This research introduces a compact, self-isolated, 12 × 12 multiple-input multiple-output (MIMO) antenna array designed for 5G mobile applications, operating within the 3.5 GHz band (3.42–3.62 GHz). The array consists of two distinct sets of six antenna elements—inverted U-shaped and T-shaped structures, each incorporating two circular and I-shaped strips—arranged symmetrically within the smartphone chassis. Each antenna element measures 15 × 5 mm² (0.17λ_o × 0.06λ_o), where λ_o represents the free-space wavelength at 3.5 GHz. These elements function simultaneously as radiators and isolators, achieving high isolation levels. Additionally, the 12-MIMO antenna elements, designed to be self-isolated, are fabricated on both sides of two compact FR-4 substrates, positioned orthogonally to the ground substrate. This perpendicular configuration augments the self-isolating mechanism. The scattering parameters (s-parameter) findings demonstrate significant decoupling measuring under −19 dB across neighboring 12-MIMO elements. Moreover, the MIMO performance metrics, including channel capacity loss (CCL), total active reflection coefficient (TARC), diversity gain (DG), and the envelope correlation coefficient (ECC), are presented to be below 0.08 bits/s/Hz, −10 dB, 9.97 dB, and 0.006 consecutively. The significant isolation and performance metrics results notably indicate that the presented 12-MIMO antenna system is well-suited for 5G communication systems.

This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capabilities. The inherent appearance of sparse data is dealt with in this work by proposing a trained data-adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber with two patches of 4000- and 7000-sample datasets. The training loss taken from the second DNN of our proposed model showed logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors were 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss was evaluated using the mean square error method, which resulted in a loss of 0.044 with the 4000-sample datasets split with the Keras method, while this was 0.020 with the adaptive method. When extending the dataset to 7000 samples, the validation loss with the Keras splitting method was 0.0073, while it was improved, reaching 0.006, with the proposed adaptive method, and achieved a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows the advantages of making the design process more efficient in sparse dataset handling, being an efficient approach in multi-resonance metasurface data pre-processing, being less time consuming, and being computationally valuable.

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement.

This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capability. The inherent appearance of sparse data is completely dealt with in this work by proposing a trained adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber and with two sized patches of 4000 and 7000 datasets. The training loss taken form the second DNN of our proposed model shows logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors are 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss is evaluated using the mean square error method, which results in a loss with 4000 datasets split with the Keras method of 0.044, while it is 0.020 with the adaptive method. When extending the dataset to 7000, the validation loss with the keras splitting method is 0.0073, while it is improved, reaching 0.006 with the proposed adaptive method, and achieving a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows its advantages of making the design process more efficient in sparse dataset handling, efficient approach in multi-resonance metasurface data pre-processing, less time consuming, and computationally valuable.

In this study, we introduce a tutorial on the use of an interactive computer-aided tool that is suitable for students enrolled in introductory course on Electromagnetics (EM). Further, the study will provide means for students to develop a solid understanding of many electromagnetic concepts through visualization of electromagnetic problems in two- and three dimensions. The simulation tool in this study is based on a MATLAB toolbox package, in which partial-differential equations (PDE) toolbox is used. This study will mainly focus on one classical electrostatic problem that pose a challenge for students to visualize, analyze and evaluate.

Hydrogen (H₂) detection under practical operating conditions requires sensing materials that simultaneously provide accessible reaction sites, efficient gas diffusion pathways, and fast interfacial charge transfer. Here, a hierarchical star-sphere ZnCo₂O₄ (ZC) architecture was integrated with graphene oxide (GO) and Pt supported on graphitized carbon (Pt/C) to develop hybrid chemiresistive sensing layers for low-temperature hydrogen detection. The synthesized ZC-based material exhibited a hierarchical morphology consisting of porous microspheres and star-shaped assemblies, providing a multiscale framework for gas access and surface reactions. By varying the GO content from 0.1 to 1 wt% at a fixed Pt/C loading, the ZC-0.5G composite achieved the most balanced structure, with well-distributed GO sheets, preserved star–sphere morphology, the highest specific surface area (53.6 m²/g), and the largest pore volume (0.09 cm³/g). The optimized sensor gave responses of 12.96%, 19.20%, 22.87%, and 26.43% for 500, 4000, 8000 and 10,000 ppm H₂ concentrations, respectively, with measurable response down to 50 ppm. The highest sensing performance was achieved at 50 °C and 60% relative humidity (RH), where the hierarchical oxide framework, GO-assisted interfacial pathways, and Pt catalytic sites acted in concert. The sensor also showed repeatable cyclic behavior and preferential response to H₂ compared to methanol, isopropanol, ethanol, acetone, and dimethylformamide. The improved sensing performance is attributed to the synergistic combination of the hierarchical ZC framework, GO-assisted interfacial pathways, and Pt-assisted catalytic activation, which together facilitate gas diffusion, surface reactions, and resistance modulation.