Applied Sciences

To address the loss of local defect information, insufficient adaptation to irregular defect patterns, and background interference in transmission-line insulator inspection, this paper proposes DRB-DEIM, a text-guided and orientation-aware multiscale detector based on DEIM-D-FINE-S. The model uses offline text prototypes to provide class-level semantic guidance and introduces three visual components: Detail-Preserving Stem for preserving local information during early downsampling, Orientation-Routed Inception ELAN for adapting feature extraction to directional and irregular defect patterns, and Bidirectional Routing-Guided Feature Pyramid Network for content-adaptive multiscale feature fusion. Experiments on an insulator defect dataset constructed from public datasets and field inspection images show that DRB-DEIM achieves 89.9% precision, 83.5% recall, 88.1% mAP@50, and 59.2% mAP@50–95. Compared with DEIM-D-FINE-S, mAP@50 and mAP@50–95 are improved by 5.3 and 8.9 percentage points, respectively. The proposed model contains 11.57 M parameters and reaches 117.4 frames per second on the workstation test platform. It also achieves an average mAP@50 of 71.7% under six degraded imaging conditions. Deployment on a Jetson Nano achieves 87.3% mAP@50 and 20.83 frames per second using TensorRT FP16. These results demonstrate improved detection of small and weakly visible insulator defects and support the feasibility of local inference on a resource-constrained edge platform.

Detecting and identifying objects outside the direct line of sight is important for security, search and rescue, and autonomous systems, and acoustic sensing offers a low-cost and environmentally robust alternative to optical non-line-of-sight (NLOS) methods. Most acoustic NLOS studies address a single object. This study addresses the classification of paired object combinations from secondary NLOS acoustic reflections. Linear chirp signals reflected from a wall were recorded with an eight-loudspeaker, eight-microphone array in a single controlled room (120 recordings, four balanced classes). A fixed-size, 513-dimensional short-time Fourier-transform (STFT) spectral summary was classified with Random Forest, Decision Tree, k-Nearest Neighbors, and Multilayer Perceptron models using recording-level stratified five-fold cross-validation repeated over five seeds. Random Forest and the Multilayer Perceptron reached accuracies of 0.9850 ± 0.0207 and 0.9600 ± 0.0231, respectively, against a chance level of 0.25. Comparisons with MFCC, Welch, and compact spectral-descriptor representations, single-microphone and multi-channel strategies, and preprocessing ablations, together with label-permutation and nuisance-baseline controls, indicate that the separation is carried by the spectral envelope rather than by gross amplitude, and that the eight-channel array gave no systematic advantage over a single microphone in this dataset. The results are limited to one room, one reflective surface, and recording-level evaluation, and they do not demonstrate generalization to unseen sessions or rooms.

As of late, gold nanoparticles (AuNPs) are gaining recognition as highly versatile nano-materials with remarkable potential in biomedical applications, owing to their unique physicochemical properties. Aside from their tunable size and shape and well-defined surface chemistry, AuNPs provide strong biocompatibility and distinctive optical behavior, arising from a phenomenon widely known as surface plasmon resonance (SPR). These metal nanoparticles’ ability to be coupled with therapeutic molecules, targeting ligands, and polymeric coatings is suggesting they could serve as encouraging platforms in the development of next-generation cancer treatment strategies, along other metal NPs such as silver nanoparticles (AgNPs). This review begins with a foundational overview of the physicochemical determinants of AuNP performance, including particle size, morphology, surface chemistry, and enhanced permeability and retention (EPR)-mediated targeting, before centering on the areas that most directly distinguish this review’s contribution: lipid-encapsulated and archaeal-lipid hybrid AuNP carriers, combined chemo-photothermal platforms, and NIR-I/NIR-II-responsive drug delivery systems. Comparative therapeutic performance, translational stage (in vitro, in vivo, or clinical evaluation), and manufacturing standardization are used as organizing criteria throughout, in place of a purely descriptive survey of individual platforms. At last, current translational challenges related mainly to toxicity, colloidal stability, large-scale synthesis, and clinical implementation are highlighted.