bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and comparison of two 3D printed scaffolds of biosilica from marine sponges for bone tissue engineering
Authors: Santo Giovanna do Espirito , de Souza Amanda , Amaral Gustavo Oliva , Alqualo Amanda Sardeli , Sousa Karolyne dos Santos Jorge , Viegas Beatriz Louise Mendes , Prado João Paulo dos Santos , Santos Francisco Vieira dos , Correa Daniel Souza , Granito Renata Neves , Rennó Ana Claudia Muniz 
Published: September 2024

Abstract

This study compared the physicochemical characteristics and biological effects of two 3D printed biosilica (BS) scaffolds (grid and gyroid). Methods included Scanning Electron Microscopy (SEM), Micro-Computed Tomography (Micro-CT), Mass Loss and pH Assessment, Fourier-Transform Infrared Spectroscopy (FTIR), and Energy-Dispersive X-ray Spectroscopy (EDS). The mechanical evaluation involved a Compression Test, and in vitro tests used cell adhesion assays with osteoblastic (MC3T3-E1) and fibroblastic (L929) cell lines. SEM showed BS spicules in both models on day 0, with signs of degradation along the experimental periods of immersion, forming a homogeneous network with the interaction with alginate. Micro-CT revealed rough surfaces in both models, with the gyroid model being more homogeneous and porous, with larger pores compared to the grid model. The gyroid model demonstrated higher values in the compression test and a decrease in pH on the first day and no differences for both models on days 3, 7, and 14. The mass loss was higher in the gyroid model by day 21. FTIR tests showed characteristic peaks for ALG and BS. EDS detected silica (Si), chlorine (Cl), calcium (Ca), carbon (C), and oxygen (O). In cell adhesion assays, both models supported adhesion and proliferation of L929 and MC3T3-E1 cells, with the gyroid model showing better cell elongation and morphology. Overall, the gyroid model demonstrated superior physicochemical properties, greater mechanical strength, and enhanced biological performance compared to the grid model, making it more promising for tissue engineering applications.
A facile optical sensing strategy for glyphosate detection based on the structure-switching signaling aptamers
Authors: Liu Ziping , Wang Xin , Huang Xinru , Bai Edith , Zhao Yuhan , Liu Shasha 
Published: September 2024

Abstract

Herein, a facile optical sensing strategy for glyphosate (GLYP) detection is established with high specificity using structure-switching signaling aptamers. The specific structure-switching aptamers (F-SSSAs) with fluorescence signal reporting functionality were initially designed. It involves two domains: the FITC-labeled signal transduction domain that controls the fluorescence signal reporting, and the functional domain (specific structure-switching aptamers) that controls the target recognition as well as high affinity for graphene oxide (GO). In this strategy, GO works as a robust quencher for F-SSSAs in the absence of GLYP. However, the structure of F-SSSAs switched in the presence of GLYP, which prominently affected the interaction between F-SSSAs and GO. And the fluorescence of the structure-switching signaling aptamer-based sensing system subsequently restored. The present optical sensing strategy exhibited two dynamic linear relationships for GLYP detection within the range from 0.2 to 80 ng·mL− 1 and from 100 to 800 ng·mL− 1 respectively, with a low detection limit (LOD) of 0.07 ng·mL− 1. Significantly, the fluorescence-based sensing strategy was successfully utilized to detect GLYP in water, soil and rice with satisfactory outcomes, which suggested that it held potential applications prospect in monitoring GLYP.
Generalized subsampled Newton methods for optimization and its application
Authors: Moghaddam Ali Shahmoradi , Ketabchi Saeed 
Published: September 2024

Abstract

In this paper, we introduce a novel generalized subsampling Newton method designed to effectively address finite-sum optimization problems. This method is particularly effective for solving iterative techniques for solving a system of linear inequalities in the form of \(Ax \leq b\) and when the objective function is not necessarily twice differentiable. The implementation of this method significantly impacts increasing speed, saving resources, and optimizing system memory usage, thereby improving algorithm performance, especially on large-scale problems. The convergence of the mentioned method is proven under the conditions of convexity and Lipschitz continuity of the objective function's gradient. Preliminary numerical experiments on large-scale generated random problems and various datasets from the UCI benchmark data sets and Netlib repositories provide empirical evidence supporting our theoretical findings of the proposed method compared to similar methods.
A Machine Learning Approach for Forecasting the Efficacy of Pyridazine Corrosion Inhibitors
Authors: Trisnapradika Gustina Alfa , Akrom Muhamad , Rustad Supriadi , Dipojono Hermawan Kresno , Maezono Ryo , Diño Wilson Agerico 
Published: September 2024

Abstract

This paper presents a machine learning (ML) methodology grounded in quantitative structure-property relationship (QSPR) principles for the prediction of corrosion inhibition efficiency (CIE) values, specifically focusing on pyridazine inhibitor compounds. The training phase incorporates the kernel density estimation (KDE) function to generate virtual samples, aiming to enhance the prediction accuracy of the ML model. The study evaluates the performance of three models, namely gradient boosting (GB), random forest (RF), and k-nearest neighbor (KNN). The results exhibit a substantial enhancement in predictive ability following the incorporation of virtual samples. Specifically, coefficient of determination (R2) values for GB, RF, and KNN models increase from − 0.33 to 0.97, -0.20 to 0.96, and − 0.17 to 0.95, respectively, with the addition of 1000 virtual samples. Correspondingly, root mean square error (RMSE) values for each model experience a significant decrease, reducing from 9.20 to 1.57, 9.07 to 1.81, and 8.60 to 2.12., respectively. This augmentation enhances the correlation between features and targets, resulting in more accurate predictions and eliminating the necessity for feature selection. Furthermore, it implies resilience to model variations, eliminating the need for model selection. The proposed methodology is a crucial link between theoretical research and experimental synthesis, providing a reliable and accurate prediction tool. This tool proves instrumental in efficiently designing and exploring corrosion inhibitor candidates, thereby contributing to the advancement of effective corrosion inhibition strategies.
Exploring Intersections of Race and Disability in the context of Canadian Employment Support Systems through the Experiences of Job Seekers/Workers, Employers, and Service Providers
Authors: Grewal Eakam , Durocher Evelyne , Premji Stephanie , Gewurtz Rebecca 
Published: September 2024

Abstract

Purpose: Racism and ableism have impacts at individual and organizational levels and can produce and reproduce inequities and injustices in diverse contexts. However, their intersection remains largely unexamined in the provision of employment supports. The objective of this qualitative study is to identify barriers and facilitators within employment supports to seek strategies to improve the employment outcomes of racialized disabled job seekers and workers and address gaps faced by service providers and employers.Methods: This study used interpretive description (1). Four racialized disabled job seekers and workers, two employers and four service providers participated in semi-structured interviews. Thematic analysis (2) was used to analyze the data.Results: Five core themes were identified: 1) managing intersectional confusion; 2) employer education; 3) contextual barriers; 4) client-service provider relationships; and 5) urgency for solutions.Conclusion: This study provides grounding evidence about common concerns and barriers within existing employment support systems and can assist policymakers and service providers to better understand the complex and nuanced lived experiences of racialized disabled job seekers and workers.
Multi-sensor Fusion Simultaneous Localization and Mapping: A Systematic Review
Authors: Fan Zheng , Zhang Lele , Wang Xueyi , Shen Yilan , Deng Fang 
Published: September 2024

Abstract

Simultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent unnamed systems to estimate their motion and reconstruct unknown environments. However, the SLAM system with merely one sensor has poor robustness and stability due to the defects in the sensor itself. Recent studies have demonstrated that SLAM systems with multiple sensors, mainly consisting of LiDAR, camera, and IMU, achieve better performance due to the mutual compensation of different sensors. This paper investigates recent progress on multi-sensor fusion SLAM. The review includes a systematic analysis of the advantages and disadvantages of different sensors and the imperative of multi-sensor solutions. It categorizes multi-sensor fusion SLAM systems into four main types by the fused sensors: LiDAR-IMU SLAM, Camera-IMU SLAM, LiDAR-Camera SLAM, and LiDAR-IMU-Camera SLAM, with detailed analysis and discussions of their pipelines and principles. Meanwhile, the paper surveys commonly used datasets and compares the effects of different algorithms. Finally, it concludes with a summary of the existing challenges and future opportunities for multi-sensor fusion SLAM.
A Literature Survey: AQI Prediction Using ML, DL and Hybrid Models
Authors: Sarkar Nairita , Keserwani Pankaj Kumar , Govil Mahesh Chandra 
Published: September 2024

Abstract

The existence of human being is not worth considering without fresh air. Rapidurbanization and industrialization is degrading air quality, which puts people’shealth at risk by causing various lung and heart diseases. It is necessary to raisepublic awareness to safeguard their well-being from these detrimental effects.An effective solution that must be implemented in conjunction with awarenesscampaigns are the use of modelling and monitoring systems for air quality predictions.Many air quality monitoring stations are installed to monitor and analysethe air quality of the respective regions. Many Machine Learning (ML) andDeep Learning (DL) models have shown their effectiveness to enhance predictivecapabilities and address the challenges of air pollution forecasting. This articleprovides a comprehensive survey based on thorough analysis of several ML,DL and hybrid models presented by the various researchers for air quality prediction.The selected papers are categorized based on the models used by theresearchers. This comprehensive survey of air quality prediction is an asset thatprovides insights that can greatly help to address the pressing problems broughton by poor air quality and its monitoring. In this survey diverse pollutants, meteorologicalfactors, several ML models, DL models and hybrid models with theirperformance efficacy are being analysed thoroughly to report the effect of thesemodels in the area of air quality forecasting. This survey facilitates the creationof realistic and efficient plans to protect public health from the adverse effects ofair pollution.
Fuzzy Logic Controller and P&amp;O-Based MPPT Techniques for Stand-Alone PV Systems: A Comparison
Authors: Kumar Rajesh , Bhattacharya Ananyo , Vardhan Aanchal Singh S. 
Published: September 2024

Abstract

The proposed work focuses on photovoltaic (PV) system monitoring and the use of maximum power point tracking (MPPT) techniques for optimal power generation. It emphasizes the widespread application of artificial intelligence (AI) in MPPT methods in solar power systems, which can significantly improve performance and efficiency. However, AI-based MPPT techniques may be more computationally intensive and costly. Hybrid MPPT methods combine traditional and AI techniques to balance performance and complexity, addressing these issues. Fuzzy logic control (FLC) emerges as a viable technique, though synchronization is required for optimal performance under variable irradiation and weather conditions. This paper uses MATLAB/Simulink to compare the four main MPPT techniques: fuzzy-based variable step size P&O, FLC, and hybrid P&O-FLC algorithms. Further, a symmetric fuzzy controller, the perturb-and-observe (P&O) method, and a mixed method that combines both fuzzy and P&O-based controllers were compared. At the same time, many other FLC-based MPPT strategies were also examined. All tests were performed in a solar PV system with a DC-DC boost converter, considering varying atmospheric conditions. Overall, the study's aim is to provide insights into the relative effectiveness of various MPPT techniques, with a particular emphasis on the use of FLC and its hybrids in a variety of environmental scenarios.
Ungrading as a Feminist Technique in the Counseling Theories Course
Authors: Horton Emily , Everett Brenda , Bradley Kailey , Schoonover TJ 
Published: September 2024

Abstract

Ungrading dismantles traditional approaches to grading and moves into growth-focused assessment. Due to its inclusive and innovative pedagogical roots, ungrading appears to align with the purpose of counseling training programs. This research study focused on how ungrading role-plays can foster a more equitable classroom environment and cultivate student autonomy in counselor education, specifically in a counseling theories course. The study used data from 40 counselors-in-training in a counseling theories course and was collected and analyzed using reflexive thematic analysis.
Mobile Applications for Diabetes Management in Iran: An App Review and Quality Assessment Using the Mobile Application Rating Scale (MARS)
Authors: Alizadeh-dizaj Ghasem , Mehrabi Nahid , Ardalan Mohammad Afshar , Gholamhosseini Leila 
Published: September 2024

Abstract

Background The increasing prevalence of diabetes worldwide necessitates the development of effective mobile applications (apps) to assist individuals in managing their condition. Despite the growing number of these applications, there is limited understanding of their quality, which is essential for ensuring that they serve their intended purpose effectively. We aimed to perform a systematic search and evaluate the content and quality of diabetes applications available in Iranian app stores on the basis of the Mobile App Rating Scale (MARS). Methods Iranian app stores for iOS and Android were searched using diabetes-related terms in August 2024. Apps that met the inclusion and exclusion criteria were added to the content and quality assessment. Two researchers independently reviewed, rated, and evaluated the retrieved apps. The Mobile Application Rating Scale (MARS) was employed to assess the included apps. MARS rates apps on a scale from 1 to 5, providing an overall score along with scores for engagement, functionality, information, aesthetics, and subject quality. Results The search terms yielded 809 apps, after removing duplicate and irrelevant apps, 33 met the inclusion criteria and were included in this review. The assessment revealed an average quality score of 2.78 (SD 1.04) across the evaluated applications, indicating a moderate level of overall performance. While certain applications demonstrated promising potential by offering valuable educational content and robust features, the overall quality exhibited significant variability, with scores ranging from 1.71 to 4.53. This variability underscores a pressing need for improvements in user engagement and functionality among many of the assessed apps. Conclusions The findings highlight the critical need for targeted enhancements in mobile health applications for diabetes management to better serve the needs of users. Collaboration among developers, healthcare professionals, and researchers is essential to create high-quality, effective tools that can significantly aid individuals in managing their diabetes. Future research should prioritize the identification of best practices and address the gaps identified in this study, thereby maximizing the potential of mobile applications as indispensable resources in diabetes care.
CALHM2 is a mitochondrial protein import channel that regulates fatty acid metabolism
Authors: Jonas Elizabeth , Mnatsakanyan Nelli , Rivera-Molina Felix , Robson Andrew , Garfinkel Alexandra MacColl , Kumar Amrendra , Batter Stephen , Padovano Valeria , Webster Kaitlin , Cardone Rebecca , Berg Justin , Toomre Derek , IV Richard Kibbey , Caplan Michael , Khokha Mustafa 
Published: September 2024

Abstract

For mitochondrial metabolism to occur in the matrix, multiple proteins must be imported across the two (inner and outer) mitochondrial membranes. Classically, two protein import channels, TIM/TOM, are known to perform this function, but whether other protein import channels exist is not known. Here, using super-resolution microscopy, proteomics, and electrophysiological techniques, we identify CALHM2 as the import channel for the ECHA subunit of the mitochondrial trifunctional protein (mTFP), which catalyzes β-oxidation of fatty acids in the mitochondrial matrix. We find that CALHM2 sits specifically at the inner mitochondrial and cristae membranes and is critical for membrane morphology. Depletion of CALHM2 leads to a mislocalization of ECHA outside of the mitochondria leading to severe cellular metabolic defects. These defects include cytosolic accumulation of fatty acids, depletion of tricarboxylic acid cycle enzymes and intermediates, and reduced cellular respiration. Our data identify CALHM2 as an essential protein import channel that is critical for fatty acid- and glucose-dependent aerobic metabolism.
Comparison between modulus of toughness and compressive strength of bamboo fiber reinforced mortar under impact loading
Authors: Aditama Vega , Dewi Sri Murni , Wibowo Ari , Wijaya Ming Narto 
Published: September 2024

The presence of bamboo fiber has been distributed to improve the structural integrity of construction. It has been used to resist impact loads and reduce crack widths in composite materials. This can be utilized for reinforcement in construction, particularly in mortar elements. Bamboo fiber has been used to resist impact loads and reduce crack widths in composite materials. This can be utilized for reinforcement in construction, particularly in mortar elements. By combining good treatment with alkaline NaOH, the quality of the fiber used as a mortar mix will increase and the capacity to absorb impact energy will be greater. In this study there were 2 stages, the first stage was to look at the effect of the effective length of bamboo fiber and alkaline molarity. The effect of treatment using alkaline is to reduce the lignin and hemicellulose substances which reduce the bond in the mortar mixture. The optimal value for the Pull-out test, which involved manipulating the molarity of NaOH as a treatment and the effective length of bamboo fiber, was determined to be 2.5 cm of embedded fiber or a total length of 5 cm when 4 molar NaOH was utilized. All fiber bamboo-composite bars had a cross section of average at 1×1 mm. And the second stage tested Charpy for impact load resistance in mortar with the optimum effective fiber length while varying the alkaline molarity. The objective of the Charpy impact test was to determine the optimal quantity of bamboo fiber to be incorporated into mortar when subjected to impact loads. standard Charpy impact test specimen is 55×10×10 mm in dimension. Experimental data show that the effective length and treatment of bamboo fiber affected the bond strength with mortar. A proportional relationship was observed between the addition of fiber to the mortar and an increase in compressive strength; The research findings indicate that the lowest modulus of toughness is 0.329 MPa, or equivalent compressive strength at 15.583 MPa, while employing 3.25 % bamboo fiber in mortar with a fiber length of 5 cm and a 12 % alkaline treatment
Cultivating Work Engagement and its Related Factors in Saudi Nurses: A Cross-Sectional Study
Authors: Al-Ahmari Khulud A. , Kattan Waleed M. 
Published: September 2024

Background
Work engagement in nursing is pivotal for quality care and workforce stability. Under the healthcare goals of Vision 2030, understanding and improving the engagement of nurses is critical in Saudi Arabia. Yet, the current literature on the work engagement of Saudi nurses is scant, presenting a knowledge gap.

Objective
This study aimed to evaluate work engagement levels among Saudi nurses at King Abdulaziz Hospital, Jeddah, and identify key contributing factors.

Methods
A quantitative cross-sectional study design was utilized. A structured questionnaire assessing demographic information, work-related characteristics, and levels of work engagement using the Utrecht Work Engagement Scale was distributed electronically to 466 Saudi nurses at the hospital in November, 2019. Two hundred twenty-four nurses responded. Data were analyzed using descriptive statistics, Mann-Whitney, Kruskal-Wallis tests, and Spearman's correlation and linear regression analysis to examine relationships between engagement and factors.

Results
The mean engagement score was 66.16%, with absorption scoring the highest. Work engagement was significantly associated with years of experience, work position, and employment status. Engagement was higher in nurses with over 20 years of experience and managers. Income level approached a significant relationship with engagement. Demographic factors did not correlate significantly with engagement.

Conclusion
The study found that work engagement among Saudi nurses was significantly influenced by experience, work position, and employment status. These findings highlight the need for a strategic focus on career development and job satisfaction to boost engagement. Future research should explore interventions that enhance engagement and their effects on healthcare outcomes.
Forecasting Delivery Time of Low-Risk Pregnant Women by Applying Linear Regression
Authors: Chaisitsa-nguan Kunnikar , Sitkulanan Piyaporn 
Published: September 2024

Background
The period of normal childbirth is the shortest, lasting no more than 24 hours, but it is the most important because up to 1 in 3 fetal deaths occur during birth. Accurate predictions of the time of birth can help health professionals provide effective care for the women during the time they give birth.

Objective
The aim of this research is to investigate the influence of cervical dilatation, the effacement of the cervix, station of the presentation, body mass index, maternal height, fetal weight, dose and duration of oxytocin exposure. The study also reviews the time a number of pregnancies take in low-risk pregnant women and to create a mathematical equation model for use in predicting the time to delivery.

Methods
This study is a retrospective descriptive study conducted from July 2023 to December 2023 at Thammasat Hospital. One hundred and eight low-risk pregnant women who had 37+ 0 to 41+ 6 weeks of gestation were selected by stratified random-sampling technique and systematic random sampling technique. The sample size was 108 participants. The research tool consisted of observation sheets and questions. Data analysis was obtained using multiple linear regression with the Stepwise regression method to examine the factor that influenced the time to delivery and create the equation.

Results
The obtained model had an R2 value of 0.316. The significant variables that mostly influence the time of delivery were the timing of oxytocin exposure (β = 0.31, p < .01) and cervical dilatation (β = -31.51, p < .01). The explanatory power of the regression model was statistically significant at 31.03%.

Conclusion
This study was designed for improving the prediction of time to delivery, which can be useful for enhancing the preparation pathways of normal childbirth. In this way, multiple regression analysis showed that the timing of oxytocin exposure and cervical dilatation can predict the time of birth.