Thiol-epoxy/thiol-acrylate hybrid networks with systematic variations (100/0, 75/25, 50/50, 25/75, 0/100, w/w) were prepared by free-radical photoinitiator (ITX) and photo-base generator (TBD$HBPh 4 ) induced photopolymerization. The enhanced spectral sensitivity of TBD$HBPh 4 at long wavelengths (320-500 nm) with aid of ITX was capable of in situ generation of a strong base (TBD) to achieve the relatively fast and essentially quantitative thiol-epoxy click reaction. The ITX/TBD$HBPh 4 photoinitiating system, which generated both the free radicals and the strong base upon UV exposure, could induce thiol-epoxy/thiol-acrylate hybrid polymerizations. The kinetics investigated with real-time IR indicated that the thiol-acrylate reactions were faster and more efficient than the thiol-epoxy reactions (>95% conversion in a matter of seconds and minutes, respectively), and the thiol conversion increased with an increase in the epoxy concentration. The incorporation of the thiol-epoxy reaction offered several advantages: the polymerization shrinkage decreased with the increase in the thiolepoxy content due to the low shrinkage factor for the thiol-epoxy system (every mole of epoxy group polymerized produces 6.3 mL of shrinkage). The glass transition temperature of the thiol-epoxy/thiolacrylate hybrid networks progressively increased as a function of the thiol-epoxy content, also resulting in enhanced mechanical and physical properties. This work will be helpful to make optical and electronic devices with low shrinkage and stress, decreased residual monomers, and improved mechanical properties.
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) are critical aspects that contribute to the success of an organization. Over the past years, IDSs and IPSs using different approaches have been developed and implemented to ensure that computer networks within enterprises are secure, reliable and available. In this paper, we focus on IDSs that are built using machine learning (ML) techniques. IDSs based on ML methods are effective and accurate in detecting networks attacks. However, the performance of these systems decreases for high dimensional data spaces. Therefore, it is crucial to implement an appropriate feature extraction method that can prune some of the features that do not possess a great impact in the classification process. Moreover, many of the ML based IDSs suffer from an increase in false positive rate and a low detection accuracy when the models are trained on highly imbalanced datasets. In this paper, we present an analysis the UNSW-NB15 intrusion detection dataset that will be used for training and testing our models. Moreover, we apply a filter-based feature reduction technique using the XGBoost algorithm. We then implement the following ML approaches using the reduced feature space: Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Logistic Regression (LR), Artificial Neural Network (ANN) and Decision Tree (DT). In our experiments, we considered both the binary and multiclass classification configurations. The results demonstrated that the XGBoost-based feature selection method allows for methods such as the DT to increase its test accuracy from 88.13 to 90.85% for the binary classification scheme.
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