Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm.

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

Peptide-nanoparticle hybrid SERS probe for dynamic detection of active cancer biomarker enzymes.

Real-time in situ detection of protease enzymes is crucial for early-stage cancer screening and cell signaling pathway study; however it is difficult to be realized using fluorescence or radioactive probes. Here we devise a hybrid optical probe by incorporating nanocrescent particle and peptides with artificial tag molecules. The peptides have high specificity to PSA, one of the most prominent prostate cancer markers, and a serine protease present in patients' seminal fluid and serum. The extrinsic Raman spectral signal from the tag molecules is enhanced by the nanocrescent and the signal is monitored as the indicator for the peptide digestion in nanomolar PSA concentration and femtoliter reaction volume. Sensitive detection of cancer-related serine protease activity of PSA proteins in low concentrations and small volumes of biofluid is critical to early cancer diagnosis, clinical staging, and therapy. The high reaction specificity of the peptide and the monitored extrinsic Raman signal also minimizes the false detection of other serine proteases and intrinsic Raman signal, which results in a high-fidelity and high-signal-to-noise-ratio cancer nanoprobe. Peptide-conjugated nanocrescents should also be applicable for measuring the intercellular and intracellular activity of other cancer-related proteases and protease activity profiling-enabled cancer cell identification.