bluuebunny/crossref_metadata_2025_split huggingface dataset:
‘A whirlwind of everything’: The lived experience of adolescents with co‐occurring chronic pain and mental health symptoms
Authors: Corser Jenny , Caes Line , Bateman Sharon , Noel Melanie , Jordan Abbie 
Published: May 2023

AbstractBackgroundCo‐occurring chronic pain and mental health issues are prevalent in adolescents, costly to society and can lead to increased risk of complications throughout the lifespan. While research has largely examined paediatric chronic pain and mental health in isolation, little is known about the unique challenges faced by adolescents who experience these co‐occurring symptoms. This idiographic study examined the lived experience of adolescents with co‐occurring chronic pain and mental health symptoms to identify salient issues for this population.MethodsSemi‐structured telephone interviews were conducted with seven adolescents (11–19 years) self‐reporting diagnoses of both pain and mental health issues for a duration of 3 months or longer. Participants were recruited from UK‐based schools, pain clinics and charities. Interview transcripts were analysed using interpretative phenomenological analysis.ResultsAnalyses generated two themes ‘a whirlwind of everything’ and ‘putting up fronts’, which describe how the experience of co‐occurring chronic pain and mental health symptoms typically disrupted adolescents' ability to regulate their physical, psychological and social wellbeing and identity. Adolescents described their symptom experience as like an internal storm over which they had no control. Such experiences required adolescents to embrace a variety of symptom management strategies, with adolescents reporting deliberate efforts to minimize their symptoms to external individuals.ConclusionCo‐occurring pain and mental health symptoms may be experienced in similar ways to individually experienced pain or mental health symptoms, but together, the experience may be both more difficult to manage and more socially isolating.SignificanceAdolescents with co‐occurring chronic pain and mental health symptoms describe the experience as if there was a storm inside of them disrupting their sense of physical, emotional and social wellbeing. This inner chaos interferes with their self‐identity and relationships with others. Challenges articulating their experiences, and negative encounters associated with their symptoms, further impact feelings of isolation and difficulties accessing support.
Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms
Authors: Yenurkar Ganesh Keshaorao , Mal Sandip , Nyangaresi Vincent O. , Hedau Anshul , Hatwar Prajwal , Rajurkar Shreyas , Khobragade Juli 
Published: May 2023

AbstractAI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.
Sustainable fuels: Lower alcohols perspective
Authors: Anekwe Ifeanyi Michael Smarte , Nyembe Nhlanhla , Nqakala Loyiso Clemence , Madikizela Mbaliyezwe , Isa Yusuf Makarfi 
Published: May 2023

AbstractIn the coming years, energy sources will play a vital role in global development. Biofuels made from sustainable sources are essential for the global economy's long‐term viability and the reduction of greenhouse gas emissions. Clean, renewable, and sustainable must be the watchwords for future energy strategy. Alcohol fuels are again becoming a popular term in the context of green fuel usage in relation to climate change mitigation and clean fuel technology. In this review, low alcohol synthesis, applications and limitations as fuel and its catalytic conversion to fuel and petrochemicals were discussed, in addition to the techno‐economic evaluation, environmental implications and prospects for practical application of low alcohols as fuels and petrochemicals. The study shows that lower alcohols have emerged as a fundamental feedstock for the synthesis of fuels and value‐added chemicals due to the increased accessibility of conversion technologies in recent years, but the cost of producing lower alcohols affects their commercialization and availability.
Contribution of home garden vegetables on reducing stunting among 6‐ to 23‐month‐old children in South Tigray, northern Ethiopia
Authors: Mahari Hailemariam Tekie , Kahsay Zenebe Abraha , Abraha Girmay Gebresamuel , Abraha Amanuel Zenebe , Henry Carol J. , Nickerson Michael T. , Mulugeta Bezabih Afework 
Published: May 2023

AbstractThe study was conducted to analyze the contribution of home garden vegetables in reducing stunting among 6‐ to 23‐month‐old children from South Tigray, Northern Ethiopia. The quasi‐experimental study design was used. Multistage sampling technique was used to select the districts and study communities. A total of 94 purposively selected vegetable producer (intervention) households and 260 randomly selected non‐producer (control) households were included in the study (1:3 ratio). The recumbent length of children was measured using horizontal wooden board to the nearest 0.1 cm. The length‐for‐ageZ‐scores were computed using WHO‐Anthro 2006 software. Propensity score‐matching and difference‐in‐difference (DID) estimates were used to analyze data using STATA software version 12. Prevalence of child stunting was 19.8 (12.7–29.4) and 21.1 (16.4–26.7)% (baseline) and 43.5 (33.5–54.1) and 46.5 (45.7–47.2)% (end line) among intervention and control groups, respectively. Child stunting was higher for boys and older children from both intervention and control households. DID estimation revealed that there was no significant difference in child stunting between intervention and control households (DID = 1.7,p = .604). However, there was an intervention effect of −0.5, 2.5, and 1.7% in the prevalence of child stunting among females, males, and both sexes, respectively. Vegetable production as an intervention strategy reduced the prevalence of stunting in children aged 6–23 months. However, vegetable production needs to be well integrated with other nutrition‐sensitive interventions to realize the objective of reducing child stunting.
A good hedge or safe haven? The hedging ability of China's commodity futures market under extreme market conditions
Authors: Huang Huilian , Xiong Tao 
Published: May 2023

AbstractUsing a proposed quantile‐based hedging analysis framework, this paper analyzes the hedging properties and portfolio performance of 15 commodities futures in China, especially under extreme market conditions. Quantile‐on‐quantile regression results show that the dependency structures of most spot and futures prices exhibit an almost similar shape with positive coefficients, which indicates that most futures products are good hedges and safe havens for the corresponding spot prices. Quantile hedge ratios vary with the quantiles, displaying a U‐shape, so the hedge ratios of most commodities should increase moderately in both bearish and bullish markets. As for portfolio performance, metal commodities perform best; agricultural commodities second best, and energy commodities relatively poorly. Out‐of‐sample quantile hedging results are consistent with in‐sample results. Therefore, participating in the futures market can effectively manage risk. The premise is that investors pay attention to market dynamics and adjust their portfolios promptly.
An empirical investigation on risk factors in cryptocurrency futures
Authors: Chi Yeguang , Hao Wenyan , Hu Jiangdong , Ran Zhenkai 
Published: May 2023

AbstractWe investigate the cross‐section asset‐pricing patterns of major cryptocurrencies from 2017 to 2021. We show that the basis, momentum, and basis–momentum factors earn statistically significant excess returns, a result consistent with the findings reported in the commodity futures literature. The basis is the strongest signal predicting cross‐sectional differences in cryptocurrency futures returns; the momentum‐induced risk premium is not statistically powerful, whereas the basis momentum‐induced risk premium disappears when accounting for the basis‐induced risk premium. Daily factor returns are statistically much stronger than weekly factor returns. Monthly factor returns are nonsignificant.
VPS72, a member of VPS protein family, can be used as a new prognostic marker for hepatocellular carcinoma
Authors: Huang Jian , Gan Jin , Wang Jian , Zheng Min , Xiao Han 
Published: May 2023

AbstractBackgroundVacuolar protein sorting (VPS) plays a crucial role in intracellular molecular transport between organelles. However, studies have indicated a correlation between VPSs and tumorigenesis and the development of several cancers. Nevertheless, the association between VPSs and hepatocellular carcinoma (HCC) remains unclear.MethodsBy analyzing databases such as The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC), we investigated the differences in VPSs expression between normal tissue and HCC transcriptomes. Furthermore, we examined the relationship between VPSs expression and overall survival (OS) in patients with HCC. Univariate and multivariate Cox analyses were employed to assess the prognostic value of VPS72 as an independent factor, and the correlation between VPS72 and the tumor immune microenvironment was also analyzed.ResultsWe observed significant overexpression of 28 VPSs in HCC tissues compared to normal tissues. The mRNA expression of VPSs displayed a negative correlation with OS, while exhibiting a positive correlation with tumor grade and stage. Additionally, both univariate and multivariate Cox analyses identified VPS72 as a potential independent risk factor for HCC prognosis. Overexpression of VPS72 demonstrated a positive correlation with various clinicopathological factors associated with poor prognosis, as well as the infiltration levels of immune cells.ConclusionTherefore, our research shows that VPSs participate in HCC occurrence and development, especially VPS72, which may act as a potential target for HCC treatment and prognosis biomarker.
Hybrid deep neural network for automatic detection of COVID‐19 using chest x‐ray images
Authors: Acharya Upendra Kumar , Ali Mohammad Taha , Ahmed Mohd Kaif , Siddiqui Mohd Tabish , Gupta Harsh , Kumar Sandeep , Mishra Ajey Shakti 
Published: May 2023

AbstractThe 2019 coronavirus (COVID‐19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre‐trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X‐ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre‐trained models such as ResNet‐50, Inception V3, VGG‐16, VGG‐19, ResNet50V2, and Xception are used for better feature extraction and Chest X‐ray image classification. The overfitting issue were resolved using K‐fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.
Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images
Authors: Söylemez Ömer Faruk 
Published: May 2023

AbstractMelanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection‐based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size‐related various properties of dermoscopy images. To this end, EfficientNet models B3–B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross‐validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.
Sprinkler irrigation uniformity assessment: Relational analysis of Christiansen uniformity and Distribution uniformity
Authors: Xue Shaopeng , Ge Maosheng , Wei Fuqiang , Zhang Qianwen 
Published: May 2023

AbstractChristiansen uniformity (CU) and Distribution uniformity (DU) are the most commonly used irrigation quality evaluation indicators, focusing on overall uniformity and local water deficit, respectively. Clarifying the quantitative relationship between the CU and DU has important production value for determining the water distribution characteristics of whole and localized areas in the evaluation of sprinkler irrigation water uniformity and improving the quality of sprinkler irrigation. This paper analyses the relationship between the calculation formulas of CU and DU based on mathematical analysis. The results reveal a prominent linear relationship between CU and DU, and the sum of the slope coefficient and the constant term of the linear relationship equation is 1. The value of the slope coefficient of the CU–DU linear relationship characterizes the differentiated form of the sprinkler water distribution. The slope coefficient is determined by the nozzle type, operating parameters and working scenarios. Notably, the specific form of the linear relationship obtained by fitting the experimental results in the literature is not universal. The mapping relationship between the value of the slope coefficient and the distribution form of sprinkler irrigation water quantity was established, and the influence of the value range of the slope coefficient on the DU of the overall and local sprinkler irrigation water quantity was demonstrated. The results show that the deviation rates of CU and DU decrease with increasing slope coefficient. When the slope coefficient is less than 1, CU is greater than DU; and the slope coefficient ranges from 0.46 to 1.36. When the slope coefficient is 0.625, the possibility of uniform irrigation is the highest. This research helps to overcome the limitations of CU and DU for irrigation uniformity evaluation and provides a reference for carrying out sprinkler irrigation uniformity evaluation with comprehensive characterization capabilities.
Teaching children to identify and avoid food allergens using behavioral skills training
Authors: Quiroz Matthew J. , Schnell‐Peskin Lauren K. , Kisamore April N. , Watkins Jessica Day , Vladescu Jason C. 
Published: May 2023

AbstractAllergic reactions to allergenic foods can pose a lethal threat to children with food allergies. Previous research has demonstrated the effectiveness of using behavioral skills training (BST) plus in situ training (IST) to teach safety responses to children. However, there has not been an evaluation of using BST to teach food safety to children with food allergies. Three elementary‐school children of neurotypical development with food allergies participated. We evaluated the efficacy of BST with IST in teaching participants to identify and respond to allergenic foods by (a) asking to see the food packaging, (b) scanning the food label for the allergenic food, and (c) reporting the safety threat to an adult while not consuming the food. Trials without allergenic foods were also presented to ensure discriminated responding. All participants demonstrated the three correct safety responses after BST and responded differentially across allergenic and nonallergenic foods, with two participants requiring feedback (IST).
Oil prices in the real economy
Authors: Shu Haicheng , Spencer Peter 
Published: May 2023

SummaryThis paper presents a macro‐finance model of the US economy and the spot and futures markets for oil. The performance of the model is greatly enhanced by using the Kalman filter to model latent variables representing the inflation asymptote, the real price of oil and the slope of the futures curve. We find that these are dominated by innovations in observed futures prices, reflecting the importance of market expectations. Using the Kalman filter to capture inflationary shocks helps solve the notorious price puzzle, the tendency for increases in interest rates to anticipate such developments and apparently cause inflation. Futures prices also depend upon risk premiums, which we find are dominated by the latent variable representing the real oil price rather than macro variables like inflation and interest rates.
Wuzi Yanzong Pill protects neural tube defects by activating PI3K/Akt signaling pathway
Authors: Wang Xinliang , Yang Chanjuan , Li Yanrong , Gong Qiang , Ru Yi , Xie Liangqi , Xiao Baoguo , Jin Xiaoming , Ma Cungen , Chai Zhi , Fan Huijie 
Published: May 2023

AbstractNeural tube defects (NTDs) are severe congenital malformations that can lead to lifelong disability. Wuzi Yanzong Pill (WYP) is an herbal formula of traditional Chinese medicine (TCM) that has been shown to have a protective effect against NTDs in a rodent model induced by all‐trans retinoic acid (atRA), but the mechanism remains unclear. In this study, the neuroprotective effect and mechanism of WYP on NTDs were investigated in vivo using an atRA‐induced mouse model and in vitro using cell injury model induced by atRA in Chinese hamster ovary (CHO) cells and Chinese hamster dihydrofolate reductase‐deficient (CHO/dhFr) cells. Our findings suggest that WYP has an excellent preventive effect on atRA‐induced NTDs in mouse embryos, which may be related to the activation of the PI3K/Akt signaling pathway, improved embryonic antioxidant capacity, and anti‐apoptotic effects, and this effect is not dependent on folic acid (FA). Our results demonstrated that WYP significantly reduced the incidence of NTDs induced by atRA; increased the activity of catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH‐Px), and content of glutathione (GSH); decreased the apoptosis of neural tube cells; up‐regulated the expression of phosphatidylinositol 3 kinase (PI3K), phospho protein kinase B (p‐Akt), nuclear factor erythroid‐2 related factor (Nrf2), and b‐cell lymphoma‐2 (Bcl‐2); and down‐regulated the expression of bcl‐2‐associated X protein (Bax). Our in vitro studies suggested that the preventive effect of WYP on atRA‐treated NTDs was independent of FA, which might be attributed to the herbal ingredients of WYP. The results suggest that WYP had an excellent prevention effect on atRA‐induced NTDs mouse embryos, which may be independent of FA but related to the activation of the PI3K/Akt signaling pathway and improvement of embryonic antioxidant capacity and anti‐apoptosis.
Investigating the tension between persistence and well‐being in engineering doctoral programs
Authors: Shanachilubwa Kanembe , Sallai Gabriella , Berdanier Catherine G. P. 
Published: May 2023

AbstractBackgroundWhile studies examining graduate engineering student attrition have grown more prevalent, there is an incomplete understanding of the plight faced by persisting students. As mental health and well‐being crises emerge in graduate student populations, it is important to understand how students conceptualize their well‐being in relation to their decisions to persist or depart from their program.Purpose/HypothesisThe purpose of this article is to characterize the well‐being of students who endured overwhelming difficulties in their doctoral engineering programs. The PERMA‐V framework of well‐being theory proposes that well‐being is a multifaceted construct comprised of positive emotion, engagement, relationships, meaning, accomplishment, and vitality.Design/MethodData were collected in a mixed‐methods research design through two rounds of qualitative semistructured interviews and a survey‐based PERMA‐V profiling instrument. Interview data were analyzed thematically using the PERMA‐V framework as an a priori coding schema and narrative configuration and analysis.ResultsThe narratives demonstrated the interconnectedness between the different facets of well‐being and how they were influenced by various experiences the participants encountered. The participants in this study faced prolonged and extreme adversity. By understanding how the multiple dimensions of well‐being theory manifested in their narratives, we better understood and interpreted how these participants chose to persist.