The release of microplastics (MPs) from nylon tea bags poses a critical concern for human exposure; however, their detection and quantification remain challenging especially in beverage matrices, and hence, this study pioneers the use of high-resolution optical coherence tomography (OCT) integrated with an image processing algorithm to rapidly detect and quantify the size and count of the MPs directly in the water extractions simulating tea brewing. The water extractions prepared by simulating tea brewing conditions, hot (100 °C, 1–5 min), cold (2 °C, 1 h), and ambient (30 °C, 1 h), were observed employing OCT imaging and validated through Nile Red (NR) staining and digital microscopy. The nylon tea bags steeped in hot water for 5 minutes released 16 000 to 24 000 LMPs (>30 µm) and SMPs (12–30 µm) per millilitre. The estimated daily intake (EDI) of MPs indicates a higher exposure for children (ranging from 0.201 to 0.349 mm³ kg⁻¹ day⁻¹) compared to adults (0.046 to 0.080 mm³ kg⁻¹ day⁻¹). In contrast, cold brewing for 1 hour released fewer LMPs but an equal quantity of small MPs (SMPs) compared to hot brewing. This OCT-based approach offers a rapid, versatile platform for the detection and quantification of MPs from diverse packaging materials and provides a powerful tool for comprehensive risk assessment when combined with chemical and toxicological analyses.

Plant disease has a tremendous impact on global food security, and bitter rot caused by Colletotrichum spp. is a greater challenge to post-harvest quality. Conventional diagnosis is precise but invasive and therefore inappropriate for real-time purposes. This study investigates optical coherence tomography (OCT) as a high-resolution, non-invasive imaging method to detect internal structural changes from disease progression. The developed OCT-based image analysis framework stages diseases by assessing morphological degradation. The discovery of unique oval-shaped internal features, invisible to other non-invasive methods, demonstrates OCT’s potential for early detection, accurate monitoring, and real-time application in precision agriculture.

The rapid evolution of Artificial Intelligence(AI) has paved the way for innovative applications in everyday objects. Among these, a smart mirror is a representation of a traditional mirror with integrated technologies to provide realtime personalized recommendations and health insights. Busy schedules make it challenging for individuals to manage their to-do lists, health, and even dress code choices. According to the past work done, both Khandaker et al. [1] and Simone et al. [2] developed smart mirrors with various features, such as face verification, weather updates, calendar events, news, traffic, and emotion recognition. Considering social event-based fashion recommendation, Federico et al [3] proposed an event classifier combined with a recommender by implementing a fashion object detector for social events. Nikita et al [4] proposed outfit recommendation by detecting user outfits, identifying the social event, and suggesting similar outfits for the detected event. However the system lacks the ability of considering outfits suitable for multiple social events. In the context of acne detection, Quan et al. [5] proposed an automatic system that employs Faster Region-based Convolutional Neural Network (R-CNN) for acne lesion detection and LightGBM for severity grading. Similarly, Hang et al. [6] introduced an ensemble neural network-based approach featuring a classification module for severity estimation and an acne localization module. However, prior research has focused on general acne detection, there is limited work addressing localizing acne within the ”Danger Triangle of the Face” which is a crucial clinical aspect. This research proposes a comprehensive Internet of Things(IoT) smart mirror framework to address past limitations. Mainly, it has features for face verification, acne detection with ”Triangle of Death” localization, and social event-based fashion recommendation. By leveraging these components, the proposed smart mirror represents a significant advancement towards smart computing, seamlessly integrated with digital intelligence.

This study presents a real-time system for detecting sheath damage in multicore wire production, addressing the limitations of conventional high-voltage testing. After evaluating multiple sensing techniques, laser-diffused reflection emerged as the most reliable, non-intrusive, and effective for continuous monitoring. A calibrated system combining a laser source and photodetector was implemented and tested on a live production line, achieving 83.75% detection accuracy. The system maintained consistent performance across wire colors and insulation conditions. This advancement offers a safer, more efficient alternative for in-process quality control. Future work aims to enhance robustness and incorporate intelligent algorithms to further optimize detection accuracy.