduate Students Toward Business Statistics: A Study of Shanker Dev Campus

This comprehensive study explored the intricate determinants of Tribhuvan University undergraduate students' attitudes toward business statistics, employing a mixed-methods approach to capture the multifaceted nature of their perceptions. Grounded in theoretical frameworks such as the Theory of Reasoned Action, Expectancy-Value Theory, Cognitive Load Theory, and Effort-Performance Theory, the research analyses the relationship and influence of cognitive competence, value, difficulty, and effort with students' attitudes. With a large sample size of 328 students, the study employs a descriptive and causal-comparative research design to uncover significant correlations between these variables. The regression results highlight the predictive power of cognitive competence, value, difficulty, and effort on students' attitudes, providing valuable insights for educators and stakeholders in enhancing the learning experience in business statistics. The findings underscore the dynamic interplay of these factors over time, emphasizing the need for tailored educational strategies and interventions to foster positive attitudes and outcomes in the field.
HPLC-DAD-MS Fingerprint of Andrographis Paniculata (Burn. f.) Nees (Acanthaceae)

An HPLC-UV fingerprint analysis was developed for the quality evaluation of Andrographis paniculata aerial parts. HPLC-DAD-MS experiments allowed the identification of eleven diterpenes and five flavonoids. Plant material of Indian and Chinese origin was evaluated employing the developed method. The chemical fingerprints of the plant material of different origins do not show significant differences.
The analysis of support mechanisms for occupational mobility in the Latvian adult education system

With increasing occupational mobility in the Western societies, where, among other drivers, economic cycles and digital transformation brought changes in labour demand, adults’ access to the education system is becoming increasingly important issue. This refers both for upgrading skills and for retraining with a view to occupational change if the labour market demand for the initial occupation has declined significantly. The aim of this article is to analyse the institutional framework of the Latvian adult education and related institutions to assess what opportunities and what obstacles it creates for individuals to undergo the necessary training to enter a new occupation. Theoretically, the article is based on institutionalism theory, which explains the impact of institutions on the economic behaviour of agents, and middle-range theories on the impact of the structure of the education and labour market institutions on occupational mobility prospects. The analysis is based on a systematic examination of the legal acts and their amendments introduced in Latvia since 2009. The data suggest that Latvian institutional environment is generally favourable for occupational change, but specially targeted public policy support is aimed at the upgrading and retraining of medium- and low-skilled workforce, leaving the occupational transition of high-skilled workforce largely to their own discretion.