Abstract

We report a light scattering study of the adsorption of end-functionalized polymers on colloidal spheres. A light scattering method is developed to measure the amount of polymer molecules adsorbed on the colloidal surfaces. The experiment reveals that only a fraction of the end-functionalized polymers is adsorbed on the colloidal surface. The results for the end-functionalized polymers are compared with those for the unfunctionalized polymer. It is found that the interaction between the colloid and the unfunctionalized polymer is repulsive, which introduces a depletion attraction between the colloidal particles. The functional end groups are found to interact attractively with the polar cores of the colloidal particles. The adsorption energy between the functional group and the colloidal surface is estimated to be ~4kaT. The experiment shows that the presence of the adsorbed polymer on the colloidal surfaces greatly reduces the depletion attraction between the colloidal particles and, therefore, enhances the stability of the colloid-polymer mixture.

Distributed Denial of Service (DDoS) attacks are one of the biggest concerns for security professionals. Traditional middle-box based DDoS attack defense is lack of network-wide monitoring flexibility. With the development of software-defined networking (SDN), it becomes prevalent to exploit centralized controllers to defend against DDoS attacks. However, current solutions suffer with serious southbound communication overhead and detection delay. In this paper, we propose a cross-plane DDoS attack defense framework in SDN, called OverWatch, which exploits collaborative intelligence between data plane and control plane with high defense efficiency. Attack detection and reaction are two key procedures of the proposed framework. We develop a collaborative DDoS attack detection mechanism, which consists of a coarse-grained flow monitoring algorithm on the data plane and a finegrained machine learning based attack classification algorithm on the control plane. We propose a novel defense strategy offloading mechanism to dynamically deploy defense applications across the controller and switches, by which rapid attack reaction and accurate botnet location can be achieved. We conduct extensive experiments on a real-world SDN network. Experimental results validate the efficiency of our proposed OverWatch framework with high detection accuracy and real-time DDoS attack reaction, as well as reduced communication overhead on SDN southbound interface.

Severity identification and size estimation is a crucial part of the quantitative diagnosis for ball bearing faults. In this paper, novel fault severity classification rules and the size estimation model based on vibration mechanism for ball bearings are proposed for more accurate estimation of the fault size. A nonlinear dynamic model, with geometric properties and deformation of the ball considered, is established to analyze the vibration characteristics of ball bearing with outer race fault. It turns out that there are different features in vibration responses with different fault sizes, and then the fault severity is classified with vibration features. Based on the vibration mechanism analysis, functional relations and mathematical expressions between the vibration characteristics and fault sizes are inferred to realize the quantitative diagnosis of faulty bearings. The experiments are performed to verify the effectiveness of the proposed method of fault severity classification and size estimation. Deep groove ball bearings designated as 6308, with seeded square-shaped surface defects of different sizes, are chosen for the experiments. The results show that the proposed method can accurately estimate the fault sizes within the acceptable error range.

No abstract