Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58% of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and three-hour precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions

SNHG22 promotes migration and invasion of trophoblasts via miR-128-3p/PCDH11X axis and activates PI3K/Akt signaling pathway

Objectives: Long non-coding RNAs (LncRNAs) act as an indispensable role in the Preeclampsia (PE)-related trophoblast function, while its relationship with Small Nucleolar RNA Host Gene 22 (SNHG22) remains unknown. Hence, this study aimed to investigate the roles of lncRNA SNHG22 in the Preeclampsia (PE)-related trophoblasts function and the underlying mechanism. Method: Normal placentas and placentas from PE patients were collected to detect the expression of lncRNA SNHG22. Then, trophoblasts HTR-8/Svneo and JEG-3 were purchased, cultured, and treated to investigate the roles of lncRNA SNHG22 on cell migration and invasion as well as its underlying regulatory mechanism. Results: The SNHG22 was downregulated in PE patients, and it was found that SNHG22 overexpression could drive migration and invasion of trophoblasts, while SNHG22 depletion exerted a suppressive effect. Mechanistically, SNHG22 was validated to regulate microRNA-128-3p (miR-128-3p), and Protocadherin 11 X-Linked (PCDH11X) was identified as the target gene of miR-128-3p. Furthermore, it was found that SNHG22 acted as a promoter in the migration and invasion of trophoblast cells in a miR-128-3p/PCDH11X dependent manner, and SNHG22 silencing weakened the activation of PCDH11X-mediated PI3K/Akt signaling pathways through inhibiting miR-128-3p, thereby preventing migration and invasion of trophoblasts. Conclusion: SNHG22 acted as a driver in the migration and invasion of trophoblasts and may be considered a candidate for the amelioration of PE

V2V Routing in VANET Based on Heuristic Q-Learning

Designing efficient routing algorithms in vehicular ad hoc networks (VANETs) plays an important role in the emerging intelligent transportation systems. In this paper, a routing algorithm based on the improved Q-learning is proposed for vehicle-to-vehicle (V2V) communications in VANETs. Firstly, a link maintenance time model is established, and the maintenance time is taken as an important parameter in the design of routing algorithm to ensure the reliability of each hop link. Aiming at the low efficiency and slow convergence of Q-learning, heuristic function and evaluation function are introduced to accelerate the update of Q-value of current optimal action, reduce unnecessary exploration, accelerate the convergence speed of Q-learning process and improve learning efficiency. The learning task is dispersed in each vehicle node in the new routing algorithm and it maintains the reliable routing path by periodically exchanging beacon information with surrounding nodes, guides the node’s forwarding action by combining the delay information between nodes to improve the efficiency of data forwarding. The performance of the algorithm is evaluated by NS2 simulator. The results show that the algorithm has a good effect on the package delivery rate and end-to-end delay

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

As cloud computing develops rapidly, the energy consumption of large-scale datacenters becomes unneglectable, and thus renewable energy is considered as the extra supply for building sustainable cloud infrastructures. In this chapter, we present a green-aware virtual machine (VM) migration strategy in such datacenters powered by sustainable energy sources, considering the power consumption of both IT functional devices and cooling devices. We define an overall optimization problem from an energy-aware point of view and try to solve it using statistical searching approaches. The purpose is to utilize green energy sufficiently while guaranteeing the performance of applications hosted by the datacenter. Evaluation experiments are conducted under realistic workload traces and solar energy generation data in order to validate the feasibility. Results show that the green energy utilization increases remarkably, and more overall revenues could be achieved

V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning

To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios

R&D modes and firm performance in high-tech companies: A research based on cross-boundary ambidexterity and network structures

This paper draws on the cross-boundary ambidexterity theory to propose that four different R&D modes impact firm performance differently and that cooperative network structure moderates the above relationships. The theoretical model is tested by using financial and patent data of 587 high-tech firms for 10 consecutive years in China. We find that different R&D modes have different impacts on a firm’s financial and innovative performance, and network structure plays different moderating roles. Practically, this work guides high-tech enterprises to optimize their resource allocation, select the most appropriate R&D mode, and establish efficient cooperative networks

Convergence and Complexity of an Adaptive Planewave Method for Eigenvalue Computations

In this paper, we study the adaptive planewave discretization for a cluster of eigenvalues of second-order elliptic partial differential equations. We first design an a posteriori error estimator and prove both the upper and lower bounds. Based on the a posteriori error estimator, we propose an adaptive planewave method. We then prove that the adaptive planewave approximations have the linear convergence rate and quasi-optimal complexity.Comment: 29 page