Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality worldwide, with therapeutic progress often limited by late-stage diagnosis and the variable effectiveness of existing treatments. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic approach, but their clinical success depends critically on accurate biomarker-based patient stratification. Current strategies for integrating biomarkers, however, remain fragmented and lack the robustness needed for reliable prediction of treatment outcomes. To address this gap, we propose a novel multimodal deep learning framework that integrates CT images, PET/CT scans, and curated biomarker profiles to improve prediction of ICI treatment response in NSCLC patients. Our model employs a pre-trained Xception encoder for advanced image feature extraction, an Encoder Attention Network for semantic representation learning, and a DenseNet-inspired metadata processor for structured biomarker data. Multimodal features are fused through a Block Dense Attention Convolutional Module with Self-Attention Multi-Head (BDAC-SAMH) mechanism, enabling richer interactions across modalities. Experimental evaluation demonstrates that our framework achieves 94.3% accuracy, 92.1% sensitivity, and 93.5% specificity, significantly outperforming conventional CNN-based unimodal methods. Importantly, the proposed system improves prediction of key ICI biomarkers, including PD-L1, TMB, and MSI, by 15.6% compared to unimodal baselines while revealing novel biomarker interactions. This highlights its potential to guide personalized immunotherapy strategies in NSCLC.

This study introduces Anisa, an advanced artificial intelligence (AI) companion designed to enhance elderly care by addressing the multifaceted needs and challenges of older adults. The system integrates the Llama 3.2 model, powered by Groq, to facilitate context-aware dialogues and empathetic interactions. This capability helps alleviate loneliness and provides essential companionship. Agenda.js is used for scheduling and managing reminders, ensuring timely notifications for medications and appointments. Additionally, Twilio enables emergency alerts when distress signals are detected. Anisa promotes physical activity, tracks daily routines, and generates activity reports shared with caregivers and healthcare providers. Expo CLI implements step-tracking and document-sharing features. By integrating these functionalities, Anisa improves the quality of life for seniors, eases caregiver responsibilities, and fosters a safer, more supportive environment.

Surface roughness (Ra) of drilled hole walls is difficult to measure non-destructively because the recessed surface is inaccessible to a stylus unless the workpiece is sectioned. This study presents an imaging rig and a stylus ground-truth procedure for 150 holes (4–9 mm) drilled in Al-7075, and evaluates whether gray-level co-occurrence matrix (GLCM), first-order and wavelet features from grayscale hole-wall images, together with spindle speed and feed rate, can estimate Ra using support vector regression (SVR) with and without mutual information feature selection. Two evaluation schemes were compared: an image-level split of 900 augmented images, and hole-level cross-validation in which all images of a hole are kept together and tests use original images of unseen holes. The image-level split gave R² = 0.80, whereas hole-level cross-validation gave R² between −0.04 and 0.08 for all feature sets, showing that image-level splitting overstates performance for this kind of data. Flips left the features unchanged, whereas noise injection and 90° rotation altered them and their correlation with Ra. The work provides a reproducible acquisition and ground-truth procedure, a 150-hole dataset and an evaluation protocol for image-based roughness estimation of drilled holes, and identifies the data and imaging conditions needed for estimation on unseen holes.

Developing a wound strength measuring device for experimental animals involves a multidisciplinary approach that combines engineering, biology, and veterinary medicine. Such a device would help researchers assess the healing progress and strength of wounds in animals, enabling a better understanding of tissue regeneration and wound healing mechanisms. In this paper, We designed a mice holder along with a full experimental setup to apply load and measure the displacement automatically. Also, we carried out an experiment to measure load versus displacement.