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Development, Reliability, and Validity of the Telerehabilitation Usability Questionnaire in Neurological Diseases
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Compatibility Of Intraoperative Frozen Section With Final Histopathological Report In A Tertiary University Hospital
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A Comparative Approach to Assess the Particle Size Effect on Performance of Immunoaffinity Surface Plasmon Sensor
No full textThis study investigates the impact of silica nanoparticle (SiNP) size on optimizing surface plasmon resonance (SPR) sensor performance, a highly sensitive technology for biological detection. Although metallic nanoparticles enhance SPR through localized plasmon generation, they can obscure the specific influence of nanoparticle size. To isolate this effect, optically transparent and non-conductive SiNPs, which do not support localized plasmon resonance, were chosen to minimize perturbations in evanescent light propagation and ensure that observed enhancements arise solely from nanoparticle size variations. SiNPs with diameters of 57, 65, and 80 nm were synthesized via the St & ouml;ber method and integrated into SPR chips. Comprehensive characterization, including zeta potential, contact angle analysis, FTIR, and SEM, confirmed successful nanoparticle integration. Real-time immunoaffinity detection using immunoglobulin G (IgG) as the model analyte showed the SiNP57@anti-IgG sensor exhibited superior sensitivity and detection limits. This improvement is attributed to the higher surface area-to-volume ratio of smaller SiNPs, which enhances analyte capture density, leading to a greater local refractive index change and a more pronounced resonance angle shift (triangle R). Kinetic and equilibrium studies showed enhanced binding affinity for sensors with smaller SiNPs, underscoring the crucial role of nanoparticle size in improving SPR sensor performance and optical biosensing technologies
Integration of Contrastive Predictive Coding and Spiking Neural Networks
No full textThis study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achieved a high classification rate in distinguishing positive sequential samples from non-sequential negative samples. The study demonstrates that CPC can be effectively combined with SNN, showing that an SNN trained for classification tasks can also function as an encoding mechanism. Project codes and detailed results can be accessed on our GitHub page: https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN</p
