bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weather forecast for the 35th America's Cup (2017) winners based on a limited area model
Authors: Messager Christophe , Badham Roger “Cloud” , Honnorat Marc , Vandenberghe François , Waterhouse Tom , Currie Ian 
Published: February 2020

AbstractThe America's Cup (AC) is the oldest trophy in international sport and sailing regatta. The 35th AC occurred during spring 2017 in Bermuda. A new generation of sailing boats was in use, while the technical advances required backstage to enable a victory were equally impressive. The availability of accurate wind fields with a grid spacing of < 1 km from numerical meteorological forecasts was one of those key advances, used for both strategic and tactical planning. The study describes the limited area model, explicitly resolving the convection, used to generate weather forecasts for Emirates Team New Zealand (ETNZ) challenger, the challenger and winning team. In particular, it illustrates an ability to forecast accurately the wind variability in space and time, which helped achieve victory.
Using social media to measure impacts of named storm events in the United Kingdom and Ireland
Authors: Spruce M. , Arthur R. , Williams H. T. P. 
Published: February 2020

AbstractDespite increasing use of impact‐based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real‐world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm‐related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.
A deep‐learning‐based approach for adenoid hypertrophy diagnosis
Authors: Shen Yi , Li Xiaohu , Liang Xiao , Xu Hai , Li Chuanfu , Yu Yongqiang , Qiu Bensheng 
Published: February 2020

PurposeAdenoid hypertrophy is a pathological hyperplasia of adenoids and may cause snoring, apnea, and impede breathing during sleep. In clinical practice, radiologists diagnose the severity of adenoid hypertrophy by measuring the ratio of adenoid width (A) to nasopharyngeal width (N) according to the lateral cephalogram, which indicates the locations of four keypoints. The entire diagnostic process is tedious and time‐consuming due to the acquisition of A and N. Thus, there is an urgent need to develop computer‐aided diagnostic tools for adenoid hypertrophy.MethodsIn this paper, we first propose the use of deep learning to solve the problem of adenoid hypertrophy classification. Deep learning driven by big data has developed greatly in the image processing field. However, obtaining a large amount of training data is hard, making the application of deep learning to medical images more difficult. This paper proposes a keypoint localization method to incorporate more prior information to improve the performance of the model under limited data. Furthermore, we design a novel regularized term called VerticalLoss to capture the vertical relationship between keypoints to provide prior information to strengthen the network performance.ResultsTo evaluate the performance of our proposed method, we conducted experiments with a clinical dataset from the First Affiliated Hospital of Anhui Medical University consisting of a total of 688 patients. As our results show, we obtained a classification accuracy of 95.6%, a macro F1‐score of 0.957, and an average AN ratio error of 0.026. Furthermore, we obtained a macro F1‐score of 0.89, a classification accuracy of 94%, and an average AN ratio error of 0.027 while using only half of the data for training.ConclusionsThe study shows that our proposed method can achieve satisfactory results in the task of adenoid hypertrophy classification. Our approach incorporates more prior information, which is especially important in the field of medical imaging, where it is difficult to obtain large amounts of training data.
Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation
Authors: Jun Guo Bang , He Xiuxiu , Lei Yang , Harms Joseph , Wang Tonghe , Curran Walter J. , Liu Tian , Jiang Zhang Long , Yang Xiaofeng 
Published: February 2020

PurposeSegmentation of left ventricular myocardium (LVM) in coronary computed tomography angiography (CCTA) is important for diagnosis of cardiovascular diseases. Due to poor image contrast and large variation in intensity and shapes, LVM segmentation for CCTA is a challenging task. The purpose of this work is to develop a region‐based deep learning method to automatically detect and segment the LVM solely based on CCTA images.MethodsWe developed a 3D deeply supervised U‐Net, which incorporates attention gates (AGs) to focus on the myocardial boundary structures, to segment LVM contours from CCTA. The deep attention U‐Net (DAU‐Net) was trained on the patients’ CCTA images, with a manual contour‐derived binary mask used as the learning‐based target. The network was supervised by a hybrid loss function, which combined logistic loss and Dice loss to simultaneously measure the similarities and discrepancies between the prediction and training datasets. To evaluate the accuracy of the segmentation, we retrospectively investigated 100 patients with suspected or confirmed coronary artery disease (CAD). The LVM volume was segmented by the proposed method and compared with physician‐approved clinical contours. Quantitative metrics used were Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), the center of mass distance (CMD), and volume difference (VOD).ResultsThe proposed method created contours with very good agreement to the ground truth contours. Our proposed segmentation approach is benchmarked primarily using fivefold cross validation. Model prediction correlated and agreed well with manual contour. The mean DSC of the contours delineated by our method was 91.6% among all patients. The resultant HD was 6.840 ± 4.410 mm. The proposed method also resulted in a small CMD (1.058 ± 1.245 mm) and VOD (1.640 ± 1.777 cc). Among all patients, the MSD and RMSD were 0.433 ± 0.209 mm and 0.724 ± 0.375 mm, respectively, between ground truth and LVM volume resulting from the proposed method.ConclusionsWe developed a novel deep learning‐based approach for the automated segmentation of the LVM on CCTA images. We demonstrated the high accuracy of the proposed learning‐based segmentation method through comparison with ground truth contour of 100 clinical patient cases using six quantitative metrics. These results show the potential of using automated LVM segmentation for computer‐aided delineation of CADs in the clinical setting.
Automatic detection of simulated motion blur in mammograms
Authors: Kamona Nada , Loew Murray 
Published: February 2020

PurposeTo use machine‐learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision‐making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety.MethodsBlur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point‐spread‐function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super‐sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine‐learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six‐way classification.ResultsFor the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively.ConclusionsPreliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists’ performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists’ lesion detection performance. As of this date, no other study has investigated the ability of machine‐learning classifiers and BM operators to detect motion blur in mammograms.
Temperament and professional quality of life among Japanese nurses
Authors: Tanaka Koji , Ikeuchi Satomi , Teranishi Keiko , Oe Masato , Morikawa Yuko , Konya Chizuko 
Published: February 2020

AbstractAimTo investigate associations between temperament and professional quality of life among Japanese nurses.DesignA descriptive‐correlational study using self‐administered anonymous questionnaires.MethodsQuestionnaires were collected from 1,267 nurses. We used analysis of covariance to examine associations between tendencies of temperament (depressive, cyclothymic, hyperthymic, irritable and anxious) and professional quality of life subscales (compassion satisfaction, burnout, compassion fatigue) first for all participants and then again after dividing the participants into two groups based on years of experience.ResultsNurses’ professional quality of life was associated with innate temperament and years of experience. Nurses with any of depressive, cyclothymic, irritable, or anxious tendencies showed significantly lower compassion satisfaction and higher burnout and compassion fatigue than those without these tendencies. Nurses with hyperthymic tendencies showed significantly higher compassion satisfaction and lower burnout than those without the tendency.
Highly stretchable wrinkled electrode based on silver ink‐elastomer nanocomposite with excellent fatigue resistance
Authors: Won Joohye , Mondal Subhadip , Park Jongho , Wang Wonseok , Lee Hyunsang , Kim Suhyun , Shin Beomsu , Sathi Shibulal G. , Nah Changwoon 
Published: February 2020

AbstractFlexible and stretchable electrodes have attracted immense attention because of their various electronic applications on the wearable sensing electronics, but challenges regarding robust and sustainable electrical performance under static and dynamic mechanical stresses still need to be overcome. Herein, with a simple effective strategy, a highly serviceable and flexible elastomeric electrode fabricated by utilizing an interpenetrating network between acrylic rubber (AR) and silicone rubber (SR), and silver (Ag) ink to increase electrical conductivity, is introduced. Oxygen (O2) plasma and sodium dodecyl sulfate (SDS) treatment on wrinkle substrate surface lead to robust adhesion of Ag ink layer, results in high electrical conductivity. Wrinkle morphology can help to stably maintain the continuous conductive Ag network during static and dynamic deformation. This stretchable electrode possessing properties such as robust and stable electrical conductivity of ~103 S/cm under 150% static tensile deformations and good sustainability after 1000 cycles of dynamic tensile deformations is a promising candidate for stretchable wearable electronics.
Interface analysis of Ag/n‐type Si contacts in n‐type PERT solar cells
Authors: Ferrada Pablo , Rudolph Dominik , Portillo Carlos , Adrian Adrian , Correa‐Puerta Jonathan , Sierpe Rodrigo , del Campo Valeria , Flores Marcos , Corrales Tomas P. , Henríquez Ricardo , Kogan Marcelo J. , Lossen Jan 
Published: February 2020

AbstractTo increase efficiencies of bifacial solar cells, emitter, back surface field (BSF), and metal patterns must be optimized. We study the influence of paste volume, through multiple prints, of two silver pastes on the contact formation at the rear side of n‐type passivated emitter and rear totally diffused (n‐PERT) solar cells with two BSF doping profiles. Differences in fingers' electrical properties were found between pastes. Contact resistivity shows a relative difference of 27.6%, partially explained by changes in the silver crystallites formation at the Ag/Si interface and in the crystallites' penetration depth. Variations in crystallites formation and penetration between pastes can reach 38.4% and 48.8%, respectively. Line resistance shows a difference between pastes, appearing as the main cause of an absolute efficiency difference of 2.9%. Fingers' structural and electrical properties are modified by increasing the paste volume. Microstructure analysis reveals that additional metallic printing does not only increase line cross sectional area but also increases the formation of silver crystallites, which can reach a relative increment of 23.9% between first and second prints. Further printing does not necessarily decrease contact resistivity, but reduces line resistance in up to 94.9%, which results in an absolute efficiency increase of 2.2%. In addition, the higher presence of silver oxide in the finger is related to a higher efficiency in the formation of silver crystallites. Finally, BSF doping has an influence in the open circuit voltage, short circuit current density, and contact resistivity, with differences that can reach 8.7 mV, 0.2 mA/cm2, and 6.1 mΩcm2, respectively, depending on paste and number of prints.
Systematic review of longitudinal studies on chemotherapy‐associated subjective cognitive impairment in cancer patients
Authors: Kim Hee‐Ju , Jung Sun‐Ok , Kim Hyang , Abraham Ivo 
Published: February 2020

AbstractObjectivesThis systematic review of longitudinal studies, assessing subjective cognitive impairment (SCI) reported by adult cancer patients, aimed to summarize evidence on the impact of chemotherapy on SCI, identify moderators of SCI, and evaluate methodological issues.MethodsData accrued from Pubmed, EMBASE, CINAHL, PsychInfo, and the Cochrane library. Inclusion criteria were original studies, an exclusively adult sample, valid and reliable subjective cognitive measures, and at least one baseline data point prior to and another after the initiation of chemotherapy. Data were collected on the sample composition, data‐collection time points, outcome measures, statistical analysis, and major findings (ie, longitudinal changes in prevalence, severity, and associated factors).ResultsForty articles published between 2004 and 2019 were retained: 21 examined chemotherapy‐treated patients only, and 19 employed control groups. Findings were mixed, with slightly more studies supporting the impact of chemotherapy on SCI. SCI tended to be more prevalent and severe after initiating chemotherapy, compared with patients' own baseline and controls not treated with chemotherapy. Impact appeared to be acute and more likely limited to subsamples. Most studies examining non–breast‐cancer samples reported the lack or limited impact of chemotherapy on SCI. The most consistent moderators were depression and fatigue. Methodological issues regarding sampling design, measurement, and statistical analysis were discussed.ConclusionMore rigorously designed longitudinal studies would clarify direct and indirect effects of chemotherapy on SCI.
Modelling the impact of crushing on the strength performance of corrugated fibreboard
Authors: Jamsari Mohamad Aiman , Kueh Celia , Gray‐Stuart Eli M. , Dahm Karl , Bronlund John E. 
Published: February 2020

This paper presents different ways of modelling the strength of corrugated fibreboard (CFB) subjected to different levels of pre‐crushing. The strength performance was measured through four‐point bending loading and edge crush test (ECT). The models used in this study were an analytical solution, an equivalent flute model, and detailed flute geometry models that consisted of idealized sine geometry and real geometry.The study found that the bending performance was dependent on the calliper of CFB rather than the flute geometry. All models showed a similar trend in predicting the drop in bending stiffness as the level of pre‐crushing increased, albeit with different absolute value.It was found that the real geometry model of the board predicted ECT performance better than the other models. However, at severe pre‐crushing levels (>50%), there was a significant drop in the experimental ECT force not predicted by the models. For these cases, there was evidence of delamination of the flute, a failure mechanism that was not included in any of the models.The analytical solution model provides the quickest prediction but could not predict the crushed ECT performance due to not considering the calliper variable in the equation. The equivalent model showed faster solving time compared with both real and idealized geometry models, although these microgeometry models predicted ECT the most accurately.
Do you bend or break? System dynamics in resilience planning for food security
Authors: Herrera de Leon Hugo Jose , Kopainsky Birgit 
Published: February 2020

AbstractThis paper discusses our experience in using system dynamics to facilitate resilience planning for food security in rural communities that are exposed to ever‐increasing climatic pressures in Guatemala. The social–ecological systems literature is rich in examples where policies to enhance resilience are deduced from factors generally accepted to be present in resilient systems (e.g. redundancy, connectivity and polycentrism). This deductive approach risks being overly simplistic. As an alternative, this paper explores how insights from analysing the structure–behaviour relationship of complex dynamic systems can be used to generate tailored policies. The results show that stability in food systems is mainly driven by key strategic resources that moderate the effects of environmental changes on food availability and affordability. Moreover, our experience highlights the importance of analysing mechanisms that determine a system's behaviour while and after the system is affected by a disturbance to formulate effective resilience policies.© 2020 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society
An energy efficient and low overhead fault mitigation technique for internet of thing edge devices reliable on‐chip communication
Authors: Ibrahim Muhammad , Baloch Naveed Khan , Anjum Sheraz , Zikria Yousaf Bin , Kim Sung Won 
Published: February 2020

SummarySoft errors in network‐on‐chip (NoC) such as single bit upsets and multibit upsets cause hazardous effects such as congestion, deadlock, livelock, and corruption of data. Error‐correcting codes (ECCs) are the best choices to handle these soft errors in links and memory buffers of NoC, which is the need of all modern systems, including internet of thing (IoT) edge devices. Many of these ECCs cannot correct both random and burst errors. Specific codes possess the correction and detection capability at the cost of an increase in area, latency, and energy. In this article, a coding technique is proposed by using a single error correction double error detection‐triple adjacent error correction‐six adjacent error detection (SEC‐DED‐TAEC‐6AED) (24,16) I5, that provides both random and burst error fault tolerance for NoC. The proposed technique decreases the area, energy, and latency cost of the whole NoC. It also reduces the area overhead to 173.41% and 117.91% compare to joint crosstalk avoidance multiple error correction (JCAMEC) and joint crosstalk multiple error correction (JMEC), respectively. Besides, the delay overhead of the proposed technique reduces to 4.2% and 91.97% compared with JCAMEC and JMEC, respectively. The simulation results show that the proposed code possesses an enhanced ability of error correction and detection with 3.5 times less redundant bits and a 30% fast code rate compared with JMEC and JCAMEC. Hence, the proposed scheme can effectively be used for detecting and correcting single and multiple bit errors for on‐chip communication.