A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

The fluctuation energy balance in non-suspended fluid-mediated particle transport

Here we compare two extreme regimes of non-suspended fluid-mediated particle transport, transport in light and heavy fluids ("saltation" and "bedload", respectively), regarding their particle fluctuation energy balance. From direct numerical simulations, we surprisingly find that the ratio between collisional and fluid drag dissipation of fluctuation energy is significantly larger in saltation than in bedload, even though the contribution of interparticle collisions to transport of momentum and energy is much smaller in saltation due to the low concentration of particles in the transport layer. We conclude that the much higher frequency of high-energy particle-bed impacts ("splash") in saltation is the cause for this counter-intuitive behavior. Moreover, from a comparison of these simulations to Particle Tracking Velocimetry measurements which we performed in a wind tunnel under steady transport of fine and coarse sand, we find that turbulent fluctuations of the flow produce particle fluctuation energy at an unexpectedly high rate in saltation even under conditions for which the effects of turbulence are usually believed to be small

The Allantois and Chorion, when Isolated before Circulation or Chorio-Allantoic Fusion, have Hematopoietic Potential

The chorio-allantoic placenta forms through the fusion of the allantois (progenitor tissue of the umbilical cord), with the chorionic plate. The murine placenta contains high levels of hematopoietic stem cells, and is therefore a stem cell niche. However, it is not known whether the placenta is a site of hematopoietic cell emergence, or whether hematopoietic cells originate from other sites in the conceptus and then colonize the placenta. Here, we show that the allantois and chorion, isolated prior to the establishment of circulation, have the potential to give rise to myeloid and definitive erythroid cells following explant culture. We further show that the hematopoietic potential of the allantois and chorion does not require their union, indicating that it is an intrinsic property of these tissues. These results suggest that the placenta is not only a niche for, but also a source of, hematopoietic cells

Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges

The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.Comment: 21 pages, 9 figure

A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes

The selection of an appropriate descriptive system and modeling framework to capture system dynamics and support process control applications is a fundamental problem in the operation of industrial processes. In this study, to account for the highly complex dynamics of industrial process and additional requirements imposed by smart and optimal manufacturing systems, an extended state space descriptive system, named comprehensive state space, is first designed. Then, based on the descriptive system, a hybrid first principles/machine learning modeling framework is proposed. The hybrid model is formulated as a combination of a nominal term and a deviation term. The nominal term covers the underlying physicochemical principles. The deviation term handles the effects of high-dimensional influence factors using regression of low-dimensional deep process features. To handle the multimodal and time-varying properties of process dynamics, the comprehensive state space is divided into subspaces indicating different operating conditions. The model parameters are identified and trained for each operating condition to form the sub-models. Then the system dynamics are formulated as a weighted sum of sub-models, with the weights being the probabilities that the current operating point belongs to different operating conditions. The weights update with the movement of the operating point in the comprehensive state space. Moreover, the descriptive system provides a platform for visualization, and can act as a digital twin of the physical process. A case study illustrates the feasibility and performance of the proposed descriptive system.The Projects of International Cooperation and Exchanges NSFC (grant no. 61860206014), the National Natural Science Foundation of China (grant nos. 61603418, 61973321, 61703441), the 111 Project (B17048), the Natural Science Foundation of Hunan Province (grant no. 2019JJ50823), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (grant no. 61621062), and the Major Program of the National Natural Science Foundation of China (grant no. 61590921).http://www.elsevier.com/locate/jprocont2021-02-01hj2020Electrical, Electronic and Computer Engineerin