This manuscript introduces a highly sensitive dual-core photonic crystal fiber (PCF) based multi-analyte surface plasmon resonance (SPR) sensor, possessing the ability to detect multiple analytes at once. A chemically stable thin plasmonic substance of gold (Au) layer, holding a thickness of 30 nm, is employed to the outer portion of the stated design that manifests a negative real permittivity. Moreover, an ultra-thin film of aluminum oxide (Al ₂ O ₃ ), having a thickness of 10 nm, is inserted into the exterior of the gold film to calibrate the resonance wavelength as well as magnify the coupling strength. The performance of the sensor is rigorously explored employing the finite element method (FEM), where numerical investigation confirms that the intended sensor model exhibits a peak amplitude sensitivity (AS) of 2606 RIU ^-1 , as well as a highest wavelength sensitivity (WS) of 20,000 nm/RIU. The achieved outcomes affirm that the sensor design can be conceivably applied in numerous biological; as well as biochemical analyte refractive index (RI) detection to realize the relevant significant applications in the visible to near-infrared (VNIR) region of 0.5 to 2 μm.

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier ( 3SHC ) and the Three Stage Hybrid Artificial Neural Network ( 3SHANN ), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.

Carbon nanotube (CNT) networks used for strain detection are one of the encouraging findings in the application of sensors. Recently, our group has discovered that in-plane strain sensors built from CNT films without surfactants exhibit a high piezoresistive response in CNT networks. In this paper, a study on modeling of piezoresistive response of CNT network based films without surfactants by percolation theory for in-plane strain detection is presented. In particular, within a conductive region, a model was built to predict piezoresistive response of CNT networks and the model predicted result shows an agreement with the experimental result.

Two-photon absorption laser experiments are conducted on a low-jitter tunable hybrid analog-digital LC-tank phase-locked loop (PLL) in a 28-nm bulk CMOS technology. The single event effect (SEE) sensitivities for different blocks as well as the effect of varying circuit parameters are analyzed. The analog loop filter and the inductor-varactor area in the VCO LC tank are identified as the most sensitive areas of the PLL, which generally agrees with previous studies. The VCO tail common-mode node, which was previously shown to be highly sensitive to SEE, exhibits very good robustness, thanks to the proposed 2nd-order harmonic LC tail filter added in this design. The laser experiments show that a lower PLL bandwidth tends to reduce the SEE sensitivities of the PLL, while the results on the effect of the VCO frequency are mixed and need further study.

Fully depleted silicon-on-insulator (FDSOI) technology nodes offer better single-event performance compared with comparable bulk technologies. However, upsets are still possible at nanoscale feature sizes and additional hardening techniques need to be explored. Single-event upset (SEU) performance of multiple flip-flop designs using the stacked-transistor hardening technique at a 22-nm FDSOI technology node is presented in this paper. Irradiation results show significant reductions in SEU cross-sections for stacked-transistor-based hardened designs compared to a conventional design. Alpha particle exposures showed zero upsets for all D-Flip-Flop (DFF) designs tested. When exposed to heavy-ions, the stacked-transistor DFF design showed a 17X improvement over a conventional DFF design at an LET value of 47 MeV-cm ² /mg. The stacked-transistor design with the charge-cancelling technique showed upsets when particle LET exceeded 93.8 MeV-cm ² /mg and at a high angle of incidence. The stacked-transistor design with the interleaving technique showed zero upsets for all test conditions.