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The publication status of dental journals in Japan was examined, with a focus on metrics such as Journal Impact Factor (JIF), Eigenfactor, Article Influence Score, and percentage of open access. A total of 18 journals published by Japanese dental organizations were identified in the Journal Citation Reports (JCR), with JIF values ranging from 0.4 to 6.6. The highest JIF was observed in The Japanese Dental Science Review. Additionally, 16 journals were not listed on the JCR. The authors explored the implications of these findings on the visibility and impact of Japanese dental research, and discussed the potential benefits of embracing open-access publications for greater global dissemination. This study highlighted the opportunities for journals to enhance their international recognition by meeting the criteria for JIF inclusion and embracing open-access publications. By adopting effective publication strategies, the dental community in Japan will be able to contribute to the advancement of dentistry globally, ensuring broader accessibility and recognition of its research contributions.

The publication status of dental journals in Japan was examined, with a focus on metrics such as Journal Impact Factor (JIF), Eigenfactor, Article Influence Score, and percentage of open access. A total of 18 journals published by Japanese dental organizations were identified in the Journal Citation Reports (JCR), with JIF values ranging from 0.4 to 6.6. The highest JIF was observed in The Japanese Dental Science Review. Additionally, 16 journals were not listed on the JCR. The authors explored the implications of these findings on the visibility and impact of Japanese dental research, and discussed the potential benefits of embracing open-access publications for greater global dissemination. This study highlighted the opportunities for journals to enhance their international recognition by meeting the criteria for JIF inclusion and embracing open-access publications. By adopting effective publication strategies, the dental community in Japan will be able to contribute to the advancement of dentistry globally, ensuring broader accessibility and recognition of its research contributions. [ABSTRACT FROM AUTHOR]

The field of agronomy and crop science plays a crucial role in ensuring global food security and sustainable agricultural practices. In the realm of scholarly publications, bibliometric indicators have emerged as invaluable tools for quantifying and comparing the impact, reach, and significance of such contributions. Various bibliometric indicators were employed for the study, including Impact Factor, Eigenfactor Score, Cite Score, and h-index. To accomplish this, we compiled a roster of 90 agronomy and crop science journals using the Journal Citation Report (JCR) within the Web of Science (WOS), journal metrics from the Scopus database (Cite Score and SNIP), and h5-index from Google Scholar. The SCImago Journal Ranking (SJR) was obtained from its official website. Subsequently, we conducted Bivariate Pearson's (r) and Spearman's (ρ) correlation coefficient tests to assess ranking correlations. The study revealed notable positive correlations particularly, a strong Pearson's (r) correlation evident between the Impact Factor (JIF) and both the Cite Score (CS) and SCImago Journal Rank (SJR) indicators, yielding values of (r = 0.962) and (r = 0.932), respectively. Interestingly, despite the distinct mathematical foundations underpinning the various citation-based bibliometric indices scrutinized, our findings underscored their robust correlations when it comes to ranking agronomy and crop science journals. [ABSTRACT FROM AUTHOR]

BackgroundImpact factor (IF) is a quantitative tool designed to evaluate scientific journals' excellence. There was an unprecedented upsurge in biomedical journals' IF in 2020, perhaps contributed by the increased number of publications since the COVID-19 outbreak. We conducted a cross-sectional study (2018–2020) to analyze recent trends in standard bibliometrics (IF, Eigenfactor, SNIP) of pediatric journals. We also estimated reference and publication counts of biomedical journals since publication volume determines the number of citations offered and IF.MethodsVarious bibliometrics of pediatric journals and reference/publication volumes of biomedical journals were compared between 2020 vs. 2019 and 2019 vs. 2018. We also compared open access (OA) and subscription journals' trends. Finally, we estimated IF changes in the journals of a different specialty, pulmonology.ResultsThe study included 164 pediatric and 4,918 biomedical journals (OA = 1,473, subscription = 3,445). Pediatric journals' IFs had increased significantly in 2020 [median (IQR) = 2.35 (1.34)] vs. 2019 [1.82 (1.22)] (Wilcoxon: p-value < 0.001). IFs were unchanged between 2018 and 2019. Eigenfactor remained stable between 2018 and 2020, while SNIP increased progressively. Reference/publication volumes of biomedical journals escalated between 2018 and 2020, and OA journals experienced faster growth than subscription journals. IFs of pulmonary journals also increased considerably in 2020 vs. 2019.ConclusionsWe report an upsurge in pediatric journals' IF, perhaps contributed by a sudden increase in publication numbers in 2020. Therefore, considering this limitation, IF should be cautiously used as the benchmark of excellence. Unlike IF, Eigenfactor remained stable between 2018 and 2020. Similar changes in IF were also observed among the journals of another specialty, pulmonology.

Extracorporeal Membrane Oxygenation (ECMO) was first used in the 1970s. Its use is increasingly common in critical care and perioperative settings and has gained newfound prominence during COVID-19. To guide future research, we conducted a bibliometric analysis of ECMO literature. Thomson Reuters Web of Science was searched to March 7, 2021. Articles were ranked by total number of citations. Data was extracted from the 100 most cited papers relevant to ECMO for study design, topic, author, year, and institution. Journal impact factor for 2019 and Eigenfactor scores were also recorded. Our search retrieved a total of 18,802 articles. Median number of citations for the top 100 articles was 220 (range 157-1,819). These were published in 34 journals, with first authors originating from 15 countries. The Annals of Thoracic Surgery had the highest number of articles (n 5 9) while Lancet publications had the most citations (n 5 3,191). Use of ECMO was most commonly observed in cardiogenic shock or acute respiratory distress syndrome. United States had the greatest article output (n 5 49). With 10 publications, 2013 was the most prolific year. Using linear regression, when controlled for time since publication, there was no statistically significant relationship between 2019 journal impact factor and number of article citations (p 5 .09). Top articles in the ECMO literature are of considerable impact and quality. As the United States produced the bulk of the prominent evidence base, and most data were regarding respiratory issues, outsized advances in ECMO may be possible within the United States during the COVID-19 era. [ABSTRACT FROM AUTHOR]

Journal impact factor (IF) inflation is suggested as a problem resulting from commentaries published by the editors in chief (EiCs) of their respective journals. However, it is unclear whether this is a systemic problem across the top thirty cardiovascular medicine journals. Therefore, the purpose of this investigation was to examine the relationship between the number of commentaries written by an EiC and their journal’s IF and Eigenfactor (Ef). Utilizing Spearman rank partial correlations controlling for length of service as the EiC, significant moderate correlations were found between the number of commentaries and the number of first-author commentaries by the EiC and the IF of their journal (r=0.568, p=0.001 and r=0.504, p=0.005; respectively). A weak but still significant correlation was found between the number of commentaries by the EiC and the Ef of their journal (r=0.431, p=0.020). The reason for these correlations is unclear, and whether the methodology used to compute the IF and Ef should be modified needs further research.

Journal impact factor (IF) inflation is suggested as a problem resulting from commentaries published by the editors in chief (EiCs) of their respective journals. However, it is unclear whether this is a systemic problem across the top thirty cardiovascular medicine journals. Therefore, the purpose of this investigation was to examine the relationship between the number of commentaries written by an EiC and their journal's IF and Eigenfactor (Ef). Utilizing Spearman rank partial correlations controlling for length of service as the EiC, significant moderate correlations were found between the number of commentaries and the number of first-author commentaries by the EiC and the IF of their journal (r=0.568, p=0.001 and r=0.504, p=0.005; respectively). A weak but still significant correlation was found between the number of commentaries by the EiC and the Ef of their journal (r=0.431, p=0.020). The reason for these correlations is unclear, and whether the methodology used to compute the IF and Ef should be modified needs further research. [ABSTRACT FROM AUTHOR]

Limited time and budgets have created a legitimate need for quantitative measures of scholarly work. The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network. The Eigenfactor and Article Influence Score use an iterative ranking scheme similar to Google's PageRank algorithm. With this approach, citations from top journals are weighted more heavily than citations from lower-tier publications. We describe these metrics and the rankings that they provide.

A recent article by Phil Davis suggested that the Eigenvalue metric does adds little useful information to the more simply calculated measure of total citations published by the ISI. This paper argues that Davis's claim is an instance of a classic statistical fallacy of spurious correlation. Based on an analysis of the entire 2006 ISI Journal Citation Reports, we show that there are statistically and economically significant differences between the Eigenfactor metrics and the ISI's impact factor and total citations.

Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design. The design of meta-optics and photonics components poses a number of high-dimensional, fabrication-constrained, computationally expensive optimization problems. Here, the authors propose a framework based on algorithmic differentiation to optimize arbitrary meta-optical devices with any shape or topology, enabling rapid inverse design of photonics and complex meta-optical devices. [ABSTRACT FROM AUTHOR]

A time‐series of yield monitor data may be used to identify field areas of consistently low or high yield to serve as productivity zones for site‐specific crop management. However, transient factors that affect yield in 1 yr, but not every year, detract from this approach. The objective of this study was to illustrate Moran eigenvector spatial filtering (MESF) with results from analysis of multi‐year crop yield data from two farm fields in the United States. The MESF method accounts for temporal autocorrelation within a common factor map representing the correlation across years and partitions stochastic geographic variation into spatially structured and unstructured components. Crop rotation data were utilized from a dryland field in east‐central South Dakota and an irrigated field in southwestern Georgia. A random effects (RE) model was estimated that utilized eigenfunctions of a geographic connectivity matrix to account for spatially structured random effects (SSRE) and unstructured random effects (SURE) in standardized z scores of multi‐year crop yield. The MESF method was evaluated with conventional averaging of unfiltered yield data as a reference for comparison. In South Dakota, the SSRE accounted for 26% of the yield variance shared across years. Distinct patterns appeared to be related to changes in soil type and landscape position. The Georgia field yielded similar results. The MESF is effective for revealing structured variation in a time series of yield monitor data and may be useful for defining productivity zones within fields. [ABSTRACT FROM AUTHOR]

This paper offers an overview of the history of the impact factor as a measure for scholarly merit; a summary of frequent criticisms of the impact factor’s calculation and usage; and a framework for understanding some of the leading alternatives to the impact factor.

Clock trees can be constructed based on static arrival time constraints or dynamic implied skew constraints. Dynamic implied skew constraints allow the full timing margins to be utilized. However, the dynamic skew constraints require a high run-time complexity to be evaluated. In contrast, static arrival time constraints are more restrictive but can be evaluated in constant time. Consequently, there is a tradeoff between timing margin utilization and run-time. In this paper, a scalable clock tree synthesis (CTS) framework is proposed for the construction of low-cost useful skew trees (USTs) with high timing quality. The scalability is based on combining the use of arrival time constraints with virtual minimum and maximum delay offsets, which facilitates that a pair of smaller subtrees can be joined into a larger subtree in constant time. The ability to quickly join subtrees is leveraged to perform a high degree of solution space exploration, which translates into the construction of USTs with low-cost. In particular, clock trees with various routing tree topologies, buffer tree topologies, buffer sizes, and stem wire lengths are explored. Moreover, the arrival time constraints are specified with the objective of being the least restrictive to reduce cost. Furthermore, the constraints are respecified throughout the tree construction process using a slack graph (SG) to expose additional timing margins. The high timing quality is obtained by seamlessly integrating arbitrary timing models using the SG. Finally, the proposed CTS framework is integrated with a clock tree optimization framework to demonstrate that the constructed USTs are capable of meeting timing constraints under the influence of on-chip variations. [ABSTRACT FROM AUTHOR]

This paper proposes a weighted generalization of the recently developed Euclidean Index. The weighting mechanism is designed to reflect the reputation of the journal within which an article appears. The weights are constructed using the Eigenfactor Article Influence percentiles scores. The rationale for assigning weights is that citations in more prestigious journals should be adjusted to logically reflect higher costs of production and higher vetting standards, and to partially counter several pragmatic issues surrounding truncated citation counts. Simulated and empirical demonstrations of the proposed approaches are included, which emphasize the flexibility and efficacy of the proposed generalization. [ABSTRACT FROM AUTHOR]

Let R k , m be the ring 𝔽 2 m [ u 1 , ... , u k ] 〈 u i 2 , u i u j − u j u i 〉 . We study cyclic codes of arbitrary length n over the ring R 3 , m and obtain a complete characterization for them by presenting a unique set of generators for these codes. For example, we try to determine all distinct cyclic codes of length 2 over the ring R 3 , m by a practical algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider a conductivity interface problem to recover the salient features of the inclusion within a body from noisy observation data on the boundary. Based on integral equations, we propose iterative algorithms to detect the location, the size and the shape of the conductivity inclusion. This problem is severely ill-posed and nonlinear, thus we should consider regularization techniques in order to improve the corresponding approximation. We give several examples to show the viability of our proposed reconstruction algorithms. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the possibility of Eigenfactor Score (ES) and SCImago Journal Rank (SJR) Indicator to be used as alternatives to the Journal Impact Factor (JIF) in the field of library and information science. Design/methodology/approach: Downloaded JIF, ES and SJR scores and ranking order of 83 iJS journals from the relevant websites. Pearson and Spearman correlation coefficients were calculated to test hypotheses on association among three journal quality metrics. Findings: A very strong positive correlation was found between sets of scores and ranking order based on JIF, ES and SJR of selected journals. Practical implications: Academics, researchers and practitioners in the field of library and information science can use ES and SJR indicator as good alternatives to JIF. Originality/value: The first study that compared three important citation-based journal quality metrics in LIS field. [ABSTRACT FROM AUTHOR]

We examine the relationships between four citation metrics (impact factor, the numerator of the impact factor, article influence score, and eigenfactor) and the library journal selection decisions made by Manhattan College faculty as part of a large-scale serials review. Our results show that journal selection status (selected or not) is only weakly or moderately related to citation impact. Faculty choosing journals for their universities do consider the citation data provided to them, although they place less emphasis on citation impact than do faculty responding to journal ranking surveys. While previous research suggests that subjective journal ratings are more closely related to size-independent metrics (those that represent the average impact of an article rather than the impact of the journal as a whole) and weighted metrics (those that give more credit for citations in high-impact journals), our current results provide no support for the first assertion and only limited support for the second. [ABSTRACT FROM AUTHOR]

This paper investigated the possibility of utilizing SCImago Journal Rank (SJR), Eigenfactor Score and Google H5 index indicator as an alternative to the Journal Impact Factor (JIF) for quality assessment in the field of biochemistry and molecular biology. Principal factors such as researchers and librarians concerns of methods of scientific journal ranking, publication of language, analysis time and self-citation impact are looked into across indicated options and alternatives. The SJR, ES, Google H5 and JIF scores and ranking order of biochemistry and molecular biology journals were downloaded from their relevant websites. Pearson's and Spearman's correlation coefficients were gauged to weigh relationship between these journal quality metrics. Nominated coefficients were embraced for evaluating direct and monotonic relationships of chosen variables and ranking measures. A constructive correlation was detected among the scores and ranking order based on SJR, ES, Google H5 and JIF of selected biochemistry and molecular biology journals. Consequently, scholars, academics and researchers in biochemistry and molecular biology can use the SJR, ES and Google H5 indicators as replacements to JIF for appraisal of scientific journals in the field. [ABSTRACT FROM AUTHOR]

Highlights • We improved a method for registering TLS forest scan with reflective marker. • It runs in parallel with near-perfect efficiency. • The error is comparable to that of markers, under very specific conditions. • Other scenario where this method will not work were identified. • Our code is free for anyone to use, and open-source. Abstract Terrestrial laser scanning (TLS) often makes use of multiple scans in forests to allow for a complete view of a given area. Combining measurements from multiple locations requires accurate co-registration of the scans to a common reference coordinate system, which currently relies on markers, an often cumbersome process in forests. Existing algorithms for achieving marker-free registration of TLS scans in forests promise to significantly decrease field work time, but are not yet operational and their results have not been validated against traditional methods. Here we present a new implementation of an existing approach which runs in parallel mode and is able to process TLS data acquired over large forest areas. To validate our algorithm, point cloud registration matrices (translation and rotation) derived from our algorithm were compared to those obtained using reflective markers in multiple forest types. The results show that our approach can be used operationally in forests with relatively clear understory, and it provides accuracy similar to that obtained from using reflective markers. Furthermore, we identified factors that can lead to this approach falling short of providing acceptable results in terms of accuracy. [ABSTRACT FROM AUTHOR]

Abstract Filtering of low vegetation with height less than approximately 1.5 m is a challenging problem, especially in mountainous areas covered by heavy low foliage, bushes and sub-shrubberies, etc. The paper proposes an approach for obtaining a more accurate Digital Elevation Model (DEM) by removing low vegetation from point cloud. The approach combines point cloud with full waveform data, and begins by filtering point cloud by way of progressive TIN densification (PTD) method. Ground points are thus extracted, but mixed with false ground points, which are mainly from low vegetation and other manmade low objects. Gaussian decomposition by grouping Levenberg–Marquardt (LM) algorithm with F test is performed for the full waveforms corresponding to the extracted ground points. Echo widths and backscattering coefficients are calculated based on the parameters extracted from the decomposition, and used to discriminate points of low vegetation from points of other low objects, allowing the false ground points reflected from low vegetation to be labeled. New elevation values are calculated from the last echoes of the waveforms from low vegetation, and the DEM is updated by replacing the original elevations with the calculated ones. The resultants are assessed both quantitatively by check points and qualitatively by rendered DEM and contour lines generated from it. The accuracy of the refined DEM with low vegetation removal increases by 31% compared with the original DEM in the experiment, showing the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Background In academic specialties such as neurosurgery, bibliometrics are often used to guide readers, academic institutes, and researchers to make objective assessment of journals. Recently, new indices have been developed to overcome the shortcomings of the widely used Impact Factor. The objective of our study was to investigate the correlations among 6 of the commonly used bibliometric indices (Impact Factor, SCImago Journal indicator, SCOPUS h-index, Google h-index, Eigenfactor, Article Influence Score) in neurosurgical and spinal surgical journals. Methods A list of all neurosurgical and spinal surgical journals was compiled using the databases of SCOPUS, Clarivate Analytics (Thomson Reuters), and National Library of Medicine Catalog. Journals that are not surgically oriented, non-English journals, and nursing journals were excluded. Bivariate Spearman ranking correlation tests were performed. Results Fifty-four journals were included, of which 14 were spine themed. High positive correlations were obtained among the bibliometric indices of neurosurgical (nonspinal) journals (P < 0.05 in all pairs). Median values were 1.54, 0.66, 53, 25, 0.0035, and 0.46 for Impact Factor, SCImago Journal rank indicator, SCOPUS h-index, Google h-index, Eigenfactor, and Article Influence Score, respectively. However, the correlations for spinal surgical journals were more variable, likely because of the small sample size. Conclusions Despite the different mathematical basis among the citation-based bibliometric indices studied, they have strong correlations in ranking neurosurgical journals. This study provides evidence that the newer indices may be used interchangeably with the Impact Factor in this context, and they may, theoretically, mitigate some the shortcomings of the Impact Factor. Highlights • Bibliometrics are used to guide researchers to make objective assessment of journals. • New indices have been developed to complement the commonly used Impact Factor. • This is the first article that assesses the correlations of indices in neurosurgery. • This study showed that there are strong correlations among the 6 studied indices. • This is a surprising finding because they have widely different mathematical basis. [ABSTRACT FROM AUTHOR]

The spectral properties of the adjacency matrix, in particular its largest eigenvalue and the associated principal eigenvector, dominate many structural and dynamical properties of complex networks. Here we focus on the localization properties of the principal eigenvector in real networks. We show that in most cases it is either localized on the star defined by the node with largest degree (hub) and its nearest neighbors, or on the densely connected subgraph defined by the maximum K-core in a K-core decomposition. The localization of the principal eigenvector is often strongly correlated with the value of the largest eigenvalue, which is given by the local eigenvalue of the corresponding localization subgraph, but different scenarios sometimes occur. We additionally show that simple targeted immunization strategies for epidemic spreading are extremely sensitive to the actual localization set. [ABSTRACT FROM AUTHOR]

The need for linear model, of the nonlinear system, while tuning controllers limits the use of classic controllers. Also, the tuning procedure involves complex computations. This is further complicated when it is necessary to operate the nonlinear system under different operating constraints. Continues Stirred Tank Reactor (CSTR) is one of those non-linear systems which is studied extensively in control and chemical engineering due to its highly non-linear characteristics and its diverse operating range. This paper proposes two different control schemes based on reinforcement learning algorithm to achieve both servo as well as regulatory control. One approach is the direct application of Reinforcement Learning (RL) with ANN approximation and another is tuning of PID controller parameters using reinforcement learning. The main objective of this paper is to handle multiple set point control for the CSTR system using RL. The temperature of the CSTR system is controlled here for multiple setpoint changes. A comparative study is also done between the two proposed algorithm and from the test result, it is seen that direct RL approach with approximation performs better than tuning a PID using RL as oscillations and overshoot are less for direct RL approach. Also, the learning time for the direct RL based controller is lesser than the later. [ABSTRACT FROM AUTHOR]

Present work studies the role of bibliometric indicators and scientometric instruments for checking the qualities of journals published in the area of construction and building technology. Journal quality review targeted comparisons between indicators of significance and merit from reputable databases. Likewise, present work stressed on performance of well cited journals and their informational correlations as predicted by selected indicators complex algorithms. Main research objective focused on review of scientific journal quality indices that included: Journal Impact Factor (JIF), Eigenfactor Score (ES), SCImago Journal Rank indicator (SJR) and H5 index. Dependable construction and building technology journals were chosen from their category within Web of Science. JIFs and ESs are obtained from Journal Citation Report and the SJR from the SCImago Journal and country rank website. Sixty one construction and building technology journals were selected for this work and their related data records and information documents retrieved from their primary sites in relating designated quality indicators (JIF, SJR, ES and H5). Correlations between indicators were elucidated by means of Pearson's and Spearman's statistical correlations produced by SPSS software. All reclaimed journals are indexed in Web of Science and Scopus citation database. [ABSTRACT FROM AUTHOR]

Purpose: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction. Methods: We collected 12 datasets (3496 patients) from prior studies on post‐(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non‐)small‐cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built‐in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open‐source R package caret. The R code and documentation for the analysis are available online ( https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100‐repeated nested fivefold cross‐validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre‐selection based on other datasets) or estimating the best classifier for a dataset (set‐specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection). Results: Random forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set‐specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively. Conclusion: Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil. [ABSTRACT FROM AUTHOR]

Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signalto- noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development. [ABSTRACT FROM AUTHOR]

Context: The decision about which journal to choose for the publication of research deserves further investigation.Aims: In this study, we evaluate the predictive ability of seven bibliometrics in the Web of Knowledge to calculate total cites over a 7-year period in neuroscience journals.Settings and Design: Coincidental bibliometrics appearing during 2007, 2008, 2009, 2010, and 2011, along with their corresponding cites in 2009, 2010, 2011, 2012, and 2013, were recorded from the journal citation reports (JCR) Science Edition. This was a retrospective study.Materials and Methods: This was a bibliographic research using data from the Web of Knowledge in the neuroscience category.Statistical Analysis Used: A linear-mixed effects design using random slopes and intercepts was performed on 275 journals in the neuroscience category.Results: We found that Eigenfactor score, cited half-life, immediacy index, and number of articles are significant predictors of 2-year-ahead total cites (P ≤ 0.010 for all variables). The impact factor, 5-year impact factor, and article influence score were not significant predictors; the global effect size was significant (R2= 0.999; P < 0.001) with a total variance of 99.9%.Conclusions: An integrative model using a set of several metrics could represent a new standard to assess the influence and importance of scientific journals, and may simultaneously help researchers to rank journals in their decision-making during the manuscript submission phase. [ABSTRACT FROM AUTHOR]

We consider the approximation of the ground state of the one-dimensional cubic nonlinear Schrodinger equation by a normalized gradient algorithm combined with a linearly implicit time integrator, and a finite difference space approximation. We show that this method, also called the imaginary time evolution method in the physics literature, is locally convergent, and we provide error estimates: for initial data in a neighborhood of the ground state, the algorithm converges exponentially toward a modified soliton that is a space discretization of the exact soliton, with error estimates depending on the discretization parameters. [ABSTRACT FROM AUTHOR]

The newest model was proposed to extract the characteristic as distinctive attribute based on the Simplification of the Laplacian Smoothing Transform (S-LST) and the Fisherface. The proposed model is composed of two primary processes, i.e., training and testing processes. A training process involved features extraction based on the S-LST and the Fisherface, selection of the principal features, and features fusion of them. The proposed model has proved that the features can preserve the particular global information and the local feature areas of the rows and columns. The testing process is the stage to obtain the primary features through the new projection of the training sets. The last operation of the testing process is to measure the similarity of the features. The proposed model was tested using three different databases, i.e., the ORL, the YALE, and the UoB. The proposed model demonstrated the best performance, which is 100% recognition rate on the ORL and YALE when five images are implemented to the training sets. The maximum average of the recognition rate is 98.7% for the ORL database, 97.7% for the YALE database, and 94.53% for the UoB database. The comparison result showed that the proposed model performs better than the other approach, i.e., LST+LDA, S-MFA, S-NPE, S-LPP, S-LDA, LPP, LDA, and PCA. [ABSTRACT FROM AUTHOR]

In this paper, a new kind of sensitivity indices based on the principal component analysis (PCA) is proposed to measure the effects of input variables on multivariate outputs. Through PCA, the outputs are projected onto a new coordinate system (eigen space), which is constructed by the eigenvector (principal components). The existent sensitivity indices based on PCA focus on the variance of principal components, which can be considered as a magnitude of the uncertainty in the corresponding coordinate axes. In addition, the direction of the coordinate axes in the eigen space also contains another part of uncertainty of outputs (the direction of the uncertainty). The new sensitivity indices measure the effect of input variables on the direction of the coordinates axes through the angles between the unconditional and conditional eigenvectors. Thus, the new sensitivity indices can reflect different effect of input variables on the output compared to the existent sensitivity indices. The results of three numerical examples and an environmental model show the difference between the new sensitivity indices and the existent sensitivity indices. Since the new sensitivity indices measure the effects of input variables on the multivariate outputs from a different perspective compared to the existent sensitivity indices, they should be mutually complementary to each other. [ABSTRACT FROM AUTHOR]

Aim of this research study is to predict the correlation, if any, between the journal impact factor, SCImago ranking, Eigenfactor score and H-index of Analytical chemistry journals and quality of these journals. JIF is considered a good parameter for evaluation of scientific journals but has been criticized on many counts such as self-citation, limitation to English language etc. Seventy five (75) analytical chemistry journals indexed in WoS and Scopus databases were selected in this research for comparison of various indices. Correlations between indices (JIF, ES, SJR, and H-index) were evaluated using Pearson and Spearson's correlation. Annual Review of Analytical Chemistry was at the top with JIF of 8.0 and the lowest for American Laboratory journal with JIF of 0.028. In case of SCImago ranking, Eigenfactor score and H-index, highest valued journals were reported as Annual Review of Analytical Chemistry (SJR = 3.078), Analytical Chemistry (ES = 0.1588) and Analytical Chemistry (H-index = 109). Lowest valued journals were reported as American Laboratory journal (SJR = 0.106), Archeosciences-Revue D Archeometrie (ES = 0.0001) and Instrumentation Science and Technology (H-index = 0). Throughout the study, none of the journals had the same ranking to compare different indicators. Pearson's correlation between JIF and SJR values reached a high value (0.936) as compared to either ES (0.558) or H5 (0.567). [ABSTRACT FROM AUTHOR]

This study uses journal ratings in criminology and criminal justice, library and information science, public administration, and social work to investigate two research questions: (1) Are stated preference (subjective) journal ratings more closely related to size-dependent citation metrics (eigenfactor and total citations, which represent the impact of the journal as a whole) or to size-independent citation metrics (article influence and CiteScore, which represent the impact of a typical article)? (2) Are stated preference ratings more closely related to unweighted citation metrics (five-year impact factor and source normalized impact per publication, which do not account for the impact of each citing journal) or to weighted citation metrics (article influence and SCImago journal rank, which do)? Within the disciplines evaluated here, respondents’ subjective ratings of journals are more closely related to size-independent metrics and weighted metrics. The relative strength of the relationship between subjective ratings and size-independent metrics is moderated by subject area and other factors, while the relative strength of the relationship between subjective ratings and weighted metrics is consistent across all four disciplines. These results are discussed with regard to popularity and prestige, which are sometimes associated with unweighted and weighted citation metrics, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid technique employing RS (Reed Solomon) coding and adaptive filter for impulsive noise suppression in OFDM (Orthogonal Frequency Division Multiplexing) systems is presented. Adaptive filter creates a more accurate estimate of the original OFDM signal after impulsive noise cancellation. The residual impulsive noise is further mitigated by RS decoder in the second stage of proposed technique. Three members of adaptive filters family i.e. NLMS (Normalized Least Mean Square) algorithm, RLS (Recursive Least Square) algorithm and Bhagyashri algorithm are tested with RS decoder in the proposed hybrid technique. Furthermore, the results in terms of steady state MSE (Mean Square Error) reduction, BER (Bit Error Rate) improvement and SNR (Signal to Noise Ratio) enhancement confirm the effectiveness of the proposed dual faceted technique when compared with the recently reported techniques in literature. [ABSTRACT FROM AUTHOR]

Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with multiple objectives that may be conflicting and require the analysis of trade-offs. We study the complexity of percentile queries in such MDPs and give algorithms to synthesize strategies that enforce such constraints. Given a multi-dimensional weighted MDP and a quantitative payoff function f, thresholds $$v_i$$ (one per dimension), and probability thresholds $$\alpha _i$$ , we show how to compute a single strategy to enforce that for all dimensions i, the probability of outcomes $$\rho $$ satisfying $$f_i(\rho ) \ge v_i$$ is at least $$\alpha _i$$ . We consider classical quantitative payoffs from the literature (sup, inf, lim sup, lim inf, mean-payoff, truncated sum, discounted sum). Our work extends to the quantitative case the multi-objective model checking problem studied by Etessami et al. (Log Methods Comput Sci 4(4), 2008) in unweighted MDPs. [ABSTRACT FROM AUTHOR]

The article offers the author's insights on the use of different metrics such as usage, citation and alternative metrics in evaluating scientific literature and deciding what to read. Topics mentioned include the impact factor which is a citation metric that was proposed by linguist Eugene Garfield, the use of eigenfactor score, and the alternative metrics (altmetrics) that aims to quantify the quality of the article.

This paper provides an overview of the main features of several bibliometric indicators which were proposed in the last few decades. Their pros and cons are highlighted and compared with the features of the well-known impact factor (IF) to show how alternative metrics are specifically designed to address the flaws that the IF was shown to have, especially in the last few years. We also report the results of recent studies in the bibliometric literature showing how the scientific impact of journals as evaluated by bibliometrics is a very complicated matter and it is completely unrealistic to try to capture it by any single indicator, such as the IF or any other. As such, we conclude that the adoption of more metrics, with complementary features, to assess journal quality would be very beneficial as it would both offer a more comprehensive and balanced view of each journal in the space of scholarly publications, as well as eliminate the pressure on individuals and their incentive to do metric manipulation which is an unintended result of the current (mis)use of the IF as the gold standard for publication quality.

Over the past decades, a number of measures for determining the impact of scientific journals were introduced and utilized. The number of citations and Impact Factor value are the indicators have been applied a lot in this area and are of a high reputation in scientometrics. In addition to these indicators, some apt indicators have been introduced to measure the impact of publications which "Eigenfactor" and "Article Influence" are the newest ones. Eigenfactor is a measure of importance and prestige of a journal in scientific community and Article Influence is a measure of average impact of an article among other articles in a journal. The main discussion in present study was on Eigenfactor which besides presenting an introduction and an explanation to this factor, its features and characteristics and its differences with Impact Factor and its calculation method have been explained in detail. Considering the fact that calculating "Article Influence" requires the value of Eigenfactor, this indicator has been shortly explained as well.