bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physiological conditions of bendi horse in Surakarta City
Authors: Syarifuddin M A , Pawestri W , Wati A K , Rahayu E T , Yanti Y , Kharismawati K 
Published: January 2025

Abstract
This study aimed to determine the physiological conditions (frequency of heart rate, respiration, and body temperature) of bendi horses in Surakarta City. The research was conducted from May to November 2023. The method used in the study was a survey with purposive sampling technique. Data collection used interview, observation, and literature study methods. The samples used were 24 bendi horses in Surakarta City. Data were processed using quantitative descriptive analysis with Microsoft Excel software and t-Test was conducted on physiological data of horses against activities. The results showed that the most widely horse breed is Javanese horses, the average horse kept is 5-10 years old, and female sex. The physiological frequency of bendi horses according to age and sex shows normal results. The t-Test results showed that bendi horses that were doing activities had higher heart rate and respiration frequency than bendi horses that were resting in the stable. The conclusion from this study is that the frequency of heart rate, respiration, and body temperature of bendi horses in Surakarta City are in normal condition.
The Determinant of Excellent Horticulture Commodity in Ngawi Regency
Authors: Yanuan Putra Mohammad Edwinsyah , Mar’atus Sholihah Elis Rahmawati , Al Ayyubi Waridad Umais , Musi Ngani Alwirya , Wardana Aditya , Firmansyah Anwar , Dhandy Rahmat 
Published: January 2025

Abstract
Ngawi Regency based on statistical data has the potential for horticultural commodities that have the 3 highest rankings, namely red onion, cayenne pepper, and big chilies. Based on these data, further studies need to be carried out to provide recommendations to further increase the optimization of one of the horticultural commodities so that it can become one of the leading commodities for Ngawi Regency. Increasing food security and sustainability of the agricultural sector is a major focus in developing agribusiness in various countries. In this context, horticultural commodities play an important role, considering their strategic role in providing nutrition and improving the local economy. The selection of superior horticultural commodities, that can provide optimal results and meet market needs, is a crucial step to ensure sustainable growth in this sector. The SWOT method helps in evaluating internal and external factors that affect the potential of a horticultural commodity. The AHP method offers a quantitative approach to overcome the complexity in decision-making.
The combination of these two methods provides a comprehensive and data-based approach in determining superior horticultural commodities and strategies that can be carried out by Ngawi Regency to improve their quality and quantity. The SWOT method provides a framework for understanding the internal and external context, while AHP offers a tool for making more structured decisions based on comprehensive criteria evaluation.
Horticultural commodities from Ngawi Regency that are considered to be superior horticultural commodities are large chili commodities with an AHP value of 0.659, and the strategy that can be carried out to maintain the sustainability of this commodity is to increase production with an AHP value of 0.558. This can be a recommendation for Ngawi Regency to be able to increase attention to large chili commodities so that they can survive as superior horticultural commodities by maintaining and improving both in terms of quality and quantity.
Determination Of Processed Products From Pomelo Orange Outer Peel Using Analytic Hierarchy Process (AHP)
Authors: Makkulawu Andi Ridwan , Soemarno  , Santoso Imam , Mustaniroh Siti Asmaul 
Published: January 2025

Abstract
The increasing production of Pomelo oranges and the need to diversify their use to enhance economic value have prompted research into developing processed products from Pomelo’s outer peel. This study aims to rank potential processed products derived from the outer peel of Pomelo using the Analytic Hierarchy Process (AHP). Data were collected by distributing questionnaires to five experts, who conducted pairwise comparisons of the selected products. The AHP method was applied to rank these products based on their priority weights. The results show that candy ranked first with a priority weight of 0.35, followed by snack (0.25), aromatherapy (0.20), scrubs (0.10), and sauce (0.09). The consistency of expert evaluations was validated through the Consistency Ratio (CR), calculated at 0.059, indicating a high level of consistency. The findings highlight candy as having the highest potential for further development, with consistent results supporting its prioritisation for product development from Pomelo Peel.
Experimental investigation of nano-additive enhanced Azolla biodiesel blends for improved diesel engine performance and emission mitigation
Authors: D Senthilkumar , Murugesan Sivanesan , T Praveenkumar , A Naresh 
Published: January 2025

Abstract
This study investigates the performance and emission characteristics of a diesel engine fueled with Azolla microphylla biodiesel blends and the effects of aluminium oxide (Al2O3) and copper oxide (CuO) nanoparticles as fuel additives. Azolla microphylla, an aquatic fern, was used to produce biodiesel through a transesterification process. Diesel fuel was blended with Azolla biodiesel in ratios of 10:90 (B10), 20:80 (B20), and 30:70 (B30). Engine performance parameters, including brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke, were evaluated. The results showed that the B30 blend exhibited the highest BTE under maximum load conditions. To address the high viscosity and poor combustion performance of higher biodiesel blends, Al2O3 and CuO nanoparticles were added to the B30 blend at concentrations of 50 ppm and 100 ppm. The B30 blend with 100 ppm Al2O3 nanoparticles demonstrated the highest BTE and the lowest emissions of HC, CO, CO2, and smoke compared to the other test blends. The improved combustion and reduced emissions were attributed to the enhanced heat transfer and catalytic properties of the Al2O3 nanoparticles. These findings suggest that Azolla biodiesel, especially when combined with Al2O3 nanoparticles, can be a viable and sustainable alternative to conventional diesel fuel.
Advanced glaucoma detection through retinal fundus image segmentation and stacking classifier
Authors: Amar Abhishek , S Rajesh I , Bilakeri Shavantrevva 
Published: January 2025

Abstract
Identification of diseases affecting the optic nerve is paramount in ophthalmology. Traditional diagnostic methods often rely heavily on manual intervention, leading to potential errors and delays. Consequently, there’s a pressing need for automated systems capable of early disease detection to initiate timely treatment, thereby averting permanent vision impairment. Glaucoma, a chronic eye condition with the potential for irreversible blindness, stands as a prime example. Recent strides in deep learning have revolutionized the diagnosis of glaucoma through the analysis of retinal fundus images. These technologies enable the development of algorithms capable of identifying subtle signs indicative of glaucoma, often imperceptible to the human eye. By harnessing vast datasets, these algorithms can learn to distinguish between healthy and diseased retinas with remarkable accuracy, facilitating early intervention and treatment. The novelty of the work comes from the combination of existing machine learning techniques on tabular datasets as well as deep learning image segmentation to increase the performance compared to using any one technique alone. The proposed methodology for glaucoma diagnosis makes use of a combination of a deep learning model and several machine learning and achieves Receiver Operating Characteristics Area Under Curve (ROC-AUC) of 88% (0.88) on healthy, glaucomatous and suspicious images in the testing data of the publicly available PAPILA dataset. The mean absolute error for the horizontal cup to disc ratio of 0.093 was obtained and the mean absolute error for the vertical cup to disc ratio of 0.082 was obtained on the testing dataset.
When is a polarised abelian variety determined by its 𝑝-divisible group?
Authors: Ibukiyama Tomoyoshi , Karemaker Valentijn , Yu Chia-Fu 
Published: January 2025

We study the Siegel modular variety

A

g

⊗

F

¯

p

\mathscr {A}_g\otimes \overline {\mathbb {F}}_p

of genus

g
g

and its supersingular locus

S

g

\mathscr {S}_g

. As our main result we determine precisely when

S

g

\mathscr {S}_g

is irreducible, and we list all

x
x

in

A

g

⊗

F

¯

p

\mathscr {A}_g\otimes \overline {\mathbb {F}}_p

for which the corresponding central leaf

C

(
x
)

\mathscr {C}(x)

consists of one point, that is, for which

x
x

corresponds to a polarised abelian variety which is uniquely determined by its associated polarised

p
p

-divisible group. The first problem translates to a class number one problem for quaternion Hermitian lattices. The second problem also translates to a class number one problem, whose solution involves mass formulae, automorphism groups, and a careful analysis of Ekedahl-Oort strata in genus

g
=
4

g=4

.
Reform Trajectories
Authors: Gentilini Ugo 
Published: January 2025

Abstract
The chapter examines the reform of cash transfers and wider relief systems. Many of such reforms rationalized “indiscriminate” transfers and reorganized them along tighter criteria and stricter provisions. The first section identifies episodes of reforms that changed the main provider in full or in part. Such a shift involves two cases: a change from ecclesiastical to state authorities across the European continent in the early 1500s; and a switch in provider in the opposing direction, that is, away from the state and towards civil society (e.g., the United States in the later 1800s). The second section shows a reform within the same provider: the new poor laws of 1834 England were a critical example of consolidation within the same (state) institutional provider, including a reconfiguration of assistance from local to central administrative units. The third section features a blend model, including a case both embedding a shift within the same provider (although from central to local authorities) and a wider role played by private sector actors. The chapter ends with a discussion on how reforms were carried out in practice and the empirical evidence on the reforms’ effects.
scHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss
Authors: Meng Hua , Qin Chuan , Long Zhiguo 
Published: January 2025

Abstract

Motivation
The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant. However, existing methods utilizing neural networks typically focus on minimizing reconstruction loss and maintaining similarity in embeddings of directly related cells, but fail to consider dissimilarity, thus lacking separability and limiting the performance of clustering.

Results
We propose a novel clustering algorithm, called scHNTL (scRNA-seq data clustering augmented by high-order neighbors and triplet loss). It first constructs an auxiliary similarity graph and uses a Graph Attentional Autoencoder to learn initial embeddings of cells. Then it identifies similar and dissimilar cells by exploring high-order structures of the similarity graph and exploits a triplet loss of contrastive learning, to improve the embeddings in preserving structural information by separating dissimilar pairs. Finally, this improvement for embedding and the target of clustering are fused in a self-optimizing clustering framework to obtain the clusters. Experimental evaluations on 16 real-world datasets demonstrate the superiority of scHNTL in clustering over the state-of-the-arts single-cell clustering algorithms.

Availability and implementation
Python implementation of scHNTL is available at Figshare (https://doi.org/10.6084/m9.figshare.27001090) and Github (https://github.com/SWJTU-ML/scHNTL-code).

Supplementary information
Supplementary data are available at Bioinformatics online.
Deep learning-enabled electrocardiogram assessment of valvular heart disease
Authors: Cinq-Mars A , Carbonati T , Fiorina L , Neylon A , Porquet P , Nguyen A , Waks J , Johnston A , Pollard T , Gow B , Mark R , Horng S , Greenbaum N , Singh J 
Published: January 2025

Abstract

Background
Valvular heart disease is a highly prevalent condition that contributes significantly to cardiovascular morbidity and mortality. Echocardiography is the gold-standard for valvular disease evaluation; however, diagnosis may be delayed due to limited resources or expertise. Deep learning analysis of electrocardiography (ECG) has demonstrated the ability to identify subtle structural and functional abnormalities within the cardiovascular system.

Purpose
The aim of this study was to evaluate the accuracy of a deep learning algorithm to identify valvular heart disease from a standard 12-lead ECG.

Methods
From 2008 to 2019, a total of 27,689 patients who were admitted to the Medical Center with an echocardiogram and ECG performed within 14 days of each other were included in the study. We trained a convolutional neural network to identify the presence of moderate or severe aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and/or tricuspid regurgitation (TR) using a 12-lead ECG. Model performance was assessed using area under the receiver-operating characteristic (AUROC). Patients were randomly divided into development (n = 22,151; 80%) and independent validation sets (n = 5,538; 20[AN1] %). Data used for the study was collected from two open access databases, MIMIC-IV-ECG and MIMIC-IV-Note.

Results
Mean age of the cohort was 65.9 ± 16.6 years old and 51% were female. On the independent validation dataset, AS was present in 126 (2.3%) of patients, AR in 51 (1%), MR in 337 (6.1%), and TR in 450 (8.1 %) patients. During validation, the model accurately identified AS, AR, MR, and TR with AUROCs of 0.80 (95% CI 0.77-0.83), 0.74 (95% CI 0.69-0.80), 0.86 (95% CI 0.84-0.88), and 0.85 (95% CI 0.83-0.87), respectively (Table 1). Test characteristics were dependent on underlying prevalence and selected sensitivity levels. For a composite of any of AS, AR, MR, or TR, the model achieved a positive predictive value of 28% with 79.4% sensitivity.

Conclusion
A deep learning-enabled ECG demonstrates robust performance to identify patients living with valvular heart disease. This model may serve as a simple and promising tool for improving the early detection of valvular heart disease, showcasing the potential for an early development valvular disease screening program.
Deep learning-driven automated assessment of left ventricular diastolic function in echocardiography
Authors: Karuzas A , Miscikas L , Sablauskas K , Kazukauskiene I , Verikas D , Naskauskas K , Sakalyte G , Jarusevicius G , Plisiene J , Lesauskaite V 
Published: January 2025

Abstract

Introduction
Assessing left ventricular (LV) diastolic dysfunction is essential, because it can lead to heart failure with preserved ejection fraction (HFpEF), significantly impacting morbidity and mortality rates. Echocardiography is the primary imaging tool for detecting LV diastolic dysfunction, however its assessment is time consuming and requires experienced clinicians to interpret multiple echocardiographic parameters. Recent advancements in deep learning might offer the potential to reduce analysis time and standardize the evaluation of LV diastolic function.

Purpose
The aim of this study was to assess the potential of deep convolutional neural networks (CNNs) for the evaluation of LV diastolic dysfunction.

Methods
Prospective observational study enrolled 233 patients, a total of 3238 2D echocardiographic images were obtained. The study included patients with normal LV systolic function, who were suspected of HFpEF. The ensemble of four CNN’s was used for the evaluation of transmitral inflow waves (E and A peak velocities), mitral (septal and lateral) velocities (e’) and peak tricuspid regurgitation velocity (TR Vmax). Additional diastolic function parameters, such as the E/A and E/e’ ratios, were calculated to assess diastolic function classes based on the 2016 ASE/EACVI recommendations. Manual measurements were performed by an expert cardiologist. CNN’s results detecting diastolic dysfunction were compared to the evaluation of expert cardiologists.

Results
The analysis revealed high yields of CNN’s measurements across various parameters, ranging from 81.12% for the E/e' ratio to 89.27% for the A wave. CNNs perform well in measuring maximum velocities using different Doppler imaging modes. The root mean square error (RMSE) values indicated the degree of deviation between the automated system and the cardiologist measurements, with the lowest RMSE observed for the E/A ratio (0.4). Bias showed minor discrepancies for the E/A ratio (-0.05), with the largest bias observed in the A wave (5.95) (Table 1).

Conclusion
This study demonstrates that automated echocardiographic analysis shows high yield and good agreement with cardiologist measurements, though some parameters require further enhancement to ensure greater accuracy and consistency. The application of CNNs can potentially simplify the assessment process, reduce analysis time, and provide standardized evaluations.
Table 1. Results of statistical analysis
Effect of image quality on automated evaluation of the left-sided heart volumes
Authors: Kazukauskiene I , Sablauskas K , Ciampi Q , Cortigiani L , Wierzbowska-Drabik K , Kasprzak J D , Lowenstein J , Karuzas A , Prota C , Gaibazzi N , Lepone A , Marconi S , Kiziela A , Arbucci R , Picano E 
Published: January 2025

Abstract

Introduction
The accuracy of echocardiography interpretations is crucial. However, echocardiography has varying degrees of image quality, which can impact diagnostic decision-making. Automated chamber quantification of the left ventricle (LV) and left atrium (LA) has been shown to be feasible using machine learning (ML) systems. However, the effect of image quality on the performance of these ML systems has not been investigated.

Purpose
To assess the effect of different echocardiographic image quality on the performance of left-sided heart quantification using an ML system.

Methods
A multi-center study enrolled 240 patients who underwent stress echocardiography across five centers. The acquired rest phase images were anonymized for subsequent analysis. Expert cardiologists reviewed the studies, selecting apical four-chamber (4Ch) and two-chamber (2Ch) view images. The cardiologists identified end-systolic (ES) and end-diastolic (ED) frames. They traced the endocardial borders for the LA in ES and the LV in ED and ES. The same ED and ES frames were then processed by an ML algorithm trained on a separate set of images. Subsequently, an expert cardiologist, blinded to the previous measurements, assessed the image quality of LA and LV. The image quality for each chamber in the ES or ED frame was categorized into one of four grades: poor, suboptimal, fair, and optimal. Agreement analysis between human and AI measurements was performed within each image quality group. Yield was defined as the percentage of studies where the ML system predicted an echocardiographic parameter compared to a cardiologist.

Results
The yield of study parameters varied significantly with image quality. The lowest yield was 45%, observed for LV end-diastolic volume in the 2Ch view when image quality was poor. In contrast, the yield of 100% was seen for LV end-diastolic volumes in both the 4Ch and 2Ch views for optimal quality images. The correlation between automated and expert cardiologist measurements of left heart volumes varied depending on image quality. For poor or suboptimal images, the Pearson correlation coefficient ranged from 0.66 to 0.91 and improved to 0.85-0.92 in fair and optimal-quality images. Additionally, there was an inverse relation between agreement metrics (bias, limits of agreement, and coefficient of variation) and improvements in image quality. However, this tendency was less pronounced or nonexistent when comparing fair and optimal-quality images. (Table 1).

Conclusions
Image quality affects the yield of automated analysis and the agreement between cardiologists and automated measurements in echocardiography, highlighting the need for high-quality image acquisition in use with ML systems.
Table 1
Performance of human expert and AI measurements in conditions of progressive image degradation
Authors: Jevsikov J , Stowell C , Ng T , Unsworth B , Zolgharni M , Francis D P , Shun-Shin M J 
Published: January 2025

Abstract

Background
A major challenge in real-world echocardiography is the difficulty in obtaining high quality images in some patients or some clinical settings. Is AI only useful when image quality is good?

Purpose
To artificially degrade adequate-quality images, and compare the ability of human experts and AI, to make measurements correctly, as image degradation worsens.

Methods
PLAX dimension measurements were made on videos of 30 patients with a range LV dimensions (mean 138mm, SD 37mm). To set the gold standard, 9 experts measured each image, blinded to the measurements made by others.
For each original image, 5 degraded versions were then made, each progressively more degraded. The degradation was designed to be maximally confusing for this measurement, namely the addition of faint ghost images of random PLAX views of other patients. This process is automatable, reproducible, and not easy to undo by conventional image processing techniques.
The 30 cases, each in original form plus 5 degraded versions, totalled 180 images, They were presented in random order for labelling by a pool of experts. They were also analysed by the Unity UK Echocardiography AI collaboratives.

Results
An example degradation sequence is shown in Figure 1 (upper panel), with images cropped for this abstract.
Across all measurements, the expert and AI suffered progressively greater measurement error as the level of image degradation increased. On average, the AI error was N smaller than the expert error (p&lt;0.05).
For example, for LV internal dimension, the progressive rise in human error was 2.2mm, 2.5mm, 3.1mm, 3.6mm, 5.3mm, 9.6 mm, p&lt;0.001 for trend, Figure 1 lower panel grey bars.
Meanwhile for the AI, the corresponding errors were: 2.5mm, 2.5 mm, 2.7 mm, 3 mm, 3.9 mm, 8 mm. (p&lt;0.001 for trend, Figure 1 lower panel grey bars).
The Minimum Heatmap Amplitude (MHA), an automatic index of AI confidence in its measurement, also declined progressively (p&lt;0.001 for trend).

Conclusion
Both humans and AI alike make less accurate measurements as image quality degrades, although the deterioration in accuracy is more predictable for AI measurements. Importantly, deterioration in image quality (and therefore doubtfulness of the measurement) can be automatically quantified through the MHA to flag measurements needing special attention.
Figure 1: Progressive degradation in images (upper panel) and corresponding increase in measurement error by both human experts and AI (lower panel)