Nanoscale Dynamics of Actin Filaments in the Red Blood Cell Membrane Skeleton

Red blood cell (RBC) shape and deformability are supported by a planar network of short actin filament (F-actin) nodes (∼37 nm length, 15-18 subunits) interconnected by long spectrin strands at the inner surface of the plasma membrane. Spectrin-F-actin network structure underlies quantitative modeling of forces controlling RBC shape, membrane curvature and deformation, yet the nanoscale organization and dynamics of the F-actin nodes in situ is not well understood. We examined F-actin distribution and dynamics in RBCs using fluorescent-phalloidin labeling of F-actin imaged by multiple microscopy modalities. Total internal reflection fluorescence (TIRF) and Zeiss Airyscan confocal microscopy demonstrate that F-actin is concentrated in multiple brightly stained F-actin foci ∼200-300 nm apart interspersed with dimmer F-actin staining regions. Single molecule STORM imaging of Alexa-647-phalloidin-labeled F-actin and computational analysis also indicates an irregular, non-random distribution of F-actin nodes. Treatment of RBCs with LatA and CytoD indicates F-actin foci distribution depends on actin polymerization, while live cell imaging reveals dynamic local motions of F-actin foci, with lateral movements, appearance and disappearance. Regulation of F-actin node distribution and dynamics via actin assembly/disassembly pathways and/or via local extension and retraction of spectrin strands may provide a new mechanism to control spectrin-F-actin network connectivity, RBC shape and membrane deformability.

The Improved Element-Free Galerkin Method for 3D Helmholtz Equations

The improved element-free Galerkin (IEFG) method is proposed in this paper for solving 3D Helmholtz equations. The improved moving least-squares (IMLS) approximation is used to establish the trial function, and the penalty technique is used to enforce the essential boundary conditions. Thus, the final discretized equations of the IEFG method for 3D Helmholtz equations can be derived by using the corresponding Galerkin weak form. The influences of the node distribution, the weight functions, the scale parameters of the influence domain, and the penalty factors on the computational accuracy of the solutions are analyzed, and the numerical results of three examples show that the proposed method in this paper can not only enhance the computational speed of the element-free Galerkin (EFG) method but also eliminate the phenomenon of the singular matrix.

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods.

Node generation in complex 3D domains for heat conduction problems solved by RBF-FD meshless method

A novel algorithm is presented and employed for the fast generation of meshless node distributions over arbitrary 3D domains defined by using the stereolithography (STL) file format. The algorithm is based on the node-repel approach where nodes move according to the mutual repulsion of the neighboring nodes. The iterative node-repel approach is coupled with an octree-based technique for the efficient projection of the nodes on the external surface in order to constrain the node distribution inside the domain. Several tests are carried out on three different mechanical components of practical engineering interest and characterized by complexity of their geometry. The generated node distributions are then employed to solve a steady-state heat conduction test problem by using the Radial Basis Function-generated Finite Differences (RBF-FD) meshless method. Excellent results are obtained in terms of both quality of the generated node distributions and accuracy of the numerical solutions.

Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.

Application of Large-Scale Cognitive Social Networks Based on Cooperative Transmission Mechanisms in Exploration of Flipped Classroom Teaching Strategy

With the deepening of the research on flipped classroom teaching theory, the flipped classroom teaching model has gradually been applied to classroom teaching at all levels and types of schools, and some beneficial results and experiences have been obtained. Due to the relatively low self-learning ability and motivation level of students, in the implementation of flipped classrooms, the quality of preclass self-study links is difficult to guarantee, resulting in unsatisfactory results of flipped classroom teaching in secondary vocational schools. This article aims to solve the current dilemma faced by the optimization of the flipped classroom teaching mode of programming courses by studying the course platform based on the flipped classroom teaching model. The source-destination node distribution is constructed with a model based on node affinity to restore the actual network node distribution architecture. The change in the distribution of source-destination nodes has led to different degrees of aggregation in the overall data flow of the network. After that, the capacity and delay performance of the primary network and the secondary network will change as the degree of data flow aggregation changes. By laying base stations in the main network, we reanalyzed the network. Through the comprehensive analysis of students’ learning status through the scores of students in class and the test situation after class, we modify the specific teaching plan of flipped classroom. Experiments have proved that the in-class flipping model we proposed effectively avoids the inherent shortcomings of students who are not strong in autonomous learning before class, solves the problem that secondary vocational students cannot do well in autonomous learning before class, and improves students to a certain extent. The results show that the flipped classroom teaching model in class can provide more powerful value for vocational teaching to achieve this goal.

Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries

This paper introduces a Model Predictive Control (MPC) strategy for the optimal energy management of a district whose buildings are equipped with vertically placed Building Integrated Photovoltaic (BIPV) systems and Battery Energy Storage Systems (BESS). The vertically placed BIPV systems are able to cover larger areas of buildings’ surfaces, as compared with conventional rooftop PV systems, and reach their peak of production during winter and spring, which renders them suitable for energy harvesting especially in urban areas. Driven by both these relative advantages, the proposed strategy aims to maximize the district’s autonomy from the external grid, which is achieved through the cooperation of interactive buildings. Therefore, the major contribution of this study is the management and optimal cooperation of a group of buildings, each of which is equipped with its own system of vertical BIPV panels and BESS, carried out by an MPC strategy. The proposed control scheme consists of three main components, i.e., the forecaster, the optimizer and the district, which interact periodically with each other. In order to quantitatively evaluate the benefits of the proposed MPC strategy and the implementation of vertical BIPV and BESS, a hypothetical five-node distribution network located in Greece for four representative days of the year was examined, followed by a sensitivity analysis to examine the effect of the system configuration on its performance.

Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm

The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models.