bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rationale for use for monoclonal antibody therapy in multisystem inflammatory syndrome in children: a systematic review
Authors: Reyes Jason , Salter Sandra , Sanfilippo Frank , Bradshaw Pamela , MacDonald Bradley 
Published: September 2023

Abstract
Objective
To review systematically the rationale for choice and use of monoclonal antibody therapy in multisystem inflammatory syndrome in children (MIS-C).
Study design
A systematic review was conducted using electronic databases (MEDLINE, Embase, Global Health, SCOPUS, and Web of Science) and preprint servers (MedRxiv and BioRxiv) between 01/01/2020 and 01/03/22. Studies were included if they had pediatric patients aged <21 years with MIS-C that had experienced the use of monoclonal antibodies. Randomized controlled trials, cohort studies, case reports, case series, case-control studies, and cohort studies were included.
Results
Twenty observational studies with 726 patients were included. Anakinra, tocilizumab and infliximab were used in 9.9%, 6.9%, and 12.4% of the cases, respectively. The most common rationale for the initiation of monoclonal antibody therapy for MIS-C was for patients who presented with severe disease or refractory to first line therapy. Infliximab tended to be used because of its precedence in managing IVIG-resistant Kawasaki Disease.
Conclusions
Use and choice of Monoclonal antibody seems to rely more on subjective clinician preferences than rigorous evidence-based studies. More studies exploring the exact pathogenesis of MIS-C are warranted to identify the key players of the cytokine storm that act as biological targets and their outcomes.
Nurses’ Preferences for Interventions to Improve Infection Prevention and Control Behaviors based on Systems Engineering Initiative to Patient Safety Model: A Discrete Choice Experiment
Authors: Zhou Qian , Liu Junjie , Zheng Feiyang , Wang Qianning , Zhang Xinping , Li Hui , Tan Li , Luo Wanjun 
Published: September 2023

Abstract
Background
The evidence of preferences for infection prevention and control (IPC) intervention from system perspective was lacked. This study aimed to elicit nurses' preferences for the intervention designed to improve IPC behaviors based on the Systems Engineering Initiative to Patient Safety (SEIPS) model using Discrete Choice Experiment (DCE).
Methods
A DCE was conducted among nurses from July 5th to 10th, 2021. Scenarios were formed by six attributes with varying levels based on SEIPS model: person, organization, tools and technology, tasks, internal environment and external environment. A conditional logit model and latent class logit model were performed to analyze the data.
Results
A total of 257 valid questionnaires were analyzed among nurses. The results from the latent class logit model show that nurses’ preferences can be divided into three classes. For class one (41.9%), positive coefficients were obtained in those six attributes. For class two (19.7%) only person was positively significant. For class three (36.4%), the most important attribute were tasks, tools and technology, internal environment and external environment.
Conclusions
This finding suggest that nurses have three latent-class preferences for interventions. Multifaceted interventions to improve IPC behaviors based on the SEIPS model are preferred by most nurses. Moreover, relevant measured should be performed targeted the latent class of person-preferred and external-environment-preferred nurses.
Profiles of well-being in French older adults and associations with successful aging and personality: findings from the SHARE project
Authors: Dendaele Elina , Kristell Pothier , Nathalie Bailly 
Published: September 2023

Abstract
Maintaining the well-being of the older adults is a primary concern in gerontology. This study determined different profiles of well-being (WB) and compared the profiles in terms of successful aging (SA), personality, and sociodemographic variables. The study sample consisted of 856 adults aged 65 to 98 years. WB was taken into account in an eudemonic and hedonic approach. SA was measured by assessing the three distinct components of Rowe and Kahn's model (1997), personality by the Big Five Inventory, and sociodemographic variables. Latent class analyses (LCA) determined the number of WB profiles, and ANOVAs and Chi2 tests to compare them. The LCA revealed three WB profiles: Profile 1 (9.35%, n=80), Profile 2 (37.38%, n=320), and Profile 3 (53.27%, n=456) in which participants reported lower, intermediate, and higher WB scores. Our results confirm that a high level of WB (Profile 3) can be linked to the components of SA and socio-demographic characteristics (age, marital status, level of education, income). This raises questions about the injunctions concerning healthy aging that older people integrate. It's also interesting to note that the intermediate profile (profile 2) can be either close to the "lower WB" profile (Profile 1) in terms of openness, conscientiousness, and agreeableness or to the "higher WB" profile (Profile 3) in terms of extraversion. However the three profiles do not have the same level of neuroticism. These results also showed the importance of adapting the support offered to older people according to their health status and/or individual characteristics.
Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain
Authors: Al-Otum Hazem Munawer 
Published: September 2023

Abstract

Image watermarking technologies have been developed to meet the concerns in multimedia copyright protection caused by the tremendous demand on multimedia sharing. In this work, a novel color image watermarking scheme is proposed for copyright protection applications. The developed scheme employs the wavelet packets decomposition (WPD) along with utilizing the back-propagation feed-forward neural networks (BPFNN). Here, a BPFNN-based color image watermarking using WPD decomposition approach is proposed and is denoted as FNN-WPD. The produced WPD-packets, from each of the RGB color channels, are reordered into chains having specific spatial- and frequency-based features. These chains are used for mark embedding and extraction using the BPFNN construction. Simulations have shown superior imperceptibility (PSNR ≥ 60 dB) and improved high robustness against incident and malicious attacks, while exhibiting low computational cost, especially at the extraction stage. Moreover, the scheme has been extended to be used for QR-code embedding with very promising robustness performance.
Salts containing different amino acids: L-argininium (+) sarcosine halogenides
Authors: Tonoyan Gayane S. , Giester Gerald , Petrosyan Aram M. 
Published: September 2023

Abstract
Recently we have discovered a new class of amino acids salts containing different amino acids. In the present paper we report crystal structures of three isostructural salts: l-argininium(+) sarcosine chloride (I), l-argininium(+) sarcosine bromide (II) and l-argininium(+) sarcosine iodide (III), formed by slow evaporation at room temperature from aqueous solutions containing stoichiometric ratios of components. The compounds crystallize in the polar space group P21 with two formula units in the asymmetric unit. The structures are stabilized due to N-H···O and N-H···X (X = Cl, Br, I) hydrogen bonds. Infrared spectra of all three crystals are shown and discussed.
Vegetation Quality Assessment: a sampling-based, loss-gain accounting framework for native, disturbed and reclaimed vegetation
Authors: Boyle Bradley L. , Franklin Warn , Burton Alison , Gullison Raymond E. 
Published: September 2023

Abstract
Governments and society increasingly are demanding that industrial projects result in a net positive impact (NPI) on biodiversity. Impacts are commonly measured in terms of losses and gains of area and quality of vegetation, where quality refers to how closely a site matches the condition of native vegetation in its undisturbed state. Existing vegetation quality frameworks share a number of limitations, including little or no replication, uncertain scope of inference, vulnerability to bias, and inability to measure error. Here we present the Vegetation Quality Assessment (VQA) framework, a sampling-based extension of Quality Hectares that measures vegetation quality in terms of overlap between the probability distributions of ecological indicators at a project site and in undisturbed (benchmark) vegetation of the same kind. Distribution overlap incorporates natural variation at the landscape scale and provides an intuitive measure of quality that varies between 0 and 1. Indicators are measured using a stratified-random sampling design that minimizes bias and supports inference at the scale of the project landscape. Confidence limits of quality and quality hectares are determined by bootstrapping; power and minimum sample sizes are estimated by Monte Carlo simulation. Multiple assessments track losses and gains of quality hectares and enable accurate accounting of progress to NPI. The VQA framework can be implemented using a variety of vegetation sampling methods, allowing existing vegetation databases to be leveraged as sources of data. We conclude by demonstrating the application of VQA at several mining operations in the Elk Valley of southeastern British Columbia, Canada.
The Adjusting Influence of Social Media’s User-Generated-Knowledge (SMUGK) in Grasping the Contribution of Cognitive and Technological Factors in Citizens' Perceptions of Satisfaction from E-Government Services in the Global South Context: The Case of Oman
Authors: Hammami Samir , Durrah Omar , El-Maghraby Lujain , Hamdoun Mohamed 
Published: September 2023

Abstract

Governments' growing presence in the social media sphere has drawn attention to the knowledge creation best practices that help serve citizens more effectively. A wide range of knowledge-creation practices can mediate critical dimensions beyond key social media usage and capability, for example, system quality and service provision, to include the associated risk and value perceptions of citizen-government interactions. These interactions can contribute to forming and encouraging virtual communities of practice collecting, verifying, storing, disseminating, reusing, and sharing knowledge that can allow better government service and more vital citizen satisfaction. Using a multi-theoretical lens: this study investigates, within the Omani context, the impact of technological and cognitive aspects of citizen social media conversations on citizen satisfaction levels as mediated by knowledge creation. The results present many interesting findings based on the Python analysis of Omani citizens’ tweets on government services. The authors build upon and contribute to existing research on e-government from the citizen perspective and the practice of government social media management targeted at civilians’ satisfaction.
Carbohydrate intake and diabetic drugs: Understanding the dynamic interplay in diabetes management
Authors: MH Mphasha , RV Vagiri 
Published: September 2023

Abstract
The body relies on carbohydrate as its main source of energy to carry out daily activities. Measuring carbohydrate allows patients to estimate the amount of carbohydrates in meals and snacks, and they can then alter their prescription as necessary. A thorough grasp of how medication therapy and carbohydrate intake interact is necessary for effective diabetic control. This systematic review attempts to summarise the most recent data on the influence of drug and carbohydrate interaction on glycaemic control in patients because there is currently no clear outline of the drug and carbohydrate interaction. Electronic databases such as PubMed, Embase, Google Scholar, and Scopus were used to conduct a thorough literature search. Included were studies examining the relationship between dietary carbohydrate intake and particular diabetes medications. In accordance with the eligibility requirements, data extraction and quality evaluation were done. Data reporting adhered to PRISMA. The systematic review included a total of 55 studies that satisfied the inclusion requirements. The studies included quantitative, qualitative and mixed method studies. The outcomes of glycaemic control, including fasting blood glucose, postprandial glucose levels, and HbA1c, were significantly influenced by the interaction between medication therapy and carbohydrate intake, according to the data. This systematic review emphasises the significance of taking into account how medication therapy and carbohydrate intake interact while managing diabetes. The results highlight the need for tailored strategies to improve glycaemic control while reducing unfavourable outcomes. To clarify the mechanisms behind these interactions and create evidence-based recommendations for customised diabetes therapy, more investigation is required.
Novel winglet design for reduction of Vortex Formation and enhancement of aircraft efficiency
Authors: Bhagat Aniket Dhanraj , Pattanaik Amitansu , Tiwari Harishankar Balram , Dethe Ganesh Prakash 
Published: September 2023

Abstract
In this research paper, we focus on creating a new winglet design to decrease vortex generation and consequently, reduce induced drag. This innovative winglet for aircraft wings helps improve overall efficiency. Winglets are aerodynamic components that are attached at the wingtips and play a crucial role in reducing drag and minimizing vortex formation, such as tip vortices, thus enhancing fuel economy. The main objective of this research paper is to mitigate the hindering effect on aircraft performance, namely, the reduction of detrimental vortices. The methodology of this research paper involves the use of software like CATIA for design and ANSYS software for analysis. The design process includes the creation of different design iterations of winglet designs with specific parameters, intending to reduce vortex formation and increase lift. Subsequent analyses were conducted on each design iteration to test performance, lift, and drag variations using ANSYS software. The results of analysis will be presented, highlighting the performance of each winglet design in comparison to the baseline wing. The study aims to identify the most efficient winglet design, aiming to minimize vortex and drag generation while maximizing lift and other aerodynamic parameters to increase overall efficiency. The study's implications will be explored for aircraft manufacturers and operators, including the potential benefits of fuel savings and a positive environmental footprint. This research significantly contributes to the aircraft domain by offering insights into innovative winglet design formulations capable of enhancing aircraft performance. This investigation serves as a foundation for further research in the realm of winglet design to enhance the sustainability and efficiency of future aircraft.
Ince-Gaussian beams propagation through turbulent atmospheric medium
Authors: Mechate Bouchaib El , Chafiq Abdelghani , Belafhal Abdelmajid 
Published: September 2023

Abstract
In this work, we reproduce the well-known Ince-Gaussian beams (IGBs) by expanding them in the Laguerre Gaussian basis. The established formula will permit us to derive the analytical expression of the average intensity of the IGBs propagating through the turbulent atmosphere by using the extended Huygens-Fresnel diffraction integral in the framework of Rytov theory. This work is concerned with finding a closed-form expression for the average intensity of IGBs propagating through weak and strong Kolmogorov turbulence and completes other scientific research works that deal with numerical approach. The numerical simulations of the analytical average intensity at the receiver plane and the effective beam spot size of the IGBs propagating through turbulent atmosphere environment are presented in this paper. Furthermore, the influences of the beam profile parameters and the turbulence strength on the evolution of the diffracted beam are investigated and discussed in detail. The present results can be considered as a useful tool to predict the behavior of IGBs for the applications in turbulent atmosphere.
Research on Food Fine Grain Image Classification Based on Improved Residual Network
Authors: WANG Dong-ri-na , DONG Ying 
Published: September 2023

Abstract
Given the many pain points in the recognition process of existing food fine-grained image recognition methods, such as insufficient extraction of effective features, limited recognition of a single data source, and low recognition accuracy, an improved residual network based classification method for food fine-grained image joint training is proposed. Firstly, in order to accurately extract food features, a convolutional attention mechanism (CAM) is integrated, which adds attention weights to different fine-grained regions of the image to highlight important features and suppress other general features, thereby improving the model's ability to extract useful features; Secondly, by incorporating CBM and Fusion network structures, features from different datasets are decoupled and dimensionally fused. This article conducted experiments on three food datasets from different countries, and the accuracy of joint training on the three datasets reached 91.76%. The experimental results show that the improved network model has better recognition performance compared to the baseline algorithm, ResNet50, proving the effectiveness and superiority of this method. Moreover, compared to previous research on fine-grained image recognition, it can not only train a "knowledgeable" network model with multiple datasets, but also achieve higher accuracy in image recognition.
Estimating the risk of Yellow fever in the Caribbean using vector competence data
Authors: GABIANE Gaelle , BOHERS Chloé , MOUSSON Laurence , Obadia Thomas , Dinglasan Rhoel , VAZEILLE Marie , DAUGA Catherine , VIGLIETTA Marine , YEBAKIMA André , Vega-Rúa Anubis , GUTIERREZ-BUGALLO Gladys , RAMIREZ Rosa Margarita GELVEZ , SONOR Fabrice , ETIENNE Manuel , DUCLOVEL-PAME Nathalie , BLATEAU Alain , SMITH-RAVIN Juliette , de Lamballerie Xavier , FAILLOUX Anna-Bella 
Published: September 2023

Abstract
Mosquito-borne diseases are spreading globally due to the expansion of the geographic range of competent mosquito vector species and increased human movement, especially infected travelers from endemic to non-endemic regions. Yellow fever (YF) has been largely controlled via mass delivery of an effective vaccine and mosquito control interventions. However, there are warning signs that YF is re-emerging in both Sub-Saharan Africa and South America. Imported from Africa to the Americas in slave ships, YF was responsible for devastating urban outbreaks for several centuries. In the Caribbean, the last YF outbreak was reported in 1908 in Martinique. The mosquito Aedes aegypti is the main vector for urban transmission in YF-endemic regions. However, it is unclear if all Ae. aegypti populations can effectively vector yellow fever virus (YFV), as there is growing evidence that intrinsic virus and mosquito host factors can significantly influence arbovirus transmission. We evaluated the vector competence of fifteen Ae. aegypti populations (nine from Martinique, two from Haiti, one from Guadeloupe, one from Cuba, one from Florida (USA), and one from Colombia) for five YFV genotypes (Bolivia, Ghana, Nigeria, Sudan, and Uganda) at 14 and 21 days post-infectious feeding. Mosquito populations from the Caribbean and the Americas were able to transmit the five YFV genotypes, in some cases achieving viral loads in mosquito saliva of up to several thousands of infectious virus particles, with YFV strains for Uganda and Bolivia having higher transmission success. We also observed that Ae. aegypti populations from Martinique were more susceptible to YFV infection than other populations from neighboring Caribbean islands, as well as North and South America. Our vector competence data suggest that the threat of re-emergence of YF in Martinique and the subsequent spread to Caribbean nations and beyond is plausible.
Applying Minority Range to Gini Index to Handle Imbalanced Dataset in Decision Tree classifiers
Authors: Silaghi Marius , Mathew Ben 
Published: September 2023

Abstract
The Class imbalance Problem is a common problem in Machine Learning where the number of instances in one class is significantly lower than the other, this can lead to biased classification models where the ma- jority class dominates and the minority class is mis- classified. Decision Tree Classifiers are commonly used for classification tasks due to their simplicity and in- terpretability. However, the class imbalance Problem can negatively impact the performance of decision tree Classifiers (Chawla et al. 2002) If this paper we discuss a new approach to training a decision tree classifier that is an improvement over pre-existing approaches. We also provide experiments to prove that the pro- posed method is an improvement over the preexisting metrics.
The impact of psychological health on acute lymphoblastic leukemia: A case report
Authors: Alkabani Joudi , Mattit Ammar , AlFawares Maamoun , Najati Ghena 
Published: September 2023

Abstract

Acute lymphoblastic leukemia (ALL) is a common childhood cancer that affects both physical and psychological well-being. Understanding the connection between psychological and physical health is crucial for optimal treatment outcomes. We present a case of an ALL patient where psychological health significantly influenced treatment, prognosis, and overall outcome. The patient underwent chemotherapy with supportive radiation therapy. Emotional distress extended the initial session of treatment to six months. Over the subsequent sessions, the patient was provided with emotional and psychological support, which participated in the completion of treatment as scheduled. Psychological health plays a vital role determining the disease prognosis and treatment response. This case highlights the significance of psychological impact during the course of treatment. We observed that the patient's condition showed fluctuations corresponding to his emotional state, with positive outcomes observed during periods of good psychological health, while emotional distress corresponded to a deterioration in overall status.
UROBORUS NO ROMANCE A HISTÓRIA SEM FIM, DE MICHAEL ENDE
Authors: Santana Luiz Henrique Costa , Rodrigues de Souza Emanoel 
Published: September 2023

Este trabalho objetiva analisar a obra A história sem fim do escritor Michael Ende (1979), no que tange aos movimentos metaliterários. A metodologia adotada é de revisão bibliográfica atrelada a análise teórico-textual intrínseca, utilizando como aporte teórico, autores que contribuem com a temática tratada, tais como: Gustavo Bernardo (1999) destacando o conceito de literatura, Gonçalo Palácios (2009) propondo uma percepção do que se entende como articulação do real e a filosofia da linguagem, Roland Barthes (1997) e o seu entendimento sobre a corrupção do silêncio como uma característica da linguagem, Tzvetan Todorov (1980) abordando a generificação do texto fantástico. A narrativa é fundamentalmente cíclica, o texto retorna a dimensão real após fazer uma incursão densa na dimensão do fantástico. Voltando-se para si, a literatura volta ao humano. O uroborus, parace não ter fim, mas o fim dele tende a ser a humanidade.