Role Of Various Features Research Articles

Identifying widely disseminated scientific papers on social media

Identifying widely disseminated papers (WDPs) on social media can help to understand dissemination mechanisms of scientific papers from academia to social media and assist in the formulation of public and science policy. This study applies machine learning methods to explore the possibility of identifying WDPs and to investigate the influence mechanisms of literature-related and social media-related features. A pre-task was first conducted to investigate whether the visibility of scientific papers on social media can be predicted, and the role of various features was analyzed. Then, we defined two predictive tasks for identifying WDPs before and after they are visible on social media. The performance of eight state-of-the-art algorithms was compared in three experiments against the dataset of the oncology field, and the contribution of literature-related and social media-related features in the tasks was explained based on the Shapley additional explanations (SHAP) value. The results show that XGBoost performs better than other algorithms, especially with an F1 score of 0.988 and AUC of 0.998 in the trend prediction task. Nearly all of the literature-related features have great effects on identifying long-term disseminated papers, and most social media-related features play more significant roles in identifying broadly mentioned papers. Moreover, journal features contribute more to identifying papers of social media visibility, while paper features, especially research topics, have a greater influence on identifying WDPs. The number and proportion of academic-related Twitter users have great impacts on the scale and duration of papers’ dissemination. The number and duration of first-generation tweets play critical roles in identifying broadly mentioned and long-term disseminated papers, respectively. This study provides profound insights into the influencing factors in the dissemination of papers from the scientific community to and across social media, and helps to understand the difference in knowledge propagation between academia and the public.

Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis

Background Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD. Objective This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF). Methods In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD. Results The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P<.01). Conclusions A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.

MEttLE: a modelling-based learning environment for undergraduate engineering estimation problem solving

Estimation is an important class of problems that engineering undergraduates must be able to solve. However teaching-learning of estimation is under emphasized in the current engineering curriculum. In this paper, we report on the first cycle of a design-based research project to design a technology-enhanced learning environment (TELE) to help students learn estimation. The TELE includes features such as a progressive higher-order modelling-based structuring of the estimation process, a problem system simulator and metacognitive scaffolds. We performed a lab study and found that learners were able to use the features in the TELE to solve the estimation problem and obtain an order-of-magnitude estimate. Further, learners learned some of the reasoning processes involved in performing estimation and recognized the role of evaluation and the need for practical considerations in estimation. We identified the roles of various features in the TELE for learning these estimation reasoning processes. These results have implications for the redesign of our TELE to improve student learning of estimation.

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

Phase fluctuations model for EM wave propagation through solar scintillation at superior solar conjunction

Traveling solar wind disturbances have a significant influence on radio wave characteristics during the superior solar conjunction communication. This paper considers the impact of solar scintillation on phase fluctuations of electromagnetic (EM) wave propagation during the superior solar conjunction. Based on the Geometric Optics approximation, the close-form approximation model for phase fluctuations is developed. Both effects of anisotropic temporal variations function of plasma irregularities and their power spectrum are presented and analyzed numerically. It is found that phase fluctuations rapidly decrease with increasing Sun–Earth–Probe angle and decrease with increasing frequency at the rate of 1/f2. Moreover, the role of various features of the solar wind irregularities and their influence on the EM wave characteristic parameters is studied and discussed. Finally, we study the phase fluctuations of typical cases in order to better understand the impact of phase fluctuations in future deep space communication scenarios during solar conjunction periods.

Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments

Selection of effective feature set and proper classifier is a challenging task in problems where machine learning techniques are used. In automatic identification of musical instruments also it is very crucial to find the right set of features and accurate classifier. In this paper, the role of various features with different classifiers on automatic identification of musical instruments is discussed. Piano, acoustic guitar, xylophone and violin are identified using various features and classifiers. Spectral features like spectral centroid, spectral slope, spectral spread, spectral kurtosis, spectral skewness and spectral roll-off are used along with autocorrelation coefficients and Mel Frequency Cepstral Coefficients (MFCC) for this purpose. The dependence of instrument identification accuracy on these features is studied for different classifiers. Decision trees, k nearest neighbour classifier, multilayer perceptron, Sequential Minimal Optimization Algorithm (SMO) and multi class classifier (metaclassifier) are used. It is observed that accuracy can be improved by proper selection of these features and classifier.

PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm

Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most 'informative' or 'discriminating' features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive. PromoterExplorer is tested based on large-scale DNA sequences from different databases, including the EPD, DBTSS, GenBank and human chromosome 22. Experimental results show that consistent and promising performance can be achieved.

Relative contributions of runoff and sediment from sources within a road prism and implications for total sediment delivery

AbstractThis study examines runoff and sediment generation rates within the road prism on unsealed road segments in the Cuttagee Creek catchment near Bermagui in New South Wales, Australia. A large (600 m2) rainfall simulator was used to measure runoff and sediment yields from each of the potential sediment and runoff sources and pathways. These included the road surface, table‐drain, upslope contributing area and cutslope face, and the entire road segment as measured at the drain outlet. Experiments were conducted on two major types of road (ridge‐top and cut‐and‐fill) of varying traffic usage and maintenance standard for two 30‐minute simulations of increasing rainfall intensity. From the range of possible sources within the road prism, the road surface produced the dominant source of excess runoff and sediment at each site with limited contributions from the table‐drain, cutslope face or contributing hillslope. Sediment generation varied significantly with road usage and traffic intensity. Road usage was strongly related to the amount of loose available sediment as measured prior to the experiments. Table‐drains acted primarily as sediment traps during the low rainfall event but changes in sediment concentration within the drains were observed as runoff volumes increased during the higher rainfall event of 110 mm h−1, releasing sediment previously stored in litter and organic dams. The experiments demonstrate the potential roles of various features of the road prism in the generation and movement of sediment and water. Copyright © 2006 John Wiley & Sons, Ltd.

Design of powder aligning systems for the compression molding of radially anisotropic permanent magnet rings

The article describes the design of powder aligning fixtures for the compression molding of radially anisotropic NdFeB magnet rings. The implications of the high fields required are discussed, with particular reference to the limitations imposed on the aspect ratio of the magnets that can be satisfactorily aligned. Features for enhancing the magnitude of the aligning field in the mold cavity, e.g., a magnetic shunt to shield the inner core and tapering of the upper plate are discussed. These issues are illustrated by means of a design study on a powder aligning fixture for a NdFeB magnet ring with an outer diameter of 26 mm and a wall thickness of 3 mm. Extensive finite element analysis is employed to investigate the role of various features, and to optimize the geometry. The findings of the design study are validated by comparison with measurements on a prototype fixture.

Extrinsic factors in the mechanics of bridged cracks

Calculations for single-edge notch specimens under uniform remote tension are used to demonstrate the profound influence of the extrinsic factors of specimen shape and load distribution on the propagation of bridged cracks when the bridging zone length is comparable to any of the dimensions of the crack or the specimen. The influence of the extrinsic factors is so strong that there is a grave risk of seriously nonconservative predictions of strength and reliability if standard engineering methods are used for materials exhibiting such bridged cracks. However, this difficulty can be resolved by regarding the relationship between the bridging tractions p and the crack opening displacement u as a fundamental material property. Extensive calculations are summarized for one class of relations p( u) that rise to a peak corresponding to ligament failure and then fall gradually to zero at large u. Resistance curves of great variety are displayed. The central role of the “bridging length scale,” the initial crack extension over which the bridging zone matures, is demonstrated. The roles of various features of p( u) in determining the transition from noncatastrophic (or ductile) failure to catastrophic (or brittle) failure are examined.