bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengaruh e-satisfaction terhadap repurchase intention produk fashion dimediasi oleh electronic word of mouth (E-WOM)
Authors: Hidajat Koerniawan , Damayanti Widia 
Published: January 2023

Penelitian bertujuan untuk mengetahui dan menjelaskan efek e-satisfaction pada repurchase intention produk fashion dimedasi electronic word of mouth (E-WOM) pada generasi milenial pengguna marketplace Shopee di DKI Jakarta. Metode penelitian ini kuantitatif eksplanatori. Teknik pengambilan sampel purposive sampling, 105 responden. Dan survei online menggunakan Google Form dan Skala Likert. Uji pengolahan data SPSS 22, AMOS V22 dan uji Zsobel test. Hasil penelitian e-mengungkapkan e-satisfaction mempengaruhi repurchase intention, selain itu juga mempengaruhi electronic word of mouth (E-WOM), selanjutnya electronic word of mouth (E-WOM) mempengaruhi repurchase intention. Temuan lain bahwa electronic word of mouth (E-WOM) memediasi efek e-satisfaction terhadap repurchase intention produk fashion pada generasi milenial pengguna marketplace Shopee di DKI Jakarta.
Integrated 3D Numerical Modelling of Pressure Behavior and Casing Response at Offset Monitor Well During Fracturing
Authors: Li Gongsheng , Morita Nobuo , Zhu Ding , Kerr Erich , Johnson Andrew , Ross Katie , Estrada Erick , Scofield Reid 
Published: January 2023

Abstract
Offset monitor wells experience stress, strain and pressure changes due to far-field fracture propagation. The pressure interference as hydraulic fractures propagate and intersect the casing directly results in a casing inner volume change and monitor well surface pressure change. The pressure behavior has been analyzed by a patented method, Sealed Wellbore Pressure Monitoring (SWPM), and it is an effective means to detect frac hits. To extend the understanding of this pressure behavior for wider applications, we proposed a methodology of simulating the casing response and corresponding pressure change of the offset well during hydraulic fracturing. The objective is to investigate the strain/pressure response along the monitor well casing for different scenarios of fracture intersection.
In this study, an analytical model for computing the stress/strain in a casing-cement-formation system was developed based on the well-known solution of stress in a thick wall cylinder. The integrated numerical model consists of a fracture propagation model, a 3D geo-mechanical model, and a transient fluid flow model. Prior to analyzing the monitoring well pressure response, fracture propagation is modeled using a commercial software to give information of fracture geometry and fracture net pressure. The 3D fluid flow model and the geo-mechanical model of fractured rock and monitor well casing were established with finite-element method. With the input parameter of fracture geometry and net pressure distribution from the fracture propagation model, the integrated model can be used to solve the total stress-displacement behavior within the simulation domain. Using this approach, the volume change of the monitor well casing, and the corresponding surface pressure behavior can be estimated. The simulated strains along the monitor well casing of a radial fracture show the numerical simulation results agree well with the existing analytical solution.
When a fracture crosses the monitor well, the standard surface pressure behavior is: (A) pressure increases due to fracture intersection; (B) pressure increase slows down as fracture tip travels some distance away from the casing; (C) pressure declines after the treatment well is shut-in. The first pressure increase is caused by casing radius reduction while the pressure fall-off is greatly affected by the far-field leakoff coefficient and permeability. The simulated results, and also the published data indicate that the typical pressure response for a single fracture hitting is on the order of 1 psi. When multiple fractures intersect the casing, a higher surface pressure increase is observed compared to single fracture case due to higher net pressure, and longer impacted section along the casing. Combined with the interpretation of DAS, this approach can identify the number and locations of the fractures that intersect the far field monitor well.
Genetic Mutation Signature for Relapse Prediction in Normal Karyotype Acute Myeloid Leukemia
Authors: Han Lijie , Wu Jiaying , Lyu Xiaodong , Yu Jifeng , Han Xiaolin , Zhao Hongmian , Bian Zhilei , Li Wei , Fan Wenjuan , He Chen , Wang Weimin , Zhang Mengmeng , Li Yafei , Liu Chao , Sun Hui , Cao Haixia , Sang Li’na , Zhang Jun , Jiang Zhongxing , Peng Jie 
Published: January 2023

Abstract
Background: Risk stratification for normal karyotype acute myeloid leukemia remains unsatisfactory, which is reflected by the high incidence of leukemia relapse. This study aimed to evaluate the role of gene mutations and clinical characterization in predicting the relapse of patients with normal karyotype acute myeloid leukemia.
Methods: A prognostic system for normal karyotype acute myeloid leukemia was constructed based on gene mutations, measurable residual disease, and clinical characteristics. A panel of gene mutations was explored using next-generation sequencing. The least absolute shrinkage and selection operator, and nomogram algorithm were used to build a genomic mutation signature (GMS) nomogram (GMSN) model that combines GMS, measurable residual disease, and clinical factors to predict relapse in 347 patients with normal karyotype acute myeloid leukemia from four centers.
Results: Patients in the GMS-high group had a higher 5-year incidence of relapse than those in the GMS-low group (P< 0.001). The 5-year incidence of relapse was also higher in patients in the GMSN-high group than in those in the GMSN-intermediate and -low groups (P< 0.001). The 5-year disease-free survival and overall survival rates were lower in patients in the GMSN-high group than in those in the GMSN-intermediate and -low groups (P < 0.001) as confirmed by training and validation cohorts.
Conclusions: This study illustrates the potential of GMSN as a predictor of normal karyotype acute myeloid leukemia relapse.
Awareness and use of tobacco products among underage individuals: Findings from the Altria Client Services Underage Tobacco Use Survey 2020-2022
Authors: Cheng Hui G. , Vansickel Andrea R. , Largo Edward G. 
Published: January 2023

Abstract
Background
Tobacco use among underage individuals is a public health concern. Timely data about tobacco products, especially emerging products such as novel oral nicotine products (NPs), can provide critical information for the prevention of underage tobacco use. With a recent federal law raising the legal age of purchase of tobacco products from 18 to 21, it is of interest to benchmark awareness and use of tobacco products in the new underage population, young adults 18–20 years old. This study provides estimates on awareness and use of tobacco products among underage individuals 13–20 years old during April 2020 to August 2022 in the United States.
Methods
Altria Client Services Underage Tobacco Use Survey (UTUS) is a repeated cross-sectional survey conducted every quarter-year. A stratified random sampling approach was used to draw nationally representative samples of household dwelling individuals 13–20 years old. Information about the awareness and use of tobacco products was obtained via online self-administration or phone interviews after a consent/assent process.
Results
A sizable portion of underage individuals were aware of NPs (~ 40% among youth and ~ 50% among underage young adults), although past 30-day use was low (< 2%). The lowest levels of awareness and use were observed for heated tobacco products and snus. Ecigarettes were the most used tobacco products among underage individuals. Underage young adults (i.e., 18–20 year olds) were more likely to use tobacco products than youth (i.e., 13–17 year olds). There was no substantial change over time in the awareness and use of tobacco products during the study period despite a slight increase in past 30-day prevalence of e-cigarette use among youth between quarter 1 of 2021 and quarter 2 of 2022.
Conclusions
The awareness and use of tobacco products remained relatively stable between April 2020 and August 2022. There is a notable level of awareness of novel NPs among underage individuals.
Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models
Authors: Das Bappa , DESAI SUJEET , Daripa Amrita , Anand Gurav Chandrakant , Kumar Uttam , Khalkho Dhiraj , Velumani T. , Kumar Nirmal , Reddy G. P. Obi , Kumar Parveen 
Published: January 2023

Abstract
Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with Analytical Hierarchy Process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF) and support vector machine (AHP-SVM) model. The model performances were assessed through area under the receiver operating characteristic (AUC). AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977) and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.
Evolution and trends of new energy vehicle policy research hotspots: An analysis based on a Citespace knowledge graph
Authors: Zhang Lei , Lin Yanfen , Zhou Siyu , Qin Quande 
Published: January 2023

Abstract
New energy vehicle (NEV) policies involve extensive and complex aspects. NEV policies have been an important issue of academic interest in the academic research community. The current research of the NEV policy has the key significance of inducing experience and guiding the future. This study conducts a comprehensive bibliometric analysis through quantitative research methods to summarize the development and evolution of NEV policy in recent years. Using Citespace visualization tools, we reviewed 2,919 papers collected from the Web of Science, identified three stages in the development of the research field and summarized features relevant to the policy in history. The most influential countries, institutions, authors, keywords and references has been examined. Through burst detection and cluster analysis, we analyzed historical trends and trajectories in this area of research. In the data we collected, a number of indicators are related to China, indicating that China’s NEV policy has a representative significance in the field of NEV policy research. Consequently, we further collected the relevant research from China, in order to summarize the policy reasons behind the rapid development of NEVs in China, and help researchers to have a better understanding of the frontiers of NEVs.
Estimation of Manning roughness coefficient in alluvial rivers with bed forms using soft computing models
Authors: Yarahmadi Mohammad Bahrami , Parsaie Abbas , Shafai-Bejestan Mahmood , Heydari Mostafa , Badzanchin Marzieh 
Published: January 2023

Abstract
The bed surface of alluvial rivers is rarely plane and takes different geometric configurations called bed forms. Bed forms are created by the movement of riverbed sediments, especially during floods. The interaction between the flow and bed form is very complex. The flow intensity controls bed forms, and the bed form significantly affects the properties of the flow (such as depth, velocity, and flow resistance). The Manning roughness coefficient is one of the most important flow resistance coefficients, which significantly affects the bed form shape and geometry. This study aimed to estimate the Manning roughness coefficient in rivers with bed forms, using soft computing models, including multilayer perceptron artificial neural network (MLPNN), group method of data handling (GMDH), support vector machine (SVM) model, and genetic programming model (GP). To this end, the energy grade line (Sf), flow Froude number (Fr), y/d50, ∆/d50, ∆\λ, and ∆/y were used as the input variables, and the Manning roughness coefficient was used as the output variable. The results showed that all the test models have acceptable accuracy, while the SVM model showed the highest level of accuracy with the coefficient of determination R2=0.99 in the verification stage. The sensitivity analysis of SVM and MLPNN models and the structural analysis of GMDH and GP models indicated that the most important parameters affecting the Manning roughness coefficient are Fr, Sf, ∆\λ.
A clinical study of the correlation between metabolic associated fatty liver disease and coronary plaque pattern
Authors: Zhang Zhijiao , Zheng Mengyao , Jiang Zimeng , Chen Yuhang , He Haiyu , Lei Hongtao , Zhao Gongfang , Huang Hua 
Published: January 2023

Abstract
Background: Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome (MetS) and has been shown to be correlated with coronary atherosclerosis (CAS). Since NAFLD was renamed metabolic associated fatty liver disease(MAFLD) in 2020, no studies have evaluated the correlation between MAFLD and CAS. The aim of this study was to evaluate the relationship between MAFLD and CAS.
Methods: A total of 1330 patients underwent continuous coronary computed tomography angiography (CCTA) and abdominal ultrasound as part of a routine physical examination. Ultrasonography was used to assess fatty liver, and CCTA was used to assess coronary artery plaques, degree of stenosis, and diseased blood vessels. Univariate and multivariate logistic regression analyses were performed with plaque type and degree of stenosis as dependent variables and MAFLD and traditional cardiovascular risk factors as independent variables to analyze the correlation between MAFLD and CAS.
Results: Among the 1164 patients, 680 (58.4%) were diagnosed with MAFLD through a combination of ultrasound and auxiliary examinations. Compared with patients in the non-MAFLD group, patients in the MAFLD group had more cardiovascular risk factors. MAFLD was correlated with calcified plaques, noncalcified plaques, mixed plaques, degree of coronary artery stenosis and multiple coronary artery lesions (p<0.05). After correction for cardiovascular risk factors, MAFLD was correlated with noncalcified plaques (1.673; 95% confidence interval (CI) 1.153-2.430; p=0.007) and mixed plaques (1.543; 95% CI 1.104-2.155; p=0.011).
Conclusions: In this study, MAFLD was correlated with cardiovascular risk factors, degree of coronary artery stenosis, multiple coronary artery lesions, and coronary atherosclerotic plaques, particularly noncalcified and mixed plaques, but not with traditional cardiovascular risk factors.
Community and Partner Engagement in Dissemination and Implementation Research at the National Institutes of Health: An Analysis of Recently Funded Studies and Opportunities to Advance the Field
Authors: Villalobos Aubrey V , Blachman-Demner Dara R , Percy-Laurry Antoinette , Belis Deshirée , Bhattacharya Manami 
Published: January 2023

Abstract
Background: As the focus has grown in recent years on both engaged research and dissemination and implementation (D&I) research, so too has federal funding to support these areas. The purpose of this analysis is to provide an overall perspective about the range of practices and approaches being used to engage partners in D&I research, with special attention to disparities-relevant research, and to identify gaps and opportunities in research funded by the U.S. National Institutes of Health (NIH) in this space.
Methods: This analysis examined a portfolio of active D&I research grants funded in fiscal years 2020 and 2021 across the NIH. Grant applications were deductively coded and summary statistics were calculated. Cross-tabulations were used to identify trends by engagement and disparities foci.
Results: There were 103 grants included in the portfolio, of which 87% contained some form of community or partner engagement, and 50% of engaged grants were relevant to health disparities. Engagement was planned across the research continuum with each study engaging on average 2.5 different partner types. Consultation was the most common level of engagement (56%) while partnership was the least common (3%). On average, each study used 2.2 engagement strategies. Only 16% of grants indicated formally measuring engagement. Compared to non-disparities studies, disparities-relevant studies were about twice as likely to engage partners at the higher levels of partnership or collaboration and were also more likely to be conducted in community settings (26% vs. 5%).
Conclusions: Based on this portfolio analysis, D&I research appears to regularly integrate engagement approaches and strategies, though opportunities to deepen engagement and diversify who is engaged remain. This manuscript outlines several gaps in the portfolio and describes opportunities for increasing engagement to improve the quality of D&I research and application to advancing health equity. In addition, opportunities for leveraging the consistent and systematic application of engagement approaches and strategies to advance the science of engagement are discussed.
A quantum assessment of the interaction between Glycine amino acid with pristine and B&amp;N doped silicon carbide nanocage (Si 12 C 12 )
Authors: Rezaei-Sameti M. , Naghibi M. Barandisheh 
Published: January 2023

Abstract
Density functional theory is utilized for scrutinizing the interaction of Glycine amino acid (Gly) with the Si12C12, BSi11C12, NSi11C12, BSi12C11, and NSi12C11 nanocages. The adsorption and deformation energy, solvent effect, topological parameters of atom in the molecule (AIM), time-dependent (TD-DFT), reduced density gradient (RDG), natural bonding (NBO), and quantum descriptors for all considered complexes of Gly&SiC nanocages are computed. The adsorption and enthalpy energy of formation all considered Gly&SiC nanocage complexes are exothermic, and interaction of Gly from oxygen site (C = O) with NSi11C12 and BSi12C11 nanocage is more favorable than other complexes. In the presence of water solvent, the formation of Gly&SiC nanocage complexes is non-spontaneous. The NBO charge and electrostatic potential (ESP) results display that the Gly molecule has a donor electron effect on the SiC nanocage surface. The gap energy and hardness property of Gly&SiC nanocage complexes depict that the conductivity and reactivity complex increases. The AIM and RDG parameters confirm that the bonding between Gly and SiC nanocage is partially covalent or electrostatic type. The electrical and optical properties of Gly with BSi11C12, NSi11C12, BSi12C11, and NSi12C11 nanocages change more than Si12C12 nanocages, and these nanocages can be applied as a selective sensor and absorber for Gly.
Effect of various methods of pre-treatment of carbon fibers on the mechanical properties of PIP-based ceramics/carbon composites
Authors: Sokolowski Krystian , Pszczola Izabela , Blazewicz Stanislaw , Bernasik Andrzej , Kornaus Kamil , Fraczek-Szczypta Aneta 
Published: January 2023

Abstract
The purpose of this study was to establish the conditions of CF surface preparation suitable for obtaining composites with favorable mechanical properties. The relationships between the interfacial properties of the carbon fiber/polymethylsiloxane composites and mechanical properties of ceramic composites (CF/SiOC, CF/SiC) formed during heat-treatment (HT) up to 1700℃ were investigated. The CF-resin interfacial strength has been modified by fiber surface treatment including nitric acid oxidation, silanization, as well as by forming a coating on the CF surface by depositing CNT or a pyrolytic carbon layer (PyC). The study of composite interphases (Interlaminar Shear Strength, ILSS and SEM) and surface tests of the modified CF (XPS, FT-IR, wettability measurements) showed different nature of the bonding between CFs and the resin matrix before and after HT. The CF silanization promoting chemical interfacial interactions significantly improved the ILSS between CFs and resin by 38.5%, while reduced mechanical properties of resulting ceramic composites. The most promising surface treatment method of CF for PIP-based ceramic composites was modification with PyC layer, which provided 2 times higher ILSS, 1.5 times higher flexural strength and improved work to fracture (WF) as compared to unmodified CF.
Multi-modal Affect Detection Using Thermal Imaging in a Gamified Exercise
Authors: Mohamed Youssef , Guneysu Arzu , Lemaignan Séverin , Leite Iolanda 
Published: January 2023

Abstract
Affect recognition, or the ability to detect and interpret emotional states, has the potential to be a valuable tool in the field of healthcare. In particular, it can be useful in gamified therapy, which involves using gaming techniques to motivate and keep the engagement of patients in therapeutic activities. This study aims to examine the effectiveness of a tangible PacMan game in inducing different emotions in participants and the accuracy of machine learning models using thermal imaging and action unit data for affect classification. A self-report survey and three machine learning models were used to assess emotions including frustration, boredom, and enjoyment in participants during different phases of the game. The results showed that the game was successful in inducing significant differences in enjoyment and frustration, but only enjoyment and frustration were significantly different from boredom. The machine learning models showed that the multimodal approach with the combination of thermal imaging and action units had the highest accuracy of 77% for emotion classification in the 7-second window, while thermal imaging had the lowest standard deviation among participants. The results suggest that thermal imaging and action units can be effective in detecting affective states and might have the potential to be used in healthcare applications, such as gamified therapy, as a promising non-intrusive method for detecting internal states.