In this paper, we propose a novel node importance evaluation method from the perspective of the existence of mutual dependence among nodes. The node importance comprises its initial importance and ...the importance contributions from both the adjacent and non-adjacent nodes according to the dependence strength between them. From the simulation analyses on an example network and the ARPA network, we observe that our method can well identify the node importance. Then, the cascading failures on the Netscience and E-mail networks demonstrate that the networks are more vulnerable when continuously removing the important nodes identified by our method, which further proves the accuracy of our method.
•We propose a novel node importance evaluation method, which considers multi-layer and uneven node importance contributions.•Experiments demonstrate the feasibility and validity of our method.•The cascading failures cause the worse network invulnerability under our method.
•The paper proposes a novel dynamic graph recurrent convolutional neural network model, named Dynamic-GRCNN, to deeply capture the spatio-temporal traffic flow features for more accurately predicting ...urban passenger traffic flows.•The paper presents incidence dynamic graph structures based on historically passenger traffic flows to model traffic station relationships. Different from existing traffic transportation network topological structures based graph relationships between stations, the incidence dynamic graph structures firstly model the traffic relationships from historical passenger flows.•For real urban passenger traffic flows, the paper demonstrates that dynamic spatial-temporal incidence graphs are more suitable to model external changes and influences.•The paper compares Dynamic-GRCNN with state-of-the-art deep learning approaches on three benchmark datasets which contain different types of passenger traffic flows for evaluation. The results show that Dynamic-GRCNN significantly outperforms all the baselines in both effectiveness and efficiency in urban passenger traffic flows prediction.
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban traffic passenger flows prediction. We first model dynamic traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical traffic data in training, which can capture the periodicity and trend of the traffic passenger flows at different stations. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.
Over the last 15 years, a number of methodological developments have enabled researchers to draw more accurate inferences concerning the relative contribution (i.e., relative importance) among ...multiple (often correlated) predictor variables in a regression analysis. One such development has been relative weight analysis (RWA). Researchers can use a RWA to decompose the total variance predicted in a regression model (R²) into weights that accurately reflect the proportional contribution of the various predictor variables. Prior to RWA, researchers were forced to rely on traditional statistics (e.g., correlations; standardized regression weights), which are known to yield faulty or misleading information concerning variable importance (especially when predictor variables are correlated with one another, which is often the case in organizational research). Although there has been a surge of interest in RWA over the last 10 years, integration of this statistical tool into organizational research has been hampered by the lack of a user-friendly statistical package for implementing RWA. Indeed, most popular statistical packages (e.g., SPSS, SAS) have yet to include RWA protocols into their regression modules. The purpose of this paper is to present a new, free, comprehensive, web-based, user-friendly resource, RWA-Web, which may be used by anyone having simple access to the internet. Our paper is structured as a tutorial on using RWA-Web to examine relative importance in the classic multiple regression model, the multivariate multiple regression model, and the logistic regression model. We also illustrate how RWA-Web may be used to conduct null hypothesis significance tests using advanced bootstrapping procedures.
•A new methodology to evaluate the stability of WBFS is proposed.•A voting approach was used to compare different importance rankings.•Experiments on simulated and real-world datasets.•Existing WBFS ...methods are not stable on real-world datasets.
Weight-based feature selection (WBFS) are methods used to measure the contribution of input to output in a trained artificial neural network (ANN). Furthermore, algorithms such as Garson’s rely upon a single best neural network model or the mean importance value of several ANNs. However, different initialization weights lead to different importance values, as reported in other studies. These differences are misleading since each rank could result in different scores, altering the position of a variable in a given rank. Therefore, we propose a new methodology to assess the stability of a WBFS method. In essence, the idea is to use a voting approach to evaluate the importance of rankings. The results showed that Garson’s, Olden’s and Yoon’s algorithms are more stable methods when applied to artificial datasets. Nevertheless, its stability is considerably reduced when applied to real-world datasets. Hence, we concluded that future work should take into consideration the aforementioned instability of existing WBFS methods as applied to complex real-world data.
Though reported capture fisheries are dominated by marine production, inland fish and fisheries make substantial contributions to meeting the challenges faced by individuals, society, and the ...environment in a changing global landscape. Inland capture fisheries and aquaculture contribute over 40% to the world's reported finfish production from less than 0.01% of the total volume of water on earth. These fisheries provide food for billions and livelihoods for millions of people worldwide. Herein, using supporting evidence from the literature, we review 10 reasons why inland fish and fisheries are important to the individual (food security, economic security, empowerment), to society (cultural services, recreational services, human health and well-being, knowledge transfer and capacity building), and to the environment (ecosystem function and biodiversity, as aquatic “canaries”, the “green food” movement). However, the current limitations to valuing the services provided by inland fish and fisheries make comparison with other water resource users extremely difficult. This list can serve to demonstrate the importance of inland fish and fisheries, a necessary first step to better incorporating them into agriculture, land-use, and water resource planning, where they are currently often underappreciated or ignored.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The growth of literature in the field of quality of service in the public transport (PT) sector shows increasing concern for a better understanding of the factors affecting service quality (SQ) in PT ...organizations and companies. A large variety of approaches to SQ have been developed in recent years owing to the complexity of the concept; the broad range of attributes required to evaluate SQ; and the imprecision, subjectivity, and heterogeneous nature of the data used to analyze it. Most of these approaches are based on customer satisfaction surveys. This paper seeks to summarize the evolution of research and current thinking as it relates to the different methodological approaches for SQ evaluation in the PT sector over the years and to provide a discussion of future directions.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The difficulty of the dauq al-lughat approach to be applied in balaghah learning for non-Arabic speakers like Indonesia raised alternative psycholinguistic approaches as a follow-up in maximizing ...language acquisition and language production. This article intended to conduct in-depth research on the urgency of psycholinguistics, especially in Balaghah learning. Library research was the research method of this article. Primary data sources were the book entitled ”An Introduction to Psycholinguistics” by Steinberg and Natalia and ”Jawahirul Balaghah” by Sayyid Ahmad Al-Hasyimy. The source of secondary data were obtained from other books and journals related to psycholinguistics and balaghah learning. Data collection techniques were carried out through two ways of reading data, namely symbolic level and semantic level. Data analysis techniques included data reduction, data display and conclusion drawing/verification. This article showed that the important role of psycholinguistics in balaghah learning was to examine the background of the problem from errors in understanding the meaning of language and the steps for handling it (problem solving). Psycholinguistic implications in learning Balaghah was to provide a more effective learning atmosphere in strengthening students’ competence and to encourage students’ achievement.
•The paper presents an active-learning oriented importance sampling method.•Importance sampling centers are optimized by using the active learning mechanism.•The surrogate model can cover all ...branches of the system limit-state surface.•Several case studies have demonstrated engineering applications of the approach.
A major issue in the structural reliability analysis is to determine an accurate estimation result of the failure probability ideally based on a small number of model evaluations. In this regard, the active-learning Kriging based importance sampling method has been received considerable attentions. However, the utility of the most probable failure point (MPP) as the unique sampling center has limited its potential applications for multi-MPP problems. To this end, the paper presents an adaptive Kriging oriented importance sampling (AKOIS) approach. The outer active-learning loop of the AKOIS procedure is used to identify importance sampling centers, whereas its inner-loop is realized based on a rather small subregion centering at the sampling center to gain new training samples. Besides, numerical convergence of local active-learning iterations will immediately trigger another round of outer global search for a new importance sampling center. In this regard, the determined importance sampling centers are able to adaptively cover all branches of the investigated limit-state surface for structural system reliability analysis. Engineering applications of the data-driven importance sampling approach are demonstrated by several system reliability examples in the literature.
To ensure safe drinking water sources in the future, it is imperative to understand the quality and pollution level of existing groundwater. The prediction of water quality with high accuracy is the ...key to control water pollution and the improvement of water management. In this study, a deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R2, i.e., R2 = 0996 against the RF (R2 = 0.886), XGBoost (R2 = 0.0.927), and ANN (R2 = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.
•Groundwater quality is assessed using EWQI method.•Machine learning (ML) algorithms are used for predicting groundwater quality.•Prediction performance of RF, XGBoost, ANN and DL models are compared.•DL based quality prediction model performs much better than other ML models.
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