bluuebunny/crossref_metadata_2025_split huggingface dataset:
EPTQA:a Chinese medical prompt learning method based on entity pair type question answering
Authors: Su Zhitong , Yu Xinran , Li Songpu , Chen Peng 
Published: October 2023

Abstract
As a mainstream method in current natural language processing tasks, the “pre-train, fine-tune” method has achieved quite good results in various scenarios. However, the “pre-train, fine-tune” method performs poorly on few-shot learning tasks. Recently, prompt learning has gained popularity. Prompt learning transforms various types of natural language processing tasks into pre-training tasks and shows good results on few-shot learning tasks. The prompt learning method based on entity pair type question answering proposed in this paper creatively applies the prompt learning method successfully to the Chinese medical relationship extraction task. The proposed model shows good results on both full data and and low resource datasets. Background Chinese medical relation extraction is an important step in building a complete medical knowledge system. Although the “pre-train, fine-tune” paradigm has shown good results in the Chinese medical relationship extraction task, the “pre-train, fine-tune” paradigm has slow model convergence, and the results are not satisfactory in the small-sample relationship extraction task. These problems are related to the scarcity of accurately labelled medical text data and the large differences between upstream and downstream models. Results Given the aforementioned problems, we propose a prompt learning method that is based on entity pair type question answering. To start with, we preprocessed the Chinese medical text dataset by transforming the data into a sentence-level relation extraction form, which is more appropriate for prompt learning. The relationship template is then devised by combining entity types, which effectively address the issue of expressing the Chinese medical relationship in an accurate and brief manner. Following the fine-tuning of the pre-trained model, this method can accurately restore the mask and present very competitive outcomes on the full data and low resource data of numerous Chinese medical datasets. Conclusions The method proposed in this paper is superior to the traditional “pre-train, fine-tune” approach, as it can efficiently extract the connections between Chinese medical entities. This method is particularly effective when dealing with small sample datasets. The efficiency of the model can be further improved by using the relationship filtering method which is based on the relevant rules of the Chinese medical entities.
Numerical Investigation of Hall and Ion Slip Effects on Prandtl Nanofluid with Non-Fourier Double Diffusion Theories: Artificial Neural Network Approach for Thermal and Mass Transportation
Authors: Habib Shazia , Khan Zeeshan , . Waseem , Islam Saeed 
Published: October 2023

Abstract
To model complex relationships between input and output data, (ANN) algorithms are extensively used in engineering and fluid mechanics. They are able to predict outcomes, enhance quality control, optimize processes, predict material properties, and recognize images and patterns. ANNs are also utilized in control systems to govern the parameters of various engineering systems in an adaptive manner. ANNs are used for fluid flow prediction, turbulence modeling, heat transfer analysis, airfoil design, environmental modeling, pipeline monitoring, hydraulic systems simulation, and cavitation prediction in fluid dynamics. In order to account for the effects of Hall and ions on a (CC) double heat flow model, this investigation employs an artificial neural network (ANN) and (LMS) to find a solution for the HIE-PNF-NFDDT model. The focus of this paper is the investigation of the temperature, velocity, and concentration of (HIE-PNF-NFDDT) flow. Researchers use the bvp4c solver to get the initial dataset and then apply the Levenberg Marquardt Scheme of Neural Network Algorithm (LMS-NNA) to analyze the flow dynamics. They use the partial differential equations (PDEs) that reflect the flow of the HIE-PNF-NFDDT to construct a chain of ordinary differential equations (ODEs), which they then employ to evaluate the reference dataset for the evolutionary method, i.e. LMS-NNA. The velocities have a direct relation with non-dimensional parameters Prandtl fluid parameters and ratio parameters. Temperature enhances while it falls for ion slip and hall parameters. Similarly, the concentration increases with the increase Hartman parameter and has an inverse relation to concentration relaxation parameters.
High-resolution projections of outdoor thermal stress in the 21st century: a Tasmanian case study
Authors: Weeding Ben , Love Peter , Beyer Kathleen , Lucieer Arko , Remenyi Tom 
Published: October 2023

Abstract

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this study we present the first hourly metre-scale projections of thermal stress driven by multivariate bias corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. This modelling projects thermal stress in central Hobart, Australia for 2040–2059 from a historical period of 1990–2005. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day.
Improving AlphaFold predicted contacts inalpha-helical transmembrane proteins structuresusing structural features
Authors: Sawhney Aman , Li Jiefu , Liao Li 
Published: October 2023

Abstract

Background: Residue contacts maps offer a 2-d reduced representation of 3-dprotein structures and constitute a structural constraint and scaffold in structuralmodeling. In addition, contact maps are also an effective tool in identifying interhelicalbinding sites and drawing insights about protein function. While mostworks predict contact maps using features derived from sequences, we believeinformation from known structures can be leveraged for a prediction improvementin unknown structures where decent approximate structures such as onespredicted by AlphaFold2 are available.
Results: Alphafold2’s predicted structures are found to be quite accurate atinter-helical residue contact prediction task, achieving 83% average precision. Weadopt an unconventional approach, using features extracted from atomic structuresin the neighborhood of a residue pair and use them to predicting residuecontact. We trained on features derived from experimentally determined structuresand predicted on features derived from AlphaFold2’s predicted structures.Our results demonstrate a remarkable improvement over AlphaFold2 achievingover 91.9% average precision for held-out and over 89.5% average precision incross validation experiments.
Conclusion: Training on features generated from experimentally determinedstructures, we were able to leverage knowledge from known structures to significantlyimprove the contacts predicted using AlphaFold2 structures. Wedemonstrated that using coordinates directly (instead of the proposed features)does not lead to an improvement in contact prediction performance.
DeepEAG: An edge-powered graph neural network using SMILES augmentation for cancer drug response prediction
Authors: Lao Chuanqi , Zheng Pengfei , Chen Hongyang , An Feng , Liu Qiao 
Published: October 2023

Abstract
Motivation:: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.
Results:: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepEAG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing Simplified Molecular Input Line Entry Specification (SMILES) data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepEAG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepEAG in guiding the design of specific cancer treatment regimens.
Learning Subtle Variances on Temporal Sequence with Inception Attention for Skeleton-based Action Recognition
Authors: Lu Jing , Li Zhuo , Li Xiao , Yuan Shaozu , Duan Maomao 
Published: October 2023

Abstract

Graph convolutional network (GCN) has been employed in the task of skeleton-based action recognition with excellent performance. Studies applying GCN on the spatial-temporal graph paid more attention to feature transfer in the sparse spatial space and the capture of the long-term dependence of each spatial node in the time dimension. However, the sequential treatment of spatial and temporal information is inadequate to capture the intimate spatial-temporal dependencies. In addition, the high-resolution temporal information in the spatial-temporal graph has not been explored adequately. For the reasons, we propose to learn subtle variances on temporal sequence based on GCN with Inception Attention for skeleton-based action recognition. To learn the subtle variances of different actions, a self-supervised spatial-temporal graph representation learning is developed, where a spatial-temporal graph is divided into several sub-graphs on the temporal dimension with a random sub-graph shuffled, and the network is asked to predict the shuffled one to learn the subtle variances of different actions. An attention mechanism called Inception Attention is proposed to highlight the cubes of interest in the spatial-temporal graph, where the dependencies are captured along the spatial and temporal space simultaneously. Experiments on three large-scale public datasets, NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 verify the effectiveness of our method.
Application of Fractal Dimension in Erosion Susceptibility Analysis of Geological Formations in Arid Areas, the Yazd-Ardakan Catchment of Iran
Authors: Khoshoui M. Mohammadi , Ekhtesasi M. R. , Talebi A. 
Published: October 2023

Abstract
The study was conducted to determine if the drainage network's fractal dimension could be used to classify the resistance of three geological formations in Iran's Yazd-Ardakan catchment. The study also compared the results of the fractal dimensions method with those of the MPSIAC, Feyznia, and Selby methods. There was a significant difference in the drainage network's mean fractal dimension among the three geological formations, with Taft limestone having the lowest fractal dimension (1.149) and the Kahar shale and sandstone formation having the highest (1.207). The MPSIAC and Feyznia methods classified the Shirkuh granite and Taft limestone formations as resistant, while the Selby method and fractal dimensions of the drainage network classified the Taft limestone formation as resistant and the Shirkuh granite formation as moderate. The fractal dimensions of the drainage network were found to correlate significantly with morphological parameters and geological formation sensitivity. In comparison to more resistant formations, more sensitive geological formations were found to have higher fractal dimensions and significantly different drainage network morphologies. This indicates that lithology plays a significant role in the development of drainage networks. In this study, fractal dimensions were effective in classifying and separating the Kahar sandstone and shale, Taft limestone, and Shirkuh granite formations with 93%, 78%, and 75% probabilities, respectively. The study's findings suggest that fractal analysis is a valuable and cost-effective tool for assessing geological formations' resistance to erosion and environmental factors compared to more time-consuming and expensive methods such as field surveys or laboratory analysis.
Integer group determinants of order 16
Authors: Yamaguchi Yuka , Yamaguchi Naoya 
Published: October 2023

Abstract

Let ${¥rm C}_{n}$ and ${¥rm Q}_{n}$ denote the cyclic group and the generalized quaternion group of order $n$, respectively. We determine all possible values of the integer group determinants of ${¥rm C}_{8} ¥rtimes_{3} {¥rm C}_{2}$ and ${¥rm Q}_{8} ¥rtimes {¥rm C}_{2}$, which are the unresolved groups of order 16 (Serrano, Paudel and Pinner also obtained a complete description of the integer group determinants of ${¥rm Q}_{8} ¥rtimes {¥rm C}_{2}$ independently of this paper and presented it a few days earlier than this paper). Also, we give a diagram of the set inclusion relations between the integer group determinants for all groups of order 16.
Crowd-Certain: Label Aggregation in Crowdsourced and Ensemble Learning Classification
Authors: majdi mohammadsadegh , Rodriguez Jeffrey J. 
Published: October 2023

Abstract

Crowdsourcing systems have been used to accumulate massive amounts of labeled data for applications such as computer vision and natural language processing.
However, because crowdsourced labeling is inherently dynamic and uncertain, developing a technique that can work in most situations is extremely challenging.
In this paper, we introduce Crowd-Certain, a novel approach for label aggregation in crowdsourced and ensemble learning classification tasks that offers improved performance and computational efficiency for different numbers of annotators and a variety of datasets.
The proposed method uses the consistency of the annotators versus a trained classifier to determine a reliability score for each annotator.
Furthermore, Crowd-Certain leverages predicted probabilities, enabling the reuse of trained classifiers on future sample data, thereby eliminating the need for recurrent simulation processes inherent in existing methods.
We extensively evaluated our approach against ten existing techniques across ten different datasets, each labeled by varying numbers of annotators.
The findings demonstrate that Crowd-Certain outperforms the existing methods (Tao, Sheng, KOS, MACE, MajorityVote, MMSR, Wawa, Zero-Based Skill, GLAD, and Dawid Skene), in nearly all scenarios, delivering higher average accuracy, F1 scores, and AUC rates.
Additionally, we introduce a variation of two existing confidence score measurement techniques. Finally we evaluate these two confidence score techniques using two evaluation metrics: Expected Calibration Error (ECE) and Brier Score Loss.
Our results show that Crowd-Certain achieves higher Brier Score, and lower ECE across the majority of the examined datasets, suggesting better calibrated results.
Edge-Aware and Relation Mining Network for RGBT Tracking
Authors: Xiao Xianbing , chen zhen , Meng Fanqin , Liu Jun , Xiong Xingzhong 
Published: October 2023

Abstract

Recently, the fusion tracking of RGB and thermal infrared images (RGBT) hasreceived most research interest. However, existing RGBT tracking algorithmsbased on deep learning mainly perform modality weight allocation to highlighttarget information, but background clutter may limit the improvement of trackingperformance. To solve this problem, we propose a new edge-aware and rela-tion mining network for RGBT tracking. This network takes advantage of thecomplementary advantages of edge information and deep information to makeup for the defect that deep information is susceptible to background interfer-ence. Meanwhile, a cross-modality relation mining module is designed to explorethe node correlation score to highlight target state information and reduce thebackground noise. Also, Transformer is utilized for bidirectional modulation tocapture cross-modality dependencies. Finally, multi-branch supervised learningand decision-level fusion are performed to improve tracking accuracy and suc-cess rate further. Extensive experiments on three publicly available datasets showthat the proposed algorithm performs excellently.
Replenishment of iron deficiency in women before the use of assisted reproductive technologies
Authors: Solovyeva A. V. , Ermolenko K. S. , Kulumbegova L. T. , Aleynikova E. Yu. , Chegus L. A. 
Published: October 2023

Introduction. Anemia is a major health problem worldwide, increasing the risks of low birth weight, preterm birth, perinatal mortality, stillbirth, postpartum hemorrhage, and maternal mortality. Replenishing iron deficiency at the stage of preconceptional preparation reduces the incidence of IDA and can reduce the incidence of pregnancy complications and improve perinatal outcomes.Aim. To evaluate the effectiveness of ferrous sulfate 80 mg and its potential to replenish iron deficiency in women with infertility before using assisted reproductive technologies (ART).Materials and methods. A prospective clinical longitudinal study was conducted from May to September 2023 at LLC Health Clinic, Moscow. The study included women (n = 34) at the stage of pregnancy planning by IVF with a diagnosed iron deficiency. Iron deficiency was established in accordance with the following criteria: decrease in serum ferritin level less than 30 μg/l, serum iron level less than 15 μmol/l.Results. After 12 weeks of taking ferrous sulfate, the average hemoglobin level increased from 112.3 ± 6.5 (101–119) g/l to 118.5 ± 6.5 (110–133) g/l (W-Wilcoxon 9300 test, p = 0.003), mean serum iron level from 15.4 ± 4.0 (5.7–22.5) μmol/l to 34.8 ± 7.1 (20.1–46.7) μmol/l (Wilcoxon W test 602.0, p < 0.001), mean ferritin level from 8.2 ± 3.1 (4.5–17.0) μg/ml to 37.6 ± 6.7 (30.6–52.6) μg/ml (Wilcoxon W test 595.0, p < 0.001).Conclusions. Timely examination of patients for iron deficiency and iron deficiency anemia at the stage of preconceptional preparation makes it possible to prescribe an effective correction, thereby helping to reduce the risks of complications of pregnancy and childbirth.
Study of the relationship between the siltation rate of Algerian dams and the runoff coefficient
Authors: Hadidi Abdelkader , Boualem Remini , Mohamed Habi , Saba Djamel 
Published: October 2023

Abstract

The accumulation of silt in water dams is a natural phenomenon and an acute problem, and every year, about 4,000 tons of soil per square kilometer goes to the dams or the sea through valleys, causing the dams to become saturated with silt or sanding the ports, coinciding with the shrinking of agricultural areas. In general, the siltation of water dams results from a complex process characterized by four successive stages, namely, runoff, erosion, transport, and sedimentation. In this field, many specialists have presented their approach to the problem of sediment transport and erosion, which can affect silt in the area, and therefore, it is important to know the rate of silt before completing the dams. In addition, many factors affect the siltation of dams, such as runoff. In this regard, this article deals with a meta-study of the relationship between the silt rate in Algerian dams and the surface runoff factor. This is to obtain a database that can be used by research professors as well as experts supervising the construction of dams in Algeria.
Murine Malaria Model: Ketoconazole Prevented Malaria while Proguanil and Sulfadoxine/Pyrimethamine Protected against Malaria-associated
Anemia and Kidney Damage
Authors: Olumide Faniran Samuel , Ademola Ayankunle Akeem , Olusola Ojurongbe , Catherine Adekunle Olutoyin 
Published: October 2023

Background::
The concern about the global spread of resistant malaria has made the researchers
not focus only on the treatment of established infections but relatively more on the prevention
of the disease.

Objective::
This study evaluates the chemopreventive activity of ketoconazole in a murine malarial
model.

Method::
Five out of seven groups of mice were pretreated for five days with proguanil (PRG), sulfadoxine/
pyrimethamine (SP), 10, 20, and 40 mg/kg body weight (b.w) of ketoconazole (KET10,
KET20, and KET40), before being infected (on the sixth day) with Plasmodium berghei. Two other
groups were infected-not-treated (INT) and not-infected-nor-treated (NINT). At 72 hours postinfection,
five out of ten mice in each group were sacrificed to assess parasitemia, chemoprevention,
hematologic, hepatic, and renal parameters. The remaining mice were observed for 28 days to determine
their mean survival day post-infection (SDPI).

Results::
All ketoconazole groups, except KET10, demonstrated 100% chemoprevention and significantly
higher mean SDPI (p<0.001) in relation to INT (negative control). There was no significant
difference in the mean SDPI observed in KET20 in relation to PRG or NINT (healthy control). A
dose-related increase (p<0.01) in the mean plasma urea was observed when ketoconazole groups
were compared to one another: KET10 versus KET20 (p<0.01) and KET20 versus KET40 (p<0.01).
Sulfadoxine/pyrimethamine demonstrated significantly reduced mean plasma urea (p<0.001) and
creatinine (p<0.05) in relation to INT and NINT, respectively. While PRG demonstrated significantly
higher mean red blood cell (RBC), hemoglobin (HGB), and hematocrit (HCT) in relation to INT.

Conclusion::
Ketoconazole possesses prophylactic antimalarial activity with associated dose-related
renal impairment. Sulfadoxine/pyrimethamine demonstrated renoprotective potentials, while PRG
prevented malaria-associated anemia.