bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Price to Book Value Still Influenced by The Main Factors on SDG's?
Authors: Nawawi Achmad , Disman  , Nugraha  , Waspada Ikaputera 
Published: August 2024

Objectives: This study aimed to obtain empirical evidence and analyze the influence of Return on Assets (ROA), Loan to Deposit Ratio (LDR), and Debt to Equity Ratio (DER) on Price to Book Value (PBV) in state-owned banks listed on the Indonesia Stock Exchange from 2011 to 2021.

Theoretical Framework: The research is grounded in financial performance and valuation theories, emphasizing how internal financial ratios impact the market valuation of firms, particularly in the banking sector.

Method: This research design is quantitative. The data used in this research comes from financial reports registered on the IDX.
The data that has been collected is then analyzed descriptively.

Results and Discussion: The analysis revealed that ROA significantly affects PBV, indicating that higher profitability leads to higher market valuation. Conversely, LDR and DER do not significantly impact PBV, suggesting that liquidity and capital structure are not primary determinants of market valuation in the context of these banks. The simultaneous effect of ROA, LDR, and DER on PBV was also observed.

Research Implications: These findings suggest that investors and financial analysts should focus more on profitability metrics such as ROA when evaluating the market value of state-owned banks. The insignificance of LDR and DER implies that other factors may play a more crucial role in influencing PBV in this sector.

Originality/Value: This study provides updated empirical evidence on the determinants of PBV in the context of Indonesian state-owned banks, contributing to the existing literature on bank valuation and offering practical insights for investors and policymakers.
Telework in Administrative Management within the Framework of the SDG, in a Peruvian Public Institution
Authors: Chacchi Denisse Vanessa García , Sotelo Cristian Gumercindo Medina , Pacherres Miguel Ángel Atoche , Ladera- Castañeda Marysela , Pacherres Patricia Atoche , Pacherres Jorge Hernán Atoche 
Published: August 2024

Objective: The objective of the research was to determine the influence of teleworking on administrative management in a Peruvian public institution. The same one that is associated with the Sustainable Development Goal (SDG) “Promote inclusive and sustainable economic growth, employment and decent work for all.”

Theoretical framework: Teleworking consists of tasks carried out without the physical presence of the worker, using computer and telecommunications means for control and supervision. This requires the employer to provide the necessary resources and manage technological dependence and ownership of the results.

Method: It was a basic study, with a non-experimental cross-sectional design. The sample was made up of 100 employees of a Peruvian public institution. The technique applied was the survey and a structured questionnaire was used for each variable.

Results and Discussion: A p value < 0.000 was found, which shows that there is a significant influence of teleworking on administrative management, showing an influence of 24%.

Research implications: Teleworking in the Peruvian administration improves implementation by reducing operating costs in the public sector, offering flexibility to collaborators, increasing productivity and allowing the hiring of talent without geographical limitations.

Originality/value: Teleworking in the administrative management of public institutions in Peru has been little researched, which makes this study an original contribution, since most previous research has focused on other contexts.
Human Talent Management in the Development of Work Skills in the Framework of The SDG in a Hospital in Peru
Authors: Pacherres Miguel Ángel Atoche , Ladera-Castañeda Marysela , Chacchi Denisse Vanessa García , Sotelo Cristian Gumercindo Medina , Pacherres Patricia Atoche , Pacherres Jorge Hernán Atoche 
Published: August 2024

Objective: To determine the influence of human talent management on the development of labor competencies of the administrative staff of a hospital in Peru. This is associated with the Sustainable Development Goal (SDG) “Decent work and economic growth”.

Theoretical framework: Globalization and digitalization generate constant changes that force institutions to orient human talent management towards the development and specialization of workers' skills, through training and feedback.

Method: The basic study had a quantitative approach and a non-experimental cross-sectional design. The sample consisted of 86 employees, the technique used was the survey and a questionnaire was used for each variable. The ordinal logistic regression test was used for the inferential analysis, considering a significance level < 0.05.

Results and Discussion: The results of the model fit test showed a p-value of 0.000 < 0.05. Likewise, the Nagelkerke coefficient yielded a value of 0.239, determining that there is a 23.9% influence of human talent management on the development of labor competencies.

Implications of the research: Adequate human talent management will allow the development of the competencies of the hospital's administrative employees.

Originality/value: Human talent management in the development of labor skills in Peruvian hospitals has been little explored, which makes this study an original contribution, given that most research has focused on other contexts.
Uso de adornos
Authors: Caram Aniceto Samara , Ferreira de Castro Larissa Gabrielle , Corrêa Gevisiez Eduardo , Castro Menezes João Vítor , Zamora Ferreira do Nascimento Liége , Ferreira Cury Rodrigo , Moraes Sampaio da Fonseca Walter Luiz 
Published: August 2024

Este artigo traz como objeto de estudo a orientação sobre a não utilização de adornos por acadêmicos de medicina no campo da prática oferecido pela instituição de ensino. A ideia inicial desse estudo partiu da aula de Aprendizagem Baseada em Projetos (PjBL), onde os acadêmicos do primeiro período deveriam desenvolver um produto educacional para orientar os próximos calouros, quanto a biossegurança. O objetivo geral desse estudo é conscientizar os acadêmicos de medicina a não usar adornos no campo de prática. Os objetivos específicos são: descrever as principais consequências causadas pelo uso de adornos; apresentar um vídeo educacional mostrando a importância do não uso de adornos, elaborado pelos alunos do PjBL. Este estudo foi elaborado em 2 tempos. Para elaboração do texto e do conteúdo do vídeo educacional, utilizou-se a pesquisa de revisão bibliográfica. Após a elaboração do conteúdo escrito, partiu-se para a elaboração do material visual. Foram selecionados artigos em português, com lapso temporal de 2019 a 2023, utilizando os descritores infecção hospitalar, equipamento de proteção individual e a palavra chave adornos. Foram encontrados para o descritor equipamento de proteção individual 26 artigos, e, para infecção hospitalar 38 artigos. As escolhas foram feitas após leitura do título e resumo. A segunda etapa que pertence a elaboração do vídeo educacional, com público alvo a se atingir. Percebe-se que a quantidade de pessoas que entram na instituição educacional, em sua maioria, são jovens entre 17 a 25 anos (também os mais velhos), com um vídeo curto da plataforma tiktok com a música de fundo “dança da mãozinha”, onde o refrão oferece a possibilidade de acrescentar tópicos de relevância do tema, em flash rápido. A principal problemática do uso de adornos é a microbiota que se mantêm presa a eles, e sua transmissão durante o toque torna-se comum. O uso de esmalte, unhas em gel, dificultam a higienização completa e eficaz das mãos o que também pode contribuir. No ambiente hospitalar, essa transmissão de microorganismos pode desencadear infecções hospitalares. A NR32 tem por finalidade estabelecer e regulamentar as diretrizes básicas para a implementação de medidas de proteção à segurança e à saúde dos trabalhadores dos serviços de saúde, principalmente aqueles que mantêm contato com agentes biológicos . Por tudo isso, pensa-se sobre a necessidade de ações promotivas e preventivas que ensinam “boas práticas” e assim, o académico consegue de forma consciente, reproduzir condutas que serão primordias para evitar a disseminação de doenças no ambiente de saúde, que além de cumprir na sua vida acadêmica como aluno, irá realizar após formado, e poderá ser um agente transformador no seu ambiente de trabalho.
A importância dos marcadores biológicos para o diagnóstico precoce e tratamento do câncer de mama
Authors: M. S. Bittencourt Cláudia , Bruno da Costa Felícia , Frascino Nesi Federico , E. S. Bittencourt Mariana 
Published: August 2024

Os marcadores tumorais são macromoléculas presentes em tumores, no sangue ou em outros fluidos biológicos, nos quais o aparecimento e/ou alterações em suas concentrações se relacionam com o crescimento de células neoplásicas. Identificar os marcadores que possam retratar o comportamento dos tumores considerados como prognóstico é de extrema relevancia no câncer de mama devido a variabilidade que a progressão clínica da doença pode apresentar. A partir de uma revisão de estudos originais, contemplando os mais importantes fatores genéticos e hormonais em prognóstico, tratamento e prevenção do câncer de mama, como história familiar, nível socioeconômico e marcadores biológicos de tumor, buscamos o objetivo de apresentar uma revisão da literatura nacional e internacional sobre o papel dos marcadores tumorais no manejo clínico de pacientes com câncer de mama, do auxílio no diagnóstico e estadiamento até a avaliação da resposta terapêutica e prognóstica. Evidenciamos a importância dos programas de acompanhamento e rastreio genéticopara a população feminina, em especial aqueles direcionados a mulheres portadoras das mutações nos genes BRCA1, BRCA2 e triplo-negativo, como o pleno funcionamento da Política Nacional de Humanização que aproxima o profissional da saúde ao paciente. Por fim, como utilizar os exames preventivos disponíveis visando a detecção precoce dessa doença, tornando o tratamento mais eficaz.
An Assessment of YouTube Educational Video Quality Through Machine Learning
Authors: Weng John 
Published: August 2024

This paper presents a machine learning-based approach to assessing the quality of educational videos on YouTube. With the growing number of educational videos available on the platform, verifying efficacy and credibility has become increasingly important. To address this issue, we propose a framework that allows for automated evaluation of video content based on analytic parameters provided by the YouTube Data API. We look at features such as view count, comment polarity, channel subscriber count, like/dislike ratio, and comment count to capture various dimensions of video quality and engagement. We introduce our target variable as the "Educational Quality Score" (EQS), which measures a video's impact on education using the RACED model - Relevance, Accuracy, Clarity, Engagement, and Depth. Our method offers educators and content creators a tool to enhance their selection of quality educational YouTube videos.
Unveiling the Impact of Key Determinants on Microloan Allocation Using Machine Learning
Authors: Ali Sheraz 
Published: August 2024

Microfinance initiatives have emerged as crucial tools in poverty alleviation, particularly in developing countries. These initiatives extend small loans to underserved populations and marginalized communities, as a means to improve their financial standing. This research leverages machine learning models to analyze a large microfinance initiative and find the key variables that drive micro-loan allocation. By conducting an in-depth feature analysis of a comprehensive microloan dataset, we aimed to pinpoint key variables that impact the allocation and size of microloans. Our study finds that the repayment period, lender networks, and the geographic location of borrowers significantly influence the loan allocation and size. The insights from our study can provide strategic guidance to microfinance programs in their loan allocation decisions. More specifically, lending decisions can be targeted more effectively to maximize impact of microfinance programs. In addition, this research also demonstrates the application of machine learning models in the realm of microfinance.
Deep Learning for Neurodevelopmental Disorder Diagnosis: Leveraging Retinal Images with Self-supervised Learning
Authors: Kim Beom , Kim Hwan , Min Sojung 
Published: August 2024

Neurodevelopmental disorders like autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have posed substantial challenges to the cognitive, social, and emotional development of children. Early and precise diagnosis of ASD and ADHD holds significant importance in facilitating timely interventions and support, ultimately enhancing outcomes for individuals affected by these conditions. Conventional diagnostic approaches for these disorders have depended on behavioral assessments, clinical observations, and structured interviews, which are labor-intensive and susceptible to subjective judgments made by healthcare professionals. In this research paper, we proposed a deep learning-based system for diagnosing neurodevelopmental disorders. The proposed system utilizes retinal images as input and generates disorder predictions. We introduce a novel self-supervised learning approach to attain superior accuracy outcomes. We demonstrated that the proposed system achieves state-of-the-art accuracy on large-scale retinal image datasets with extensive experimental results.
CardioXNet: Representation Learning for Accurate Cardiovascular Disease Diagnosis from X-ray Images
Authors: Hwang Jiwon , Klingebiel Jesse 
Published: August 2024

Cardiovascular Disease (CVD) is a prevalent, incurable condition affecting the heart and blood vessels. Due to its significant impact on mortality in the United States, there is a pressing need for enhanced risk stratification methods. Coronary Artery Calcium Score (CACS), reliant on CT scans, is most commonly employed as a risk stratification method but suffers from limitations, including accessibility and early detection challenges. To address the problem, this research study proposes a representation learning-based CVD diagnosis framework utilizing X-ray images, offering expedited and earlier detection, as well as improved accessibility. This system comprises two distinct stages: representation learning to extract CVD-related features and transfer learning to train a CVD classifier. Representation learning enhances the quality of extracted features, thereby leading to more precise results in the subsequent CVD diagnosis network. Comprehensive experiments and training validate the efficacy of the proposed method, demonstrating its superiority over the existing methods. These promising results suggest the potential utility of X-rays as a valuable biomarker for diagnosing CVD disease.
A Deep Semi-Supervised Domain Generalization Approach for Epileptic Seizure Prediction using Electro Encephalo Graphy (EEG)
Authors: An Sunwoo , Osei-Mensah Koby 
Published: August 2024

According to the World Health Organization, nearly 50 million people suffer from epilepsy, one of the most common neurological disease. Epilepsy is characterized by abnormal brain activity, leading to recurrent seizures. Each seizure manifests as sudden, uncontrolled bursts of electrical activity in the brain, and the injuries and restrictions on daily life underscores the urgency of finding effective methods for epileptic seizure prediction. With the use of deep learning techniques, early predictions of epileptic seizures, an unsolved problem, are attempted in this paper. Previous research has limitations of being sensitive to noise as it is dependent on specific electroencephalogram (EEG) devices and datasets, a serious issue this paper solves. In this paper, a semi-supervised based domain generalization method to develop an accurate seizure prediction system is proposed. It consists of two phases: representation learning and transfer learning phase. To achieve high precision, the proposed method utilizes a representation learning approach. Here, a feature-swapping mechanism that effectively disentangles seizure-related features is introduced. During transfer learning, the pre-trained network is trained to output the probability of whether the input EEG indicates a seizure or not. The proposed model achieves state-of-the-art performance, with an accuracy of 90.53% and 94.88% on the NICU and Epileptic Seizure Recognition datasets respectively in within-dataset evaluations. It outperforms the previous methods by 19.35% in cross-dataset evaluations. This robust improvement opens up promising possibilities for real-world clinical applications. The proposed feature disentangling method is also expected to contribute to developing reliable medical tools.
The impact of trade and globalization on the economic development of nations
Authors: Pitman Jasmine 
Published: August 2024

The rise of globalization has exacerbated disparities between developing and developed nations. This economic imbalance may be explained through the different export strategies (e.g., commodity vs non-commodity) that countries employ. This paper explores how trade and globalization impacts the economic development of nations with a specific focus on how different export strategies impact outcomes.

Focusing on one developing nation, Bolivia in Latin America, and one developed nation, the United States, this paper provides an explanation of why exporting non-commodities leads to greater development. Fundamental learnings to support this include the promotion of more diversity in a workplace, higher rates of innovation, higher-skilled jobs, higher national wages, better working conditions, and non-value detracting FDI. It also enables countries to specialize in goods or services that they have comparative advantage in, in order to foster greater revenue.

This research allows for essential insights into notions for development trajectories; developing nations can adapt their export strategies by diversifying into manufactured goods, increasing government expenditure into the secondary sector, and capitalizing on natural resources by revising resource-extracting trade agreements.
Per- and Polyfluorinated Compounds in Menstrual Products and its Relationship with Natural Labeling
Authors: Shvartsman Ava , Varnold Robert 
Published: August 2024

Per- and Polyfluorinated compounds (PFAS), also known as “forever chemicals,” gain their name from their persistence in both the environment and the human body. From pots to drinking water to period products/menstrual products, PFAS are found in seemingly every product both in and out of markets, yet PFAS in period products is of particular interest within this study due to its potential for harm. Due to the permeability of the vaginal mucosa and the misleading labeling of packaging, numerous people are being exposed to these compounds unknowingly, and thus their large range of potential side effects. Alongside the lack transparency within the composition of period products and the unknowing addition of PFAS through manufacturing and a lack of oversight, labels like “natural” and “non-toxic” lose their meaning. This study is performed to heighten awareness to this issue to potentially promote change in both transparency policy with “medical devices” and consumer/brand awareness of their products. This study attempts to show the lack of relationship between labeling and PFAS detection in menstrual products through an analysis of data from a Mamvation study alongside product listings on common consumer sites. By showing the lack of relationship, this study helps initiate an important discussion that can be used to spur change within accepted systems (such as United States policy on labeling and a lack of transparency and knowledge of product production chains behind brand names.
Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network
Authors: Verma Garima 
Published: August 2024

PurposeTo detect foot ulcers in diabetic patients by analysing thermal images of the foot using a deep learning model and estimate the effectiveness of the proposed model by comparing it with some existing studies.Material and methodsOpen-source thermal images were used for the study. The dataset consists of two types of images of the feet of diabetic patients: normal and abnormal foot images. The dataset contains 1055 total images; among these, 543 are normal foot images, and the others are images of abnormal feet of the patient. The study’s dataset was converted into a new and pre-processed dataset by applying canny edge detection and watershed segmentation. This pre-processed dataset was then balanced and enlarged using data augmentation, and after that, for prediction, a deep learning model was applied for the diagnosis of an ulcer in the foot. After applying canny edge detection and segmentation, the pre-processed dataset can enhance the model’s performance for correct predictions and reduce the computational cost.ResultsOur proposed model, utilizing ResNet50 and EfficientNetB0, was tested on both the original dataset and the pre-processed dataset after applying edge detection and segmentation. The results were highly promising, with ResNet50 achieving 89% and 89.1% accuracy for the two datasets, respectively, and EfficientNetB0 surpassing this with 96.1% and 99.4% accuracy for the two datasets, respectively.ConclusionsOur study offers a practical solution for foot ulcer detection, particularly in situations where expert analysis is not readily available. The efficacy of our models was tested using real images, and they outperformed other available models, demonstrating their potential for real-world application.
ĐÁNH GIÁ ỨNG DỤNG CÂN NGANG TRÊN MỞ RỘNG PHỐI HỢP CẮT NGẮN CƠ NÂNG MI ĐIỀU TRỊ SỤP MI
Authors: Nguyễn Thị Thu Hiền , Nguyễn Thị Phương Thảo , Phạm Trọng Văn , Dương Diệu Hương , Vũ Thị Quế Anh 
Published: August 2024

Mục tiêu: Nghiên cứu này được thực hiện nhằm đánh giá hiệu quả của kỹ thuật cắt ngắn cân cơ nâng mi phối hợp treo cân ngang trên mở rộng trong điều trị bệnh nhân sụp mi tại Khoa Tạo hình thẩm mỹ mắt và vùng mặt Bệnh viện Mắt Trung ương. Đối tượng và phương pháp nghiên cứu: Nghiên cứu can thiệp mô tả không đối chứng trên 2 nhóm bệnh nhân sụp mi bẩm sinh mức độ nặng và sụp mi tái phát. Lựa chọn cỡ mẫu thuận tiện với cỡ mẫu là 44 mắt. Kết quả: 22 bệnh nhân với 30 mắt sụp mi bẩm sinh mức độ nặng và 14 mắt trên 12 bệnh nhân sụp mi tái phát được phẫu thuật lại bằng phương pháp cắt ngắn cân cơ nâng mi phối hợp treo cân ngang trên mở rộng. Tỉ lệ nam:nữ = 1:1,37 tuổi thấp nhất là 2 tuổi, tuổi cao nhất là 21 tuổi. MRD1 trước phẫu thuật trung bình 0,67 mm, MRD1 trung bình sau phẫu thuật là 3,25 mm. Theo dõi sau 3 tháng tỷ lệ thành công của nhóm sụp mi bẩm sinh mức độ nặng là 93.3% và của nhóm sụp mi tái phát là 92.9% Kết luận: Phương pháp cắt ngắn cơ nâng mi trên phối hợp với treo cân ngang trên mở rộng là một phương pháp an toàn, hiệu quả đối với những bệnh nhân sụp mi bẩm sinh mức độ nặng và sụp mi tái phát. Vật liệu cân ngang trên mở rộng là một vật liệu tự thân, do đó hạn chế được những biến chứng thải loại vật liệu, nhiễm trùng, giá thành rẻ, phù hợp với nhiều đối tượng bệnh nhân