Efficiency Droop and Degradation in AlGaN-Based UVB Light-Emitting Diodes

In this study, we found that the current droop (J-droop) in AlGaN-based UVB light-emitting diodes was more obvious at higher temperatures, despite both the main and parasitic peaks undergoing monotonic decreases in their intensity upon an increase in the temperature. The slower temperature droop (T-droop) did not occur when the forward current was increased to temperatures greater than 298 K. After an aging time of 6000 h, the emission wavelengths did not undergo any obvious changes, while the intensity of the parasitic peak barely changed. Thus, the degradation in the light output power during long-term operation was not obviously correlated to the existence of the parasitic peak.

Global view on virus infection in non-human primates and implications for public health and wildlife conservation.

Viruses can be transmitted from animals to humans (and vice versa) and across animal species. As such, host-virus interactions and transmission have attracted considerable attention. Non-human primates (NHPs), our closest evolutionary relatives, are susceptible to human viruses and certain pathogens are known to circulate between humans and NHPs. Here, we generated global statistics on VI-NHPs based on a literature search and public data mining. In total, 140 NHP species from 12 families are reported to be infected by 186 DNA and RNA virus species, 68.8% of which are also found in humans, indicating high potential for crossing species boundaries. The top 10 NHP species with high centrality in the NHP-virus network include two great apes (Pan troglodytes, Pongo pygmaeus) and eight Old World monkeys (Macaca mulatta, M. fascicularis, M. leonina, Papio cynocephalus, Cercopithecus ascanius, C. erythrotis, Chlorocebus aethiops, and Allochrocebus lhoesti). Given the wide distribution of Old World monkeys and their frequent contact with humans, there is a high risk of virus circulation between humans and such species. Thus, we suggest recurring epidemiological surveillance of NHPs, specifically Old World monkeys that are in frequent contact with humans, and other effective measures to prevent potential circulation and transmission of viruses. Avoidance of false positives and sampling bias should also be a focus in future work.

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion.

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.