In the context of in-vitro testing of implantable medical devices, multi-layered body phantom development poses significant challenges in terms of complexity, cost and possible formation of unwarranted air gaps between two layers or mixing chemicals of two layers while one layer is placed on other layer in hot semi-solid form. Therefore, it is recommended to use its equivalent homogeneous phantom with same dielectric properties. In this article, equivalent complex permittivity of three-layer human-body-model at 2.45 GHz is estimated by using Transmission Matrix Method (TMM) and FEM solver. The transmission matrix of the required body model is calculated based on the electrical properties of each layer. Solving nonlinear equations from TMM, multiple solutions of complex permittivity values are obtained. A two-antenna system is designed in FEM solver where one of the antennas is placed within homogeneous phantoms with obtained complex permittivity values and other is in free space to choose exact complex permittivity using scattering parameters. To validate the final phantom properties practically, the simulated setup is replicated. The practical results show good agreement with simulated responses. This approach can reduce complexity of designing multilayer-models considering only 0.53
The design and development of a novel scalp-implantable, nan-invasive, battery free, hybrid wireless sensor system for continuous intracranial pressure (ICP) monitoring are presented. Traditional methods for ICP measurement are highly invasive and pose risks such as infection and trauma. In contrast, the proposed system comprises a radio frequency (RF) powered, hybrid RF biosensor utilising a miniaturised implantable antenna and a wearable non-invasive transceiver. The dual-band operation at 2.45 GHz and 4.55 GHz enables high-data-rate transmission suitable for real-time brain telemetry and for energy harvesting. Electromagnetic simulations utilising human tissue phantoms exhibit robust impedance characteristics, featuring reflection coefficients of − 20.61 dB at 2.45 GHz and − 12.48 dB at 4.55 GHz, alongside fractional bandwidths of 20.42
Monitoring changes in the brain is fundamental for diagnosing brain diseases, selecting appropriate treatments, and long-term management of patients with neurological disorders. Radio frequency (RF) antenna biosensor technologies have increasingly gained interest as a promising, non-invasive or minimally invasive technique for neurological assessment. This approach enables the detection of a wide range of physiological and pathological conditions, including stroke events, neural activity, temperature variations, and the progression of neurodegenerative diseases. This paper presents a designed monolithic microwave integrated circuit low-noise amplifier and RF link budget and examines the recent developments in RF antenna designs specifically tailored for brain monitoring applications, with a comparative focus on operating frequencies, antenna geometries, bandwidth characteristics and safety performance evaluated through Specific Absorption Rate. In addition, an illustrative design case study of an ISM-band monolithic microwave integrated circuit (MMIC) low-noise amplifier (LNA) and associated RF link budget is presented to demonstrate how antenna-level requirements translate into front-end transceiver specifications. At 13.7 oK average receiver noise temperature, the designed low-noise amplifier yields a carrier link margin of 38.5 dB and the data link margin, 15.3 dB over the 5.725–5.875 GHz ISM band for non-invasive high-resolution microwave brain physiological monitoring applications.
This study aims to develop an innovative bilayered dental implant design featuring a titanium alloy core with a porous composite titanium (Ti) and hydroxyapatite (HA) outer layer to enhance implant stability and patient outcomes. Using SolidWorks 2017, 3D models of the implants and a mandibular bone segment were created. A Finite Element (FE) analysis was then conducted with ANSYS Workbench to assess the mechanical behavior under a 250 N axial compressive load, comparing the bilayered implant to a conventional titanium implant. Variables like porosity (ranging from 10 to 90% in 10% increments), HA content (ranging from 10 to 50% in 5% intervals), and outer layer thickness (2 mm, 1.5 mm and 1 mm) were systematically analyzed. Each configuration was evaluated based on von Mises stress distribution and interfacial strain in peri-implant bone. Results indicated that all porous designs of bilayered implants had significantly lower von Mises stress than traditional implant, with reductions ranging from approximately 69 to 94%, depending on HA/Ti composition and shell thickness. The non-porous bilayer configurations also showed clear stress reductions, with decreases from approximately 72 to 90%, depending on the HA/Ti composition and shell thickness. However, these reductions were slightly lower than those observed in porous designs, with maximal reductions occurring in the porous core of some 2 mm bilayered implant configurations. The combined evaluation of strain and von Mises stress analyses identified the 2 mm core diameter with a 2 mm porous shell as the optimal design, providing favorable microstrain, improved load transfer, and reduced stress concentrations. This modification promotes a more favorable mechanical interaction between the implant and surrounding bone. These findings underscore the potential of bilayered porous implants to improve stability and bone integration, marking a significant step forward in dental implant technology. Further research, including experimental validation, is encouraged to verify these results and investigate other loading conditions, promoting the development of more effective and sustainable dental implant solutions.
The rapid advancement of ML algorithms and the increasing availability of large datasets have significantly transformed the landscape of predictive modelling in scientific research. In this context, we introduce Optima (OPTimized Interpretable Model Building & Analysis Toolkit), a user-friendly, Python-based GUI designed to simplify and accelerate the development of interpretable ML-based classification QSAR/QSPR/QSTR models. This toolkit offers an intuitive graphical user interface (GUI), enabling users with domain knowledge but limited coding experience to efficiently optimize, construct, and interpret various ML-based classification models. A most highlighting feature of this GUI is its fully customizable settings panel, allowing users to modify colour schemes, font sizes, axis labels, and plot dimensions to suit publication or presentation needs. By combining robust optimization with an explainable approach, the Optima toolkit improves classification QSAR model performance while ensuring transparency and reproducibility. This platform addresses a critical need by providing an intuitive GUI for rational dataset splitting, efficient feature selection, and the development of seven different ML-based classification QSAR models, covering the entire workflow from optimization to interpretation. The toolkit is presently available for Windows and can be downloaded from the provided link (https://github.com/Rahul-Roy-21/OPTIMA).
