Al-Iraqia Journal for Scientific Engineering Research

In this research, the influence of the location, area and shape of the openings on the seismic behavior and performance of the concrete slab roof was evaluated and studied using the finite element numerical method, and recommendations were given for the optimal design. In order to model and analyze slab samples and perform parametric studies, ABAQUS software was used in this research. Studies were carried out based on different parameters such as different positions of the opening in the slab, the placement of the opening in the corner-side-center of the slab, different ratios of the area of the opening to the area of the slab,to be less than and more than 50% and finally different forms of the opening. In order to evaluate the seismic behavior of the slab, the time history dynamic analysis method was used based on the Tabas (Iran) earthquake record. The results of the analysis showed that for the opening located in the corner and on the side of the slab, the conditions were critical, and for the opening located on the edge of the slab (side of the slab), the conditions were more critical. Also, the results showed that the presence of an opening on the side of the slab increased theenergy, force and shear caused by the earthquake to at least 40% compared to other positions of the opening in the slab, and as a result, the most critical conditions related to the placement of the opening in the slab It is considered the edge of the slab. Also, the results of the analysis showed that in the case where the opening surface is more than 50% (slab with a large opening), the condition of the slab's vulnerability is lower, but the maximum energy of the slab with a larger opening is always up to 60% more than the slab with a small opening. Of course, this energy level is related to the early times of the earthquake, and finally, the high ductility of the slab with a larger opening can include better seismic behavior and performance of the slab. Finally, the results of the analysis showed that circular and oval openings and, in general, openings with curved and non-sharp corners have better seismic conditions and performance and experience lower energy values. Trapezoidal opening has better conditions in irregular trapezoidal slabs and square opening has the highest amount of energy among different opening shapes, which is not desirable

Autism is a disorder of brain function that can appear in children younger than two years and affects their communication and learning. The incidence of Autism in children has been increasing, and at the same time, numerous schemes have been proposed for its diagnosis. However, the researchers continue to face challenges in the early and accurate diagnosis of Autism. Early and accurate detection of autism allows doctors to determine the severity of the disease and begin appropriate treatment protocols to develop communication skills in children. This paper proposes feature ranking methods integrated with four classifiers for the accurate diagnosis of autism. Multiple experiments are conducted by integrating ranking features methods (chi-square, Anova-F, and chi2-yates) with Decision Tree (DT), Multi-layer perceptron (MLP), NaiveBayes Bernoulli (NB), and Support Vector Machine –Radial Basis Function (SVM-RBF) classifiers to achieve accurate early detection. The feature ranking is applied with five sets (5, 10, 20, 40, and 46), and each set is individually evaluated to assess its contribution to autism diagnosis. The best diagnosis model (chi2-yates-based SVM-RBF) has achieved 0.971611, 0.94797, and 0.997335 for the accuracy, F1-score, and ROC-AUC, respectively. The proposed framework would be effective in helping therapists in early detection and selecting suitable treatment for autism.

This paper presents a dynamic quantization-aware neural architecture search (NAS) framework for real-time encrypted traffic classification in 5G networks. The framework jointly optimizes model architecture and quantization policies using reinforcement learning with hardware-in-the-loop feedback, addressing both accuracy and efficiency on edge devices. Unlike conventional static methods, the proposed system supports runtime bitwidth switching to adapt precision levels based on fluctuating traffic loads and hardware conditions. Extensive experiments were conducted on ISCX-VPN, USTC-TFC2016, and QUIC-5G datasets. The results show that the proposed approach achieves 94.2% accuracy on ISCX-VPN, 92.7% on USTC, and 89.4% on QUIC-5G, outperforming baseline methods while reducing inference latency by 28–42% and lowering energy consumption by up to 26%. The framework maintains robustness under low-precision constraints, with a mean Quantization Stability Score (QSS) of 0.91, compared to 0.82–0.87 for existing approaches. Hardware-specific optimizations provide additional gains, such as a 2.1 × speedup on Raspberry Pi 4, reduced latency on Jetson Xavier and lower energy consumption on Intel NCS2. The results of Ablation studies affirm that dynamic quantization, hardware feedback, and stability and mechanisms are an absolute necessity, improving accuracy by up to 3.1% and decreasing latency by 31% over sequential optimization. These results prove the efficiency of dynamic quantization-aware NAS to classify encrypted traffic and emphasize its prospects in broader 5G edge AI applications.