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Although paternal age has been linked to certain psychiatric disorders, the nature of any causal relationship remains elusive. Here, we aimed to comprehensively assess the magnitude of a wide range of offsprings psychiatric risk conferred by paternal age, leveraging a pedigree inferred from covered-insurance relationship (accuracy >98%) in Taiwans single-payer compulsory insurance program. We also examined whether there is an independent role of paternal age and explored the potential effect of parental age difference. A total cohort of 7,264,788 individuals born between 1980 and 2018 were included; 5,572,232 with sibling(s) were selected for sibling-comparison analyses and 1,368,942 and 1,044,420 children with information of paternal-grandparents and maternal-grandparents, respectively, were selected for multi-generation analyses. Using inpatient/outpatient claims data (1997-2018), we identified schizophrenia, autism, bipolar disorder (BPD), attention deficit-hyperactivity disorder (ADHD), major depressive disorder (MDD), eating disorder (ED), substance use disorder (SUD), mental retardation (MR), tic disorder, obsessive-compulsive disorder (OCD), anxiety, and somatoform disorder. We identified suicides using death certificates. Logistic regression analysis was used to estimate the paternal/maternal/grand-paternal age association with psychiatric risk in the offspring. The total cohort and sibling-comparison cohort resulted in similar estimates. Paternal age had a U-shaped relationship with offsprings MDD, ED, SUD, and anxiety. A very young maternal age (<20 years) was associated with markedly higher risk in offsprings SUD, MR, and suicide. Older paternal age (>25 years) was linearly associated with offsprings schizophrenia, autism, BPD, ADHD, MDD, ED, SUD, MR, OCD, anxiety, and suicide. Older grand-paternal age was linearly associated with offsprings schizophrenia, autism, ADHD, and MR. Dissimilar parental age was positively associated with offsprings ADH

Readability of Chinese texts considered in this paper is a multiclass classification problem with 12 grade classes corresponding to 6 grades in primary schools, 3 grades in middle schools, and 3 grades in high schools. A special property of this problem is the strong ambiguity in determining the grades. To overcome the difficulty, a measurement of readability assessment methods used empirically in practice is adjacent accuracy in addition to exact accuracy. In this paper we give mathematical definitions of these concepts in a learning theory framework and compare these two quantities in terms of the ambiguity level of texts. A deep learning algorithm is proposed for readability of Chinese texts, based on convolutional neural networks and a pre-trained BERT model for vector representations of Chinese characters. The proposed CNN model can extract sentence and text features by convolutions of sentence representations with filters and is efficient for readability assessment, which is demonstrated with some numerical experiments. © 2022. Mathematical Foundations of Computing. All Rights Reserved.

The use of wireless sensor networks for indoor localization application has emerged as a significant area of interest over the last decade, primarily motivated by its low cost and convenient deployment. The weighted centroid localization algorithm is a suitable positioning technique in a wireless sensor network due to its easy implementation. However, the performance of this method is easily affected by outliers and interference in the measurement of radio signal strength. In order to overcome this limitation, a more robust ARMA filter using generalized t-distribution noise model based on influence function approach is proposed. A hardware prototype was implemented to demonstrate that the ARMA filter could improve system performance, especially when dealing with the case of measurement outliers.

This paper investigates the clustering of Chinese electronics manufacturers, both large and small, alongside established foreign producers in Qingdao, North China. It examines how the supplier-buyer linkages between foreign invested enterprises (FIEs) and local firms has driven the development of a successful electronic industry cluster around development zones established by the local authority, underpinning the success of the large domestic firms both at home and abroad. We also analyze how FIE’s collaboration with local firms foster local economic development in Qingdao. By integrating the theoretical perspectives of hub-and-spoke model from economic geography and flagship-and-five partner’s model in the science of strategic management, we develop our own theoretical lens to investigate the inter-firm network between the FIEs and local firms. The secret of the success of the Chinese electronic producers lies in their ability to drive, rather than being subordinate to, this network.

This paper presents a case study of a discrete event simulation model of an Accident and Emergency Unit in a hospital in the UK. The objective of the study is to create a simulation study of the A&E Unit, to evaluate alternative scenarios and hence reducing patient waiting time. The case study uses a novel approach to predict the arrival time of patients and hence results in a more realistic platform on which to base the subsequent scenario analysis. The scenario analysis illustrates that significant reductions in the waiting time of patients can be obtained by relatively minor changes in operations.