Solution-phase hierarchical self-organization of ultralong Se nanowires into diverse macroarchitectures and their enhanced field emission.

A simple solution-phase route was developed for the large-scale synthesis of self-organized, closely packed ultralong single crystalline Se nanowire superstructures with diverse morphologies and macroscopic dimensions even extending over several millimeters. The hierarchical architectures of self-organized Se nanowires were formed by reducing H2SeO4 with a bisubstituted aniline, such as 3,5-dimethoxyaniline, 2,5-dimethoxyaniline, 2,6-dimethoxyaniline, and 2-methoxy-5-nitroaniline under solvothermal conditions. Scanning electron microscopy studies show 100% morphological yield and morphological uniformity of the self-organized hierarchical architectures. Based on the dependence of the Se nanostructures on the synthetic conditions, especially the molecular structures of reductants and solvent, we proposed a plausible mechanism to account for the formation of the distinctive morphologies of the self-organized nanowire architectures. The field emission characteristics of the Se nanowires synthesized using 2,6-dimethoxyaniline and 2-methoxy-5-nitroaniline as the reductants are studied. These well-aligned Se nanowires show very low turn-on field (Eto) and threshold field (Ethr) as well as high emission current densities under low applied electric fields, which are superior to most of the one-dimensional (1D) nanostructures reported previously, due to their exceptional aspect ratios (>20 000) and sharp tips in combination with the nature of low band gap and high conductivity of Se. Furthermore, the Se nanowire emitters exhibit good emission current stability with small fluctuations (typically, less than 3%) over a period of 1000 min.

Enhancing Medical Signal Processing and Diagnosis with AI-Generated Content Techniques.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

[Preliminary study on the health status among the "left-behind" children in the Xiantao rural area of Hubei Province].

OBJECTIVE: To study the health status of the primary school children who remain in their home villages (the "left-behind" children) in a rural area of Hubei Province, Central China, whilst their parents are migrant workers in the cities of China. METHODS: A total of 1000 pupils in the 4th to 6th grade from six rural primary schools in Xiantao City, Hubei Province were enrolled. All subjects were surveyed with questionnaires and received physical examinations. Pupils whose parents had no history of migrant work and who lived with both parents were defined as the control groups. RESULTS: Among the 875 valid questionnaires, there were 590 "left-behind" children and 285 controls. The mean body weight was significantly lower among the "left-behind" children (35.5 ± 7.1 kg) than the controls (36.3 ± 8.8 kg) (P<0.05). The weight/age z score of "left-behind" children (-0.9811 ± 0.54) was also significantly lower than that of the controls (-0.7012 ± 0.34) (P<0.05). However, the other physical indicators including body height, height/age z score, thickness of sebum, and body mass index and the common nutrition status showed no significant differences between the two groups. The "left-behind" children scored significantly higher in the Children's Depression Inventory than the controls (11.4 ± 7.2 vs 8.0 ± 5.8, P<0.01), and the incidence of depression was also significantly higher in "left-behind" children than in controls (15.3% vs 6.0%, P<0.01). Compared with the controls, the "left-behind" children had significantly higher incidences of antiadoncus (32.0% vs 23.2%; P<0.01), respiratory tract infections (14.6% vs 9.5%; P<0.05), and gastrointestinal infections (7.6% vs 3.9%; P<0.05). CONCLUSIONS: Although the "left-behind" children have normal nutrition status, they tend to have poor mental health and are more susceptible to infections.