Generalized Estimating Equations for Hearing Loss Data with Specified Correlation Structures

Due to the nature of pure-tone audiometry test, hearing loss data often has a complicated correlation structure. Generalized estimating equation (GEE) is commonly used to investigate the association between exposures and hearing loss, because it is robust to misspecification of the correlation matrix. However, this robustness typically entails a moderate loss of estimation efficiency in finite samples. This paper proposes to model the correlation coefficients and use second-order generalized estimating equations to estimate the correlation parameters. In simulation studies, we assessed the finite sample performance of our proposed method and compared it with other methods, such as GEE with independent, exchangeable and unstructured correlation structures. Our method achieves an efficiency gain which is larger for the coefficients of the covariates corresponding to the within-cluster variation (e.g., ear-level covariates) than the coefficients of cluster-level covariates. The efficiency gain is also more pronounced when the within-cluster correlations are moderate to strong, or when comparing to GEE with an unstructured correlation structure. As a real-world example, we applied the proposed method to data from the Audiology Assessment Arm of the Conservation of Hearing Study, and studied the association between a dietary adherence score and hearing loss.Comment: 14 pages, 5 tables, 4 supplementary tables; submitted to Biometrical Journa

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method

Serum Oxytocin Levels and an Oxytocin Receptor Gene Polymorphism (rs2254298) Indicate Social Deficits in Children and Adolescents with Autism Spectrum Disorders

The neuropeptide oxytocin (OT) and its receptor (OXTR) have been predicted to be involved in the regulation of social functioning in autism spectrum disorders (ASD). Objective of the study was to investigate serum OT levels and the OXTR rs2254298 polymorphism in Chinese Han children and adolescents with ASD as well as to identify their social deficits relevant to the oxytocinergic system. We tested serum OT levels using ELISA in 55 ASD subjects and 110 typically developing (TD) controls as well as genotyped the OXTR rs2254298 polymorphism using PCR-RFLP in 100 ASD subjects and 232 TD controls. Autistic symptoms were assessed by the Autism Behavior Checklist (ABC) and the Childhood Autism Rating Scale (CARS). There were no significant associations between OXTR rs2254298 polymorphism and ASD, serum OT levels and age, as well as serum OT levels and intelligent quotient (IQ) in both ASD and TD groups. However, ASD subjects exhibited elevated serum OT levels compared to TD controls and positive correlations between serum OT levels and adaptation to change score in the CARS and CARS total scores. Moreover, in the ASD group, significant relationships were revealed between the single-nucleotide polymorphism (SNP) rs2254298 and serum OT levels, the category stereotypes and object use in the ABC and the category adaptation to change in the CARS. These findings indicated that individuals with ASD may exhibit a dysregulation in OT on the basis of changes in OXTR gene expression as well as environmentally induced alterations of the oxytocinergic system to determine their social deficits

Bearing remain life prediction based on weighted complex SVM models

Aiming to achieve the bearing remaining life prediction, this research proposed a method based on the weighted complex support vector machine (SVM) model. Firstly, the features are extracted by time domain, time-frequency domain method, so as the extract the original features. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis (PCA) is used to merge the features and reduce the dimension. And the bearing degradation indicator is constructed based on the first principal component, which can indicate the bearing early failure state precisely. Then, based on the life condition indicator, the weighted complex SVM model is used to achieve the bearing remain life prediction, in this model, the particle swarm algorithm (PSO) method is used to select the SVM internal parameters, the phase space reconstruction algorithm is used to determine the structure of the SVM. Cases of actual were analyzed, the results proved the effectiveness of the methodology

KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS) - a groundbreaking facial landmark detection framework that stands out from existing methods. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead. The code is available at https://github.com/zhiqic/KeyPosS.Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the code is at https://github.com/zhiqic/KeyPos