Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

The fit of cognitive diagnostic models (CDMs) to response data needs to be evaluated, since CDMs might yield misleading results when they do not fit the data well. Limited-information statistic M 2 and the associated root mean square error of approximation (RMSEA 2 ) in item factor analysis were extended to evaluate the fit of CDMs. The findings suggested that the M 2 statistic has proper empirical Type I error rates and good statistical power, and it could be used as a general statistical tool. More importantly, we found that there was a strong linear relationship between mean marginal misclassification rates and RMSEA 2 when there was model-data misfit. The evidence demonstrated that .030 and .045 could be reasonable thresholds for excellent and good fit, respectively, under the saturated log-linear cognitive diagnosis model.

Tropical cyclone (TC) size, usually measured with the radius of gale force wind (34 kt or 17 m s−1), is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Previous studies have reported that there is a very weak relationship between TC size and TC intensity. A close examination presented here using satellite-based wind analyses suggests that the relationship between TC size and intensity is nonlinear. TC size generally increases with increasing TC maximum sustained wind before a maximum of 2.50° latitude at an intensity of 103 kt or 53.0 m s−1 and then slowly decreases as the TC intensity further increases. The observed relationship between TC size and intensity is compared to the relationships produced by an 11-yr seasonal numerical simulation of TC activity. The numerical simulations were able to produce neither the observed maximum sustained winds nor the observed nonlinear relationship between TC size and intensity. This finding suggests that TC size cannot reasonably be simulated with 9-km horizontal resolution and increased resolution is needed to study TC size variations using numerical simulations.

Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role define? in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for computing the item parameter error covariance matrix. This method has been subsequently implemented in commercial IRT software programs such as IRTPRO and flexMIRT. Jamshidian and Jennrich noted that Supplemented EM is among a class of methods based on numerically differentiating the EM map, and they proposed noniterative alternatives, such as forward difference and Richardson extrapolation, that are mathematically simpler and may lead to a reduction in computational burden when compared with Supplemented EM. However, the relative merits of the various numerical differentiation methods have not been evaluated in the context of IRT modeling. We perform such an evaluation, using both simulated and empirical data. It is found that the accuracy of the simpler noniterative alternatives is heavily dependent on the choice of the numerical differentiation perturbation constants. On the other hand, Supplemented EM consistently maintains accuracy and does not require the selection of perturbation constants. Furthermore, when implemented with an adaptive iteration scheme, an updated Supplemented EM algorithm can be as computationally efficient as the alternatives. The expected (Fisher) information matrix, while accurate, requires too heavy computation for realistic test lengths. Therefore, we recommend the routine use of the updated Supplemented EM algorithm in IRT applications.

Do scientists follow hot topics in their scientific investigations? In this paper, by performing analysis to papers published in the American Physical Society (APS) Physical Review journals, it is found that papers are more likely to be attracted by hot fields, where the hotness of a field is measured by the number of papers belonging to the field. This indicates that scientists generally do follow hot topics. However, there are qualitative differences among scientists from various countries, among research works regarding different number of authors, different number of affiliations and different number of references. These observations could be valuable for policy makers when deciding research funding and also for individual researchers when searching for scientific projects.

Previous studies found that tropical cyclone (TC) formation is generally suppressed over the western North Pacific (WNP) following strong El Niño events. The 2015/2016 event is identified as one of the three major El Niño events since 1950. However, a climatological average of 26 named TCs occurred over the WNP in 2016. The plausible causes for this inconsistency are investigated in this study. By examining the historical records, we also found that 28 named TCs occurred over the WNP following 1991/1992 El Niño. For most strong El Niño cases, the suppressed TC formation in the ensuing early season (January-June) can persist to the peak season and lead to the negative TC frequency anomalies. However, TC formation turns to be active during August-October in 1992 and 2016, offsetting the suppressed TC formation in the early season and thus resulting in the climatological annual TC counts. It is found that anomalous sea surface temperature (SST) cooling over the north Indian Ocean and SST warming over the tropical North Pacific contribute to the enhanced TC formation in 1992 by stimulating an anomalous cyclonic circulation over the WNP, while the tri-polar SST pattern across the tropical Indo-western Pacific Ocean and the related convergence zone over 130 -160 E are responsible for the enhanced TC formation in 2016. The results indicate the crucial role of SST evolution over the north Indian Ocean and tropical Pacific in TC formation following strong El Niño events, which has important implication for the seasonal forecasting of TC activity over the WNP. K E Y W O R D S genesis frequency, large-scale conditions, strong El Niño ensuing TC season