bluuebunny/crossref_metadata_2025_split huggingface dataset:
Musical Tempo Matters? Impact of Musical Rhythm on Blood, Physiological and Welfare Parameters in Stabled Horses
Authors: de Oliveira Fernanda Yumi Ueno , Caldara Fabiana Ribeiro , Odakura Agnês Markiy , Burbarelli Maria Fernanda de Castro , Ouros Caio César dos , Paz Ibiara Correia de Lima Almeida , Braz Jaqueline Murback , Garcia Rodrigo Garófallo 
Published: December 2024

Abstract

The aim of this study was to evaluate the effects of two styles of classical music, based on different tempos (BPM), on the physiological and blood parameters of horses during social isolation and restriction of movements. First experiment were carried out using nine horses of no defined breed, distributed in Control, Slow-tempo music and Moderate-tempo music .For social isolation and restriction of movement, the animals were housed daily in individual stalls for two hours and exposed to the stimuli for 60 minutes, and eye temperature, heart rate, and respiratory rate were assessed.The second experiment were carried out using ten horses of no defined breed were used, distributed in a randomized design in treatments: Slow-tempo Music and Moderate-tempo Music. Blood samples were taken at the start and end of the experimental period to assess hematological and biochemical parameters and serum serotonin levels. Horses exposed to moderate-tempo music showed an increase in serum calcium levels, mean corpuscular hemoglobin (MCH), and total hemoglobin concentration, as well as a reduction in lymphocytes.Both types of music led to a significant increase in serotonin levels after one week of stimulation. Both musical rhythms are appropriate for promoting the well-being and health of stabled horses.
Optimizing Pt catalyst performance for oxygen reduction reaction via surface functionalization of Vulcan XC-72R carbon black support
Authors: Najafli Erkin , Grossberg Maarja , Mikli Valdek , Walke Peter , Ratso Sander , Kruusenberg Ivar 
Published: December 2024

Abstract

In this study, the impact of Vulcan XC-72R carbon black support pre-treatment on the oxygen reduction reaction performance of the Pt catalyst was investigated. Catalysts were synthesized via the polyol method using ethylene glycol, with different pre-treatment pathways (with H2O2 and HNO3 solutions) applied to the carbon black for surface functionalization. Additional samples using non-treated XC-72R and N-doped XC-72R were prepared for comparison, alongside a commercial Pt/C catalyst. Electrochemical characterization revealed that H2O2 pre-treatment resulted in the largest electrochemically active surface area and enhanced oxygen reduction reaction activity, surpassing both non-treated and commercial catalysts. The H2O2-treated catalyst exhibited superior stability during accelerated stress testing. SEM and XRD analyses confirmed the presence of Pt nanoparticles with sizes between 2.93-4.46 nm (that are considered optimal) for the H2O2-treated sample. Raman spectroscopy indicated that H2O2 pre-treatment led to a less ordered carbon structure with more defects, which is favorable for improved Pt utilization. It was concluded that H2O2 pre-treatment of XC-72R significantly improves the catalytic activity of the Pt catalyst, demonstrating the potential for optimizing Pt loading while maintaining desired ORR kinetics in fuel cell applications.
Development of shallow-water contourite deposits on the muddy continental shelf off southeastern Korea
Authors: Chun Jong-Hwa , Ha Jiho , Shin Jungkyun , Um In Kwon 
Published: December 2024

Abstract

On the southeastern continental shelf of Korea, within the Korea Strait Shelf Mud deposit, seafloor undulations trending north-northwest‒south-southwest have been identified offshore of Ulsan. This study utilized multibeam echo sounding, two- and three-dimensional (3D) high-resolution CHIRP seismic surveys, and piston core analysis to investigate the formation and timing of these undulations on the muddy continental shelf. The multibeam data revealed irregular seafloor undulations aligned with the bathymetric contours, and the sea-bottom reflected amplitude of 3D CHIRP profiles identified elongated mounds within this region. The surface undulations extend approximately 30 km at a depth of 46‒56 m, running parallel to the bathymetric contours. These features vary laterally in width from 50 to 400 m and including small, elongate mounds. Core 20MAP-P05, which is located closer to the coast, contains coarse-grained typhoon deposits from the high-energy environment above a storm wave base, dating to 225‒287 cal yr BP. However, this interval is absent in core 20MAP-P04, which was sampled from the undulating seafloor at depth of 51.2 m. This core contains vertical burrows, 10‒20 mm in diameter, dating to around 183 cal yr BP at a depth of 220 cm, suggesting the rapid redeposition of resuspended sediments. These results indicate the formation of elongate mounds in an area with seafloor undulations off Ulsan, and that sudden re-sedimentation on the muddy continental shelf occurred around 183 cal yr BP due to the influence of contourite currents beneath the storm wave base.
Researching on Insulator Defect Recognition based on Context Cluster CenterNet++
Authors: Meng Bo 
Published: December 2024

Abstract

The existing UAV inspection images are faced with many challenges for insulator defect recognition. A new multi-resolution Context Cluster CenterNet + + model is proposed. First, this paper proposes the Context Cluster method to solve the problem of low recognition accuracy caused by non-uniform distribution of targets. The cluster region is used to identify and predict the location of the target, and the improved loss function is used to modify the cluster center. Secondly, this paper uses deformable convolution operator (DCNv2) combined with path aggregation network (PAN) to carry out deformable convolution operation on the image, and accurately predicts the regression box and key point triplet (KP), so as to improve the accurate positioning of the target position of any shape and any scale. The sensitivity of the model to target scale change and deformation is reduced, and the recognition accuracy of the model is improved. Then, Bhattacharyya distance is used to calculate the triplet prediction loss of key points and the target center point offset loss, so as to significantly improve the positioning accuracy of the same target in different frames. Finally, experiments are carried out on the MS-COCO dataset and the National Grid standardized UAV inspection insulator image dataset.
Random Impact Force Localisation Enabled by Weighted Reference Database Method
Authors: Qiu Binbin , Liu Siqi , Li Weidong , Feng Chunhua , Zhu Yu , Liu Haoran 
Published: December 2024

Abstract

The mechanical properties of an engineering structure can be substantially influenced by a random impact force (RIF), which may compromise the integrity and safety of the structure. Nevertheless, accurately localising the RIF applied to a structure presents a significant challenge. To address this issue, this study introduces a novel method known as the weighted reference database method (WRDM). Its innovations are reflected in three aspects: (i) constrained by the sparse construction of a reference database, bicubic interpolation is utilised to increase the reference impact point density and improve the localisation accuracy; (ii) a weighted random impact localisation framework is constructed, in which a cosine distance variant is chosen as the weight to further improve the localisation accuracy; and (iii) to overcome the region limitation of interpolation, the boundary range of the WRDM is extended. Experiments on a suspended rectangular plate were conducted to validate and demonstrate the effectiveness of the WRDM in terms of localisation accuracy. The experimental results indicate that the average absolute error of the method is 16.67 mm (the interpolation interval size is 2 mm and the prioritisation point number (PPN) is 108), and its localisation accuracy is higher than that of previously published methods (21.54 mm for PRMCSM; 20.80 mm for the hybrid method).
Bioinformatic Analysis of the Protective Effects of Dexmedetomidine and Thrombopoietin against Hypoxia/Reoxygenation-Induced Injury in AC16 Cells
Authors: Xing Cuiyan , Wu Mingyi , Zhou Xiaoyang , Gong Benhang 
Published: December 2024

Abstract

Objective: To investigate the protective mechanisms of dexmedetomidine (Dex) and thrombopoietin (TPO) against hypoxia/reoxygenation (H/R)-induced myocardial injury.
Methods: Human cardiomyocyte AC16 cells were subjected to hypoxic conditions and treated with Dex and TPO. Cellular responses were assessed through proliferation, apoptosis, and autophagy assays. RNA sequencing and bioinformatic analyses were conducted to identify differentially expressed genes, followed by functional pathway enrichment analysis.
Results: Dex and TPO significantly promoted cell proliferation, reduced apoptosis and autophagy, and inhibited caspase-3 activity and LC3B expression. Both agents induced changes in gene expression, with pathway enrichment analysis revealing involvement in MAPK, TGF-β, and TNF signaling pathways. While both treatments demonstrated overlapping effects, each exhibited unique gene regulation mechanisms.
Conclusion: Dex and TPO mitigate H/R-induced myocardial injury through complex gene regulatory mechanisms, offering potential intervention strategies for myocardial ischemia-reperfusion injury (MIRI).
Improved European heat event simulation by resolving oceanic-mesoscale eddies
Authors: Krueger Julian , Kjellsson Joakim , Lohmann Katja , Matei Daniela , Kedzierski Robin Pilch 
Published: December 2024

Abstract

Owing to the rapid increase of European heat event occurrences over the past decades, the understanding of their physical drivers has become increasingly important for the scientific community. Recently, it has been shown that cold North Atlantic sea surface temperatures (SSTs) are strongly linked to European heat events such as in summer 2015 and 2018. Thereby, an accurate representation of this connection in climate models is crucial for a more realistic European heat event simulation. Here, we investigate the connection by employing seven global coupled climate models, from which six models are embedded in the High Resolution Model Intercomparison Project (HighResMIP). High-resolution versions of these models capture mesoscale eddies, thus enabling to study their impact onto the (North Atlantic SST- European summer temperature extremes) connection. Our results show that high-resolution model versions better capture the North Atlantic trough and the downstream ridge anomalies over central Europe, although still underestimated in intensity
and duration compared to observations. Improvements in high-resolution configurations are due to the increased ocean model resolution, which reduces the North Atlantic surface biases and improves air-sea interactions, thus having implications for the prediction and projection of climate extremes in the North Atlantic-European region.
Evaluation of an artificial intelligence model for identification of obstructive hydrocephalus on computed tomography of the head
Authors: Ghatak Ankita , Newbury-Chaet Isabella , Mercaldo Sarah , Chin John , Halle Madeleine , L'Italien Eric , MacDonald Ashley , Schultz Alex , Buch Karen , Conklin John , Mehan William , Pomerantz Stuart , Rincon Sandra , Bizzo Bernardo , Hillis James Michael 
Published: December 2024

Abstract

Introduction:
Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head computed tomography (CT) by an artificial intelligence (AI) model could facilitate sooner life-saving interventions, although there are common co-occurring findings including intracranial hemorrhage that can confound this interpretation. This study assessed the accuracy of an AI model (Annalise Enterprise CTB) at identifying obstructive hydrocephalus including in the presence or absence of other findings.
Methods
This retrospective cohort included 177 thin (≤ 1.5mm) series and 194 thick (> 1.5 and ≤ 5mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model.
Results
The AI model performed with area under the curve 0.988 (95% confidence interval (CI): 0.971 to 0.998) on thin series and 0.986 (95% CI: 0.969 to 0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality and ventricular drain, and across demographic and scanner manufacturer subgroups.
Conclusions
The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness.
Technological Adjuncts to Streamline Patient Recruitment, Informed Consent, and Data Management Processes in Clinical Research: Observational Study
Authors: Koh Jodie , Caron Stacey , Watters Amber N , Vaidyanathan Mahesh , Melnick David , Santi Alyssa , Hudson Kenneth , Arguelles Catherine , Mathur Priyanka , Etemadi Mozziyar 
Published: December 2024

Background
Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.

Objective
This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.

Methods
Using one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act–compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act–compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID.

Results
Over a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy.

Conclusions
Technological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies.
Determinants of Bond Performance (Study of Companies Listed on the Indonesian Stock Exchange)
Authors: Andarwati Andarwati 
Published: December 2024

Bond ratings are critical for firms that issue them, as they reflect the company's risk level. A higher bond rating typically leads to lower costs for the company. Most investors tend to avoid bonds with poor credit ratings. This research aims to examine how profitability, liquidity, and leverage influence bond prices, using bond ratings as a key variable. The study population comprises all firms listed on the Indonesian stock market from 2019 to 2021, totaling 116 entities. Through purposive sampling, the research focused on 72 companies that met specific criteria: firms that issued reports in rupiah, generated profits, and were rated by PT Pefindo. The study employed path analysis to test the hypotheses. The findings reveal that profitability, liquidity, and leverage have minimal impact on bond ratings. Furthermore, changes in liquidity and leverage do not affect bond prices. However, profitability and bond ratings significantly influence bond prices. The study also shows that bond ratings do not mediate the effects of liquidity and leverage on bond prices. Nonetheless, bond ratings reflect the yield of bond prices. In conclusion, this study finds that while profitability, liquidity, and leverage do not significantly affect bond ratings, bond prices are influenced by profitability and bond ratings. The practical implications suggest that bond ratings and bond prices play a pivotal role in enhancing company performance, particularly in terms of managing liquidity to meet interest and installment obligations and leveraging strategies to boost income.
Developing synergy learning model for science learning: A case study at islamic junior high school
Authors: Halimatussa’diah Halimatussa’diah , Sitompul Harun , Mursid R. 
Published: December 2024

This research aims to develop a Synergy Learning Model in the context of science learning. This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.
Integumentary and musculoskeletal indications for surgery predominate in a 15-year study of search-and-rescue dogs
Authors: Powers Gretchen J. , Lucas Abigail , Isaksen Katja E. , Seeley Abigail C. , Cogger Naomi , Otto Cynthia M. 
Published: December 2024

Abstract

OBJECTIVE
Analyze data from a 15-year longitudinal study of search-and-rescue (SAR) dogs to determine the indications for surgery and evaluate whether the risk varied by breed, sex, neutering, or deployment status during 9/11.

METHODS
150 SAR dogs: 95 dogs deployed to sites from the September 11, 2001, terrorist attacks and 55 SAR dogs not deployed to these sites. Search-and-rescue dog handlers completed an annual health survey until the dog’s death, or the handler withdrew from the study. The reported surgical events were categorized by body system and etiology. Incidence risk rates, with 95% CIs, were calculated for the most common surgical indications. Incidence rate ratios were calculated stratified by deployment status, sex, and breed, and significance was assessed.

RESULTS
Over the study period, 103 surgical events were recorded in 67 of the 150 dogs (45%; 95% CI, 37% to 53%); 41 dogs had 1 event, 18 had 2 events, 6 had 3 events, and 2 had 4 events. Deployment status during 9/11, breed, sex, and neuter status were not significantly associated with the affected body system.

CONCLUSIONS
The most affected body systems were the integumentary and musculoskeletal systems. The most common etiologies were trauma, neoplasia, inflammatory, and degenerative conditions. Search-and-rescue dogs experienced relatively routine or minor conditions despite the occupational hazards of their careers.

CLINICAL RELEVANCE
Surgery will impact working dog health and performance. Preventive efforts to reduce and interventions to treat surgical conditions in SAR dogs should focus on the integumentary and musculoskeletal systems.
A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting
Authors: O’Leary Christian , Lynch Conor , Toosi Farshad Ghassemi 
Published: December 2024

Abstract
Reliable and accurate electricity price forecasting algorithms can be used to inform efficient energy consumption schedules and maximise profits for electricity traders. Operating within Ireland’s Integrated Single Electricity Market (I-SEM), traders can buy and sell electricity at fluctuating hourly rates whose day-ahead prices are published at approximately 13:00 GMT day-1. Access to electricity price predictions earlier than this publication time allows stakeholders an expanded timeframe to facilitate energy cost-aware scheduling.
While many studies have been conducted to espouse various machine learning and statistical approaches to electricity price forecasting, these models tend to be bespoke and require in-depth knowledge regarding model implementation. The problem of requiring such expertise is not unique to time series forecasting, and research into mitigating such limitations exists in the form of Automated Machine Learning (AutoML). AutoML aims to derive effective models while automating various steps typically required for machine learning experimentation, such as pre-processing, model selection, validation, etc.
Given the increasing proliferation of AutoML tools and frameworks, this paper applies eight Python-based AutoML libraries to day-ahead electricity price forecasting on an excerpt of I-SEM data. These libraries are compared across a series of error metrics and training times to produce an empirical benchmark that can be utilised to select high-performing AutoML tools for further price forecasting research and other forms of time series forecasting. AutoKeras is found to produce accurate forecasts but requires careful configuration to avoid long runtimes. PyCaret, Ludwig, FLAML and FEDOT also generate favourable results while being significantly easier to configure.
An index for measuring departure from an anti-sum-symmetry model for square contingency tables with ordered categories
Authors: Ando Shuji 
Published: December 2024

Abstract
In the analysis of square contingency tables, which are two-way contingency tables in which the row and column variables consist of the same classification, statistical models regarding the symmetry of row and column variables are often used rather than the independence. This study proposes an index for measuring the degree of departure from the anti-sum-symmetry model. The proposed index is constructed using the Kullback–Leibler divergence. The anti-sum-symmetry model is useful to evaluate whether symmetric and asymmetric structures exist with respect to the anti-diagonal of the table. We derive the plug-in estimator and large-sample confidence interval for the proposed index. The usefulness of the proposed index is demonstrated by applying it to real data.