The growing environmental issues regarding plastic waste have necessitated the need for sustainable and biodegradable alternatives to traditional plastic pots. Present study focuses on the production of eco-friendly biodegradable plant containers using various bio composites consisting of natural fibers such as pineapple leaves (PC), water hyacinth (WC), dried leaf litter (DC), banana fibers (BC), and coco peat with cornstarch acting as a natural adhesive. The main aim is to produce a nature-friendly alternative to conventional plastic pot maintaining structural integrity and durability. To assess the viability of the bio composite, several mechanical and environmental tests were carried out, such as compression, flexural strength, impact resistance, water absorption, and biodegradability tests. A direct planting test with the money tree (Epipremnum aureum) was conducted to qualitatively assess the practical applicability of the biopots under real-use conditions. All experimental data were analyzed statistically, and differences among the samples were considered significant at p < 0.05. Water absorption analysis showed that BC exhibited the highest absorption (96.40 ± 3.59
Digital micromirror devices (DMDs) are a popular choice for wavefront-shaping applications. We describe the restructuring of commercial digital light projectors to utilise their DMD for consistently replicable and budget-friendly diffraction and wavefront-shaping application experiments. Techniques for validating the optical parameters of DMDs are described. The experimental validation of the concept of the Fresnel zone plate using the set-up is discussed. We verified the experimental and simulated reconstruction of two dots and their Fourier transforms. We also describe the generation of orbital angular momentum states of light using the experimental set-up.
We present StegoMed, a deep learning-based framework intended to facilitate secure transmission of medical images in teleradiology environments. Drawing inspiration from GAN-based representations, StegoMed uses a lightweight two-stage encoder-decoder architecture without a discriminator which enables stable training and computational efficiency. The proposed framework embeds MRI brain images within natural cover images and subsequently reconstructs them with high fidelity. To enhance both imperceptibility and recoverability, the model is optimized by combining pixel-wise reconstruction, perceptual, and structural similarity losses. Experimental results demonstrate that StegoMed achieves high average reconstruction quality (PSNR = 47.7 dB, SSIM = 0.9976, MSE = 0.00006) and outperforms several existing baselines. The method also shows robustness to common distortions such as JPEG compression and Gaussian noise. These results demonstrate StegoMed’s potential as an effective and privacy-preserving solution for secure medical image transmission in modern teleradiology workflows.
The covalent functionalization of the graphene surface is successfully employed using a silane coupling agent, 3-aminopropyl trimethoxy silane (APTMS). The silane modification increased the interlayer spacing between the rGO layers. The composite electrode loaded with 1.5 g APTMS displayed 97.3 % pseudocapacitance, implying the successful silylation of the oxygenated functional groups on the graphene structure. The silylation imparts a sheet-like internal morphology with sharp edges to rGO's morphology. Among binary electrodes, Si-rGO with 20 wt% loading of acid-treated multi-walled carbon nanotubes (A-CNT) (PSRC20 binary electrodes) show excellent electrochemical properties and the highest specific capacitance. PSRC20 binary electrode displayed 82.6 %
Skin cancer, particularly malignant melanoma, remains one of the most life-threatening yet preventable cancers worldwide, where early and accurate detection is essential for improving patient survival rates. This work investigates the application of the YOLOv11 real-time object detection framework for multi-class skin lesion detection using dermoscopic images from the HAM10000 dataset. Five clinically significant lesion categories—AKIEC, BCC, BKL, MEL, and NV—were selected to evaluate detection performance in a practical medical imaging setting. In Phase I, a baseline YOLOv11 model was developed with comprehensive preprocessing and augmentation strategies, and its performance was evaluated using standard object detection metrics including mAP, precision, recall, F1-score, and inference time. In Phase II, selected architectural variations were explored by replacing the default backbone with MobileNetV3 and the detection head with an SSD-style head to analyze the trade-offs between computational efficiency and detection accuracy. Experimental results demonstrate that the baseline YOLOv11 configuration provides strong multi-class detection performance, while the lightweight architectural variants offer insights into deployment-oriented efficiency trade-offs. This study highlights the adaptability of modern object detection frameworks for medical image analysis and provides a practical foundation for efficient AI-assisted skin lesion detection systems.
