bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physiological regulation of moderate-intensity exercise in improving the biomarkers visfatin and myonectin as a modulator of increasing metabolic performance in obese
Authors: Sugiharto  , Pranoto Adi , Ihsan Nurul , Goenawan Hanna , Merawati Desiana , Rejeki Purwo Sri , Siantoro Gigih , Widyatama Fikri Sasongko , Angga Prayogi Dwina 
Published: February 2025

Abstract

Objectives
A well-structured, regular, and programmed physical exercise regimen is believed to be an effective physiological modulator for preventing the dysfunction of myonectin and visfatin secretion. Therefore, this study aims to analyze the physiological regulation of moderate-intensity programmed exercise in improving myonectin and visfatin biomarkers in obese women.

Methods
The study involved 30 physically healthy obese women, nonathletes, selected based on predetermined criteria, who voluntarily agreed to participate. They were then divided into two groups: a control group (CRT; n=15) and a programmed exercise group (ART; n=15). The exercise program lasted for 8 weeks, with a frequency of 5 times per week at moderate intensity. Metabolic performance was analyzed using the myonectin and visfatin biomarkers with the Sandwich-ELISA method. Data analysis was conducted using parametric paired sample t-tests and independent sample t-tests at a 5 % significance level.

Results
The study results showed a significant increase in myonectin secretion and a decrease in visfatin secretion (p≤0.05) after 8 weeks of programmed exercise, demonstrated by an increase in myonectin of 97.85 pg/mL and a decrease in visfatin of 0.05 ng/mL. Conversely, in the control group, myonectin decreased by 3.04 pg/mL, and visfatin increased by 0.03 ng/mL.

Conclusions
These findings confirm that an 8-week programmed exercise regimen can improve the secretion of myonectin and visfatin biomarkers, thereby enhancing metabolic performance in obese women. Thus, myonectin and visfatin biomarkers induced by programmed exercise serve as modulators for enhancing metabolic performance in obese women.
‘Why do they not want to play with me?’: a multimodal critical discourse analysis of the construction of colourism in cartoon films
Authors: Otaif Fahad Ahmed , Asiri Amal 
Published: February 2025

Abstract
Multimodal discourse is an influential way of social communication where thoughts, ideology, and cultural perspectives, including superiority and racism, are conveyed. The current study is a critical multimodal discourse analysis of the ‘Ugly Duckling’ video cartoons that were designed to be shown internationally for children’s TV entertainment. While the textual comparison has revealed that the theme of physical ugliness has almost multiplied four times in the English translation of the story than it was first introduced in Danish by Hans Christian Andersen in 1843, the critical multimodal analysis of the ‘Ugly Duckling’ cartoons has shown the intertwined use of verbal and visual elements to construct the meaning of racist social rejection and colourism. The multimodal discourse of racial exclusion and colourism employed several tools including lexical choices, textual narrative, colour, angles, size and the positioning of characters on the cartoon screen. Cartoon filmmakers are recommended to take a critical stance towards the multimodal narratives they (re)produce to help avoid producing material that may bring about social discrimination against people of different races and colours. Children of all races should be able to observe positive reflections of themselves and feel empowered through the multi-ethnic stories they watch and engage with daily.
СОВРЕМЕННОЕ СОСТОЯНИЕ ПОВЕРХНОСТНЫХ ВОД И ПОЧВ ВЕРХНЕГО ПРИАНГАРЬЯ
Authors: ВЛАСОВА Н.В. , ВОРОБЬЕВА И.Б. , БЕЛОЗЕРЦЕВА И.А. 
Published: February 2025

Приведены данные ландшафтно-геохимических исследований на территории Верхнего Приангарья. Изучены химические свойства почв и вод малых рек правобережья р. Ангары. Установлены различия характеристик почв, сформировавшихся в двух округах Прибайкальской провинции и одном округе Иркутско-Черемховской провинции. Общий гидрохимический анализ показал принципиальные различия поверхностных вод двух образующих правобережную часть бассейна верхнего участка р. Ангары природных структур - Байкальской рифтовой зоны и южной части Сибирской платформы. Полученные данные подтвердили и дополнили сделанные 20 лет назад выводы о ландшафтно-геохимических характеристиках территории.
The paper presents data from the landscape-geochemical studies in the Upper Angara region. The chemical properties of soils and waters of small rivers on the right bank of the Angara River have been studied. Differences in the characteristics of soils formed in two districts of the Pribaikalskaya province and one district of the Irkutsk-Cheremkhovskaya province have been established. The general hydrochemical analysis showed fundamental differences between the surface waters of two natural structures forming the right-bank part of the basin of the upper section of the Angara River, namely: the Baikal rift zone and the southern part of the Siberian platform. The data obtained confirmed and supplemented the conclusions made 20 years ago about the landscape-geochemical characteristics of the territory.
ОСОБЕННОСТИ АТМОСФЕРНОЙ ЦИРКУЛЯЦИИ ПРИ ЭКСТРЕМАЛЬНЫХ НАВОДНЕНИЯХ В ПРИБАЙКАЛЬЕ
Authors: ОСИПОВА О.П. , КИЧИГИНА Н.В. 
Published: February 2025

Представлены результаты исследований взаимосвязей атмосферной циркуляции и наводнений в Южном Прибайкалье. Во время наводнений в июле 1971 г., июне и июле 2019 г. на метеостанциях исследуемой территории осадков выпало существенно выше нормы - в среднем 229 %, 163 % и 156 % от нормы соответственно. Проведен комплексный синоптический анализ для выявления основных причин экстремально высоких значений осадков, которые спровоцировали подъем уровней воды на левобережных притоках Ангары и реках Южного Байкала в июле 1971 г., июне и июле 2019 г. Синоптические процессы были классифицированы по типам погоды согласно методу Дженкинсона и Коллисона. Для наводнения в июле 1971 г. было идентифицировано шесть типов погоды, а для наводнения в июне и июле 2019 г. - 15. В основном это были циклонические и адвективные типы с восточными компонентами, коррелирующие с влажными погодными условиями. Направления переноса воздушных масс определены по данным анализа 5-дневных обратных траекторий HYSPLIT. Установлено, что основные факторы, определившие интенсивность катастрофических паводков следующие: активный процесс циклогенеза; резкое усиление меридиональных затоков холода и тепла и развитие блокирующих процессов; наличие высокого содержания потенциально накопленной влаги, поля восходящих вертикальных движений и вынужденной конвекции в предгорьях; местные орографические условия.
The article presents the results of studies of the relationship between atmospheric circulation and floods in Southern Cisbaikalia. During the floods (in July 1971, June and July 2019), precipitation at the meteorological stations of the study area was significantly higher than the norm: on average 229 %, 163 % and 156 % of the norm, respectively. A comprehensive synoptic analysis was carried out to identify the main causes of extremely high precipitation values, which provoked a rise in water levels on the left-bank tributaries of the Angara river and on the rivers of Southern Baikal in July 1971, June and July 2019. Synoptic processes were classified by weather types according to the Jenkinson and Collison method. Six and fifteen weather types were identified for the July 1971 flood and for the June and July 2019 flood, respectively. These were mainly cyclonic and advective types with eastern components, correlating with humid weather conditions. The directions of air mass transfer were determined based on the analysis of 5-day HYSPLIT backward trajectories. It was found that the main factors determining the intensity of catastrophic floods were as follows: 1) active process of cyclogenesis; 2) sharp increase in meridional cold and heat flows and development of blocking processes; 3) the presence of a high content of potentially accumulated moisture, a field of upward vertical movements and forced convection in the foothills, and 4) local orographic conditions.
Classification and quantification of minor iron-sulfide concentrations in concrete aggregate using automated mineralogy
Authors: Oberhardt Nikolas , Lode Stefanie , Lindgård Jan , Aasly Kurt 
Published: February 2025

Abstract
Iron sulfide concentrations and mineral associations triggering the internal deterioration of concrete structures are still enigmatic. Incidences of internal sulfate attacks induced by iron sulfide-containing concrete aggregates appear worldwide. Severe cases are reported from Canada, the United States of America, and Ireland. Moreover, conservative limits for the total sulfur content of aggregates increased the need to dispose of otherwise high-quality resources for concrete production. The maximum threshold values for total elemental sulfur in the European standard EN-12620 for concrete aggregates are

$$\le$$

≤

1 wt.

$$\%$$

%

, and as little as

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≤

0.1 wt.

$$\%$$

%

if the non-stoichiometric iron-sulfide pyrrhotite (Fe(1-x)S) is present in the rock. This study investigates the potential of scanning electron microscopy-based automated mineralogy for mineral classification and the quantitative quality assessment for concrete aggregate material. Identifying the stoichiometrically closely related disulfide pyrite and monosulfide pyrrhotite is emphasized. The iron/sulfur ratio and greyscale variations in the electron backscatter images between pyrite and pyrrhotite were tested as additional differentiation criteria when acquiring mineral mapping and point-of-interest analysis. The added greyscale criterion yielded a better distinction between the two chemically similar phases. A good correlation was achieved when comparing results from energy-dispersive X-ray spectroscopy in automated mineralogy with wavelength-dispersive spectroscopy point analyses on the electron microprobe. Semi-quantification of the chemical data from automated mineralogy was computed for the total sulfur content in the petrographic samples. The total sulfur content of bulk samples, investigated by high-temperature combustion and inductively coupled plasma atomic emission spectroscopy, was consistent with the semi-quantitative results of automated mineralogy.
Cost Burden of Cancer Screening in Kansas by Region and Rural/Urban Designation
Authors: Mills Kale , Freund Nicole 
Published: February 2025

Introduction. In 2022, the U.S. healthcare expenditure totaled $4.5 trillion, representing 17.3% of its gross domestic product. Despite this, 26 million Americans remain uninsured, often relying on out-of-pocket payments for essential services like cancer screenings. Kansas, with its high uninsured rate, faces unique challenges, emphasizing the need to analyze the cost burden of these critical yet repeatable interventions.
Methods. Authors of this cross-sectional study analyzed hospital pricing transparency data for breast, lung, and colon cancer screening costs across 124 Kansas hospitals. Data on self-pay costs were collected and compared between urban and rural regions, as well as geographic price variations. Statistical analyses included measures of central tendency, Kruskal-Wallis tests, and Mann-Whitney U tests to evaluate differences.
Results. Pricing disparities were evident across Kansas. Urban hospitals charged higher prices for chest computed tomography (CT) scans, while rural hospitals had elevated costs for colonoscopies and mammograms. Notable price variation included Northeast Kansas colonoscopy prices, which ranged from $595 to $11,684. Rural residents faced a greater financial burden, spending 7% of their income on screenings compared to 6% for urban residents. Median screening prices statewide were $2,247 for colonoscopies, $1,109 for chest CT scans, and $228 for mammograms.
Conclusions. These disparities call for targeted policy interventions, such as Medicaid expansion, standardized pricing regulations, and increased support for low-cost clinics. Enhanced hospital pricing transparency is critical for empowering patients and reducing financial burdens. This study highlights the urgent need for equitable access to cancer screenings in Kansas.
Relationships Between PROMIS and Legacy Patient-Reported Outcome Measure (PROM) Scores in the MARS Cohort at 10-Year Follow-up
Authors:   MARS Group
Published: February 2025

Background:
Patient-reported outcome measures (PROMs) are used to evaluate the impact of musculoskeletal conditions and their treatment on patients’ quality of life, but they have limitations, such as high responder burden and floor and ceiling effects. The Patient-Reported Outcomes Measurement Information System (PROMIS) was developed to address these issues but needs to be further evaluated in comparison with legacy PROMs. The goals of this study were to evaluate the floor and ceiling effects of, the correlations between, and the predictive ability of PROMIS scores compared with traditional legacy measures at 10-year follow-up in a cohort who underwent revision anterior cruciate ligament (ACL) reconstruction.

Methods:
A total of 203 patients (88.7% White; 51.7% female) who underwent revision ACL reconstruction completed the PROMIS via computer adaptive tests as well as legacy PROMs at the cross-sectional, 10-year follow-up of the longitudinal MARS cohort study (MARS cohort n = 1,234). Floor and ceiling effects and Spearman rho correlations between PROMIS and legacy measures are reported. Linear regression with quadratic terms were used to develop and evaluate conversion equations to predict legacy scores from the PROMIS.

Results:
No floor or ceiling effects were reported for the PROMIS Physical Function (PF) domain, whereas a floor effect was found for 37.9% of the participants for the PROMIS Pain Interference (PI) domain, and a ceiling effect was found for 34.0% of the participants for the PROMIS Physical Mobility (PM) domain. PROMIS domains correlated moderately with the International Knee Documentation Committee total subjective score (absolute value of rho [|ρ|] = 0.68 to 0.74), fairly to moderately with the Knee injury and Osteoarthritis Outcome Score and Western Ontario and McMaster Universities Osteoarthritis Index scores (|ρ| = 0.52 to 0.67), and fairly with the Marx Activity Rating Scale (|ρ| = 0.35 to 0.44). None of the legacy-measure scores were accurately predicted by the PROMIS scores.

Conclusions:
The PROMIS PF domain has value in assessing patients 10 years after revision ACL reconstruction. Because of floor and ceiling effects, using the PI and PM domains may not allow for precision when measuring long-term changes in pain and mobility. Although the PROMIS measures correlated with the legacy measures, with effect sizes ranging from fair to moderate, the legacy scores were not accurately predicted by the PROMIS. The results suggest that knee-specific legacy measures should not be eliminated from long-term follow-up when the goal is to capture the specific knee-related information that they provide.

Level of Evidence:
Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.
Characterizing Joint Distribution of Uncertainty Parameters and Production Forecasts Using Gaussian Mixture Model and a Two-Loop Expectation-Maximization Algorithm
Authors: Gao Guohua , Lu Hao , Blom Carl 
Published: February 2025

Summary
Uncertainty quantification of reservoirs with multiple geological concepts and robust optimization are key technologies for oil/gasfield development planning, which require properly characterizing joint distribution of model parameters and/or production forecasts after conditioning to historical production data. In this work, we generate an ensemble of conditional realizations by a multirealization history-matching workflow. The posterior probability density function (PDF) of model parameters and/or production forecasts is non-Gaussian, and we approximate it by a Gaussian mixture model (GMM) using an expectation-maximization (EM) algorithm.
We first discuss major limitations of the traditional EM algorithm—not robust and converging to suboptimal solutions. We develop a two-loop (TL) EM algorithm [EM-eigenvalue decomposition (EVD)-TL] using the compact form of EVD and propose new strategies to overcome these limitations: (1) Reduce the dimension of a Gaussian component if its covariance matrix becomes singular and (2) introduce an inner EM loop in which only the diagonal matrix in EVD of the covariance matrix is updated. The first strategy improves the stability and convergence of the EM algorithm in dealing with degeneration of Gaussian components. The second strategy reduces the computational cost and further improves the convergence rate and accuracy of solutions.
The proposed EM-EVD-TL algorithm was validated on an analytical testing example, and its performance is compared against the single-loop, traditional EM algorithms, which use either Cholesky decomposition (EM-CD) or EVD (EM-EVD). An ensemble of conditional realizations is generated from sampling the actual PDF using the Markov Chain Monte Carlo (MCMC) approach. For the analytical example, the GMMs approximated by three EM algorithms are very close to the actual distribution with negligible difference. Finally, we applied the proposed EM-EVD-TL algorithm to realistic history-matching problems with different number of uncertainty parameters and production forecasts. We first generate an ensemble of conditional realizations using either the MCMC method or the distributed Gauss-Newton (DGN) optimization method. Then, we construct GMMs using different EM algorithms by fitting the conditional realizations, starting from different initial configurations and settings. Our numerical results confirm that the proposed EM-EVD and EM-EVD-TL algorithms perform robustly. In contrast, the traditional EM-CD algorithm fails to converge for most testing cases. The EM-EVD-TL algorithm converges faster to better solutions than the EM-CD algorithm.
Identifying Regional Differences in Correlations Between Transportation Infrastructure and Carbon Emissions with Data Analysis and Machine Learning
Authors: Baglio Nolan 
Published: February 2025

Abstract

Transportation supports global connections in the world. Urban planning requires much thought about transportation to ensure connectivity and simplicity within a city. However, what should also be considered is the environmental impact of their decisions. The research aims to understand how different types of transportation infrastructure impact carbon emissions. Each country was categorized by region, and the study looked into six different regions. The ideal features for predicting the carbon emissions per capita were determined using random forest regression, a machine-learning model that creates many decision trees to make predictions. The carbon emissions per capita with the percentage of roads paved, the feature deemed most valuable to predicting carbon emissions per capita by the random forest regressor, in each region was compared. With linear regression, a relationship was determined in each region to understand the variance from the line and the slope of the line and to know how the data progresses as the values increase. The results showed that economic and governmental factors were often at the root of environmental factors, and transportation infrastructure reflects economic strength. This was understood from both correlations between carbon emission changes and financial events, as well as from policy reviews of countries that were anomalies in the data, showing that they had differing policies regarding transportation. In this research, transportation infrastructure was proven essential to environmental factors. The study also indicates that the jobs of urban planners and road workers play a significant role in how a country approaches its carbon-neutral goals.
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences
Authors: Danish Muhammad Umair , Sureshkumar Mathumitha , Fonseka Thanuri , Uthayakumar Umeshika , Galwaduge Vinura 
Published: February 2025

Abstract

This research provides an in-depth evaluation of various machine learning models for energy forecasting, focusing on the unique challenges of seasonal variations in student residential settings. The study assesses the performance of baseline models, such as LSTM and GRU, alongside state-of-the-art forecasting methods, including Autoregressive Feedforward Neural Networks, Transformers, and hybrid approaches. Special attention is given to predicting energy consumption amidst challenges like seasonal patterns, vacations, meteorological changes, and irregular human activities that cause sudden fluctuations in usage. The findings reveal that no single model consistently outperforms others across all seasons, emphasizing the need for season-specific model selection or tailored designs. Notably, the proposed Hyper Network-based LSTM and MiniAutoEncXGBoost models exhibit strong adaptability to seasonal variations, effectively capturing abrupt changes in energy consumption during summer months. This study advances the energy forecasting field by emphasizing the critical role of seasonal dynamics and model-specific behavior in achieving accurate predictions.
Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning
Authors: Dogra Prashant , Cave Joseph , Christiono Anne , Schiavone Carmine , Pownall Henry , Cristini Vittorio , Staquicini Daniela , Brinker C , Campen Matthew , Wang Zhihui , Nguyen Hien Van , Noureddine Achraf 
Published: February 2025

Abstract

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine-learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse mesoporous silica NPs validated the framework’s predictive accuracy for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically-based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential Novel Alternative Method (NAM) to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.