The Set Splittablity Problem

A collection of sets is called splittable if there is a set S such that for each set B in the collection, the intersection of S and B is half the size of B. Splittability is a generalization of graph colorability, which is an active area of research with numerous applications such as scheduling and matching. We show that the problem of deciding whether a collection is splittable is NP-complete. Nevertheless we characterize splittability for some special collections. Finally we study a further generalization called p-splittability, in which the splitter S is required to contain a given fraction of each set B

A Scalable M-Channel Critically Sampled Filter Bank for Graph Signals

We investigate a scalable M-channel critically sampled filter bank for graph signals, where each of the M filters is supported on a different subband of the graph Laplacian spectrum. For analysis, the graph signal is filtered on each subband and downsampled on a corresponding set of vertices. However, the classical synthesis filters are replaced with interpolation operators. For small graphs, we use a full eigendecomposition of the graph Laplacian to partition the graph vertices such that the mth set comprises a uniqueness set for signals supported on the mth subband. The resulting transform is critically sampled, the dictionary atoms are orthogonal to those supported on different bands, and graph signals are perfectly reconstructable from their analysis coefficients. We also investigate a fast version of the proposed transform that scales efficiently for large, sparse graphs. Issues that arise in this context include designing the filter bank to be more amenable to polynomial approximation, estimating the number of samples required for each band, performing non-uniform random sampling for the filtered signals on each band, and efficient reconstruction methods. We empirically explore the joint vertex-frequency localization of the dictionary atoms, the sparsity of the analysis coefficients for different classes of signals, the ability of the proposed transform to compress piecewise-smooth graph signals, and the reconstruction error resulting from the numerical approximations

Flatness Prediction of Cold Rolled Strip Based on Deep Neural Network with Improved Activation Function

With the improvement of industrial requirements for the quality of cold rolled strips, flatness has become one of the most important indicators for measuring the quality of cold rolled strips. In this paper, the strip production data of a 1250 mm tandem cold mill in a steel plant is modeled by an improved deep neural network (the improved DNN) to improve the accuracy of strip shape prediction. Firstly, the type of activation function is analyzed, and the monotonicity of the activation function is deemed independent of the convexity of the loss function in the deep network. Regardless of whether the activation function is monotonic, the loss function is not strictly convex. Secondly, the non-convex optimization of the loss functionextended from the deep linear network to the deep nonlinear network, is discussed, and the critical point of the deep nonlinear network is identified as the global minimum point. Finally, an improved Swish activation function based on batch normalization is proposed, and its performance is evaluated on the MNIST dataset. The experimental results show that the loss of an improved Swish function is lower than that of other activation functions. The prediction accuracy of a deep neural network (DNN) with an improved Swish function is 0.38% more than that of a deep neural network (DNN) with a regular Swish function. For the DNN with the improved Swish function, the mean square error of the prediction for the flatness of cold rolled strip is reduced to 65% of the regular DNN. The accuracy of the improved DNN is up to and higher than the industrial requirements. The shape prediction of the improved DNN will assist and guide the industrial production process, reducing the scrap yield and industrial cost

Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

Summer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982 to 2010 are used to make the forecast. The results show that the SST‐based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations

Review of Study on the Coupled Dynamic Performance of Floating Offshore Wind Turbines

Floating offshore wind turbines (FOWT) have attracted more and more attention in recent years. However, environmental loads on FOWTs have higher complexity than those on the traditional onshore or fixed-bottom offshore wind turbines. In addition to aerodynamic loads on turbine blades, hydrodynamic loads also act on the support platform. A review on the aerodynamic analysis of blades, hydrodynamic simulation of the supporting platform, and coupled aero- and hydro-dynamic study on FOWTs, is presented in this paper. At present, the primary coupling method is based on the combination of BEM theory and potential flow theory, which can simulate the performance of the FOWT system under normal operating conditions but has certain limitations in solving the complex problem of coupled FOWTs. The more accurate and reliable CFD method used in the research of coupling problems is still in its infancy. In the future, multidisciplinary theories should be used sufficiently to research the coupled dynamics of hydrodynamics and aerodynamics from a global perspective, which is significant for the design and large-scale utilization of FOWT

Study on the Rotation Effect on the Modal Performance of Wind Turbine Blades

With the large-scale development of wind turbines, large flexible blades bear heavier loads. In the actual rotating work of blades, the coupling of structural deformation and motion produces a dynamic stiffening effect and spin softening effect, which affects the dynamic characteristics of blades. In this study, the finite element method is used to model the NREL 5MW blade, and the dynamic stiffening and spin softening effects are investigated using the modal analysis. The influence of rotating effects on the blade’s natural frequency is revealed. It is concluded that the effect of dynamic stiffening is more significant than that of spin softening, and the comprehensive result of the two effects is not simply the superposition of them but presents obvious nonlinearity

Biomass fuels related-PM2.5 promotes lung fibroblast-myofibroblast transition through PI3K/AKT/TRPC1 pathway

Emerging evidence has suggested that exposure to PM2.5 is a significant contributing factor to the development of chronic obstructive pulmonary disease (COPD). However, the underlying biological effects and mechanisms of PM2.5 in COPD pathology remain elusive. In this study, we aimed to investigate the implication and regulatory effect of biomass fuels related-PM2.5 (BRPM2.5) concerning the pathological process of fibroblast-to-myofibroblast transition (FMT) in the context of COPD. In vivo experimentation revealed that exposure to biofuel smoke was associated with airway inflammation in rats. After 4 weeks of exposure, there was inflammation in the small airways, but no significant structural changes in the airway walls. However, after 24 weeks, airway remodeling occurred due to increased collagen deposition, myofibroblast proliferation, and tracheal wall thickness. In vitro, cellular immunofluorescence results showed that with stimulation of BRPM2.5 for 72 h, the cell morphology of fibroblasts changed significantly, most of the cells changed from spindle-shaped to star-shaped irregular, α-SMA stress fibers appeared in the cytoplasm and the synthesis of type I collagen increased. The collagen gel contraction experiment showed that the contractility of fibroblasts was enhanced. The expression level of TRPC1 in fibroblasts was increased. Specific siRNA-TRPC1 blocked BRPM2.5-induced FMT and reduced cell contractility. Additionally, specific siRNA-TRPC1 resulted in a decrease in the augment of intracellular Ca2+ concentration ([Ca2+]i) induced by BRPM2.5. Notably, it was found that the PI3K inhibitor, LY294002, inhibited enhancement of AKT phosphorylation level, FMT occurrence, and elevation of TRPC1 protein expression induced by BRPM2.5. The findings indicated that BRPM2.5 is capable of inducing the FMT, with the possibility of mediation by PI3K/AKT/TRPC1. These results hold potential implications for the understanding of the molecular mechanisms involved in BRPM2.5-induced COPD and may aid in the development of novel therapeutic strategies for pathological conditions characterized by fibrosis

Dependence of daily precipitation and wind speed over coastal areas: evidence from China's coastline

Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socio-economic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead–lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation. HIGHLIGHTS Dependence of daily rainfall and wind speed over China's coastline is investigated.; Daily wind speed increased 2 days before rainfall occurrence and decreased after then.; Heavier rainfall events are more likely to occur with higher wind speeds.