bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protocol for a mixed-methods study to develop a Core Outcome Set for assessing interventions and care for parents after neonatal death (iCHOOSE Neonatal study)
Authors: Davies Anna , Williams Charlotte , Burden Christy , Mullin Sadie , Moy Lucy , Redshaw Maggie , Patel Mehali , Fraser Abigail , Hinton Lisa , Lynch Mary , Merriel Abi , Siasakos Dimitris , Webbe James , Odd David , Timlin Laura , Watts Leanne , Bakhbakhi Danya 
Published: May 2024

Abstract

Introduction: Neonatal death exerts long lasting impact on parents’ mental health, finances and relationships, and the wider family. There is national and international momentum to evaluate interventions to support parents after the death of a baby. Core Outcome Sets provide a minimum set of outcomes, agreed by stakeholders to be important, that should be evaluated in all studies to support evidence syntheses and identification of the most effective interventions.
Aim: We aim to develop a Core Outcome Set (COS) for assessing interventions and care after neonatal death, to support future evidence syntheses and enable identification of effective interventions and care for parents.
Methods and analysis: We will develop the COS in 6 phases. A parent involvement group and stakeholder steering committee have been established and have informed each planned phase: 1) Systematic review of quantitative studies evaluating care and interventions provided after neonatal death, to describe interventions, outcomes, and outcome measurement tools used to assess intervention effectiveness; 2) Qualitative interviews with parents who have experienced neonatal death to identify outcomes important and relevant to them; 3) Think-Aloud interviews with stakeholders (bereaved parents and professionals) to develop and refine an online survey; 4) Real-Time online international Delphi survey with bereaved parents and professionals to shortlist outcomes for consideration in the COS; 5) Adapted nominal group online consensus meetings with parents and professionals to agree final COS; 6) Identification of a preliminary set of measurement tools.
Ethics and dissemination: Ethical approval has been granted for all activities to be undertaken by the University of Bristol Health Sciences Faculty Research Ethics Committee (reference: 15121). We will disseminate the findings via peer-reviewed publications and relevant academic and professional conferences.
Registration: This COS has been registered on the COMET database.(1) The systematic review has been registered on the PROSPERO database (CRD42020151365)
Highly Sensitive Molecular Assay for the Detection of Treponema pallidum infection
Authors: Shiluli Clement , Kamath Shwetha , Kanoi Bernard N. , Kimani Racheal , Maina Michael , Waweru Harrison , Kamita Moses , Ndirangu Ibrahim , Abkallo Hussein M. , Oduor Bernard , Pamme Nicole , Dupaty Joshua , Klapperich Catherine M. , Lolabattu Srinivasa Raju , Gitaka Jesse 
Published: May 2024

Abstract

Introduction: According to the World Health Organization (WHO) more than 7 million new Treponema pallidum (T. pallidum) infections were reported among people aged 15–49 years in 2020 globally, the majority of them in developing countries. Syphilis, which is caused by T. pallidum is transmitted through contact with active lesions of a sexual partner or from an infected pregnant woman to her foetus. Gold standard T. pallidum laboratory diagnosis methods include dark-field microscopy, silver staining, direct fluorescence immunoassays and the rabbit infectivity test. However, these tests are associated with false positive or negative results. The gold standard 16S ribosomal (rRNA) gene polymerase chain reaction (PCR) is used for routine amplification of T. pallidum conserved genes. Here we report on an ultrasensitive syphilis diagnostic method, based on de novo genome mining of the T. pallidum DNA to identify identical multi repeat sequences (IMRS) as amplification primers.
Methods: We used genome-mining approaches to find IMRS distributed throughout the T. pallidum genome to design a primer pair that target four repeat sequences. Genomic T. pallidum DNA was diluted from 8.14×104 to 8.14×10− 2 genome copies/𝜇l and used as template in the IMRS-based amplification assay. For performance comparison, 16S rRNA PCR assay was employed. Probit analysis was used to calculate the lower limit of detection of the T. pallidum-IMRS PCR and the conventional 16S rRNA PCR assays.
Results: Probit analysis confirmed that the T. pallidum-IMRS primers offered higher test sensitivity of 0.03 fg/𝜇L compared to the 16S rRNA PCR (0.714 pg/𝜇L). Using the T. pallidum-IMRS primers, we were able to observe considerable isothermal amplification of genomic DNA at a starting concentration of 0.01 pg/µL.
Conclusion: De novo genome mining of T. pallidum IMRS as amplification primers can serve as a platform for developing ultrasensitive diagnostics for Syphilis and potentially a wide range of infectious pathogens.
Impact of Fiscal Policy on Manufacturing Sector Growth Performance in Nigeria
Authors: Abiola Olawale 
Published: May 2024

Abstract

This research delves into the correlation between Nigeria's manufacturing sector and Government Capital Expenditure. Employing regression analysis, we leverage time series data spanning 1981 to 2022 to shed light on this dynamic relationship. The study affirms the stationarity of all variables after first differencing. Furthermore, the Johansen co-integration test unveils a long-run equilibrium relationship among the selected variables, namely Value of Manufacturing output, Government Capital Expenditure, Value Added Tax, and Customs and Excise Duty. The analysis uncovers a robust and positive connection between the growth performance of the manufacturing sector and Government Capital Expenditure throughout the examined timeframe. The findings indicate that increased Government Capital Expenditure correlates with enhanced growth in the manufacturing sector output. This study underscores the critical role of public investment in fostering economic growth and industrial development. The results suggest that strategic allocation of resources towards infrastructure, regulatory frameworks, and supportive policies can significantly impact the vibrancy and competitiveness of Nigeria's manufacturing sector.
An operational framework to track individual farmland trees over time at national scales using PlanetScope imagery
Authors: Reiner Florian , Gominski Dimitri , Fensholt Rasmus , Brandt Martin 
Published: May 2024

Abstract

Trees outside forests, in particular on croplands, play a crucial role for food security and climate resilience in the Global South, but are threatened by increasing climatic change and human pressures. The dynamics of agroforestry systems and national cropland tree stocks are largely unknown, as currently no robust monitoring system exists to remotely detect single field trees and track changes at national scales. Here we present a framework to track cropland trees at the single tree level across multiple years, using a combination of satellite imagery, deep learning, and object-based change classification. The approach matches annual tree centre predictions to detect changes, such as individual tree losses from logging or tree mortality events. The slope between annual tree prediction confidence heatmaps is also used to detect areas of gains, with possible applications for monitoring plantation and restoration areas. The framework is designed for PlanetScope nano-satellite imagery, which offers unprecedented opportunities for detailed tree monitoring given the combined high spatial and temporal resolution. PlanetScope imagery, however, also come with a range of challenges, which are discussed and for which solutions are proposed. We demonstrate the framework by applying it to a national-scale case study of cropland trees in Tanzania from 2018 to 2022.
Performance of Bamboo Fibre on Modifying Mechanical Properties of Concrete
Authors: Osmi Siti Khadijah Che , Husen Hapsa , Othman Maidiana , Nor Norazman Mohamad , Sojipto Suriyadi , Termizi Muhammad Asyrah Hafry Mohd 
Published: May 2024

Introduction
Towards Go-green’s concept, the use of natural fibres in the construction industry has received substantial attention from many engineering fields. Literature shows that the waste has been used as a replacement or addition material in the concrete mixture for producing lightweight concrete and improving the mechanical properties of the concrete. This innovation is one of the alternative solutions in converting waste into valuable, sustainable materials and consequently reducing the abundance of the waste that may permit environmental pollution.

Aims
This study aims to investigate the mechanical properties of concrete with the addition of bamboo fibre (BF) from species type Bambusa Vulgaris, specifically focusing on the compressive and tensile strength of concrete. The chosen bamboo fibre is adopted because it is largely found in Peninsular Malaysia and has a high tensile strength, as suggested by previous researchers.

Methods
To achieve this, three percentages of the BF, i.e., 0.5%, 1%, and 1.25%, were added to concrete grade M20. The plain concrete i.e., 0% BF, was prepared as a control sample. The performance of BF on modifying the mechanical properties of concrete for both fresh (i.e., slump test) and hardened (i.e., compression and split tensile tests) states were examined after 7 to 28 days of the curing process.

Results
The findings indicate the effectiveness of BF in improving the mechanical properties of the concrete grade M20. It is noted that the concrete samples with the addition of 0.5% BF is considered as the optimum percentage because it able to increase the compressive and tensile strength of concrete grade M20.

Conclusion
This study provides significant information on the ability of BF as a concrete strength enhancer which subsequently converts the abundance of unwanted agriculture by converting the waste to new green and sustainable concrete material.
Facebook Challenge: Integration of Breastfeeding for Sick Babies and E-Training to Enhance the Knowledge of Healthcare Workers
Authors: Kaewwimol Patcharaporn 
Published: May 2024

Background
Although the Thailand Breastfeeding Center has supported breastfeeding for sick babies since 2014, it has been found that breastfeeding in infants is still 40% lower than national targets.

Objective
This research aimed to test the effectiveness of online breastfeeding training through social media to further the knowledge of healthcare workers.

Methods
A quasi-experimental research study of a sample group of 60 workers was conducted between the neonatal step-down unit and the postpartum care unit. Research instruments consisted of breastfeeding e-training through a Facebook platform, knowledge testing on the breastfeeding of sick newborns, an online training satisfaction assessment form, data analysis by frequency and percentage, averaging, and testing by t-test, chi-square, and the Fisher exact test.

Results
The mean and standard deviation of knowledge before training in the experiment and control group were 22.40±2.11and 23.43±3.32, respectively. After the intervention, the experimental group was 31.93±1.98, and the control group was 24.06±2.14. It was found that the average knowledge score pertaining to the breastfeeding of high-risk neonates in the group that had undergone online training was higher than the group that had undergone ordinary supervision (p< 0.001).

Conclusion
It can be seen that asynchronous e-training may be one alternative for the knowledge development of healthcare workers in promoting breastfeeding for families with sick newborns.
Delving into Machine Learning's Influence on Disease Diagnosis and Prediction
Authors: Shivahare Basu Dev , Singh Jagendra , Ravi Vinayakumar , Chandan Radha Raman , Alahmadi Tahani Jaser , Singh Prabhishek , Diwakar Manoj 
Published: May 2024

Introduction/ Background
Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses.

Materials and Methods
This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions.

Results
The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics.

Discussion
The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enhance diagnostic accuracy, yet it also emphasizes the need for continuous refinement and understanding of the underlying factors affecting algorithm performance. Future research should focus on optimizing these algorithms within diverse clinical settings to fully harness their diagnostic capabilities.

Conclusion
This study highlights the transformative potential of machine learning in medical diagnostics, demonstrating how various algorithms can effectively analyze digitized images and patient records to diagnose diseases. While the performance of these algorithms varies based on dataset characteristics, the overall high accuracy underscores machine learning's promise in healthcare. As the field continues to evolve, machine learning is poised to become an integral part of clinical diagnosis, enhancing the accuracy and efficiency of medical evaluations and treatments.
INFLUENCE OF A PREPARATION TRAINING FOR STUDENT COMPETENCY COMPETITIONS IN CAD MECHANICAL
Authors: Ardianto Fiki , Subandi Subandi , Jarwopuspito Jarwopuspito , Efendi Yosep 
Published: May 2024

Lomba Ketrampilan Siswa (LKS) is an annual activity in student competency competition among students at the Vocational High School (VHS) [R1] level in accordance with the areas of expertise taught at each participant's school. This activity is one part of a series of selections to get the best students from all over Indonesia who will be guided by teams in their respective areas of expertise and included in international level competency competencies. Students who take part in the LKS competition are students who are chosen by their school. To get the title of champion, of course, there is a lot of preparation that must be undertaken and one of them is training. This study aims to present data regarding the training program that students undergo as preparation for regional-level LKS competitions. This research uses a qualitative approach with a descriptive research type. The results of this study found that there were several scheduled, independent and incidental training programs
Health Information System Strengthening During Antenatal Care in Haiti: Continuous Quality Improvement Study
Authors: Casella Jean-Baptiste Meredith , Vital Julmiste Thamar Monide , Ball Ellen 
Published: May 2024

Background
Journey to 9 Plus (J9) is an integrated reproductive, maternal, neonatal, and child health approach to care that has at its core the goal of decreasing the rate of maternal and neonatal morbidity and mortality in rural Haiti. For the maximum effectiveness of this program, it is necessary that the data system be of the highest quality. OpenMRS, an electronic medical record (EMR) system, has been in place since 2013 throughout a tertiary referral hospital, the Hôpital Universitaire de Mirebalais, in Haiti and has been expanded for J9 data collection and reporting. The J9 program monthly reports showed that staff had limited time and capacity to perform double charting, which contributed to incomplete and inconsistent reports. Initial evaluation of the quality of EMR data entry showed that only 18% (58/325) of the J9 antenatal visits were being documented electronically at the start of this quality improvement project.

Objective
This study aimed to improve the electronic documentation of outpatient antenatal care from 18% (58/325) to 85% in the EMR by J9 staff from November 2020 to September 2021. The experiences that this quality improvement project team encountered could help others improve electronic data collection as well as the transition from paper to electronic documentation within a burgeoning health care system.

Methods
A continuous quality improvement strategy was undertaken as the best approach to improve the EMR data collection at Hôpital Universitaire de Mirebalais. The team used several continuous quality improvement tools to conduct this project: (1) a root cause analysis using Ishikawa and Pareto diagrams, (2) baseline evaluation measurements, and (3) Plan-Do-Study-Act improvement cycles to document incremental changes and the results of each change.

Results
At the beginning of the quality improvement project in November 2020, the baseline data entry for antenatal visits was 18% (58/325). Ten months of improvement strategies resulted in an average of 89% (272/304) of antenatal visits documented in the EMR at point of care every month.

Conclusions
The experiences that this quality improvement project team encountered can contribute to the transition from paper to electronic documentation within burgeoning health care systems. Essential to success was having a strong and dedicated nursing leadership to transition from paper to electronic data and motivated nursing staff to perform data collection to improve the quality of data and thus, the reports on patient outcomes. Engaging the nursing team closely in the design and implementation of EMR and quality improvement processes ensures long-term success while centering nurses as key change agents in patient care systems.
Impact of Drought in Northeastern Algeria: Comparative Study of the SPI and SPEI Índices
Authors: Ziari Abderrahmane , Medjerab Abderrahmane 
Published: May 2024

Purpose: The study aimed to evaluate drought trends in Annaba, Algeria, considering global warming and extreme weather, using quantitative indices for improved drought comprehension and prediction.

Theoretical Framework:The study assesses changes in drought frequency and severity in the Annaba region of Algeria due to global warming and extreme weather conditions. It employs quantitative indices to enhance drought understanding and prediction.

Methodology: The study analyzes temperature and precipitation data (1981-2021) from an Annaba weather station to assess drought characteristics.Standardized indices (SPI, SPEI) and extreme value analysis (Generalized Pareto Distribution) are used to quantify drought intensity, duration, and temperature impacts.

Findings: Our analyses reveal a critical divergence in drought assessment tools. While stable monthly temperatures suggest a constant influence on evapotranspiration, the SPEI index (including temperature) indicates increasing drought compared to the precipitation-only SPI. This highlights the importance of considering temperature alongside precipitation for accurate drought assessment.

Research Practical and Social Implication: This study emphasizes the significance of considering the interplay among temperature, precipitation, and evapotranspiration for better understanding and predicting drought changes in the Annaba region, Algeria. These insights are crucial for sustainable water resource management and climate change adaptation in the region.

Originality/Value:This study distinguishes itself by its comprehensive analysis of long-term climate data to examine drought trends in a specific region (Annaba, Algeria) severely impacted by climate change. By comparing the effectiveness of two drought indices (SPEI and SPI), it offers valuable insights into how to improve drought assessments by considering the role of temperature in evapotranspiration. This research enriches the existing literature on drought management and climate change adaptation, providing a valuable case study for similar arid and semi-arid regions worldwide.
Analysis of Determinants Factors Affecting Organizational Citizenship Behaviour in Post-Merger Sharia Banks
Authors: Hasan Diana , Sudiarditha I. Ketut R. , Wibowo  , Hakim Luqman , Susita Dewi 
Published: May 2024

Purpose: This study investigates the determinants of Organizational Citizenship Behavior (OCB), focusing on the relationship between Organizational Culture, Organizational Commitment, Career Development, and OCB.

Method: Using a Literature Review approach, this study analyzes scholarly articles published from 2016 to 2020 to identify gaps in research and explore the mediation effect of Career Development on the relationship between Organizational Culture and Organizational Commitment in influencing OCB.

Results and conclusion: The findings highlight the importance of Organizational Culture, Organizational Commitment, and Career Development in shaping OCB. While existing literature extensively examines OCB's relationship with organizational factors, such as culture and commitment, this study emphasizes the need for further research on the mediating role of career development. It also suggests that OCB is influenced by changes in organizational strategy and the implementation of human resource management practices, particularly in Indonesian companies.

Research implications: The study provides theoretical insights into OCB determinants and practical implications for organizational management in fostering OCB. It underscores the significance of considering career development alongside cultural and commitment factors.

Originality/value: This research contributes to the literature by addressing gaps in research and emphasizing the importance of career development in shaping OCB. It offers valuable insights for organizational management and contributes to the advancement of knowledge in organizational behavior and human resource management.