Abstract
This study proposes a two-stage Intrusion Detection System (IDS) for Controller Area Networks (CAN) that leverages protocol-specific timing characteristics. Modern vehicular networks are vulnerable to injection attacks due to the CAN protocol's lack of built-in authentication. Our methodology transforms raw CAN traffic into a structured feature space consisting of CAN IDs, message offsets, and inter-message intervals derived from the CAN Remote Frame request-response mechanism. The first stage applies unsupervised z-score statistical thresholding, requiring no labeled attack data. The second stage employs three independent binary Random Forest (RF) classifiers for precise characterization. Individual classifiers achieve F1-scores of 0.96 (Fuzzy), 0.77 (DoS), and 0.79 (Impersonation). In the integrated end-to-end pipeline, while the system effectively filters 97% of legitimate traffic, a performance stratification is observed: high detection is maintained for timing-disruptive attacks (Fuzzy), whereas timing-preserving attacks (DoS, Impersonation) exhibit lower recall due to the restrictive nature of the timing-only first-stage gating mechanism. Hardware profiling confirmed an inference latency of similar to 0.018 ms and footprint of 8.8-19.2 MB, offering a deployable, computationally efficient defense for legacy automotive environments.

Abstract
A point-of-care test (POCT) based on low-cost and highly sensitive disposable chips was designed for the sensitive and selective detection of proteins. In particular, a pollen-based plasmonic nanostructured probe coupled, for the first time, with biomimetic receptors custom-designed as molecularly imprinted nanoparticles (MIP-NPs) for protein recognition, was developed and interrogated by an extrinsic optical fiber (OF)-based scheme. To this purpose, bovine serum albumin (BSA) was chosen in a proof-of-concept frame as an example of a protein. © 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

Abstract
Objective: To evaluate the impact of sensor integration on YBT outcomes, including data precision, injury risk prediction, real-time monitoring, and athletic performance assessment. A secondary objective is to identify gaps in automated real-time evaluation methods. Methods: A systematic review was conducted using a search window covering studies published between 2020 and 2026. After screening and eligibility assessment, the final included studies were published between 2021 and 2025. This review focused on studies that either applied sensor-based or technology-assisted methods directly to Y-Balance Test assessment or used YBT as a functional outcome alongside technology-supported measurement approaches. Results: After screening and eligibility assessment, 21 studies met the inclusion criteria and were included in the qualitative and descriptive synthesis. The reviewed studies suggest that technology-assisted approaches can broaden the assessment of Y-Balance Test performance by adding biomechanical, functional, or task-related information beyond conventional manual scoring. Several studies reported improved monitoring of balancerelated outcomes or intervention-related changes, but direct evidence for improved measurement precision and formal injury prediction was limited. Conclusions: Sensor-assisted approaches in YBT show promising potential to improve measurement objectivity and broaden functional assessment in clinical and athletic settings. However, the current literature does not yet demonstrate a fully automated or real-time YBT system, and further development is required before such applications can be considered established for routine practice. Future progress will require larger and more diverse cohorts, methodological standardization, robust validation procedures, and the development of portable realtime YBT-specific systems suitable for routine implementation. Significance: This review contributes a structured evidence map of sensor-assisted YBT research and highlights the gap between existing technology-supported assessment approaches and truly automated, real-time, YBT-specific systems.

Abstract
Optical coherence tomography angiography (OCTA), known for its high-resolution and noninvasive imaging capability, has become a key modality for visualizing retinal vasculature. Accurate and automated segmentation of capillaries, arteries, veins, and foveal avascular zone in OCTA images is essential for quantitative analysis and disease assessment. In this paper, we propose a depth enhanced cascaded framework specifically designed for multi-class OCTA segmentation. Our method investigates the spatial distribution of vasculature in retinal images and integrates a novel self-supervised depth prediction module to learn implicit depth cues from volumetric data, thereby improving the discrimination of overlapping vascular layers. In addition, we design two topology-aware loss functions that explicitly encourage structural integrity and continuity of vessel segmentation, particularly at bifurcations and endpoints. Experiments on the OCTA-6 mm and OCTA-3 mm datasets demonstrate that our method outperforms existing state-of-the-art approaches, with mIoU gains of around 2% over prior method, IPNv2, thereby highlighting enhanced segmentation accuracy and vascular topology preservation.

Abstract
Artificial intelligence (AI), complex mathematical algorithms, is currently employed across various fields to perform tasks quickly and effectively. In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: (Escherichia coli and Staphylococcus aureus), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224 & times; 224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm's performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model's high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9 min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis's speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error.

Abstract