RESUMO
Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91-99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.
RESUMO
INTRODUCTION: Penile fracture is a relatively common phenomenon. The main problem associated with this condition is the lack of patients' awareness on the urgency of the situation. This study reports the different modes of presentations and treatment results. MATERIALS AND METHODS: We reviewed 21 cases of penile fracture over 5 years. Parameters were mode of injury, age group, time interval before presentation, management, site of injury, urethral involvement, results, complications and erectile function at follow-up. RESULTS: The mean age of patients was 34 years, the mean time interval until presentation was 26 h. Cases involving the right corpus cavernosum comprised 57.14% and 42.85% were cases involving the left corpus cavernosum. Two patients had full circumferential urethral tear. Two patients developed wound infections and 2 patients developed mild penile curvature (<30°). These 4 patients had all presented late for treatment (>40 h). CONCLUSION: Urologists need to consider penile fracture a urological emergency and atypical presentations need to be considered when deciding on management.
