bluuebunny/crossref_metadata_2025_split huggingface dataset:
From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services
Authors: Garcia‐Marti Irene , Overeem Aart , Noteboom Jan Willem , de Vos Lotte , de Haij Marijn , Whan Kirien 
Published: June 2022

AbstractNational meteorological services (NMS) are limited by practical and financial boundaries in the number of official meteorological measurements that it can collect. This means that large regions are often unobserved. These gaps can be filled by novel data sources, including measurements from personal weather stations that are owned and operated by amateur citizen scientists, or opportunistic sensing from devices that are not designed to measure meteorological variables, like commercial microwave links (radio connections between mobile phone towers). These types of data are known as “third‐party data” (3PD) as they are not owned or operated by NMS or research institutes (e.g., university, government department). Demonstration of the quality and value of these novel data sources is an active area of research. NMS, like the Royal Netherlands Meteorological Institute (KNMI), are faced with some unique challenges when it comes to transferring research to operations. KNMI is in the early stages of developing an operational pipeline for 3PD. We outline some use cases where we have demonstrated the quality of 3PD. We discuss our experiences with some of these challenges that can occur when transferring between proof‐of‐concepts on 3PD developed in research settings into real operational workflows providing valuable services. Hence, in this work we introduce our third‐party data life cycle, in which we provide an integral overview of this transitioning process considering business and social aspects, technical feasibility assessments, the importance of quality control, and aspects related to data integration and alignment with the existing official data sources. We also reflect on how these potential new applications could fit into KNMIs long‐term strategies and contribute to the high‐resolution weather forecast and early warning issuing. We hope that sharing these experiences will provide some general guidelines to organizations in need of providing new services stemming from 3PD and transform them into “daily business.”
Photoinduced effects of monochromatic visible light with different wavelengths on realgar
Authors: Wu Furong , Zhang Yongkang , Li Yan , Wang Yuhang , Ma Junjie , Wang Fengping 
Published: June 2022

AbstractRealgar is a well‐known light‐sensitive material. Under illumination, realgar is oxidized into arsenolite and uzonite, and uzonite generates pararealgar. To further study the effect of light with different wavelengths on realgar, herein, the light‐induced reaction of realgar under irradiation of four monochromatic visible lights, red light (628.0–648.0 nm), amber light (585.4–604.6 nm), green light (506.0–544.0 nm), and blue light (428.9–447.1 nm), have been studied by confocal Raman, X‐ray diffraction, and ultraviolet–visible absorption spectroscopy. The results of Raman spectra show that the photoinduced effect of different monochromatic lights on realgar is different. Realgar is stable under red light but is sensitive to other visible lights like amber, green, and blue light. The light‐induced reaction of realgar is the fastest under the green light. The effect of the blue light on realgar is complicated. Pararealgar, as the light‐induced product of realgar, transforms into uzonite under the blue light, which is discussed based on the bandgap of pararealgar calculated using the density functional theory method.
Effect of Plasticization on Stretching Stability of Poly(Vinyl Alcohol) Films: A Case Study Using Glycerol and Water
Authors: Fu Zhen‐zhen , Yao Yi‐hang , Guo Sheng‐jie , Wang Ke , Zhang Qin , Fu Qiang 
Published: June 2022

AbstractAdding small molecular plasticizers is the most common route to tailor the stretchability of poly(vinyl alcohol) (PVA). However, how the plasticization along with the nature of the plasticizer governs the structural homogeneity during stretching remains an open question to answer. Herein, two representative plasticizers, glycerol (GLY) and water, are chosen to endow the PVA films with ductility. It is found that large strain cavitations cause obvious stress whitening in the PVA/H2O films; on the contrary, most of the PVA/GLY films maintain transparent undergoing tensile deformation. Through a combination of experimental inspections and molecular dynamic simulation, it is revealed that partial water molecules that behave as free water will aggregate into microdomains, which serve as mechanical defects responsible for yielding voids. Whereas, the GLY plasticizer homogeneously disperses at a molecular level and interacts with PVA chains through strong hydrogen bonds. More interestingly, it is illustrated that the dispersion and bound states of plasticizers are closely related to the mechanical character of the plasticized PVA films. These findings offer new insight into the working mechanism of plasticization on the structural stability during stretching, and guide the design of PVA/plasticizer system to obtain excellent comprehensive mechanics.
Using the Analytic Hierarchy Process method to develop two efficiency indicators for the Food Acquisition Program – Milk modality
Authors: Neves Josemir Araújo , Rangel Adriano Henrique do Nascimento , Neto Manoel Pereira , Matos Marta Maria Souza , Silva Rita de Cássia de Andrade , Novaes Luciano Patto , Urbano Stela Antas , Paulino Hideljundes Macedo 
Published: June 2022

AbstractThis work describes the process of building two indicators in order to measure the efficiency of the Food Acquisition Program – Milk modality (PAA‐Milk) in the States which implement it. The Analytic Hierarchy Process (AHP) methodology was used to develop the first indicator, while the Principal Component Analysis was used as a tool for cutting and simplifying the structure of the first indicator to obtain the second indicator. The results demonstrate the great potential of the AHP tool together with the statistical tools to develop indicators to diagnose and monitor public policies in Brazil. The states of Alagoas, Paraíba, and Ceará presented the best efficiency in the performance of the Program, while Bahia and Pernambuco presented the worst results.
The Impact of Type 2 Diabetes in Parkinson's Disease
Authors: Athauda Dilan , Evans James , Wernick Anna , Virdi Gurvir , Choi Minee L. , Lawton Michael , Vijiaratnam Nirosen , Girges Christine , Ben‐Shlomo Yoav , Ismail Khalida , Morris Huw , Grosset Donald , Foltynie Thomas , Gandhi Sonia 
Published: June 2022

ABSTRACTBackgroundType 2 diabetes (T2DM) is an established risk factor for developing Parkinson's disease (PD), but its effect on disease progression is not well understood.ObjectiveThe aim of this study was to investigate the influence of T2DM on aspects of disease progression in PD.MethodsWe analyzed data from the Tracking Parkinson's study to examine the effects of comorbid T2DM on PD progression and quality of life by comparing symptom severity scores assessing a range of motor and nonmotor symptoms.ResultsWe identified 167 (8.7%) patients with PD and T2DM (PD + T2DM) and 1763 (91.3%) patients with PD without T2DM (PD). After controlling for confounders, patients with T2DM had more severe motor symptoms, as assessed by Movement Disorder Society Unified Parkinson's Disease Rating Scale, Part III (25.8 [0.9] vs. 22.5 [0.3] P = 0.002), and nonmotor symptoms, as assessed by Non‐Motor Symptoms Scale total (38.4 [2.5] vs. 31.8 [0.7] P < 0.001), and were significantly more likely to report loss of independence (odds ratio, 2.08; 95% confidence interval [CI]: 1.34–3.25; P = 0.001) and depression (odds ratio, 1.62; CI: 1.10–2.39; P = 0.015). Furthermore, over time, patients with T2DM had significantly faster motor symptom progression (P = 0.012), developed worse mood symptoms (P = 0.041), and were more likely to develop substantial gait impairment (hazard ratio, 1.55; CI: 1.07–2.23; P = 0.020) and mild cognitive impairment (hazard ratio, 1.7; CI: 1.24–2.51; P = 0.002) compared with the PD group.ConclusionsIn the largest study to date, T2DM is associated with faster disease progression in Parkinson's, highlighting an interaction between these two diseases. Because it is a potentially modifiable metabolic state, with multiple peripheral and central targets for intervention, it may represent a target for alleviating parkinsonian symptoms and slowing progression to disability and dementia. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
Compression behavior of 3D printed isogrid cylindrical shell structures using experimental and finite element modeling
Authors: Johnson Anish Jafrin Thilak , Paramasivam Suresh 
Published: June 2022

AbstractLattice structures have been widely used in aircraft and automobile industries due to their excellent mechanical properties namely high specific strength, specific stiffness, and energy absorption capability. On the other hand, additive manufacturing that is considered as an essential for Industry 4.0 offers incredible opportunities for product development and production flexibility. Previous research on 3D printed isogrid structures focused on isogrid panels and their buckling behavior, whereas isogrid lattice cylindrical shells garnered less attention. This work reports the effect of short carbon fiber reinforcement with polyamide three‐dimensional printing material on the compression response of isogrid lattice shell structures by experimental and numerical modeling. Isogrid cylindrical shells were three dimensionally printed using fused deposition modeling. Initially, test coupons were printed using polyamide and carbon fiber reinforced polyamide and their mechanical properties were found using uniaxial tensile testing. The obtained tensile properties were given as an input to the numerical modeling performed using LS‐DYNA®. The peak load and the maximum displacement of the printed isogrid lattice shells subjected to axial compression loads were experimentally evaluated. The numerical findings were compared with those produced using experimental methods. The error in estimating the peak load of lattice cylinders through numerical modeling was limited to 5.35%. The effect of geometric parameters namely rib width (helical and hoop), shell thickness, helical angle of ribs on the buckling strength was also studied.
Alterations in cardiac output in fetuses with congenital heart disease
Authors: Sethi Neeta , Gai Jiaxiang , Bost James , Donofrio Mary T. 
Published: June 2022

AbstractObjectiveFetuses with severe congenital heart disease (CHD) have altered blood flow patterns. Prior work to assess fetal combined cardiac output (CCO) is limited by sample size and lack of longitudinal gestational data. Our aim was to evaluate CCO in CHD fetuses to determine whether the presence of single ventricle (SV) physiology or aortic obstruction impacts fetal blood flow and cardiovascular hemodynamics.MethodProspective study including singleton fetuses with CHD (n = 141) and controls (n = 118) who underwent a mid‐ and late‐gestation fetal echocardiogram. Ventricular cardiac output was calculated using the standard computation. Combined cardiac output was derived as the sum of the right and left cardiac outputs and indexed to estimated fetal weight.ResultsFetuses with two ventricle (2V) CHD had significantly higher CCO compared to controls and SV CHD fetuses. Fetuses with SV‐CHD had similar CCO compared to controls. Fetuses with 2V‐CHD and aortic obstruction had significantly higher CCO than fetuses with SV‐CHD and aortic obstruction.ConclusionOur findings suggest that the SV can compensate and increase CCO despite the lack of a second functioning ventricle, however, the degree of compensation may be insufficient to support the increased blood flow needed to overcome the hemodynamic and physiologic alternations seen with severe CHD.
Site‐specific glycosylation of SARS‐CoV‐2: Big challenges in mass spectrometry analysis
Authors: Campos Diana , Girgis Michael , Sanda Miloslav 
Published: June 2022

AbstractGlycosylation of viral proteins is required for the progeny formation and infectivity of virtually all viruses. It is increasingly clear that distinct glycans also play pivotal roles in the virus's ability to shield and evade the host's immune system. Recently, there has been a great advancement in structural identification and quantitation of viral glycosylation, especially spike proteins. Given the ongoing pandemic and the high demand for structure analysis of SARS‐CoV‐2 densely glycosylated spike protein, mass spectrometry methodologies have been employed to accurately determine glycosylation patterns. There are still many challenges in the determination of site‐specific glycosylation of SARS‐CoV‐2 viral spike protein. This is compounded by some conflicting results regarding glycan site occupancy and glycan structural characterization. These are probably due to differences in the expression systems, form of expressed spike glycoprotein, MS methodologies, and analysis software. In this review, we recap the glycosylation of spike protein and compare among various studies. Also, we describe the most recent advancements in glycosylation analysis in greater detail and we explain some misinterpretation of previously observed data in recent publications. Our study provides a comprehensive view of the spike protein glycosylation and highlights the importance of consistent glycosylation determination.
Pulmonary artery enlargement predicts poor survival in patients with COPD: A meta‐analysis
Authors: Chen Huaqiao , Shu Tingting , Wang Lu , Yang Lingzhi , Hu Changchun , Du Shanshan , Wei Huang 
Published: June 2022

AbstractNo previous meta‐analysis has evaluated the relationship between pulmonary artery enlargement (PAE) measured by computed tomography (CT) and prognosis for patients with chronic obstructive pulmonary disease (COPD). Recently, several studies have suggested poor survival and reduced exercise capacity in COPD patients with PAE on CT scan, but there were conflicting results. We aimed to assess the prognostic value of PAE‐CT in patients with COPD. Relevant studies were identified by searching major databases. Pooled outcomes were determined to assess the prognostic value of PAE‐CT in COPD patients. Eighteen studies including 5694 participants were included. PAE indicated higher mortality in COPD patients (odds ratio [OR] = 3.06; 95% confidence interval [95% CI]: 1.76–5.32; p < 0.0001), shorter 6‐minute walk distance (mean difference [MD] = −67.53 m; 95% CI: −85.98 to −49.08; p < 0.00001), higher pulmonary artery systolic pressure (MD = 15.65 mmHg; 95% CI: 13.20–18.11; p < 0.00001), longer length of hospital stay (MD = 2.92 days; 95% CI: 0.71–5.12; p = 0.009) and more severe symptom such as dyspnea (COPD Assessment Test MD = 3.14; 95% CI: 2.48–3.81; p < 0.00001). We also conducted a subgroup analysis regarding the lung function and blood gas analysis for a stable period and acute exacerbation of COPD patients. In conclusion, PAE is significantly associated with mortality, lower exercise tolerance, and poor quality of life in patients with COPD. PAE may serve as a novel imaging biomarker for risk stratification in patients with COPD in the future.
Pleurisy with pneumothorax due to CAM‐resistant MAC lung disease
Authors: Nukui Yoshihisa , Kawahara Tatsuo , Hanzawa Satoshi 
Published: June 2022

AbstractA 72‐year‐old man received rifampicin, ethambutol and sitafloxacin to treat clarithromycin (CAM)‐resistant Mycobacterium avium complex (MAC) lung disease. He was admitted because of fever. Pneumothorax and pleural effusion were present in the right lung, and a new consolidation appeared in the right upper lobe. Based on positive culture of the pleural effusion for CAM‐resistant M. avium and findings on chest computed tomography, he was diagnosed with pleurisy due to M. avium, with rupture of the subpleural lung parenchymal lesion into the pleural space. Additional treatment with streptomycin (SM) improved the patient's high‐grade fever. SM might be effective for pleurisy caused by CAM‐resistant MAC lung disease.
Needle path planning in semiautonomous and teleoperated robot‐assisted epidural anaesthesia procedure: A proof of concept
Authors: Haro‐Mendoza Daniel , Pérez‐Escamirosa Fernando , Pineda‐Martínez Diego , Gonzalez‐Villela Victor J. 
Published: June 2022

AbstractBackgroundEpidural anaesthesia is a Percutaneous Procedure (PP) which plays a crucial role in surgical procedures, where accurate needle insertion is still challenging. The objective of this work is to present a Tuohy needle path planning, which allows an anaesthesiologist to drive semiautonomously, with the assistance of a teleoperated robot, the tip of the needle during this PP.MethodsWe capture, analysed and modelled the anaesthetist hands' motion during the execution of this procedure, by synthetising, programing and simulating a parametrised and normalised kinematic constrains dependent on an insertion variable in a virtual robot.ResultsTwo preoperative path planning models were obtained, which provide a teleoperated robot with kinematic constraints to semiautonomously drive a Tuohy needle in the epidural anaesthesia procedure.ConclusionsA semiautonomous robot can assist in the execution of this PP using the kinematic constraints obtained from the study of the movement of a specialist's hands.
An H∞ approach to data‐driven fault estimation, and isolation for Hammerstein‐Wiener systems
Authors: Salim Mina , Mahdavi Zahra , Kharrati Hamed 
Published: June 2022

AbstractThis article considers the fault estimation problem in a data‐driven setup for a class of nonlinear systems called Hammerstein‐Wiener. The fault estimation problem is converted to a finite‐horizon minimax optimization problem. To estimate faults, a subspace predictor is utilized to predict the output sequence. Our proposed method is robust against disturbances. The proposed method is briefly expressed as an algorithm that can be implemented by MATLAB software. The suggested algorithm is applied to a numerical MIMO example to verify its validity.
Deep learning detects invasive plant species across complex landscapes using Worldview‐2 and Planetscope satellite imagery
Authors: Lake Thomas A. , Briscoe Runquist Ryan D. , Moeller David A. 
Published: June 2022

AbstractEffective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge (Euphorbia virgata), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites. Worldview‐2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short‐term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview‐2 model. Across models, most false‐positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid‐season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red‐edge and near‐infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large‐scale, remote and data‐sparse areas.
Life Capital: An Epistemic and Methodological Lens for TESOL Research
Authors: Consoli Sal 
Published: June 2022

AbstractThis paper draws on one of Pierre Bourdieu’s core dimensions of social theory: the notion of capital. Bourdieu's work has percolated various academic domains and transcended disciplinary boundaries, thereby leading to new vistas and questions. It is in the spirit of generating “new vistas” that this paper offers considerations which may serve as an epistemic and methodological drive to reinvigorate our social research practices within TESOL. In particular, I argue for a new application of Bourdieu's sociological concept to account more holistically for the complexities of TESOL research practices, and I present a proposal that may support the TESOL research community in addressing the “realities” and “dilemmas” of our field (McKinley & Rose, 2017). Ultimately, life capital will act as a catalyst to promote a fresh perspective on our research—a lens which, in the long run, may yield TESOL research that fully acknowledges, values, and celebrates the humanness of our inquiries.
Virtual sensors for wind turbines with machine learning‐based time series models
Authors: Dimitrov Nikolay , Göçmen Tuhfe 
Published: June 2022

AbstractModern wind turbines have multiple sensors installed and provide constant data stream outputs; however, there are some important quantities where installing physical sensors is either impractical or the sensor technology is not sufficiently advanced. An example of such a problem is, for example, sensing the shape and location of wake‐induced wind deficits caused by upwind turbines—a feature which would have relevant application in wind farm control; however, it is hard to detect physically due to the need of scanning the airflow in front of the turbine in multiple locations. Another control‐related example is monitoring and predicting the blade tip‐tower clearance. A “virtual sensor” can be created instead, by establishing a mathematical relationship between the quantity of interest and other, measurable quantities such as readings from already available sensors (e.g., SCADA, lidars, and met‐masts). Machine Learning (ML) approaches are suitable for this task as ML algorithms are capable of learning and representing complex relationships. This study details the concept of ML‐based virtual sensors and showcases three specific examples: blade root bending moment prediction, detection of wind turbine wake center location, and forecasting of blade tip‐tower clearance. All examples utilize sequence models (Long Short‐Term Memory, LSTM) and use aeroelastic load simulations to generate wind turbine response time series and test model performance. The data types used in the examples correspond to channels that would be available from high‐frequency SCADA data combined with a blade and tower load measurement system. The resulting model performance demonstrates the feasibility of the ML‐based virtual sensor approach.