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Cs2LiLaBr6: Ce (CLLB) scintillator with the size of Φ 21mm × 25 mm coupled with PMT was used to detect neutron and gamma rays. The pulse shape discrimination (PSD) of neutrons and gamma rays by charge comparison method, the neutrons and gamma rays from AmBe source and fast neutron beam can be separated with figure-of-merit (FOM) values of 0.9 and 1.3, respectively. However, some neutron and gamma rays are difficult to distinguish, so new algorithms need to be investigated to improve the PSD performance of neutron and gamma. Artificial neural networks (ANN) have a very good image recognition capability, thus the ANN model was constructed to discriminate the waveforms of neutron and gamma rays. After ANN model training, the neutron and gamma signals of the CLLB detector were recognized with an accuracy of 98%, and the FOM value of the ANN method was calculated to be 19.4. This result is much higher than the charge comparison method, indicating better discrimination between neutrons and gamma rays with the ANN method.

This study investigated the number of operator errors, task completion time, and workload of subjects at different levels by imposing conditions such as focused text boldness, noise disturbance, and time pressure to simulate a realistic cloud change business process in the laboratory. Results of the study showed that the text bolding of important content reduced the number of errors, whereas noise interference increased the number of errors. Text boldness only reduced the number of corrected errors, and noise interference only increased the number of uncorrected errors. Moreover, bolding was found to have different effects on the number of errors under different noise levels and time pressure levels, with text boldness significantly reducing the number of total errors only in quiet or low time pressure states. Time pressure had no effect on cloud change task error counts, but high time pressure resulted in higher subjective workload. Practitioner Summary: Operator error is one of the main causes of service failure, and reducing operator error in cloud change operations is of practical importance. In this study, we found focused text boldness could reduce operator errors, while noise could increase the number of errors. High time pressure would lead to a high workload. [ABSTRACT FROM AUTHOR]

Although most of the damage caused by lepidopteran insects to plants is caused by the larval stage, chemosensory systems have been investigated much more frequently for lepidopteran adults than for larvae. The fall armyworm Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) is a polyphagous and worldwide pest. To understand the larval chemosensory system in S. frugiperda, we sequenced and assembled the antennae and maxillae transcriptome of larvae in the sixth instar (larval a-m) using the Illumina platform. A total of 30 putative chemosensory receptor genes were identified, and these receptors included 11 odorant receptors (ORs), 4 gustatory receptors (GRs), and 15 ionotropic receptors/ionotropic glutamate receptors (IRs/iGluRs). Phylogeny tests with the candidate receptors and homologs from other insect species revealed some specific genes, including a fructose receptor, a pheromone receptor, IR co-receptors, CO2 receptors, and the OR co-receptor. Comparison of the expression of annotated genes between S. frugiperda adults and larvae (larval a-m) using RT-qPCR showed that most of the annotated OR and GR genes were predominantly expressed in the adult stage, but that 2 ORs and 1 GR were highly expressed in both the adult antennae and the larval a-m. Although most of the tested IR/iGluR genes were mainly expressed in adult antennae, transcripts of 3 iGluRs were significantly more abundant in the larval a-m than in the adult antennae of both sexes. Comparison of the expression levels of larval a-m expressed chemosensory receptors among the first, fourth, and sixth instars revealed that the expression of some of the genes varied significantly among different larval stages. These results increase our understanding of the chemosensory systems of S. frugiperda larvae and provide a basis for future functional studies aimed at the development of novel strategies to manage this pest.