Using SWOT Analysis for Defining Strategies of Mobile Learning

After exploring the concept of m-learning, this paper focuses on defining a SWOT analysis of mobile learning with detail discussion of each element. Finding important relationships by matching well know SWOT variables in a systematic fashion is a next step of research where using a algorithm which can provide a help in defining appropriate strategy. The result of our research is a conceptual framework for defining strategies for mobile learning, which will help primarily universities or other educational institutions in estimating their own position and creating the strategy for adopting m-learning into their curriculums which make a valuable contribution for the future research in this area.

Development and Validation of a Novel Microbiome-Based Biomarker of Post-antibiotic Dysbiosis and Subsequent Restoration

Background: The human gut microbiota are important to health and wellness, and disrupted microbiota homeostasis, or “dysbiosis,” can cause or contribute to many gastrointestinal disease states. Dysbiosis can be caused by many factors, most notably antibiotic treatment. To correct dysbiosis and restore healthier microbiota, several investigational microbiota-based live biotherapeutic products (LBPs) are in formal clinical development. To better guide and refine LBP development and to better understand and manage the risks of antibiotic administration, biomarkers that distinguish post-antibiotic dysbiosis from healthy microbiota are needed. Here we report the development of a prototype Microbiome Health Index for post-Antibiotic dysbiosis (MHI-A).Methods: MHI-A was developed and validated using longitudinal gut microbiome data from participants in clinical trials of RBX2660 and RBX7455 – investigational LBPs in development for reducing recurrent Clostridioides difficile infections (rCDI). The MHI-A algorithm relates the relative abundances of microbiome taxonomic classes that changed the most after RBX2660 or RBX7455 treatment, that strongly correlated with clinical response, and that reflect biological mechanisms believed important to rCDI. The diagnostic utility of MHI-A was reinforced using publicly available microbiome data from healthy or antibiotic-treated populations.Results: MHI-A has high accuracy to distinguish post-antibiotic dysbiosis from healthy microbiota. MHI-A values were consistent across multiple healthy populations and were significantly shifted by antibiotic treatments known to alter microbiota compositions, shifted less by microbiota-sparing antibiotics. Clinical response to RBX2660 and RBX7455 correlated with a shift of MHI-A from dysbiotic to healthy values.Conclusion: MHI-A is a promising biomarker of post-antibiotic dysbiosis and subsequent restoration. MHI-A may be useful for rank-ordering the microbiota-disrupting effects of antibiotics and as a pharmacodynamic measure of microbiota restoration.

Algorithm of axial compressor stall warning based on BP neural network and fuzzy logic

Stall warning of axial compressor is very challenging and the existing warning margin is not enough. A algorithm based on BP neural network fusion fuzzy logic is proposed. Firstly, BP neural network is used for training recognition, next the identification results are fused with fuzzy logic reasoning to form the result judgment of time sequence, finally the stall early warning of axial compressor is realized. The simulation results of the experimental data show that the stall data at all speeds are at least 0.1s in advance of the early warning. Compared with other methods, this method has a better surge early warning margin performance and engineering practicability.

OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

Trajectory and Conveyance Validation of a Micro Conveyor Based on a Digital Electromagnetic Actuators Array for the Micro-Factory

Micro-factories are characterized by high modularity, reconfigurability and mobility. To achieve this, the micro-factory needs a conveyor which is able to transport objects in as many degrees of freedom (DoF) as possible, executes optimal trajectories of these objects in terms of energy and precision and is robust to withstand possible malfunctions. In this article, we present the planar conveyance of objects on a digital actuation array following trajectories generated by an adapted A* algorithm. The A* algorithm exploits the predictions of a developed dynamic model of the system to find the optimal paths (in terms of energy) on the conveyor surface. The dynamic model predictions were compared to experimental measurements, obtaining low root-mean-square-errors for all conditions. Uni-dimensional conveyance tests characterized the influence of the control parameters. Then, bi-dimensional motions characterized the conveyor’s performance. From the bi-dimensional test, a position root-mean-square-error of 20 μm was measured for a 1109 μm open-loop controlled trajectory. The modular nature of the array allows easy scaling and avoiding possible malfunctioning zones, increasing the robustness of the micro-conveyor. The experimental tests demonstrate that the proposed device is an interesting alternative for the micro-factory.

AN EFFICIENT GRAPH THEORY-BASED ALGORITHM FOR SHIP TRAJECTORY PLANNING

The research presented in this paper is dedicated to the development of a path planning algorithm for a moving object in a dynamic environment. The marine environment constitutes the application area. A graph theory-based path planning method for ships is introduced and supported by the results of simulation tests and comparative analysis with a heuristic Ant Colony Optimization approach. The method defines the environment with the use of a visibility graph and uses the A* algorithm to find the shortest, collision-free path. The main contribution is the development of an effective graph theory-based algorithm for path planning in an environment with static and dynamic obstacles. The computational time does not exceed a few seconds. Obtained results allow to state that the method is suitable for use in an intelligent motion control system for ships.