The Plastic Scintillator Detector at DAMPE

he DArk Matter Particle Explorer (DAMPE) is a general purposed satellite-borne high energy $\gamma-$ray and cosmic ray detector, and among the scientific objectives of DAMPE are the searches for the origin of cosmic rays and an understanding of Dark Matter particles. As one of the four detectors in DAMPE, the Plastic Scintillator Detector (PSD) plays an important role in the particle charge measurement and the photons/electrons separation. The PSD has 82 modules, each consists of a long organic plastic scintillator bar and two PMTs at both ends for readout, in two layers and covers an overall active area larger than 82 cm $\times$ 82 cm. It can identify the charge states for relativistic ions from H to Fe, and the detector efficiency for Z=1 particles can reach 0.9999. The PSD has been successfully launched with DAMPE on Dec. 17, 2015. In this paper, the design, the assembly, the qualification tests of the PSD and some of the performance measured on the ground have been described in detail

Segmentation, Clustering and Timing Relationship Analysis of MANET Traffic Flow

Users in mobile Ad Hoc networks (MANET) usually encrypt their data packets to resist the evasdroppers, which makes the network management and Intrution detection difficult. However, user behavior, ultimately displayed as traffic flow, shows regularity along time. This paper aims to study the regularity through studding the timing relationship between traffic flows, whose results provide the technical support for user behavior analysis. First, segment the end-to-end flows based on the information of time intervals and packet lengths. Second, cluster the segments by an improved maximum-distance method. Third, analyze the time relationship between the clusters, i.e., traffic flow types, based on the clustering results. Simulation results verify the effectiveness of the method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3145

Resolving the genetic heterogeneity of prelingual hearing loss within one family: Performance comparison and application of two targeted next generation sequencing approaches.

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Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism

Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. Recent studies have shown that token labels can convey crucial task-specific information and enrich token semantics. However, as far as we know, due to completely abstain from sequence tagging mechanism, all prior span-based work fails to use token label in-formation. To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER. By stacking multiple atten-tion layers in depth, we design a deep neu-ral architecture to build STSN, and each atten-tion layer consists of three basic attention units. The deep neural architecture first learns seman-tic representations for token labels and span-based joint extraction, and then constructs in-formation interactions between them, which also realizes bidirectional information interac-tions between span-based NER and RE. Fur-thermore, we extend the BIO tagging scheme to make STSN can extract overlapping en-tity. Experiments on three benchmark datasets show that our model consistently outperforms previous optimal models by a large margin, creating new state-of-the-art results.Comment: 10pages, 6 figures, 4 table

Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition

For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know. In our previous work, we proposed a paradigm known as Bundling Learning (BL) to address the above problem. The BL paradigm bundles the two NER paradigms, enabling NER models to jointly tune their parameters by weighted summing each paradigm's training loss. However, three critical issues remain unresolved: When does BL work? Why does BL work? Can BL enhance the existing state-of-the-art (SOTA) NER models? To address the first two issues, we implement three NER models, involving a sequence labeling-based model--SeqNER, a span-based NER model--SpanNER, and BL-NER that bundles SeqNER and SpanNER together. We draw two conclusions regarding the two issues based on the experimental results on eleven NER datasets from five domains. We then apply BL to five existing SOTA NER models to investigate the third issue, consisting of three sequence labeling-based models and two span-based models. Experimental results indicate that BL consistently enhances their performance, suggesting that it is possible to construct a new SOTA NER system by incorporating BL into the current SOTA system. Moreover, we find that BL reduces both entity boundary and type prediction errors. In addition, we compare two commonly used labeling tagging methods as well as three types of span semantic representations

GJB2 mutation spectrum in 2063 Chinese patients with nonsyndromic hearing impairment

Background: Mutations in GJB2 are the most common molecular defects responsible for autosomal recessive nonsyndromic hearing impairment (NSHI). The mutation spectra of this gene vary among different ethnic groups. Methods: In order to understand the spectrum and frequency of GJB2 mutations in the Chinese population, the coding region of the GJB2 gene from 2063 unrelated patients with NSHI was PCR amplified and sequenced. Results: A total of 23 pathogenic mutations were identified. Among them, five (p.W3X, c.99delT, c.155_c.158delTCTG, c.512_c.513insAACG, and p.Y152X) are novel. Three hundred and seven patients carry two confirmed pathogenic mutations, including 178 homozygotes and 129 compound heterozygotes. One hundred twenty five patients carry only one mutant allele. Thus, GJB2 mutations account for 17.9% of the mutant alleles in 2063 NSHI patients. Overall, 92.6% (684/739) of the pathogenic mutations are frame-shift truncation or nonsense mutations. The four prevalent mutations; c.235delC, c.299_c.300delAT, c.176_c.191del16, and c.35delG, account for 88.0% of all mutantalleles identified. The frequency of GJB2 mutations (alleles) varies from 4% to 30.4% among different regions of China. It also varies among different sub-ethnic groups. Conclusion: In some regions of China, testing of the three most common mutations can identify at least one GJB2 mutant allele in all patients. In other regions such as Tibet, the three most common mutations account for only 16% the GJB2 mutant alleles. Thus, in this region, sequencing of GJB2 would be recommended. In addition, the etiology of more than 80% of the mutant alleles for NSHI in China remains to be identified. Analysis of other NSHI related genes will be necessary.Version of Recor

What are the important factors influencing the physical activity level of junior high school students: a cross-sectional survey

BackgroundEngaging in regular physical activity has been consistently linked to improved physical health and academic performance. Despite its known benefits, there is a concerning trend of decreased physical activity among children globally. The study primarily aims to investigate the level of physical activity among junior high school students in Taiyuan and analyse the main affecting factors from a socio-ecological perspective.MethodsA cross-sectional study was conducted, involving 650 junior high school students from 7 schools in Taiyuan, and 648 valid questionnaires were ultimately collected. The data on students’ physical activity levels were collected through the Children’s Leisure Activities Study Survey Questionnaire, and the data on factors affecting students’ physical activity were collected through the Student Perceived Factors Affecting Physical Activity Questionnaire.ResultsIn this study, students from the 7th, 8th, and 9th grades participated in physical activities, averaging 214.500 min per week in moderate-intensity and 25.000 min in high-intensity activities, cumulatively averaging 280.000 min weekly. Notably, a significant disparity (p = 0.012) was observed in the combined duration of moderate and high-intensity activities, with male students engaging more time compared to their female counterparts (307.500 vs. 255.000 min). This variance extended across different grades, particularly evident in 8th graders who recorded the highest weekly high-intensity activity duration (31.000 min) and overall physical activity time (320.500 min), surpassing the 7th graders(p = 0.007 for high-intensity activities). Furthermore, an exploratory factor analysis of a 32-item questionnaire, designed to identify determinants of physical activity, revealed six principal components. These components were found to positively correlate with both moderate and high-intensity physical activities.ConclusionResults emphasize that educational institutions and community programs should collaborate to offer engaging weekend physical activity programs. Schools should develop and implement tailored physical education curricula addressing gender and grade differences. Furthermore, schools and local governments should invest in high-quality sports equipment and facilities