bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Chitosan Addition on Porosity of Rice Husk Calcium Silicate Used for Free Fatty Acid Adsorbent from Palm Oil
Authors: Lumban Raja Saur , Butarbutar Olwin 
Published: July 2024

Studies have been done on developing calcium silicate pores derived from rice husk with and without including chitosan as a template. The chitosan solution was supplemented with sodium silicate derived from rice husk, and then CaCl2 was added. The calcium silicate was calcinated at 900ºC for 4 h. The calcium silicate obtained was characterized using X-ray diffraction (XRD), fourier transform infrared (FT-IR), and Brunauer-Emmett-Teller (BET) analysis. The XRD analysis showed the presence of diffraction peaks corresponding to Wollastonite (CaSiO3) crystals, Wollastonite 2m (CaSiO3), Alite (Ca3SiO5), and larnite (Ca2SiO4). Next, the FT-IR spectrum exhibits Si-O-Si, Si-O-Ca, and Si-OH functional groups, suggesting the presence of calcium silicate material. The BET study of calcium silicate using chitosan templates revealed a pore size of 3.88 nm, a pore volume of 0.1577 cc/g, and a surface area of 81.1519 m2/g. The BET analysis results obtained differed from those of the BET analysis of calcium silicate conducted without using chitosan templates, which were 2.06 nm, 0.069 cc/g, and 13.531 m2/g, respectively. The developed mesoporous calcium silicate was employed as an adsorbent to decrease the concentrations of free fatty acids in palm oil. The acid-base titration method is used to evaluate quantities of free fatty acids. The results obtained from the computation of free fatty acid levels indicate a decline in the concentration of free fatty acids in palm oil. The concentration of free fatty acids derived from palm oil was 1.23%. Therefore, mesoporous calcium silicate-free fatty acids at a concentration of 1.06% resulted in an 86.1% reduction.
DETERIMINAN HARGA SAHAM INDEKS INFOBANK15 PADA MASA PANDEMI DITINJAU DARI FAKTOR EKONOMI MAKRO
Authors: Candy Candy , Calystania Verren 
Published: July 2024

Tujuan studi ini yaitu menemukan dampak faktor ekonomi makro, yakni inflasi, suku bunga, nilai tukar, dan jumlah uang beredar terhadap harga saham indeks InfoBank15 periode Januari 2020-Februari 2023. Data sekunder dalam penelitian ini dihimpun melalui lembaga terpercaya seperti Badan Pusat Statistik, Bank Indonesia, dan Yahoo! Finance. Data kuantitatif dianalisis menggunakan metode linear berganda dan pengujian hipotesis melalui uji t dan uji F. Hasil penelitian mengungkapkan bahwa harga saham indeks InfoBank15 dipengaruhi inflasi dan jumlah uang beredar secara positif, sementara nilai tukar berkorelasi negatif dengan harga saham indeks InfoBank15. Penelitian ini juga menemukan bahwa pergerakan suku bunga Indonesia selama periode penelitian tidak mampu mempengaruhi harga saham indeks InfoBank15. Hasil penelitian ini merupakan cerminan pengaruh faktor makro terhadap harga saham perbankan di masa pandemi, sehingga hasilnya mungkin berbeda di masa depan karena pandemi yang sudah mulai hilang. Maka dari itu, studi berikutnya dapat memperpanjang periode penelitian dan menambahkan variabel ekonomi makro lainnya seperti produk domestik bruto dan harga minyak dunia, untuk melihat pengaruhnya terhadap harga saham perbankan setelah pandemi berlalu.
Deteksi Berita Hoax dengan Perbandingan Website Menggunakan Pendekatan Deep Learning Algoritma BERT
Authors: Ripa'i Asep , Santoso Firman , Lazim Farihin 
Published: July 2024

Berita hoax merupakan informasi yang salah dan menyesatkan yang dapat menyebabkan provokasi dan kebencian bagi pembaca. Dengan adanya akses internet yang mudah membuat persebaran berita hoax semakin masif. Oleh karena itu, perlu ada metode yang dapat melakukan deteksi berita hoax. Penelitian menggunakan metode deep learning dengan mengintegrasikan text mining untuk mencari informasi dan pola berita yang berhubugan dengan hoax. Dengan menggunakan dataset dari situs kaggle berjumlah sekitar 2700-an kemudian dilakukan text preprocessing agar data lebih terstruktur untuk diolah lanjut. Kemudian membuat feature engineering dari BERT agar data dapat diproses oleh machine learning dengan tiga metode klasifikasi yaitu BERT, SVM dan random forest kemudian dilakukan pengujian dan evaluasi. Pada penelitian ini model yang menghasilkan performa yang paling tinggi yaitu BERT dengan (akurasi = 0.99, ROC-AUC = 0.99) dibanding model machine learning tradisional.
Project-based methodology for English language learning in High School
Authors: Monteverde-Del Rio Maria , Acurio-Recalde Mónica 
Published: July 2024

High School students in Ecuador's Fiscal Educational Units have not developed skills in the English foreign language, nor can they recognize environmental problems and contribute with an effective and efficient solutions to them. The application of active communication methodologies such as project-based learning can help solve the problem together with the use of technology, thus contributing to the practice of oral skills to present innovative proposals for a specific project that can be sustainable over time. It is evident the lack of active methodological practices in the country's fiscal institutions, added to the little control in the educational establishments of the application of this type of tools that promote teamwork, hinders the improvement of the quality of education, especially in the subject of English. It is necessary to follow up on the teaching performance and the application of this methodology to generate new challenges in the educational area, especially in high school students, and complete their exit profile successfully in the English language. An article template and instructions on the editorial process and the text, figure, and references format are provided.
The approach of this study is qualitative with a descriptive scope of the content for which the previous bibliography on the proposed topic is used, making a review of the literature on the project-based methodology (MBP) and its influence on the development of oral communication in the English language in the students.
Desarrollo de aprendizaje colaborativo para estudiantes con necesidades educativas especiales: El caso de la herramienta Canva
Authors: Mena-Muñoz Edison , Porras-Villacis Heidi , García-Cobas Rudy , Nivela-Cornejo María 
Published: July 2024

La colaboración en la educación es una metodología óptima para ofrecer una educación más inclusiva y enriquecedora, al basarse en el trabajo en equipo, fomenta las habilidades sociales, la confianza y la autoestima. La investigación se orienta al análisis del aprendizaje colaborativo para estudiantes con necesidades educativas especiales, analiza especialmente la herramienta Canva. Para efecto se empleó una metodología regida bajo en el marco sociocrítico, consistió en una investigación de campo, de carácter descriptivo y enfoque cuali cuantitativo. La técnica empleada fue la encuesta bajo tres dimensiones de estudio, además se empleó la prueba de chi cuadrado y la estadística de Cronbach. La población corresponde a 110 docentes del cantón Latacunga. Los principales resultados destacan la importancia percibida del aprendizaje colaborativo y una aceptación generalizada y positiva sobre su uso de Canva en el contexto educativo. la prueba de chi cuadrado y la estadística de Cronbach respalda la validez y fiabilidad de los ítems para medir las dimensiones evaluadas en el estudio. En conclusión, los hallazgos subrayan la necesidad de proporcionar mayores recursos tecnológicos y apoyo para los docentes en la implementación efectiva de estrategias colaborativas adaptativas, lo que podría mejorar aún más la calidad del proceso educativo inclusivo para estos estudiantes.
Análisis de gustos y preferencias del consumidor de alimentos y bebidas de cafeterías en la ciudad de Ambato
Authors: Zambrano-Real Rebeca , Frias-Pérez Edisson , López-Flores Renato 
Published: July 2024

En el Ecuador el sector de alimentos y bebidas es una actividad de alta demanda y que impulsa el avance económico del país que según datos del SRI (servicios de rentas internas) (SRI, 2022) en el año 2021 se registró una recaudación por actividad de $115.104.005 de dólares, 13% más en el año 2020; sin embargo, en el 2019 se registró una recaudación total del sector de $134.896.344 de dólares, un 17 % más que en el año 2022, indicando que el sector de alimentación no ha recuperado su estabilidad económica en su totalidad, anterior a los acontecimientos económicos y sociales que reflejaron un cambio drástico en todos los sectores catalogados como no estratégicos.
Si bien es cierto la lenta recuperación global de este sector impacta en relación a sus ingresos, de manera paulatina va recuperando su estabilidad, lo que implica que los actores de este sector deberán buscar estrategias y acciones que les generen ventajas competitivas, y que permita a uno o más actores sobresalir, utilizando la investigación de los factores de elección y gustos y preferencias de un consumidor tipo para que las acciones sean mas eficientes, en un entorno cada vez más competitivo y en un sector que lentamente se recupera.
El desarrollo de la investigación con los criterios y variables que se asocian al comportamiento del consumidor es amplia, y sus fundamentos están asociados a investigaciones de campo que permiten estandarizar y regular factores intervinientes al momento de la toma de decisiones.
La presente investigación focalizada en el análisis de gustos y preferencias del consumidor de alimentos y bebidas específicamente direccionado a establecimientos de cafeterías, proporciona una visión general de algunos criterios considerados por el investigador y evaluados a través de un instrumento cuantificable y medible, de cómo es el comportamiento del consumidor tipo respecto a los factores intervinientes al momento de la toma de decisión y sobre sus hábitos de consumo.
Se fundamenta el análisis en la conceptualización de las variables y los hallazgos de manera interpretativa, relacionando la investigación de campo con dicha información y conceptos de mercadeo; ampliando la visión para la tomar de definiciones sobre el comportamiento del consumidor en la frecuencia de compra, motivantes de compra, preferencias de productos, el entorno competitivo frente el cual el sector tendrá que considerar su participación, y definir gracias a la investigación un perfil de consumidor para establecimientos de cafeterías en la ciudad de Ambato.
Case Report: Vasospastic angina presenting as phantom odor perception
Authors: Zubair Haroon , Suma Valentin , Masood Faisal , Jan M. Fuad , Bajwa Tanvir , Haddadian Babak 
Published: July 2024

BackgroundVasospastic angina usually presents with intermittent episodes of chest pain. It can rarely be associated with the perception of phantom odors.Case summaryA 69-year-old woman presented for evaluation of intermittent shortness of breath and chest pain. She reported that she often experienced an abnormal smell sensation just prior to the event. The patient had abnormal smell sensation and shortness of breath at the initiation of exercise stress echocardiography with transient electrocardiographic changes and new regional wall motion abnormalities. Subsequent invasive coronary angiography showed no obstructive epicardial coronary artery disease. The patient was started on calcium channel blocker therapy with resolution of symptoms.ConclusionPhantom odor perception has been rarely reported as an angina-equivalent symptom. Clinicians should have a high index of suspicion in patients presenting with atypical anginal symptoms.
Patient-specific 3D in vitro modeling and fluid dynamic analysis of primary pulmonary vein stenosis
Authors: Devlin Christian , Tomov Martin L. , Chen Huang , Nama Sindhu , Ali Siraj , Neelakantan Sunder , Avazmohammadi Reza , Dasi Lakshmi Prasad , Bauser-Heaton Holly D. , Serpooshan Vahid 
Published: July 2024

IntroductionPrimary pulmonary vein stenosis (PVS) is a rare congenital heart disease that proves to be a clinical challenge due to the rapidly progressive disease course and high rates of treatment complications. PVS intervention is frequently faced with in-stent restenosis and persistent disease progression despite initial venous recanalization with balloon angioplasty or stenting. Alterations in wall shear stress (WSS) have been previously associated with neointimal hyperplasia and venous stenosis underlying PVS progression. Thus, the development of patient-specific three-dimensional (3D) in vitro models is needed to further investigate the biomechanical outcomes of endovascular and surgical interventions.MethodsIn this study, deidentified computed tomography images from three patients were segmented to generate perfusable phantom models of pulmonary veins before and after catheterization. These 3D reconstructions were 3D printed using a clear resin ink and used in a benchtop experimental setup. Computational fluid dynamic (CFD) analysis was performed on models in silico utilizing Doppler echocardiography data to represent the in vivo flow conditions at the inlets. Particle image velocimetry was conducted using the benchtop perfusion setup to analyze WSS and velocity profiles and the results were compared with those predicted by the CFD model.ResultsOur findings indicated areas of undesirable alterations in WSS before and after catheterization, in comparison with the published baseline levels in the healthy in vivo tissues that may lead to regional disease progression.DiscussionThe established patient-specific 3D in vitro models and the developed in vitro–in silico platform demonstrate great promise to refine interventional approaches and mitigate complications in treating patients with primary PVS.
Comparative efficacy and safety of SGLT2is and ns-MRAs in patients with diabetic kidney disease: a systematic review and network meta-analysis
Authors: Yang Si-Qi , Zhao Xi , Zhang Jing , Liu Huan , Wang Yu-Han , Wang Yao-Guang 
Published: July 2024

ObjectivesTo evaluate the efficacy and safety of non-steroid mineralocorticoid receptor antagonists (ns-MRAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) in patients with diabetic kidney disease (DKD).MethodsSystematic literature searches were performed using PubMed, Embase and Web of Science encompassing inception until January 20, 2024. Randomized control trials (RCTs) comparing ns-MRAs and SGLT2is in DKD were selected. The efficacy outcomes of interest included kidney-specific composite outcome, cardiovascular (CV)-specific composite outcome, end-stage kidney disease (ESKD), and overall mortality. We also investigated safety outcomes, including acute kidney injury (AKI) and hyperkalemia.ResultsA total of 10 randomized clinical trials with 35,786 patients applying various treatments were included. SGLT2is (SUCRA 99.84%) have potential superiority in kidney protection. SGLT2is (RR 1.41, 95%CI 1.26 to 1.57) and ns-MRAs (RR 1.17, 95% CI 1.08 to 1.27) were associated with significantly lower kidney-specific composite outcome than the placebo. Regarding the reduction in CV-specific composite outcome and ESKD, SGLT2is (SUCRA 91.61%; 91.38%) have potential superiority in playing cardiorenal protection. Concerning the CV-specific composite outcome (RR 1.27, 95%CI 1.09 to 1.43) and ESKD (RR 1.43, 95%CI 1.20 to 1.72), SGLT2is significantly reduced the risks compared to placebo. Regarding the reduction in overall mortality, SGLT2is (SUCRA 83.03%) have potential superiority in postponing mortality. Concerning the overall mortality, SGLT2is have comparable effects (RR 1.27, 95%CI 1.09 to 1.43) with placebo to reduce the risk of overall mortality compared to placebo. For AKI reduction, ns-MRAs (SUCRA 63.58%) have potential superiority. SGLT2is have comparable effects (RR 1.24, 95%CI 1.05 to 1.46) with placebo to reduce the risk of AKI. For hyperkalemia reduction, SGLT2is (SUCRA 93.12%) have potential superiority. SGLT2is have comparable effects (RR 1.24, 95%CI 1.05 to 1.46) with placebo to reduce the risk of AKI. Concerning hyperkalemia reduction, nsMRAs (RR 1.24 95%CI 0.39 to 3.72) and SGLT2is (RR 1.01 95%CI 0.40 to 3.02) did not show significant benefit compared to placebo.ConclusionConcerning the efficacy and safety outcomes, SGLT2is may be recommended as a treatment regimen for maximizing kidney and cardiovascular protection, with a minimal risk of hyperkalemia in DKD.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023458613.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Authors: Royer Sarah-Jeanne , Wolter Helen , Delorme Astrid E. , Lebreton Laurent , Poirion Olivier B. 
Published: July 2024

The characterization of beached and marine microplastic debris is critical to understanding how plastic litter accumulates across the world’s oceans and identifying hotspots that should be targeted for early cleanup efforts. Currently, the most common monitoring method to quantify microplastics at sea requires physical sampling using surface trawling and sifting for beached microplastics, which are then followed by manual counting and laboratory analysis. The need for manual counting is time-consuming, operator-dependent, and incurs high costs, thereby preventing scalable deployment of consistent marine plastic monitoring worldwide. Here, we describe a workflow combining a simple experimental setup with advanced image processing techniques to conduct both quantitative and qualitative assessments of microplastic (0.05 cm &lt; particle size &lt;0.5 cm). The image processing relies on deep learning models designed for image segmentation and classification. The results demonstrated comparable or superior performance in comparison to manual identification for microplastic particles with a 96% accuracy. Thus, the use of the model offers an efficient, more robust, standardized, highly replicable, and less labor-intensive alternative to particle counting. In addition to the relative simplicity of the network architecture used that made it easy to train, the model presents promising prospects for better-standardized reporting of plastic particles surveyed in the environment. We also made the models and datasets open-source and created a user-friendly web interface for directly annotating new images.
Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis
Authors: Wu Yinteng , Hu Haifeng , Wang Tao , Guo Wenliang , Zhao Shijian , Wei Ruqiong 
Published: July 2024

PurposeOsteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development.Materials and methodsWe acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes.ResultsIn OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA.ConclusionSIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.
Characterizing the skeletal muscle immune microenvironment for sarcopenia: insights from transcriptome analysis and histological validation
Authors: Shen Linhui , Zong Yuan , Zhao Jiawen , Yang Yi , Li Lei , Li Ning , Gao Yiming , Xie Xianfei , Bao Qiyuan , Jiang Liting , Hu Weiguo 
Published: July 2024

BackgroundSarcopenia is a condition characterized by the age-related loss of skeletal muscle mass and function. The pathogenesis of the disease is influenced by chronic low-grade inflammation. However, the specific changes in the immune landscape changes of sarcopenic muscle are not yet fully understood.MethodsTo gain insights into the immune cell composition and interactions, we combined single-nucleus RNA sequencing data, bulk RNA sequencing dataset, and comprehensive bioinformatic analyses on the skeletal muscle samples from young, aged, and sarcopenic individuals. Histological staining was then performed on skeletal muscles to validate the distribution of immune cells in clinical samples.ResultsWe analyzed the transcriptomes of 101,862 single nuclei, revealing a total of 10 major cell types and 6 subclusters of immune cell types within the human skeletal muscle tissues. Notable variations were identified in the immune microenvironment between young and aged skeletal muscle. Among the immune cells from skeletal muscle microenvironment, macrophages constituted the largest fraction. A specific marker gene LYVE1 for skeletal muscle resident macrophages was further identified. Cellular subclasses included four distinct groups of resident macrophages, which play different roles in physiological or non-physiological conditions. Utilizing bulk RNA sequencing data, we observed a significant enrichment of macrophage-rich inflammation in sarcopenia.ConclusionsOur findings demonstrate age-related changes in the composition and cross-talk of immune cells in human skeletal muscle microenvironment, which contribute to chronic inflammation in aged or sarcopenia muscle. Furthermore, macrophages emerge as a potential therapeutic target, thus advancing our understanding of the pathogenesis of sarcopenia.