A new design for a tunable multi-channel plasmonic bandpass filter was numerically investigated using the two-dimensional finite element method (2D-FEM). The proposed multi-channel plasmonic bandpass filter consists of a metal-insulator-metal waveguide (MIM-WG) and double-sided arrow-shaped cavities. Silver (Ag) and a non-linear optical medium (InGaAsP) are used in the designed filter. InGaAsP fills the bus waveguide and arrow-shaped cavities. The refractive index of InGaAsP is sensitive to the incident light intensity, therefore the resonance wavelengths can be controlled. Utilizing different incident light intensities (such as 10¹⁷ v²/m² and 2 × 10¹⁷ v²/m²) on the InGaAsP, the filter wavelengths can be tuned over a range from 600 nm to 1200 nm. The proposed filter with a confinement area of 0.5 μm² can be used in wavelength division multiplexing (WDM), photonic systems, coloring filters, sensing, and 5G+ communication.

Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML). In this study, we used ML classifiers and explainable artificial intelligence (XAI) to predict stroke. Six different ML classifiers that trained on available datasets for stroke patients. Six feature selection methodologies were used to extract essential features from the dataset. The XAI methods applied (Shapley Additive Values (SHAP), ELI5, and Local Interpretable Model-agnostic Explanations (LIME)). This study will help medical practitioners to effectively manage their patients and develop a screening tool that incorporates technologies to revolutionize the approach to preventing and treating stroke.

The rapid and accurate detection of blood components plays a vital role in modern medical diagnostics and health monitoring. In this work, we propose a two-dimensional photonic crystal (PhC) biosensor capable of efficiently identifying ten different blood components and biochemical analytes with high precision. The sensor is constructed from a square-lattice arrangement of silicon rods with a radius of 100 nm and a lattice constant of 540 nm, supporting a TE photonic bandgap in the range of 1250–2100 nm. Two-line defects and an eye-shaped defect are incorporated to track resonance wavelength shifts arising from variations in the refractive index of blood samples. Numerical analysis was conducted using the two-dimensional finite element method (2D FEM) and plane wave expansion (PWE) method to evaluate the optical characteristics of the proposed PhC biosensor. Simulation results demonstrate that the proposed design achieves outstanding transmission efficiency, ranging from 83.6% to 99.1%, coupled with high sensitivity values between 270 and 327.2 nm RIU ⁻¹ . The biosensor further exhibits remarkable quality factors (Q-factor) (27,122.2–54,377) and a figure of merit (FoM) in the range of 5,167–10,907, along with a low limit of detection of 9.3 × 10 ⁻⁶ RIU and stable response. These findings highlight the potential of the proposed biosensor as a powerful platform for advanced biomedical sensing applications.

A high-performance optical modulator based on three graphene (Gr) layers integrated with a dual elliptic-shaped silicon waveguide is proposed and investigated. The device is optimized for transverse electric mode operation, where the elliptic geometry is engineered to enhance modulation efficiency and compactness. The key performance indicators—including propagation loss (Lp), modulation depth (MD), bandwidth, energy consumption, and device footprint—are comprehensively analyzed through numerical simulations employing the finite element method. Simulation results reveal that the proposed modulator achieves a MD of 0.745 dB μ m ⁻¹ , a compact cross-sectional area of 1.08 μ m ² , and a low Lp of 0.045 dB μ m ⁻¹ at the telecommunication wavelength of 1550 nm. Further geometry optimization demonstrates enhanced modulation capability and reduced energy consumption. These results highlight the potential of the proposed structure as an energy-efficient and scalable candidate for future photonic integrated circuits and for advancing active nanophotonic devices incorporating two-dimensional materials.