Virtual Reality-Based Attention Prediction Model in Gaming for Autistic Children.

Nowadays, virtual reality (VR) has emerged as a successful new therapeutic strategy in a variety of sectors of the health profession, including rehabilitation, the promotion of inpatients' emotional wellness, diagnostics, surgeon training and mental health therapy. This study develops a new model VRAPMG for children with ASD with the following steps that consider 3D gaming. In this work, the face image is considered to evaluate the attention of the children.

Revolutionizing Drug Delivery: A Design Professional's Approach to Drug-loaded Transferosomal Vesicles for Transdermal Use.

Aim: This study aimed to develop and evaluate lornoxicam (LXM) and thiocolchicoside (TCS) transferosomal transdermal patches.

Background: Oral administration of LXM and TCS can lead to gastric irritation, necessitating alternative delivery methods for pain and inflammation relief. Incorporating LXM & TCS into transferosomes within a transdermal patch offers a potential solution.

De novo transcriptome assembly of the Perna viridis: A novel invertebrate model for ecotoxicological studies.

Mussels, particularly Perna viridis, are vital sentinel species for toxicology and biomonitoring in environmental health. This species plays a crucial role in aquaculture and significantly impacts the fisheries sector. Despite the ecological and economic importance of this species, its omics resources are still scarce.

Incremental Structure from Motion for Small-Scale Scenes Based on Auxiliary Calibration.

Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas.

A Combined Frame Difference and Convolution Method for Moving Vehicle Detection in Satellite Videos.

To address the challenges of missed detections caused by insufficient shape and texture features and blurred boundaries in existing detection methods, this paper introduces a novel moving vehicle detection approach for satellite videos. The proposed method leverages frame difference and convolution to effectively integrate spatiotemporal information. First, a frame difference module (FDM) is designed, combining frame difference and convolution.