Consciousness trajectories and functional independence after acute brain injury in children with prolonged disorder of consciousness.

Aim: To explore the trajectories of consciousness recovery and prognosis-associated predictors in children with prolonged disorder of consciousness (pDoC).

Method: This single-centre, retrospective, observational cohort involved 134 (87 males, 47 females) children diagnosed with pDoC and hospitalized at the Department of Rehabilitation at the Children's Hospital of Chongqing Medical University in China. The median onset age was 30 (interquartile range [IQR] 18-54) months, with onset ages ranging from 3 to 164 months.

Burden and attributable risk factors of non-communicable diseases and subtypes in 204 countries and territories, 1990-2021: a systematic analysis for the global burden of disease study 2021.

Background: Understanding based on up-to-date data on the burden of non-communicable diseases (NCDs) is limited, especially regarding how subtypes contribute to the overall NCD burden and the attributable risk factors across locations and subtypes. We aimed to report the global, regional, and national burden of NCDs, subtypes, and attributable risk factors in 2021, and trends from 1990 to 2021 by age, sex, and socio-demographic index (SDI).

Materials And Methods: We used data from the Global Burden of Disease Study 2021 to estimate the prevalence, deaths, and disability-adjusted life years (DALYs) for NCDs and subtypes, along with attributable risk factors.

Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing.

To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%.

LDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips.

Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss.