bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlates of the number of children ever born to women in rural Ethiopia: Application of truncated generalized Poisson model with exposure
Authors: Gabreyohannes Emmanuel 
Published: October 2024

Abstract

Background
Rapid population growth and high fertility rates in low-income countries adversely affect the provision of maternal and child healthcare services and are roadblocks to the achievement of sustainable development goals. A high fertility rate has serious health implications for both the mother and the children she bears, and thus, investigating the factors behind this phenomenon is of paramount importance.
Method
The data for this study were extracted from the 2016 Ethiopia Demographic and Health Survey (EDHS 2016). The response variable was the number of children ever born (NCEB) per woman in rural Ethiopia. This responsible variable was zero-truncated since only women who had at least one live birth at the time of the survey were considered. Data from a total of 6256 women were analyzed using a zero-truncated generalized Poisson model, which takes into account any type of dispersion and the truncated nature of the response variable simultaneously.
Results
The mean NCEB for reproductive-age women residing in rural areas of Ethiopia was found to be about 4.5. Factors that are associated with bearing more children include early age at first birth, number of deceased children in the family, low economic status of the household and land ownership. On the other hand, contraceptive use, women’s education and media exposure had a negative impact on the NCEB per woman. The results also revealed significant regional variation, with women in the Somali region registering the highest number of child births.
Conclusion
To curb high maternal fertility, interventions that selectively target regions with high child births (e.g., Somali), uneducated women and poorest women; awareness creation campaigns to combat early initiation of childbearing, particularly teenage pregnancies; unreserved efforts aimed at reducing child mortality; and promoting the use of birth control measures are recommended.
Mental Health Monitoring And Intervention Using Unsupervised Deep Learning On EEG Data
Authors: Yadulla Akhila Reddy , Sajja Guna Sekhar , Addula Santosh Reddy , Maturi Mohan Harish , Nadella Geeta Sandeep , Cruz Elyson De La , Meduri Karthik , Gonaygunta Hari 
Published: October 2024

Abstract

This study explores the analysis of EEG signal data for real-time mental health monitoring using advanced unsupervised deep learning models. Employing algorithms such as autoencoders, Principal Component Analysis (PCA), K-means clustering, and Gaussian Mixture Models (GMM), this research aims to uncover patterns and biomarkers indicative of various mental health conditions. The study utilizes a comprehensive dataset comprising EEG signals from different brain regions, focusing on the extraction of significant features and the training of models to detect subtle yet crucial changes in brain activity. Our findings demonstrate enhanced capability for early detection of mental health issues, with improved predictive accuracy and potential for personalized therapy, underscoring a promising future for mental health care. Furthermore, the study rigorously addresses the ethical implications of using algorithmic approaches in healthcare, such as potential biases, patient privacy, and the welfare of individuals. By implementing these unsupervised deep learning models, our research offers compelling opportunities for the prevention, tailored intervention, and improved treatment outcomes in mental health care while also emphasizing the importance of navigating the ethical complexities to ensure responsible technology deployment for enhancing patient well-being and safety.
Commensal dysbiosis promotes the development of colorectal cancer liver metastasis via the CCL6/CCR1 axis
Authors: Li Zhongchao , Li Mingming , Yang Yue , Sun Zhicheng , Chang Zhibin , Chen Yunsong , Zhao Lei 
Published: October 2024

Abstract

Purpose: Gut microorganisms are involved in the occurrence and progression of various cancer types, including colorectal cancer. Several studies have indicated that the destruction of commensal homeostasis can promote tumor metastasis. The purpose of our study was to clarify the effect of gut commensal dysbiosis on the risk of liver metastasis of colorectal cancer (CRLM) and its mechanisms.
Methods: We established a mouse model of CRLM with a background of commensal dysbiosis. Using this model, we studied the impact of commensal dysbiosis on CRLM.
Results: Commensal dysbiosis promotes the development of CRLM via the C-C chemokine ligand 6 (CCL6) and the C-C chemokine receptor 1 (CCR1) axis. It was found that commensal dysbiosis altered the tumor microenvironment (TME) of the liver by recruiting tumor-associated macrophages (TAMs), notably M-2-like TAMs and promoted the growth of liver metastasis. The effect of promoting liver metastasis was achieved by upregulating the expression levels of CCL6 to drive CCR1+TAMs infiltration into the TME. It is important to note that inhibition of CCR1 expression can reduce CRLM.
Conclusion: Commensal dysbiosis could promotes the development of CRLM via CCL6/CCR1 signaling and represent an approach to prevent CRLM. The results of the current research study suggested that targeting this axis could be an effective method to inhibit the CRLM by regulating the TME.
FlareNet: A Feature Fusion Based Method for Fire Detection under Diverse Conditions
Authors: Yousaf Balal , Mirza Adeel Feroz , Irfan Muhammad , Mansoor Majad , Yang Zaiyue 
Published: October 2024

Abstract

Fire detection is crucial for safeguarding lives and property. In recent years, advancements in deep learning and Internet of Things (IoT) architectures have significantly enhanced the accuracy of fire and smoke detection systems. In this study, we introduce FlareNet, a feature fusion based deep learning model that leverages a DenseNet architecture combined with Spatial Pyramid Pooling (SPP) and a Contextual Feature Pyramid Network (CFPN). FlareNet is further augmented with dual attention mechanisms and a Feature Enhancement Attention (FEA) mechanism to selectively emphasize critical features for distinguishing between fire and non-fire scenes. Our proposed model is rigorously evaluated across five diverse datasets: Sharma, Deep Quest, BoWFire, FD dataset, and our novel MixFire dataset, achieving an impressive average accuracy of 99.2%. A comparative evaluation against state-of-the-art (SOTA) algorithms reveals that FlareNet outperforms existing methods with a notable improvement in accuracy, precision, recall, and F1-score, thereby setting a new benchmark in the domain of fire detection. Furthermore, A comprehensive analysis with baseline models such as VGG16, VGG19, ResNet18, MobileNetV2, and DenseNet is also presented. These advancements underscore FlareNet’s capability to enhance fire detection systems for a more sustainable environment. FlareNet code and MixFire dataset can be accessed by https://github.com/adeelferozmirza/FlareNet.
A data-driven approach to refine the partially stirred reactor closure for turbulent premixed flames
Authors: Piu Lorenzo , Péquin Arthur , Freitas Rodolfo S. M. , Iavarone Salvatore , Pitsch Heinz , Parente Alessandro 
Published: October 2024

Abstract

Accurately predicting turbulent combustion processes is fundamental for optimizing efficiency, reducing pollutant emissions, and ensuring operational safety in combustion systems. To this purpose, Computational Fluid Dynamics (CFD) simulations are widely employed. In particular, Large Eddy Simulations (LES) balance prediction accuracy with computational efficiency by resolving only the most energy-containing scales of turbulence and rely on modeling the Turbulence-Chemistry Interactions (TCI) occurring at the smallest scales. Among the existing closures, the Partially Stirred Reactor (PaSR) model incorporates finite-rate chemistry and estimates a cell reacting fraction based on the local Damköhler number to account for the subfilter-scale TCI. Although widely validated in CFD computations, the PaSR model was found limited by the way it computes the cell reacting fraction. To tackle this point, our study proposes a Machine Learning (ML) enhanced Partially Stirred Reactor model for LES. A fully connected neural network is trained on Direct Numerical Simulation (DNS) data of turbulent premixed jet flames to compute a correction coefficient for the cell reacting fraction. Maintaining the original model shape, this ML-enhanced closure aims at bridging the gap between physics-based models and advanced data-driven techniques. The proposed formulation not only improves the prediction accuracy of quantities of interest such as the heat release rate but also features computational feasibility and generalisation capabilities over a large range of LES grid refinement. This demonstrates the significant potential of ML-aided TCI closures in future applications of combustion engineering.
The Mediating Role of Oxidative Stress in the Association Between Phthalate Exposure and Body Mass Index Among Adults: A Cross-Sectional Analysis Using NHANES Data
Authors: Ripon Rezaul Karim 
Published: October 2024

Abstract

Background
The study investigates the mediating role of oxidative stress in the relationship between phthalate exposure, measured via urinary Bisphenol A (BPA) levels, and Body Mass Index (BMI) in adults. Given the global prevalence of obesity and the widespread exposure to phthalates, understanding this relationship is critical. Methods: Data were derived from the National Health and Nutrition Examination Survey (NHANES), focusing on adult participants. Key variables included urinary BPA levels (independent variable), BMI (dependent variable), oxidative stress indicators (mediator), and covariates such as age, serum creatinine, poverty index, and race/ethnicity. Descriptive statistics summarized baseline characteristics, and mediation analysis quantified the direct, indirect, and total effects of phthalate exposure on BMI. Results: The study sample comprised 19,022 adults, with a nearly equal distribution of males and females. The median BMI and urinary BPA levels differed significantly by gender. Mediation analysis revealed that oxidative stress mediated approximately 19% of the total effect of phthalate exposure on BMI. The Average Causal Mediation Effect (ACME) was − 0.000976 (95% CI: -0.001147 to 0.001, p < 0.001), and the Average Direct Effect (ADE) was − 0.004155 (95% CI: -0.008239 to 0.001, p = 0.016). The total effect was − 0.005132 (95% CI: -0.008997 to 0.001, p = 0.004). Conclusions: This study highlights the significant role of oxidative stress in mediating the relationship between phthalate exposure and BMI. The findings suggest that strategies to reduce oxidative stress could mitigate the obesogenic effects of phthalates, underscoring the need for regulatory policies to limit phthalate exposure and public health interventions targeting oxidative stress. Future research should explore longitudinal data and a broader range of phthalate exposures to further elucidate these relationships.
Persistently primed microglia restrict the reactivation of latent cytomegalovirus at the expense of neuronal synaptic connectivity
Authors: Brizić Ilija , Mihalić Andrea , Kveštak Daria , Lisnić Berislav , Krstanović Fran , Hosseini Shirin , Sitnik Katarzyna , Golemac Mijo , Lisnić Vanda Juranić , Rashidi Ahmad , Bochow Isabell , Arossa Alessia , Furione Milena , Britt William , Verjans Georges , Korte Martin , Čičin-Šain Luka , Jonjić Stipan 
Published: October 2024

Abstract

Microglia are myeloid cells that reside within the central nervous system (CNS), where they maintain homeostasis under normal, non-pathological conditions. In addition, microglia also perform numerous immune functions upon different pathogenic stimuli, including CNS infections with various neurotropic viruses. Herpesviruses establish a lifelong latent infection from which they reactivate intermittently upon waning of immune control. The role of microglia in preventing reactivation of latent herpesviruses remains unclear. In this work, we used congenital cytomegalovirus (CMV) infection as a model to investigate the impact of a persistent virus infection of the brain on microglia. We show that mouse CMV (MCMV) latency in the CNS is associated with permanent microglial priming. The changes induced by persistent infection include continuous, interferon-gamma-dependent microglia activation and extensive transcriptional reprogramming at the single-cell level, leading to the expansion of a microglia subset associated with latent infection. Notably, the maintenance of microglia in a primed state provides enhanced control of latent infection and superior recall response but is associated with excessive loss of synaptic dendritic spines mediated by primed microglia. Altogether, our results indicate that latent CMV infection in the brain causes perturbation of microglial homeostasis, which leads to chronic neuroinflammation that successfully restricts virus reactivation but simultaneously compromises neuronal synaptic connectivity in the brain.
Navigating Human Resource Diversity Management in Higher Education: A Case Study of University of Botswana
Authors: Nyamutswa Tapiwa Oliver , Lilian Mutseura 
Published: October 2024

Abstract

This study investigates the challenges and benefits of human resource diversity management at the University of Botswana, addressing a significant gap in existing literature concerning human resource practices in Botswana's state universities. Utilizing a descriptive research design, data were collected through a combination of quantitative and qualitative methods, including surveys and interviews with 98 administrative staff members. Findings reveal a diverse workforce characterized by variations in gender, age, race, and religion, which offers opportunities for enhanced innovation and problem-solving. However, challenges such as language barriers, religious differences, and social groupings hinder effective diversity management. The study recommends the implementation of a comprehensive diversity policy prioritizing qualifications and experience, alongside continuous diversity training to foster inclusivity. Additionally, it advocates for policy reforms to attract international talent. The insights gained contribute to the broader understanding of diversity management within higher education contexts in Botswana and beyond.
Exploring the role of human brands in developing the sharing economy: A futures research study
Authors: Asiabar Mojtaba Ghorbani , Asiabar Morteza Ghorbani , Asiabar Alireza Ghorbani 
Published: October 2024

Abstract

The sharing economy, as an emerging economic model, has profoundly transformed the way goods and services are consumed and exchanged. This futures research study investigates the role of human brands in developing the sharing economy. Utilizing scenario analysis and Delphi methods, data were collected from 150 experts in marketing, economics, and technology fields. Results indicate that human brands can act as catalysts in expanding sharing platforms and enhancing consumer trust. Multiple regression analysis revealed that human brand credibility (β = 0.42, p < 0.001) and online engagement (β = 0.38, p < 0.01) are significant predictors of sharing platform success. Furthermore, an independent t-test showed a significant difference in the adoption rate of sharing economy platforms with and without human brand presence (t(148) = 3.76, p < 0.001). This study provides a conceptual framework for better understanding the interaction between human brands and the sharing economy, offering important implications for managers and policymakers in shaping the future of this domain. The findings suggest that strategic integration of human brands could potentially accelerate the growth and acceptance of sharing economy platforms, particularly in emerging markets where trust is a critical factor. Additionally, the study highlights the need for a balanced approach in leveraging human brands while maintaining the decentralized ethos of the sharing economy.
The impact of financial literacy on financial well-being: a systematic literature review
Authors: Antwi Prince Kwabena Agyemang , Addai Bismark , Duah Evans , Kubi Maxwell Tetteh 
Published: October 2024

Abstract

Background: The harsh reality of financial difficulty can be concerning, requiring that an opportunity to break free from these limitations be seized at the earliest opportunity. This systematic literature review was therefore necessary to enhance understanding of financial literacy as a means of achieving financial well-being, and to carefully examine the extant literature in order to unravel the key themes and future research agenda of the impact of financial literacy on financial well-being.
Methods: Following the PRISMA guidelines, we searched the Scopus database from inception to August 16, 2024 and included articles that had the key words: financial literacy, financial education, financial outcome, financial well-being, economic outcome, economic well-being in their titles, abstracts, or keywords. After applying various inclusion and exclusion criteria, 35 articles were included in the final review. The synthesis was performed in a narrative way. Descriptive and thematic analysis were conducted to answer the research questions.
Findings: The study showed that numerous studies have consistently found financial literacy to significantly influence financial well-being. Other variables such as financial behavior, pronounced behavioral elements such as non-impulsive future-oriented behavior and impulsiveness, and financial socialization were discovered as having significant influence on financial well-being. The following themes were developed in answer to the research questions: financial literacy and its determinants; the influence of financial literacy on financial well-being; the impact of financial socialization on financial well-being; the interplay of financial literacy, psychology, and behavioral economics; digital financial literacy in contemporary contexts, and emerging trends. The study made some future research recommendations.
Implications: Future research can build on this work to improve our understanding of financial literacy’s impact on financial well-being and help create effective strategies to support individuals and families.
Role of Sales and Marketing Information Systems in Companies’ Sales Rate Increase
Authors: Parvin Mohammad , Ramyar Fatemeh 
Published: October 2024

Abstract

The modern condition of business environments has made use of some processes and systems to control effects of such environments and continuity of activity through the organizations “inevitable”. From among these processes and systems, it can be referred to the most important one i.e. sales and marketing information system. This research was carried out with the aim of studying role of sales and marketing information systems in increase of companies’ sales rate through the use of descriptive-survey research method. The present study includes seven (7) hypotheses. Senior managers and marketing managers of five vegetable oil production companies are considered subject of this study, in which, its sample size was calculated 93 persons through the use of Cochran Formula. Simple random sampling method was used for selecting sample.
Questionnaire was used for collecting data. The reliability of the questionnaire was obtained 0.96 through Cronbach’s Alpha Method. The results of statistical tests showed that sales and marketing information systems are effective in increase of companies’ sales rate.
Identifying and Visualizing Global Research Trends and Hotspots of Artificial Intelligence in Medical Ultrasound: A Bibliometric Analysis
Authors: Xiao Jinting , Shen Fajuan , Lu Weizhao , Yu Zaiyang , Li Shengjie , Wu Jianlin 
Published: October 2024

Background:
Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and
visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation.

Objective:
This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by
combining quantitative and visualization methods.

Methods:
Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited
papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent
institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis.

Results:
The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580.
The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United
States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on
Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were
“ultrasound”, “computer-aided diagnosis”, and “segmentation”. Ultrasonography of Superficial Organs was the main site that was studied the most.

Conclusion:
This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the
most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within
this domain.
Diuretics-Induced Acute Pancreatitis: Case Series with a Review of the Literature
Authors: Patel Dhruvkumar M. , Patel Maitri M. , Patel Lalitkumar B. , Patel Vahin B. , Patel Mukundkumar V. , Patel Dhara K. 
Published: October 2024

Background:
Although diuretic-induced Acute Pancreatitis (AP) cases are typically
mild to moderate, severe and potentially fatal occurrences can arise.

Case Series and Literature Review:
We have, herein, presented a series of diuretic-induced AP
cases from March 2018 to February 2024 of a 54-year-old woman treated with chlorthalidone, a
45-year-old male treated with hydrochlorothiazide, and a 48-year-old male treated with frusemide.
The literature search has identified 26 cases published to date, 10 from frusemide and 16
from thiazide diuretics. The Naranjo adverse reaction probability scale has categorized all three
drugs as “probable”. All cases have responded to conservative treatment and cessation of the offending
drug. Various mechanisms, such as hypersensitization, ischemia, direct cytotoxic effects,
hypercalcemia, and dose-dependent idiosyncrasy, have been found to lead to intrapancreatic activation
of pancreatic enzymes, resulting in drug-induced AP.

Conclusion:
Further research into the mechanisms and genetic factors contributing to diureticinduced
AP is essential for enabling early diagnosis and management of diuretic-induced AP.