Teleworkbench: Validating Robot Programs from Simulation to Prototyping with Minirobots (Demonstration)

Tanoto A, Werner F, Rückert U, Li H. Teleworkbench: Validating Robot Programs from Simulation to Prototyping with Minirobots (Demonstration).This paper describes a Demo showing the role of the Teleworkbench in the validation process of a multi-agent system, e.g., a traffic management system. In the Demo, we show the capability of the Teleworkbench in seamlessly bridging the simulation and experimentation with real robots. During experiments, important information is logged for analysis purpose. Additionally, a graphical user interface enables geographically distributed users to perform some levels of interactivity, e.g., watch the video or command the robots

Informal traders lock horns with the formal milk industry: the role of research in pro-poor dairy policy shift in Kenya

A polarizable atomic multipole-based force field for the membrane bilayer models 1,2-dioleoyl-phosphocholine (DOPC) and 1-palmitoyl-2-oleoyl-phosphatidylethanolamine (POPE) has been developed. The force field adopts the same framework as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) model, in which the charge distribution of each atom is represented by the permanent atomic monopole, dipole and quadrupole moments. Many-body polarization including the inter- and intra-molecular polarization is modelled in a consistent manner with distributed atomic polarizabilities. The van der Waals parameters were first transferred from existing AMOEBA parameters for small organic molecules and then optimised by fitting to ab initio intermolecular interaction energies between models and a water molecule. Molecular dynamics simulations of the two aqueous DOPC and POPE membrane bilayer systems, consisting of 72 model molecules, were then carried out to validate the force field parameters. Membrane width, area per lipid, volume per lipid, deuterium order parameters, electron density profile, etc. were consistent with experimental values

Effect of a combination of Tuina therapy and budesonide inhalation on asthma in children, and its influence on lung function and pro inflammatory f actors

Purpose: To determine the effect of a combination of Tuina therapy and budesonide inhalation on pediatric asthma, and its influence on lung function and levels of inflammatory factors. Methods: Eligible 100 asthmatic children admitted to Provincial Maternity and Child-care Hospital, Lanzhou, Gansu Province, from January 2019 to January 2021 were randomized either to a control group or study group (1:1). The patients in control group were treated with budesonide inhalation, while the study group was given Tuina therapy in combination with budesonide inhalation. Treatment effectiveness, levels of inflammatory factors, immune functions and number of infections were evaluated in the patients. Results: The study group exhibited higher effectiveness profile versus the control group (96 vs 82 %; p < 0.05). After treatment, decreases were observed in the frequency of asthmatic attacks and number of respiratory infections in the two groups, with lower results in the study group than in the control group (p < 0.05). There were marked decreases in the levels of IgG, TNF-α and IL-8 in both groups, with the study group showing higher reductions (p < 0.05). Conclusion: Combined treatment with Tuina and budesonide inhalation decreases the levels of inflammatory factors, regulates immune function, and improves lung function of asthmatic children. Further investigation in a larger population would be required to establish the mechanism and clinical value of this therapy

Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network

Financial frauds cause billions of losses annually and yet it lacks efficient approaches in detecting frauds considering user profile and their behaviors simultaneously in social network . A social network forms a graph structure whilst Graph neural networks (GNN), a promising research domain in Deep Learning, can seamlessly process non-Euclidean graph data . In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc. as well as their social connectivity. Currently, most GNNs are incapable of selecting important neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored. In this paper, we propose a novel behavior information aggregation network (BIAN) to combine the user behaviors with other user features. Different from its close "relatives" such as Graph Attention Networks (GAT) and Graph Transformer Networks (GTN), it aggregates neighbors based on neighboring edge attribute distribution, namely, user behaviors in financial social network. The experimental results on a real-world large-scale financial social network dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with the State-Of-The-Art models.Comment: 6 pages, 1 figur

Fast and deterministic optical phased array calibration via pointwise optimisation

Owing to the structural errors in the optical phased array, an initial random phase reduces the quality of the deflection beam. The most commonly applied approach to phase calibration is based on adaptive optics. However, adaptive optimisation approaches have slow convergence and low diffraction efficiency. We proposed a pointwise optimisation approach to achieve fast and accurate beam deflection. This approach conducts phase calibration, combining global traversal and local searches individually for each array element. We built a phase-calibration optical system containing a one-dimensional optical waveguide phase array for further verification and designed the relevant mechanics. The simulation and experimental results demonstrate that the pointwise optimisation approach accelerates the calibration process and improves the diffraction efficiency