Postmodernism represents skepticism toward metanarratives and universal ideologies that dominated the modernist era. The study focuses on three key postmodernist concepts - alternative assessment, process syllabi, and self-directed learning. Alternative assessment emphasizes evaluating the learning process over summative testing. Process syllabi focus on the learning experience rather than rigid outcomes. Self-directed learning enables student responsibility in setting learning goals and processes. This paper examined the effects of postmodernist educational concepts on improving Iranian EFL learners’ autonomy. A quasi-experimental pre-test/post-test design was utilized with 60 intermediate level Iranian EFL students divided into an experimental and control group (n=30 each). The experimental group received instruction utilizing the three postmodernist concepts over 14 weeks, while the control group received traditional modernist instruction. Autonomy was measured using a validated questionnaire before and after the intervention. The results showed the experimental group demonstrated significant increases in autonomy compared to the control group. Paired sample t-tests revealed significant differences between pre-test and post-test autonomy for the experimental group across all three postmodern concepts - alternative assessment, process syllabi, and self-directed learning. This suggests postmodernist concepts that decentralize instruction and emphasize student process over outcomes can enhance Iranian EFL learners’ self-direction and responsibility for language acquisition. The study implies EFL syllabus designers and instruction should transition to postmodern models centered on individualized assessment, flexible syllabi, and student-guided learning to boost autonomy. Further research can expand sample sizes and explore additional postmodernist concepts across diverse demographics.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

When the density current smashes into the lower dense fluid mass a plunge region will occur. Since the plunge depth bounds between density current and ambient fluid, determining of this point is important. In this research, the hydraulic parameters of density current and bed slope of stagnant fluid were investigated. To achieve the purpose of this research a physical model was built in the hydraulic laboratory of Shahid Chamran University (SCU) and various experiments were performed. In the experiments, the plunge depth at the different discharge of density current and density difference 6, 9, 13 and 16 kg/m3 at three slopes 8, 12 and 16% was measured. Then using Flow-3D under RNG turbulence model, plunge depth was simulated for the same experimental condition. The comparison of results obtained showed that the predicted plunge depth with Flow-3D under RNG turbulence model had a high and satisfactory accuracy. Finding showed that for the 8% bed slope, the results of RNG turbulence model 14% was more than experimental data. While, for the entire slope, the results of RNG turbulence model was predicted 10.5% in average more than measured data. Generally, the statistical investigations showed that the RNG turbulence model with satisfactory precision estimated the plunge point depth.