bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transformers as a classifier for solar flare time series: a comparative study
Authors: Ferreira Juliana Sabino , Gradvohl André Leon Sampaio , Silva Ana Estela Antunes da , Coelho Guilherme Palermo , Cinto Tiago 
Published: March 2024

Abstract
Solar flares are violent and sudden eruptions that occur in the solar atmosphere and release energy in the form of radiation. They can affect technological systems on Earth and in its orbit, causing financial losses and damage to human life. Therefore, it is necessary to predict the occurrence of such flares to mitigate their effects. Specialized instruments gather data for solar activity monitoring. Hence, we can create prediction models using machine learning from this data. From an analysis of the literature, we noticed the prevalence of some algorithms, such as Multi-layer Perceptrons (MLP), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), which presented good results, mainly considering the True Skill Statistic (TSS) metric. In parallel, in 2017, a new deep learning-based neural network architecture called Transformers emerged. Researchers initially created it for natural language processing. However, Transformers were successfully employed in other domains, such as time series forecasting. Solar activity data is considered a time series due to its continuous capture over time. Consequently, we can employ Transformers to develop a solar flare forecast model. Considering a significant lack of work using Transformers for solar flare forecasting, we ran experiments to test the Transformers' viability and performance in solar flare forecast models. We created models using other algorithms (MLP, SVM, LSTM, Transformers) to investigate the Transformers' performance and compared them using accuracy, TSS, and Area Under the ROC Curve (AUC) metrics. We observed that the Transformers had superior performance compared to the other models. For instance, the Transformers' TSS metric average was 0.9, contrasting the other models' TSS=0.4. The difference was slightly smaller in AUC, where Transformers reached 0.9, and the others reached no more than 0.7. Therefore, we can use the Transformers to classify solar flare data and obtain superior results compared to other models. We also conducted experiments using different forms of data balancing, including unbalanced data, balanced with undersampling, oversampling, and SMOTE techniques. The MLP, SVM, and LSTM models showed significant improvements in balance, where the average TSS increased from 0.1 to 0.4. On the other hand, Transformers were not sensitive to data balancing, presenting the most stable TSS in all cases.
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors
Authors: Lindahl Karl-Olof , Lindenberg Björn 
Published: March 2024

Abstract

We consider Thompson sampling for linear bandit problems with finitely many independent arms, where rewards are sampled from normal distributions that are linearly dependent on unknown parameter vectors and with unknown variance. Specifically, with a Bayesian formulation we consider multivariate normal-gamma priors to represent environment uncertainty for all involved parameters. We show that our chosen sampling prior is a conjugate prior to the reward model and derive a Bayesian regret bound for Thompson sampling under the condition that the 5/2-moment of the variance distribution exist.
WITHDRAWN: Developing an artificial intelligence framework to combat tax noncompliance in Botswana
Authors: Edmore Munjeyi 
Published: March 2024

Abstract
Orientation: Artificial intelligence (AI) is implemented in tax administration to reduce tax noncompliance and improve tax ratio. Despite these highly publicised benefits, BURS continue to use traditional measures to enforce tax compliance behaviour in Botswana than current AI technologies within its operational divisions.
Research purpose: The purpose of the study is to develop an AI framework to combat tax noncompliance in Botswana.
Motivation for the study: A lacuna was discovered, highlighting the need for a framework that can effectively eradicate tax noncompliance in Botswana. The aim is that BURS fully embrace AI, thereby improving revenue [collection] yields and tax compliance in Botswana.
Research design, approach and method: In order to accomplish the objectives of this study, a qualitative-exploratory, descriptive and contextual research paradigm grounded within phenomenological examination was employed. Sixteen (N=16) interviews were carried out to collect qualitative data. Semantic thematic data analysis was used to analysis data and for theme identification.
Main findings: The findings reveal that most transactions in Botswana are cash based which fuels tax noncompliance. High level of cash transactions was witnessed in second hand car dealership, Indian and Chinese business owners. The participants further highlight that most business transactions in Botswana are cashless that should be a part of the AI framework to enhance tax compliance.
Practical/ managerial implications: The study demonstrates the potential impact of AI in solving tax noncompliance. The framework presented in this study provides conditions and guideline for BURS to implement permanent solutions to achieve total compliance and sustainable economic growth.
Contribution/value-add: A framework is developed and recommended for use by BURS that is explicitly aimed at providing practical solutions to tax noncompliance in Botswana.
The characteristics and clinical analysis of Lorenz plot of neonatal atrial tachycardia
Authors: Yu Tingting , Wei Xin , Wen Jie , Zheng Fei , Li Hualian 
Published: March 2024

Abstract

The Lorenz plot analysis improves the accuracy of arrhythmia diagnosis and is an appealing diagnostic tool for patients with arrhythmias. In this study, we explored the utility of the Lorenz plot in the classification of various types of neonatal atrial tachycardia and evaluated the treatment effects and prognosis. Ninety-six newborns (mean age of 9.43 ± 11.7 days) with atrial tachycardia had their dynamic electrocardiograms and clinical data collected between January 2018 and June 2023. They were divided into multifocal (30 cases), parasystole (19 cases), and atrial premature beat-induced atrial tachycardia (47 cases) groups based on the morphological characteristics of the Lorenz plot. The clinical features of the patients in each group were also analyzed and it was proved that the prevalence of cardiac structural abnormalities, viral infections, myocarditis, and heart failure significantly increased in the multifocal atrial tachycardia group. Additionally, the treatment, length of hospitalization and outcome varied notably among the different types of atrial tachycardia. The multifocal atrial tachycardia group's hospitalization days were the longest (19.6 ± 5.4 days). All patients were followed up for a median period of 8.2 months (range 2months–4years) after discharge, and all instances were controlled except for four lost to follow-up. The analysis of distinct Lorenz plot patterns allowed for the identification and classification of neonatal atrial tachycardia, which could improve the diagnostic efficiency of neonatal atrial tachycardia and assist clinical physicians in making better judgments based on the type of atrial tachycardia.
Discovery of Novel Diagnostic Biomarkers for Common Pathogenic Nocardia through Pan- genome and Comparative Genome Analysis, with Preliminary Validation
Authors: Wang Chaohong , Jia Xinmiao , Wei Ming , Yan Jun , Sun Qing , Long Sibo , Zheng Maike , Shi Yiheng , Jiang Guanglu , Zhao Yan , Huang Hairong , Gu Li , Yang Xinting , Wang Guirong 
Published: March 2024

Abstract
Purpose This study aims to utilize pan-genome and comparative genome analysis techniques to reveal diagnostic biomarkers of considerable importance for common pathogenic Nocardia, thereby facilitating accurate identification of clinical Nocardia infections.
Methods Complete or assembled genome sequences of common pathogenic Nocardia and closely related species were obtained from NCBI (https://www.ncbi.nlm.nih.gov/) as discovery and validation sets, respectively. Genome annotation was performed using Prokka software, and pan-genomic analysis and extraction of Nocardia core genes were performed using BPGA software. Comparative genome analysis of these core genes with the validation set gene sequences was then performed using BLAT, with a threshold of 30% amino acid coverage and identity to distinguish specific core genes. Finally, candidate gene-specific primers were designed using Snapgene software and DNA samples were obtained from clinical Nocardia strains and closely related species for validation.
Results The analysis identified 18 core genes specific to Nocardia spp., 4 core genes specific to N. farcinica, and 46 core genes specific to N. cyriacigeorgica. After rigorous clinical validation, 1 gene (F6W96_34950) from Nocardia spp. and 5 genes (NCTC10797_02287, NCTC10797_01760, NCTC10797_05842, NOCYR_2299 and C5B73_13220) from N. cyriacigeorgica all showed relatively high specificity, suggesting their potential as promising biomarkers for the diagnosis of Nocardia infections.
Conclusion This pioneering research reveals diagnostic biomarkers of considerable significance, with the potential to substantially enhance the precise diagnosis of common pathogenic Nocardia infections, thereby laying the groundwork for innovative diagnostic methodologies in subsequent studies.
One-Step Preparation of Indole-Based Porous Magnetic Composite via Cation-π Interaction-Driven and -Induced Strategy
Authors: Mao Yangqing , Zhu Haoran , Zhang Boyuan , Wu Ziqi , Zhao Bing , Yuan Rui , Zhou Mingru , Zheng Min , Chang Guanjun , Xu Yewei 
Published: March 2024

Abstract
A strategy driven and induced by cation-π interaction was employed to obtain magnetic porous composite material with a uniform dispersion of ferroferric oxide (Fe3O4). Ferroferric oxide/indole-based porous composite (Fe3O4/PIN) was prepared in a one-step process. Cation-π interaction dispersed iron ions (Fe3+) next to the indole groups in the indole-based porous polymer (PIN), and subsequently, after the in-situ reaction, Fe3O4 was uniformly distributed around the indole groups within PIN. At 298 K, Fe3O4/PIN displayed remarkable adsorption effectiveness, attaining 290.697 mg/g, and within 1 hour, had achieved 74% of the highest capacity. In addition, due to the excellent magnetic properties of Fe3O4/PIN, it exhibited rapid recovery characteristics for TNT. Despite five cycles of adsorption-desorption, with acetone as the eluent, the adsorption capacity of Fe3O4/PIN for TNT still remains at 90% of its highest potential. Hence, Fe3O4/PIN is anticipated to serve as an effective adsorbent for TNT. The uniform distribution of Fe3O4 in porous materials through the driving and inducing effects of cation-π is an unprecedented innovation, providing a new perspective and approach for the preparation and application of similar composite materials.
Carbonyl cyanide 3-chlorophenylhydrazone promotes of mitophagy in gastric cancer cells MKN1 and MKN45 via PINK1/parkin pathway
Authors: Yao Jun , LV Sun-yuan , Xia Jia-jie , Xu Chen-nan , Ying Xiao , Guo Jing-jing 
Published: March 2024

Abstract
BACKGROUND
Carbonyl cyanide 3-chlorophenylhydrazone (CCCP), as a common mitochondrial uncoupling agent, inducer of mitochondrial autophagy, has been used in nervous system and digestive system diseases. Mitochondrial autophagy is rarely reported in gastric cancer (GC). In this study, we used CCCP to treat MKN1 and MKN45 gastric cancer cells.
METHODS
We studied its effects on cell viability, migration and invasion, mitochondrial membrane potential level, apoptosis, cell cycle and mitophagy of GC were determined respectively by CCK8, Wound-Healing, Transwell, JC-1 dye, Western blot analysis and Flow cytometry assays. CCCP drugs and saline were injected intravenously, respectively, to clarify the effects of the drugs on HGC27 xenograft tumours in nude mice.
RESULTS
CCCP significantly inhibited the activity of MKN1 and MKN45 cells in a dose-dependent manner, and inhibited cell proliferation, migration and invasion. CCCP induced MKN1 and MKN45 cell apoptosis and arrested cell cycle in G1 phase. Furthermore, CCCP inhibited the expression of CyclinD1, CDK4 and CDK6, thus inhibited the cell cycle, and promoted the decline of mitochondrial membrane potential, which induced cells to enter PINK1 / parkin pathway mitophagy. Intravenous CCCP drugs significantly inhibited nude mouse HGC27 xenograft tumour Growth.
CONCLUSIONS
In general, CCCP, can regulate gastric cancer cells by inducing mitophagy, and may be a potential therapy for gastric cancer.
Product quality prediction based on integrated feature selection
Authors: Liu Yong , Xu Xiao-jie 
Published: March 2024

Abstract
Product quality prediction models based on big data and artificial intelligence technologies can quickly and accurately analyze massive amounts of data to ensure that products meet higher quality standards. However, in the process of data fitting, how to screen data sets with linear and nonlinear characteristics to improve the accuracy of the prediction model is an urgent problem to be solved. Therefore, it is necessary to carry out research on data feature screening in product quality prediction models. We construct a prediction model based on the Stacking structure and adopted an integrated learning approach for feature screening considering the diversity of data. In this paper, firstly, we briefly summarize the process of the product processing quality prediction problem; secondly, we introduce the data acquisition and preprocessing in this chapter; then, we focus on the improved support vector regression and Radio Free Ethernet (SVR-RFE) integrated feature selection algorithm for feature screening and construct a model including Convolutional Neural Network, Bi-directional Long Short-Term Memory, and Multi Head Self Attention (CNN-BiLSTM-SE attention) under the integrated feature selection; finally, we put the constructed model into a real case for validation to confirm the Finally, the constructed model was validated in real cases to confirm the feasibility and effectiveness of the constructed model.
E-learning and Emergency conditions: Issues, opportunities and Challenges for Botswana Higher Education Institutions
Authors: Edmore Munjeyi , Ruramayi Tadu 
Published: March 2024

Abstract
Purpose- E-learning and teaching became more common as a method of imparting knowledge to a wide range of learners following the outbreak of the global pandemic SARS-CoV-2. However, e-learning and teaching have received considerable attention in recent years. While the extant scholarship literature has focused on the efficacy of e-learning and teaching in countries in the Global North, there is still no comprehensive knowledge or assessment to gain insights into the preparedness of a HEIs to handle emergencies and adapt to future conditions effectively. As a result, this study assesses HEIs' preparedness to handle emergencies like COVID-19, opportunities and challenges of adopting e-learning and teaching in HEIs from the perspective of the heads of departments and lecturers of selected institutions in Botswana.
Design/methodology/approach- A qualitative research approach and in-depth interviews were used to gain insights from 5 HODs and 5 lecturers. The qualitative data collected were analysed via thematic analysis to determine the research findings (outcomes) of the study.
Findings- HoDs and lecturers appreciate the merits of using e-learning facilities and perceive the method to be more effective and economical for lesson delivery and student learning. In contrast, common challenges encountered by both HEIs and students include a lack of technological resources, poor internet coverage, inadequate staff training and a lack of management support.
Originality/value- This article provides novel insights into the efficacy and preparedness of HIEs in the use of e-learning and teaching during future serious pandemics. This article has both theoretical, research and policy implications for the adoption of e-learning agendas in HEIs.
Extraction and Quantification of a Carcinogenic and Cytotoxic compound: 5-hydroxymethyl-2-furfural (HMF) in honey using HPLC
Authors: Singh Pranjali , Sree C. 
Published: March 2024

Abstract
As there is a rise in adulteration of honey which leads to the formation of a carcinogenic and cytotoxic compound, 5-hydroxymethyl-2-furfural (HMF) in honey, it has become very essential to test honey for the presence of this compound. Honey is a natural sweet product that is produced by honey bees (Apis mellifera). This carcinogenic compound, 5-hydroxymethyl-2-furfural is developed on dehydration of reducing sugars present in honey in acidic conditions. The concentration of this toxic compound in fresh honey is mostly absent or is present in very low amounts, but its concentration increases during processing, upon storage for longer periods, and upon heating/storing at higher temperatures. This compound causes toxic fatal effects (mutagenic, chromosomal aberrations, genotoxic, organotoxic, carcinogenic, cytotoxicity towards mucous membranes, the skin, the eyes, and the upper respiratory tract). An accurate and specific liquid chromatographic method was developed due to the urgent requirement for an analytical method to estimate the quantity of 5-hydroxymethylfurfural in honey. A mobile phase with a combination of Acetonitrile and Water with 0.2% Formic Acid was used with a flow rate of 0.6 ml/minute and the separation was done on Phenomenex Non-Polar C18 Column with dimensions of 150 mm X 4.6 mm X 3 µm at a wavelength of 282 nm. The Calibration curve with R2 value of 0.999 was obtained. These results were repeated two times in the laboratory, hence Repeatability Precision was performed to validate the results. The amount of HMF in honey in the samples were found to be within the limit of 40 mg/kg as per FSSAI. The amount of HMF present in Sample 1 and Sample 2 was found to be 3.80 mg/kg and 6.50 mg/kg, respectively. This new HPLC method developed would be economical, reproducible and repeatable.
Delving into the Motivations behind Tech Adoption in Entrepreneurship
Authors: Park Yuyeong 
Published: March 2024

Abstract
This qualitative study delves into the intricate motivations driving technology adoption within entrepreneurial endeavors. By employing in-depth interviews and thematic analysis, it uncovers the diverse array of factors shaping entrepreneurs' decisions to integrate technology into their ventures. Economic incentives, strategic imperatives, and personal aspirations emerge as key drivers behind technology adoption. Economic motivations encompass the desire to enhance operational efficiency, reduce costs, and gain a competitive edge. Strategic imperatives drive entrepreneurs to leverage technology for long-term growth, scalability, and market penetration. Personal motivations, including a passion for innovation, autonomy, and societal impact, also play a significant role in shaping technology adoption decisions. The study reveals a dynamic and iterative technology adoption process characterized by experimentation, learning, and adaptation. Despite challenges such as technical complexity, resource constraints, and resistance to change, entrepreneurs report positive outcomes from technology adoption, including improved efficiency, increased productivity, and enhanced competitiveness. Moreover, technology adoption has broader implications for innovation, job creation, and societal impact. These findings contribute to a deeper understanding of the dynamics between technology adoption and entrepreneurship. The study offers actionable insights for stakeholders, including policymakers, practitioners, and researchers, seeking to support and promote technology-enabled entrepreneurship. By aligning technological investments with entrepreneurial goals and values, fostering a supportive ecosystem for technology adoption, and promoting digital literacy and access to resources, stakeholders can harness the transformative potential of technology to drive innovation, growth, and positive change in entrepreneurship and society.
Assessment of many WQI models and development of new WQI model
Authors: Aldoury Muzher Mahdi Ibrahem , Hammood Mohammed Taha 
Published: March 2024

ABSTRACT

The water quality index (WQI) is widely used to assess water quality. In all WQI models, selected parameter data are transformed into many steps to a single number. Since the first model was developed in 1965, a lot of WQI models have been developed. However, all models suffer from many problems such as they are introduced based on site-specific standards for a particular region and give eclipsing, dichotomous, ambiguity, rigidity, and uncertainty problems. In the present work, an effort is made to test many of the applicable WQI models using many logical conditions. The results showed that no one of the tested models satisfies these conditions. The aim of the present work is to develop a WQI model that can be used universally (regardless of the standard used) and covers different uses of water (drinking, recreational, irrigation, or industrial). Moreover, it can overcome the previously mentioned problem. The suggested new WQI model developed in this work succeeded in achieving these two goals.
Prediction of Deleterious Single Amino Acid Polymorphisms with a
Consensus Holdout Sampler
Authors: Álvarez-Machancoses Óscar , Faraggi Eshel , deAndrés-Galiana Enrique J. , Fernández-Martínez Juan L. , Kloczkowski Andrzej 
Published: March 2024

Background:
Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide
Variants (nsSNVs) are the most common genetic variations. They result from missense
mutations where a single base pair substitution changes the genetic code in such a way that the triplet
of bases (codon) at a given position is coding a different amino acid. Since genetic mutations
sometimes cause genetic diseases, it is important to comprehend and foresee which variations are
harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a
classification problem.

Methods:
Computational methods using machine intelligence are gradually replacing repetitive and
exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities
of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently,
strong and more exact approaches are needed to address these problems. In the present
work paper, we show a consensus classifier built on the holdout sampler, which appears strong and
precise and outflanks all other popular methods.

Results:
We produced 100 holdouts to test the structures and diverse classification variables of diverse
classifiers during the training phase. The finest performing holdouts were chosen to develop a
consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined
which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs.

Conclusion:
Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent
results, highly accurate with low standard deviation. The advantage of our method emerges
from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.