bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of anisotropy and disorder on the superconducting properties of niobium
Authors: Zarea Mehdi , Ueki Hikaru , Sauls J. A. 
Published: October 2023

We report results for the superconducting transition temperature and anisotropic energy gap for pure niobium based on Eliashberg’s equations and electron and phonon band structures computed from density functional theory. The electronic band structure is used to construct the Fermi surface and calculate the Fermi velocity at each point on the Fermi surface. The phonon bands are in excellent agreement with inelastic neutron scattering data. The corresponding phonon density of states and electron–phonon coupling define the electron–phonon spectral function, α2F(p, p′; ω), and the corresponding electron–phonon pairing interaction, which is the basis for computing the superconducting properties. The electron–phonon spectral function is in good agreement with existing tunneling spectroscopy data except for the spectral weight of the longitudinal phonon peak at ℏωLO = 23 meV. We obtain an electron–phonon coupling constant of λ = 1.057, renormalized Coulomb interaction μ⋆ = 0.218, and transition temperature Tc = 9.33 K. The corresponding strong-coupling gap at T = 0 is modestly enhanced, Δ0 = 1.55 meV, compared to the weak-coupling BCS value Δ0wc=1.78kBTc=1.43meV. The superconducting gap function exhibits substantial anisotropy on the Fermi surface. We analyze the distribution of gap anisotropy and compute the suppression of the superconducting transition temperature using a self-consistent T-matrix theory for quasiparticle-impurity scattering to describe niobium doped with non-magnetic impurities. We compare these results with experimental results on niobium SRF cavities doped with nitrogen impurities.
Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer
Authors: Xu Luyou , Zhang Jinxi , Li Zhen , Liu Yu , Jia Zhuang , Han Xiaowei , Liu Chenglin , Zhou Zhixiong 
Published: October 2023

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion.Methods: A total of 100 subjects aged 18–30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland–Altman analysis.Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland–Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias &lt; 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed.Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.
Global evaluation of key factors influencing nitrogen fertilization efficiency in wheat: a recent meta-analysis (2000-2022)
Authors: Yokamo Solomon , Irfan Muhammad , Huan Weiwei , Wang Bin , Wang Yiliu , Ishfaq Muhammad , Lu Diajun , Chen Xiaoqin , Cai Qiuliang , Wang Huoyan 
Published: October 2023

Improving nitrogen use efficiency (NUE) without compromising yield remains a crucial agroecological challenge in theory and practice. Some meta-analyses conducted in recent years investigated the impact of nitrogen (N) fertilizer on crop yield and gaseous emissions, but most are region-specific and focused on N sources and application methods. However, various factors affecting yield and N fertilizer efficiency in wheat crops on a global scale are not extensively studied, thus highlighting the need for a comprehensive meta-analysis. Using 109 peer-reviewed research studies (published between 2000 and 2022) from 156 experimental sites (covering 36.8, 38.6 and 24.6% of coarse, medium, and fine texture soils, respectively), we conducted a global meta-analysis to elucidate suitable N management practices and the key factors influencing N fertilization efficiency in wheat as a function of yield and recovery efficiency and also explained future perspectives for efficient N management in wheat crop. Overall, N fertilization had a significant impact on wheat yield. A curvilinear relationship was found between N rates and grain yield, whereas maximum yield improvement was illustrated at 150-300 kg N ha-1. In addition, N increased yield by 92.18% under direct soil incorporation, 87.55% under combined chemical and organic fertilizers application, and 72.86% under split application. Site-specific covariates (climatic conditions and soil properties) had a pronounced impact on N fertilization efficiency. A significantly higher yield response was observed in regions with MAP &gt; 800 mm, and where MAT remained &lt; 15 °C. Additionally, the highest yield response was observed with initial AN, AP and AK concentrations at &lt; 20, &lt; 10 and 100-150 mg kg-1, respectively, and yield response considerably declined with increasing these threshold values. Nevertheless, regression analysis revealed a declining trend in N recovery efficiency (REN) and the addition of N in already fertile soils may affect plant uptake and RE. Global REN in wheat remained at 49.78% and followed a negative trend with the further increase of N supply and improvement in soil properties. Finally, an advanced N management approach such as “root zone targeted fertilization” is suggested to reduce fertilizer application rate and save time and labor costs while achieving high yield and NUE.
The impact of a changed writing environment on students' motivation to write
Authors: Myhill Debra , Cremin Teresa , Oliver Lucy 
Published: October 2023

IntroductionThe act of writing is widely acknowledged to be a complex and challenging activity, and in parallel, we know that student motivation to write is a predictor of writing performance. So understanding what characteristics of the writing classroom support or foster motivation remains a salient concern. Research has shown that UK teachers are more likely to see themselves as readers than writers, which may affect how they teach writing.MethodsThis paper reports on student focus group interview data from a study which sought to strengthen teachers' sense of themselves as writers, and to examine the impact of this on students' classroom experience of writing and their writing outcomes. The participant teachers experienced a creative writing residential, which established a writing community led by two professional writers, with the goal of changing teachers' professional practice in their own writing classrooms. The study was mixed methods, comprising a randomized controlled trial and a comprehensive qualitative dataset collating data from both the residential and the classroom. This paper presents the qualitative analysis of 32 interviews with 16 student focus groups, exploring their responses to their teachers' changed practices and how it connected with their motivation to write.ResultsThe interview analysis shows how many students responded positively to new teaching practices which gave them greater autonomy and choice, and established a more collaborative way of working. This led to increased confidence in and motivation to write.DiscussionThe study highlights the importance of the classroom environment in supporting and sustaining motivation to write, and underlines that motivation is not simply an internal characteristic of an individual but is situated within the context of a community of writers.
Recognition of masked and unmasked facial expressions in males and females and relations with mental wellness
Authors: Huc Marie , Bush Katie , Atias Gali , Berrigan Lindsay , Cox Sylvia , Jaworska Natalia 
Published: October 2023

BackgroundWhile the effects of mask wearing/facial occlusion are known to impair facial expression recognition, little is known about the role of mental wellness on facial expression recognition, as well as the influence of sex on misattribution errors (i.e., confusions between emotions). In this large study, we aimed to address the relation between facial expression recognition and loneliness, perceived stress, anxiety, and depression symptoms in male and female adults.MethodsWe assessed the influence of mask-wearing on facial expression recognition [i.e., accuracy and response time (RT)] via an online study in N = 469 adult males and females across Canada.ResultsExpectedly, recognition was impaired under masked conditions (i.e., lower accuracy, longer RTs, more misattribution errors). Females were faster and more accurate than males, with less misattribution errors. A novel finding was that people with higher perceived stress were less accurate at identifying masked fearful faces. Perceived stress influenced the relation between sex and RT to masked happy faces; males with high stress scores were slower to recognize masked happy faces, the opposite was true for females. Finally, this study was among the first to show that higher loneliness predicted shorter RT to unmasked faces.ImpactOur results show that facial expression recognition is impaired by mask-wearing, and that sex and mental health features are important predictors of performance. Such insight could be detrimental in certain sectors of the population (e.g., health care or education), and inform policies being adopted in future pandemics.
Spatial variation and determinants of inappropriate complementary feeding practice and its effect on the undernutrition of infants and young children aged 6 to 23 months in Ethiopia by using the Ethiopian Mini-demographic and health survey, 2019: spatial and multilevel analysis
Authors: Derseh Nebiyu Mekonnen , Shewaye Daniel Alayu , Agimas Muluken Chanie , Alemayehu Meron Asmamaw , Aragaw Fantu Mamo 
Published: October 2023

BackgroundInappropriate complementary feeding practices (IACFPs) are major public health issues in Ethiopia, which usually result in stunting and becoming an intergenerational cycle. However, the spatial patterns and determinants of IACFP and its effect on undernutrition are not well understood in Ethiopia. Therefore, the aim of this study was to explore the spatial patterns and determinants of IACFPs and their effects on the undernutrition of infants and young children (IYC) in Ethiopia.MethodsThis was a nationwide community-based survey study among 1,463 mothers of IYC aged 6–23 months in Ethiopia. The global spatial autocorrelation was assessed using the global Moran’s-I to evaluate the spatial clustering of IACFPs. Significant clusters with high and low rates of IACFPs were explored. A multilevel mixed-effect logistic regression with cluster-level random effects was fitted to identify determinants of IACFPs with an AOR and 95% CI.ResultsThe prevalence of IACFPs was 90.22%. The spatial pattern of IACFP in Ethiopia was clustered across regions (Global Moran’s I = 0.63, Z-score = 12.77, value of p ≤0.001). Clusters with a high rate of IACFP were detected in southern, northwest, and eastern Ethiopia. Individual and community-level variables accounted for 63% of IACFP variation. Mothers with no education were 3.97 times (AOR = 3.97; 95% CI: 1.64–9.60) more likely to have IACFPs than those with higher education. The poorest HHs had 4.80 times the odds of having IACFPs as the richest HHs (AOR = 4.80, 95% CI: 1.23–18.71). The odds of having IACFPs were 2.18 times (AOR = 2.18; 95% CI: 1.28–3.72) higher among babies with no postnatal checkup. Non-breastfed IYC were 2.8 times (AOR = 2.80; 95% CI: 1.29–6.10) more likely to have IACFP when compared with breastfed ones. IYC with the inadequate introduction of solid, semi-solid, and soft foods, inadequate minimum dietary diversity, and inadequate minimum acceptable diet were more likely to have wasting, underweight, and stunting.ConclusionThis study showed the prevalence of IACFPs was very high, which had an effect on undernutrition and showed spatial variation in Ethiopia. Therefore, the government of Ethiopia and stakeholders should focus on women with no education and the poorest HHs, encourage PNC checkups, and encourage breastfeeding in the hotspot areas to minimize IACFPs in Ethiopia.
Clinical outcome prediction of acute neurological patients admitted to the emergency department: Sequential Organ Failure Assessment score and modified SOFA score
Authors: Donoso-Calero María I. , Sanz-García Ancor , Polonio-López Begoña , Maestre Miquel Clara , Durantez Fernández Carlos , Mordillo-Mateos Laura , Mohedano-Moriano Alicia , Conty-Serrano Rosa , Otero-Agra Martin , Jorge-Soto Cristina , Martín-Conty José L. , Martín-Rodríguez Francisco 
Published: October 2023

BackgroundThe aim of this study was to determine the ability of the Sequential Organ Failure Assessment score (SOFA) and modified SOFA score (mSOFA) as predictive tools for 2-day and 28-day mortality and ICU admission in patients with acute neurological pathology treated in hospital emergency departments (EDs).MethodsAn observational, prospective cohort study in adults with acute neurological disease transferred by ambulance to an ED was conducted from 1 January 2019 to 31 August 2022 in five hospitals in Castilla-León (Spain). Score discrimination was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the score.ResultsA total of 640 adult patients with neurological disease were included. For the prediction of 2-day mortality (all-cause), mSOFA presented a higher AUC than SOFA (mSOFA = 0.925 vs. SOFA = 0.902). This was not the case for 28-day mortality, for which SOFA was higher than mSOFA (mSOFA = 0.852 vs. SOFA = 0.875). Finally, ICU admission showed that SOFA was higher than mSOFA (mSOFA = 0.834 vs. SOFA = 0.845).ConclusionBoth mSOFA and SOFA presented similar predictive ability, with mSOFA being the best predictor for short-term mortality and SOFA being the best predictor for medium-term mortality, as well as for ICU admission. These results in a cohort of patients with acute neurological pathology pave the way for the use of both predictive tools in the ED. The inclusion of these tools could improve the clinical assessment and further treatment of neurological patients, who commonly present the worst outcomes.
The impact of COVID-19 on sexual behavior, HIV prevention interest, general healthcare access, and other HIV risk factors among trial participants in Malawi, South Africa, Uganda, and Zimbabwe
Authors: Mancuso Noah , Mathebula Florence , Chitukuta Miria , Matambanadzo Kudzai V. , Tenza Siyanda , Reddy Krishnaveni , Nobula Lumka , Kemigisha Doreen , Stoner Marie C. D. 
Published: October 2023

IntroductionThe COVID-19 pandemic greatly impacted HIV prevention and care globally. The pandemic also had disproportionate impacts on the financial, emotional, and physical wellbeing of women and girls in East and Southern Africa, who were already at increased HIV vulnerability. This study aimed to understand how the COVID-19 pandemic and its response efforts impacted the sexual behavior, HIV prevention interest, general healthcare access, and other HIV risk factors of women and girls in HIV prevention studies.MethodsUsing the socio-ecological model (SEM), an explanatory sequential mixed-methods analysis was performed with data from four Microbicide Trial Network (MTN) studies on different populations—adolescent girls and young women (AGYW), pregnant persons, breastfeeding persons, and couples—in Malawi, South Africa, Uganda, and Zimbabwe. Descriptive statistics for outcomes of interest were calculated within each study separately and Chi-squared tests of independence were performed to evaluate associations between study population and outcomes. Excerpts from study qualitative interviews were stratified into code reports which were then summarized into memos with key themes and considerations of the SEM framework to provide context to quantitative findings.ResultsFew participants (8/731) had known or suspected COVID-19 infection. Sexual frequency and alcohol use decreased most often among AGYW compared to pregnant or breastfeeding women and couples (p-value &lt; 0.001). The pandemic had little impact on changes in reported HIV prevention interest or access to HIV prevention study products. Healthcare access was impacted for everyone, with couples most likely to report decreases in access (p-value &lt; 0.001). From qualitative interviews, economic instability, adverse mental health, and increased violence due to COVID-19 caused increased strain on other factors related to HIV vulnerability.ConclusionsWhile interest in HIV prevention did not change and a few HIV risks decreased for most women and girls, other vulnerabilities to HIV increased due to the COVID-19 pandemic, highlighting the importance of continued access to HIV prevention for women and girls. More research is needed to better understand the long-term impact of COVID-19 on HIV prevention and vulnerability in community populations.
Stable Isotope Labeling-Based Nontargeted Strategy for Characterization of the In Vitro Metabolic Profile of a Novel Doping BPC-157 in Doping Control by UHPLC-HRMS
Authors: Tian Tian , Jing Jing , Li Yuanyuan , Wang Yang , Deng Xiaojun , Shan Yuanhong 
Published: October 2023

Traditional strategies for the metabolic profiling of doping are limited by the unpredictable metabolic pathways and the numerous proportions of background and chemical noise that lead to inadequate metabolism knowledge, thereby affecting the selection of optimal detection targets. Thus, a stable isotope labeling-based nontargeted strategy combined with ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) was first proposed for the effective and rapid metabolism analysis of small-molecule doping agents and demonstrated via its application to a novel doping BPC-157. Using 13C/15N-labeled BPC-157, a complete workflow including automatic 13C0,15N0-13C6,15N2 m/z pair picking based on the characteristic behaviors of isotope pairs was constructed, and one metabolite produced by a novel metabolic pathway plus eight metabolites produced by the conventional amide-bond breaking metabolic pathway were successfully discovered from two incubation models. Furthermore, a specific method for the detection of BPC-157 and the five main metabolites in human urine was developed and validated with satisfactory detection limits (0.01~0.11 ng/mL) and excellent quantitative ability (linearity: 0.02~50 ng/mL with R2 > 0.999; relative error (RE)% < 10% and relative standard deviation (RSD)% < 5%; recovery > 90%). The novel metabolic pathway and the in vitro metabolic profile could provide new insights into the biotransformation of BPC-157 and improved targets for doping control.
Effects of Lactobacillus plantarum and Weissella viridescens on the Gut Microbiota and Serum Metabolites of Mice with Antibiotic-Associated Diarrhea
Authors: Yan Zhiwei , Liu Zhuangzhuang , Ma Yong , Yang Zhao , Liu Gang , Fang Jun 
Published: October 2023

Antibiotic-associated diarrhea (AAD) refers to diarrhea caused by gut microbiota disorders after the use of antibiotics, which seriously threatens the health of humans and animals. Therefore, it is necessary to find an effective therapy to treat AAD. This research aimed to explore the effects of Lactobacillus plantarum H-6 (L. plantarum H-6) and Weissella viridescens J-1 (W. viridescens J-1) on alleviating antibiotic-associated diarrhea induced by lincomycin hydrochloride (LH) in mice. The results show that L. plantarum H-6 could significantly reduce the expression of pro-inflammatory factors such as IL-1β and IL-6 in colon tissue. At the same time, L. plantarum H-6 significantly increased the abundance of Lactobacillus and Akkermansia, decreased the abundance of Bacteroides, and increased the contents of L-tryptophan, LysoPC (20:4 (8Z, 11Z, 14Z, 17Z)), reduced riboflavin, threoninyl–methionine, and N-palmitoyl in serum. However, W. viridescens J-1 had little effect on the treatment of AAD. It can be concluded that L. plantarum H-6 can regulate mice’s colonic microbial composition, improve their serum metabolic process, and alleviate antibiotic-associated diarrhea. This research may provide a novel therapeutic option for AAD.
Determination of the Classification Success of KNN Algorithm Distance Metric Methods on Wheat Seeds Dataset
Authors: ÇELİK Ahmet 
Published: October 2023

Machine learning algorithms are widely used in product sorting processes in the food industry. The
attributes of the products are used in the classification process. Attributes vary for each product. In this
study, using the k nearest neighbor (KNN) algorithm, the classification of the wheat groups of Kama,
Rosa and Canada was performed. The Seeds dataset provided in UCI (University of California, Irvine)
machine learning open source data storage was used. There are 70 examples of each wheat class in the
data set. In addition, the classification estimation success of distance metrics and the number of training
data was measured. Each of the wheat samples was randomly selected and a soft X-ray technique was
used to visualize the inner core structure of the wheat in the experimental environment with high
quality. According to the training rates ranging from 50% to 90% of the data set, the classification
success of the KNN algorithm was tested. In the KNN algorithm, the neighborhood values 1, 3 and 5
were selected to affect the classification success. The successes of the Euclidean, Chebyshev,
Manhattan and Mahalanobis distance metric methods of the KNN algorithm were tested according to
each k neighborhood value. According to the results obtained, with the Mahalanobis metric method, a
classification success rate of 0.9924 accuracy was obtained according to the AUC (Area Under the Curve)
success metric by using the neighborhood value of k = 3. In the literature, there is no study comparing
the KNN algorithm, neighborhood values and distance vectors together on food data sets using varying
training and test data. Therefore, it is thought that the study will make an important contribution to
the literature.