bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors associated with selection of practice in primary care and rural health among medical and nursing students in China
Authors: Fan Victoria , Guo Mary , Hou Jianlin , Talagi Deveraux , Ke Yang , Wang Weimin 
Published: September 2022

Background China has a shortage of physicians and nurses in primary care and rural health. This study explores factors that influence the choices of medical and nursing students in China to select a career in primary care, or in rural health. Methods A total of 3826 medical students and 1771 nursing students were surveyed in China. Data were analysed using descriptive statistics, Chi-squared tests, and logistic regression models. Results The majority of medical and nursing students were willing to practice primary care (55% and 59%, respectively). Yet, only 16% and 5% of medical and nursing students, respectively, desired to work in a village or small city. The most common reasons cited to not practice primary care is the lack of opportunities for clinical skills improvement, academic and personal development, and networking. Medical students who were living in a rural residence between ages 1 and 15 years were more likely to report a willingness to work in a rural location (OR: 2.18, 95% CI: 1.33–3.58) or in primary care (OR: 1.72, 95% CI: 1.31–2.25). Conclusion More efforts are needed to understand how preferences among medical and nursing students influence their career choices and change in choices over time. Understanding the concerns of students can help to tailor interventions in healthcare education and training to increase student satisfaction with their career choice and enrolment counts in medical and nursing fields.
Inferring the initiation and development of myeloproliferative neoplasms
Authors: Hermange Gurvan , Rakotonirainy Alicia , Bentriou Mahmoud , Tisserand Amandine , El-Khoury Mira , Girodon François , Marzac Christophe , Vainchenker William , Plo Isabelle , Cournède Paul-Henry 
Published: September 2022

The developmental history of blood cancer begins with mutation acquisition and the resulting malignant clone expansion. The two most prevalent driver mutations found in myeloproliferative neoplasms—JAK2V617FandCALRm—occur in hematopoietic stem cells, which are highly complex to observe in vivo. To circumvent this difficulty, we propose a method relying on mathematical modeling and statistical inference to determine disease initiation and dynamics. Our findings suggest thatCALRmmutations tend to occur later in life thanJAK2V617F. Our results confirm the higher proliferative advantage of theCALRmmalignant clone compared toJAK2V617F. Furthermore, we illustrate how mathematical modeling and Bayesian inference can be used for setting up early screening strategies.
Applying the performed culture approach in collegiate beginning and intermediate level Chinese courses
Authors: Chai Donglin 
Published: September 2022

AbstractThis paper discusses how to use the Performed Culture Approach (PCA) to design collegiate Chinese beginning and intermediate level courses. As a pedagogical framework to improve learners’ intercultural communication skills, PCA has gained increasing recognition by scholars in the field. However, many instructors still face the challenge of deciding how to apply PCA in different Chinese language programs and with non-PCA informed pedagogical materials. Through a three-year implementation at an American university which adoptsIntegrated Chinese(IC), one of the most popular pedagogical materials in American college beginning- and intermediate-level Chinese courses, the article analyzesIC’s instructional challenges, PCA-based curriculum design, learning outcomes, and student feedback. The article finally discusses general principles of applying PCA in different programs with various pedagogical materials.
Cross-domain few-shot defect recognition for metal surfaces
Authors: Duan Guifang , Song Yiguo , Liu Zhenyu , Ling Shiquan , Tan Jianrong 
Published: September 2022

Abstract
Defect recognition for metal surfaces in the industry has attracted more and more attention. However, defect data scarcity presents a huge challenge for defect recognition in real industrial scenarios. The traditional few-shot defect recognition method can address this problem when the training data and test data are collected from the same or a similar metal surface. However, the defect data from similar metal surfaces are difficult to acquire to a certain extent. In this paper, we introduce a novel task setting that can achieve few-shot defect recognition by transferring knowledge across domains. The method consists of two levels: image-level and feature-level. At the image-level, a meta-augmentation method is proposed to improve the recognition generalization in each meta-task by joint parameter updating from the original and augmented domains. At the feature-level, a class covariance-guided feature perturbation method is proposed to perturb the feature distribution to enhance the cross-domain generalization capability. The extension of cross-domain experiments from textured to metal surfaces shows the superior performance of the proposed method compared to other mainstream methods.
Influence of the spatially inhomogeneous electric field on the thermodynamic property of the particle confined in a quantum well
Authors: Liu Xue , Wang De-Hua , He Xue , Fa Mei-Chen , Chu Bin-Hua , Zhang Jie , Zhang Shu-Fang 
Published: September 2022

Abstract
The effect of the spatially inhomogeneous electric field on the quantum thermodynamic property of the particle confined in a quantum well has been investigated theoretically. The Schrodinger equation was solved for the particle subjected to a spatial confinement by an inhomogeneous electric field and energy eigen-values were obtained. Using the energy eigen-values, we have calculated the average energy, free energy, entropy, and heat capacity of this system. As an important parameter in the inhomogeneous electric field, the inhomogeneity gradient of the field plays an important role. Compared with the spatially homogenous electric field, some novel quantum effect appears. The energy eigen-value for the particle corresponding to each bound state increases as the inhomogeneity gradient increases, resulting in the increase of the average energy, free energy, and heat capacity; however, the entropy, which measures the disorder of the particle confined in the inhomogeneous field, gradually decreases with the increase of the inhomogeneity gradient. In addition, the confinement effect of the quantum well on the thermodynamic properties of this system has also investigated in great detail. The results show that the larger size of the quantum well is, the stronger the influence of inhomogeneous field becomes, which makes the thermodynamic properties of the particle in the spatially inhomogeneous field quite different from those in the homogeneous field. Our work may provide some references for the future research on the thermodynamic property of the confined atom and molecule in the inhomogeneous field and has some practical applications in statistical physics, chemical physics, and condensed physics, etc.
A Simulation Experiment of a Pipeline Based on Machine Learning for Neutral Hydrogen Intensity Mapping Surveys
Authors: Li Lin-Cheng , Wang Yuan-Gen 
Published: September 2022

Abstract
We present a simulation experiment of a pipeline based on machine learning algorithms for neutral hydrogen (H i) intensity mapping (IM) surveys with different telescopes. The simulation is conducted on H i signals, foreground emission, thermal noise from instruments, strong radio frequency interference (sRFI), and mild RFI (mRFI). We apply the Mini-Batch K-Means algorithm to identify sRFI, and Adam algorithm to remove foregrounds and mRFI. Results show that there exists a threshold of the sRFI amplitudes above which the performance of our pipeline enhances greatly. In removing foregrounds and mRFI, the performance of our pipeline is shown to have little dependence on the apertures of telescopes. In addition, the results show that there are thresholds of the signal amplitudes from which the performance of our pipeline begins to change rapidly. We consider all these thresholds as the edges of the signal amplitude ranges in which our pipeline can function well. Our work, for the first time, explores the feasibility of applying machine learning algorithms in the pipeline of IM surveys, especially for large surveys with the next-generation telescopes.
Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning
Authors: Conrad Erin C , Bernabei John M , Sinha Nishant , Ghosn Nina J , Stein Joel M , Shinohara Russell T , Litt Brian 
Published: September 2022

Abstract

Objective. To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases. Approach. We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a ‘spatial null model’ to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates. Main results. About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63–0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78, p = 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72, p = 0.38). Significance. Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.
Algebraic and differential-geometric constructions of set-theoretical solutions to the Zamolodchikov tetrahedron equation
Authors: Igonin Sergei , Konstantinou-Rizos Sotiris 
Published: September 2022

Abstract
We present several algebraic and differential-geometric constructions of tetrahedron maps, which are set-theoretical solutions to the Zamolodchikov tetrahedron equation. In particular, we obtain a family of new (nonlinear) polynomial tetrahedron maps on the space of square matrices of arbitrary size, using a matrix refactorisation equation, which does not coincide with the standard local Yang–Baxter equation. Liouville integrability is established for some of these maps. Also, we show how to derive linear tetrahedron maps as linear approximations of nonlinear ones, using Lax representations and the differentials of nonlinear tetrahedron maps on manifolds. We apply this construction to two nonlinear maps: a tetrahedron map obtained in Dimakis and Müller-Hoissen (2019 Lett. Math. Phys.
109 799–827) in a study of soliton solutions of vector Kadomtsev–Petviashvili equations and a tetrahedron map obtained in Konstantinou-Rizos (2020 Nucl. Phys. B 960 115207) in a study of a matrix trifactorisation problem related to a Darboux matrix associated with a Lax operator for the nonlinear Schrödinger equation. We derive parametric families of new linear tetrahedron maps (with nonlinear dependence on parameters), which are linear approximations for these nonlinear ones. Furthermore, we present (nonlinear) matrix generalisations of a tetrahedron map from Sergeev’s classification Sergeev (1998 Lett. Math. Phys.
45 113–9). These matrix generalisations can be regarded as tetrahedron maps in noncommutative variables. Besides, several tetrahedron maps on arbitrary groups are constructed.
Graphene/carbon-based materials for advanced energy conversion applications
Authors: Mohan Kurra , Narsimhaswamy D. , Ravi Varala 
Published: September 2022

Abstract
A brief review reporting the recent advances on the applications of graphene/carbon based nanocomposites is presented. In particular, due to the convenient preparatory procedures available for the bulk synthesis of graphene and graphene derived composites as a top-down approach, the number of research contributions has also raised exponentially. The combination of graphene with metal, metal oxide composites resulted in exploration of various applications ranging from energy to biomedical fields. Multidimensional graphene materials were subdivided into supercapacitor electrodes, lithium ion battery electrodes, fuel cells and dye sensitized solar cells for utilization in flexible, clean and safe electrochemical energy storage and conversion. Further, this review explored the fields of sensors, adsorption, anaerobic digestion and drug delivery.
Influence of synthesis routes on the performance of Ni nano-sized catalyst supported on CeO2-Al2O3 in the dry reforming of methane
Authors: Pham Cham Q , Cao Anh Ngoc T , Pham Le Kim Hoang , Phuong Pham TT , Tran Thi Tuong Vi , Vo Cao-Minh , Nguyen Hong-Ha T , Nguyen Quang-Anh , Nguyen Tung M , Vo Dai-Viet N 
Published: September 2022

Abstract
The dry reforming of methane is a potential solution to mitigate the global warming effect. In this study, the effects of various preparation methods including the impregnation, coprecipitation, and combined coprecipitation-impregnation on physical characteristics and DRM catalytic performance of Ni-CeO2-Al2O3 were investigated. The synthetic procedure was discovered to have a crucial role in the basicity and reducibility, as well as nickel dispersion of catalyst. With affordable basicity and high reducibility, the combination of coprecipitation-impregnation exhibited the best performance with CH4 and CO2 steady-state conversions of 72% and 82%, respectively. Moreover, the deactivation of catalysts was also studied. The formation of low graphitic type showed less harmful to catalyst.
Label-free monitoring of proteins in optofluidic hollow-core photonic crystal fibres
Authors: Heck Jan R , Miele Ermanno , Mouthaan Ralf , Frosz Michael H , Knowles Tuomas P J , Euser Tijmen G 
Published: September 2022

Abstract
The fluorescent detection of proteins without labels or stains, which affect their behaviour and require additional genetic or chemical preparation, has broad applications to biological research. However, standard approaches require large sample volumes or analyse only a small fraction of the sample. Here we use optofluidic hollow-core photonic crystal fibres to detect and quantify sub-microlitre volumes of unmodified bovine serum albumin (BSA) protein down to 100 nM concentrations. The optofluidic fibre’s waveguiding properties are optimised for guidance at the (auto)fluorescence emission wavelength, enabling fluorescence collection from a 10 cm long excitation region, increasing sensitivity. The observed spectra agree with spectra taken from a conventional cuvette-based fluorimeter, corrected for the guidance properties of the fibre. The BSA fluorescence depended linearly on BSA concentration, while only a small hysteresis effect was observed, suggesting limited biofouling of the fibre sensor. Finally, we briefly discuss how this method could be used to study aggregation kinetics. With small sample volumes, the ability to use unlabelled proteins, and continuous flow, the method will be of interest to a broad range of protein-related research.
Fabrication of gemcitabine and losmapimod-prodrug loaded nanoparticles for the treatment and care of lung cancer
Authors: Zhao Rong , Qi Juan 
Published: September 2022

Abstract
Lung carcinoma (LC) is rare cancer in most parts of the world but is common cancer in southern Asia. Local recurrent disease and distant metastasis of LC are still mysterious challenges. This investigation was to effectively fabricate and evaluate the therapeutic efficiency of Gemcitabine (GEM) and Losmapimod (LOS) encapsulated into polyethylene glycol (PEG)-polylactic-co-glycolic acid (PLGA)-NPs (GEM/LOS@NPs), which exhibited inhibition of in vitro lung cells proliferation. GEM/LOS@NPs with a diameter of ∼100 nm have been constructed employing an enhanced double-emulsion (W/O/W) method. The GEM/LOS@NPs’ particle size was studied through transmission electron microscopic (TEM) techniques and dynamic light scattering (DLS) measurement. After successfully fabricating GEM/LOS@NPs, the anticancer ability of A549 and H460 lung cancer cells was investigated. Biochemical assays such as AO/EB (acridine bromide/orange ethidium) and nuclear staining investigated the cancer cell’s morphology and death. The cell death of the cancer cells was synergically examined by ROS analysis. Overall, these results suggest that highly toxic drugs can be rationally transformed into self-deliverable and pharmacologically efficient monotherapy.
Investigation of machining characteristics during micro-hole fabrication in CFRP composite by ECDM process
Authors: Yadav Santosh Kumar , Singh Abhishek , Debnath Kishore 
Published: September 2022

Abstract
Carbon fiber reinforced polymer (CFRP) composites have gained significant popularity in the field of aerospace, automotive, electronic, and sports due to its versatile properties. Although, fabrication of micro-holes in CFRP composite is troublesome by the conventional processes because of its anisotropic and abrasive behaviour. To overcome this difficulty, recently, an unconventional hybrid process such as electrochemical discharge machining (ECDM) is extensively used to machine such types of materials. This paper highlights the experimental and statistical analysis of machined micro-holes which was fabricated in CFRP composite using ECDM process. The experimentation was performed according to Taguchi’s L9 orthogonal array. The input parameters were (a) voltage, (b) tool rotation, (c) tool travel rate, and (d) duty cycle, each at three levels. Hole overcut and taper was measured as output responses. TOPSIS-Entropy based multi-response optimization technique was applied to optimize the chosen parameters. The hole overcut and taper was measured and analysed using SEM images. The parametric combination of 50 V, 360 RPM, 0.5 mm min−1, and 60% resulted minimum overcut and taper of 227 μm and 72 μm, respectively. The morphology of the machined hole exhibited better surface characteristics at a high tool travel rate and low duty cycle. Tool wear in terms of lateral wear and flank wear was observed during machining at high voltage and high duty cycle. From the TOPSIS Entropy analysis, the optimal condition for both overcut and taper was found as 50 V, 360 RPM, 0.5 mm min−1, and 60%.
Natural course of visual snow syndrome: a long-term follow-up study
Authors: Graber Michael , Scutelnic Adrian , Klein Antonia , Puledda Francesca , Goadsby Peter J , Schankin Christoph J 
Published: September 2022

Abstract
Visual snow syndrome is characterized by a continuous visual disturbance resembling a badly tuned analogue television and additional visual and non-visual symptoms causing significant disability. The natural course of visual snow syndrome has not hitherto been studied. In this prospective longitudinal study, 78 patients with the diagnosis of visual snow syndrome made in 2011 were re-contacted in 2019 to assess symptom evolution using a semi-structured questionnaire. Forty patients (51% of 78) were interviewed after 84 ± 5 months (mean ± SD). In all patients, symptoms had persisted. Visual snow itself was less frequently rated as the most disturbing symptom (72 versus 42%, P = 0.007), whereas a higher proportion of patients suffered primarily from entopic phenomena (2 versus 17%, P = 0.024). New treatment was commenced in 14 (35%) patients, of whom in seven, visual snow syndrome was ameliorated somewhat. Three (7%) experienced new visual migraine aura without headache, and one (2%) had new migraine headache. There were no differences in the levels of anxiety and depression measured by the Patient Health Questionnaire 8 and the Generalized Anxiety Disorder Scale 7. Thirty-eight patients (49%) were lost to follow-up. In visual snow syndrome, symptoms can persist over 8 years without spontaneous resolution, although visual snow itself might become less bothersome.
A Validated Stability-Indicating HPLC-PDA Method for Tolnaftate: Identification, Characterization andIn SilicoToxicity Predictions of Major Degradation Products
Authors: Waghela Nikky , Parmar Ishvarchandra , Devale Titiksh , Desai Sonal 
Published: September 2022

AbstractSimple and rapid stability indicating High Performance Liquid Chromatography-Photo Diode Array (HPLC-PDA) method was developed and validated for the estimation of tolnaftate in the presence of its forced degradation products. The method employed SunQSil C18 column (250 mm × 4.6 mm, 5 μm) as stationary phase and acetonitrile:water (85:15, v/v) as mobile phase. Retention time of tolnaftate was 6.9 min. Acid and alkali hydrolysis, oxidation, photo degradation and thermal degradation studies were carried out to evaluate the degradation behavior of tolnaftate. The developed and optimized method was validated as per International Conferences on Harmonization (ICH) guidelines. Limit of detection and limit of quantitation were found to be 0.092 and 0.276 μg/mL, respectively. Linearity was observed in a concentration range of 0.276–6 μg/mL with R2 = 0.9936. %Recovery was found to be between 98.28% and 100.71%. The developed and validated Reversed Phase-High Performance Liquid Chromatography (RP-HPLC) method was successfully applied for quantification of tolnaftate in in-house topical solution. Major base and oxidative degradation products were identified and characterized by liquid chromatography–electrospray ionization mass spectrometry. The probable mechanisms for the formation of degradation products were predicted based on the fragmentation pattern of degradation products. The in silico dermal penetration predictions and carcinogenicity of degradation products were evaluated by using QikProp and CarcinoPred-EL functionality.