Ertay, Agah Oktay; Ertay, Muhammed Mustafa

Dual-parameter photonic sensors are usually reported through a nominal sensitivity and one detection limit, without stating which statistic that limit is or whether it survives transfer between devices. This computational study supplies that evaluation for one structure, a 36-layer one-dimensional multilayer read in transmission, whose Zak-phase-distinct TiO2/SiO2 photonic-crystal sections enclose a 600 nm analyte cavity and a 500 nm thermo-optic reference cavity, each carrying a 5 nm ITO/5 nm TiO2 nanolaminate insert. Two coupled interface resonances at 1517 and 1651 nm, with loaded Q of 232 and 208 and refractive-index (RI) sensitivities of 90.71 and 329.41 nm/RIU, are inverted by a bounded nonlinear calibration to (Formula presented.) RIU and (Formula presented.) (Formula presented.) ; the temperature channel reports the device temperature. Probability-of-detection limits at 1% false alarm and 95% detection are (Formula presented.) RIU and (Formula presented.) (Formula presented.) ; they are set by the calibration standards and the wavelength reference, not by the linewidth. Transferring one calibration between devices worsens them 81-fold and 203-fold; a three-point per-device correction removes 84–93% of that loss. A trivial control matched on wavelength, Q, transmission, thickness and RI sensitivity shows no topological robustness advantage. Applied to the design itself, the same evaluation shows that the modes are cavity-selected, that hyperbolicity brings no benefit, and that the nanolaminate-free stack is preferred.

Ozden, Melih; Coban, Omer; Karacali, Tevhit

This study presents the successful fabrication of lead zirconate titanate (PZT) thin films on silicon (Si) substrates using a hybrid deposition method combining spin-coating and RF sputtering techniques. Initially, a PZT layer was deposited through four successive spin-coating cycles, followed by an additional layer formed via RF sputtering. The resulting multilayer structure was annealed at (Formula presented.) C for 2 h to improve crystallinity. Comprehensive material characterization was conducted using XRD, SEM, cross-sectional SEM, EDX, and UV–VIS absorbance spectroscopy. The analyses confirmed the formation of a well-crystallized perovskite phase, a uniform surface morphology, and an optical band gap of approximately 3.55 eV, supporting its suitability for sensing applications. Building upon these findings, a multilayer PZT-based touch sensor was fabricated and electrically characterized. Low-frequency I–V measurements demonstrated consistent and repeatable polarization behavior under cyclic loading conditions. In addition, (Formula presented.) –f measurements were performed to assess the sensor’s dynamic electrical behavior. Although expected dielectric responses were observed, the absence of distinct anti-resonance peaks suggested non-idealities linked to (Formula presented.) ion diffusion from the electrode layers. To account for these effects, the classical Butterworth–Van Dyke (BVD) equivalent circuit model was extended with additional inductive and resistive components representing parasitic pathways. This modified model provided excellent agreement with the measured impedance and phase data, offering deeper insight into the interplay between material degradation and electrical performance. Overall, the developed sensor structure exhibits strong potential for use in piezoelectric sensing applications, particularly for tactile and pressure-based interfaces.

Huyut, Mehmet Tahir; Velichko, Andrei

Since February 2020, the world has been engaged in an intense struggle with the COVID‐19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID‐19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID‐19 tests. The LogNNet‐model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and acti-vated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID‐19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neu-trophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID‐19 using the key fea-tures. The method is promising to create mobile health monitoring systems in the Internet of Things.

Velichko, Andrei; Huyut, Mehmet Tahir; Belyaev, Maksim; Izotov, Yuriy; Korzun, Dmitry

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.