An New Efficient Cluster Based Detection Mechanisms for Distributed Denial of Services (DDoS) Attacks

Cluster aggregation of statistical anomaly detection is a mechanism for defending against denial of service attack (dos) and distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestioncontrol problem; because most of the congestion is occurred in the malicious hosts not follow the normal endto- end congestion control. Upstream routers are also notified to drop such packets in order that the router’s resources are used to route legitimate traffic hence term cluster aggregation. If the victim suspects that the cluster aggregations are solved by most of the clients, it increases the complexity of the cluster aggregation. This aggregation solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In this proposal, the aggregation solving mechanism is cluster aggregation to the core routers rather than having at the victim. The router based cluster aggregation mechanism checks the host system whether it is legitimate or not by providing a aggregation to be solved by the suspected host.

A stochastic model, simulation, and application to aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix

A stochastic model of nanocrystals clusters formation is developed and applied to simulate an aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix. Simulations are compared with our experimental results. The stochastic model suggested governs mobilities both of individual nanocrystals and its clusters (arrays). We give a comprehensive analysis of the patterns simulated by the model, and study an influence of the surrounding medium (solvent) on the aggregation processes. In our model, monomers have a finite probability of separation from the cluster which depends on the temperature and binding energy between nanocrystals, and can also be redistributed in the composition of the cluster, leading to its compaction. The simulation results obtained in this work are compared with the experimental data on the aggregation of CdS nanocrystals upon evaporation of the Langmuir–Blodgett matrix. This system is a typical example from real life and is noteworthy in that the morphology of nanocrystals after evaporation of the matrix cannot be described exactly by a model based only on the motion of individual nanocrystals or by a cluster-cluster aggregation model.

Valley Vortex Assisted and Topological Protected Microparticles Manipulation with Complicated 2D Patterns in a Star-Like Sonic Crystal

Arranging microparticles into desired patterns, especially in a complicated pattern with a reliable and tunable manner, is challenging but highly desirable in the fields such as biomedicine and tissue engineering. To overcome these limitations, here, by using the concept of topology in acoustics, the valley vortex is utilized to manipulate particles on a large scale with complicated 2D patterns in the star-like sonic crystals at different frequencies. A topologically protected edge state is obtained at the interface of the crystals with different valley Hall phases, which shows the ability of reliable microparticles control along the sharp corner and the capability of robust particles cluster aggregation in a defective system. The results may provide intriguing resources for future microfluidic systems in a complicated and brittle environment.

Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain

Background On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks. Aim To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain. Methods A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf’s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results Analyses were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August. Conclusion STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Supramolecular Fractal Growth of Self-Assembled Fibrillar Networks

Complex morphologies, as is the case in self-assembled fibrillar networks (SAFiNs) of 1,3:2,4-Dibenzylidene sorbitol (DBS), are often characterized by their Fractal dimension and not Euclidean. Self-similarity presents for DBS-polyethylene glycol (PEG) SAFiNs in the Cayley Tree branching pattern, similar box-counting fractal dimensions across length scales, and fractals derived from the Avrami model. Irrespective of the crystallization temperature, fractal values corresponded to limited diffusion aggregation and not ballistic particle–cluster aggregation. Additionally, the fractal dimension of the SAFiN was affected more by changes in solvent viscosity (e.g., PEG200 compared to PEG600) than crystallization temperature. Most surprising was the evidence of Cayley branching not only for the radial fibers within the spherulitic but also on the fiber surfaces.

Real Time Surveillance of COVID-19 Space and Time Clusters During the Summer 2020 in Spain

Background:On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.Aim:To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.Methods:A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf´s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results:Analysis were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.Conclusion:STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Machine learning-based structure-property predictions in silica aerogels

The structural features in silica aerogels are known to be modelled effectively by the diffusion-limited cluster-cluster aggregation (DLCA) approach. In this paper, an artificial neural network (ANN) is developed for...