Cancer associated with the nervous system and brain tumors ranks among the leading causes of death in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) capture brain images. MRI is pivotal in diagnosing brain tumors and analyzing other brain disorders. Typically, radiologists or experts manually assess MRI images to detect brain tumors and abnormalities in the early stages for appropriate treatment. However, early brain tumor diagnosis is complex, necessitating computerized methods. This research introduces a novel approach for the automated segmentation of brain tumors and a framework for classifying brain tumor regions. The proposed methods comprise several stages: preprocessing, enhancing the coherence of MRI brain images using Contrast Limited Adaptive Histogram Equalization (CLAHE) and diffusion filtering in the first two steps, followed by the segmentation of the region of interest using the Fuzzy C-Means (FCM) clustering technique in the third step. The last step involves classification using the Support Vector Machine (SVM) classifier. The classifier is applied to different brain tumor types, from meningioma to pituitary tumors, utilizing the CE-MRI database. The proposed method exhibits significantly improved contrast and proves the effectiveness of the classification framework, achieving an average sensitivity of 0.977, specificity of 0.979, accuracy of 0.982, and a Dice score (DSC) of 0.961. Furthermore, this method demonstrates a shorter processing time of 0.42 seconds compared to existing approaches. The performance of this method underscores its significance when compared to state-of-the-art methods in terms of sensitivity, specificity, accuracy, and DSC. For future enhancements, it is possible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Heavy metal ion contamination of water supplies has significantly increased during the last century due to advances in industry and technology. Therefore, a lot of effort was put into developing chemical and physical methods for detecting and tracking the presence of these potentially harmful solutions. Despite their comparatively high sensitivity and low detection limits, these methods are hindered by complex instrumentation and tedious, expensive, and difficult chemical processes. Therefore, in this study, we present a straightforward and effective sensing method based on the graphene metasurface for detecting several classes of heavy metal ions. A graphene-metasurface-inspired optical sensor with a glass substrate is developed that can detect Cu²⁺ and Mg²⁺ with a sensitivity of 113.92 GHz/RIU and 113.9 GHz/RIU, respectively. In addition to that, the linear fitting curve for both the metal ions is established, and R² score of 0.9997 and 0.9982 is achieved, respectively. Furthermore, the lowest value of the figure of merit (FOM) of 2.98 RIU⁻¹ and the maximum Quality factor (Q factor) of 11.22 is obtained. The proposed structure also exhibited a low detection limit as well as a resolution of 0.52 RIU and 78.14 THz, respectively. As a result of these findings, a simple and accurate tool for detecting water contamination with heavy metals and aqueous solutions with relatively high performance is developed.

MXene (Ti₃C₂T_x) has emerged very recently as an interacting material for surface plasmon resonance (SPR) configuration. It was discovered that Ti₃C₂T_x can facilitate the adsorption of biomolecules due to its higher binding energies, stronger interaction between matter and light, and larger surface area. In this work, a two-dimensional Ti₃C₂T_x and silicon layer-based SPR refractometric sensor is proposed for the sensitive and fast detection of milk fat concentration due to the high significance of this issue to people all over the world. The proposed SPR structure employs BK7 (BK7 is a designation for the most common Borosilicate Crown glass used for a variety of applications in the visible range) as a coupling prism and silver as a metal layer. The layer thicknesses and the number of Ti₃C₂T_x sheets are optimized for the highest performance. The highest reached sensitivity is 350 deg./RIU with 50 nm silver and 4 nm silicon with a monolayer of Ti₃C₂T_x, which is ultra-high sensitivity compared to the latest work that utilizes SPR configuration. The proposed SPR-based sensor’s ultra-high sensitivity makes it more attractive for usage in a variety of biosensing applications.

This paper presents a metamaterial-based flexible wearable ultra-wideband (UWB) antenna for breast imaging and wireless body area network (WBAN) applications. The wearable antenna is required to be a planar and low-profile structure using flexible materials. The proposed antenna comprises two layers of denim (10 × 10 mm²) and felt (10 × 15 mm²). The antenna was integrated with six metamaterial unit cells using a modified grain rice shape within a split ring resonator to enhance the bandwidth, gain, and directivity and reduce the specific absorption rate value to less than 2 W/kg. The proposed antenna operates within a broad bandwidth range (6.5 GHz–35 GHz) with the maximum gain and directivity of 8.85 dBi and 10 dBi, respectively, and a radiation efficiency of more than 70% over its operating frequency band. The results verified good agreement between the simulation and measurement of the proposed technique in detecting an existing tumor with a diameter of 4 mm from any location inside the breast. The results convincingly proved the capability of the proposed wearable UWB antenna system for both WBAN and breast imaging applications.

Diabetes is rapidly becoming a serious and life-threatening disease. It affects 415 million persons worldwide and is a leading cause of death among those aged 20 to 59. It is essential to develop a rapid-detection, accurate and sensitive glucose detector. In this work, a biosensor based on surface plasmon resonance (SPR) is proposed theoretically for the detection of glucose concentration. To realize higher sensitivity, the proposed SPR sensor contains a barium titanate layer placed between the metal (Ag) thin film and the molybdenum disulphide layer. Barium titanate material shows notable dielectric properties, such as low loss and a high index of refraction. It is expected to give a large shift in the resonance angle caused by a tiny change in the analyte refractive index. By optimizing the thicknesses of barium titanate and Ag and the number of molybdenum disulphide layers, the proposed biosensor can exhibit an ultra-high sensitivity of 307.36 deg RIU⁻¹. The extremely high sensitivity makes the proposed SPR-based biosensor encouraging to be used in many fields of biosensing.