Real-time Accurate Runway Detection based on Airborne Multi-sensors Fusion

Existing methods of runway detection are more focused on image processing for remote sensing images based on computer vision techniques. However, these algorithms are too complicated and time-consuming to meet the demand for real-time airborne application. This paper proposes a novel runway detection method based on airborne multi-sensors data fusion which works in a coarse-to-fine hierarchical architecture. At the coarse layer, a vision projection model from world coordinate system to image coordinate system is built by fusing airborne navigation data and forward-looking sensing images, then a runway region of interest (ROI) is extracted from a whole image by the model. Furthermore, EDLines which is a real-time line segments detector is applied to extract straight line segments from ROI at the fine layer, and fragmented line segments generated by EDLines are linked into two long runway lines. Finally, some unique runway features (e.g. vanishing point and runway direction) are used to recognise airport runway. The proposed method is tested on an image dataset provided by a flight simulation system. The experimental results show that the method has advantages in terms of speed, recognition rate and false alarm rate

High-dimensional Clustering onto Hamiltonian Cycle

Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outliers. This paper proposes a new framework called High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above problems. First, HCHC combines global structure with local structure in one objective function for deep clustering, improving the labels as relative probabilities, to mine the similarities between different clusters while keeping the local structure in each cluster. Then, the anchors of different clusters are sorted on the optimal Hamiltonian cycle generated by the cluster similarities and mapped on the circumference of a circle. Finally, a sample with a higher probability of a cluster will be mapped closer to the corresponding anchor. In this way, our framework allows us to appreciate three aspects visually and simultaneously - clusters (formed by samples with high probabilities), cluster similarities (represented as circular distances), and outliers (recognized as dots far away from all clusters). The experiments illustrate the superiority of HCHC

Serum 25-hydroxyvitamin D3 is associated with advanced glycation end products (AGEs) measured as skin autofluorescence: The Rotterdam Study

Advanced glycation end products (AGEs) accumulate in tissues with aging and may influence age-related diseases. They can be estimated non-invasively by skin autofluorescence (SAF) using the AGE Reader™. Serum 25-hydroxyvitamin D3 (25(OH)D3) may inhibit AGEs accumulation through anti-oxidative and anti-inflammatory properties but evidence in humans is scarce. The objective was to investigate the association between serum 25(OH)D3 and SAF in the population-based cohort study. Serum 25(OH)D3 and other covariates were measured at baseline. SAF was measured on average 11.5 years later. Known risk factors for AGE accumulation such as higher age, BMI, and coffee intake, male sex, smoking, diabetes, and decreased renal function were measured at baseline. Linear regression models were adopted to explore the association between 25(OH)D3 and SAF with adjustment for confounders. Interaction terms were tested to identify effect modification. The study was conducted in the general community. 2746 community-dwelling participants (age ≥ 45 years) from the Rotterdam Study were included. Serum 25(OH)D3 inversely associated with SAF and explained 1.5% of the variance (unstandardized B = − 0.002 (95% CI[− 0.003, − 0.002]), standardized β = − 0.125), independently of known risk factors and medication intake. The association was present in both diabetics (B = − 0.004 (95% CI[− 0.008, − 0.001]), β = − 0.192) and non-diabetics (B = − 0.002 (95% CI[− 0.003, − 0.002]), β = − 0.122), both sexes, both smokers and non-smokers and in each RS subcohort. Serum 25(OH)D3 concentration was significantly and inversely associated with SAF measured prospectively, also after adjustment for known risk factors for high SAF and the number of medication used, but the causal chain is yet to be explored in future studies. Clinical Trial Registry (1) Netherlands National Trial Register: Trial ID: NTR6831 (http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6831). (2) WHO International Clinical Trials Registry Platform: under shared catalogue number NTR6831 (www.who.int/ictrp/network/primary/en/)

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies

Dynamics of multipartite quantum correlations under decoherence

Quantum discord is an optimal resource for the quantification of classical and non-classical correlations as compared to other related measures. Geometric measure of quantum discord is another measure of quantum correlations. Recently, the geometric quantum discord for multipartite states has been introduced by Jianwei Xu [arxiv:quant/ph.1205.0330]. Motivated from the recent study [Ann. Phys. 327 (2012) 851] for the bipartite systems, I have investigated global quantum discord (QD) and geometric quantum discord (GQD) under the influence of external environments for different multipartite states. Werner-GHZ type three-qubit and six-qubit states are considered in inertial and non-inertial settings. The dynamics of QD and GQD is investigated under amplitude damping, phase damping, depolarizing and flipping channels. It is seen that the quantum discord vanishes for p>0.75 in case of three-qubit GHZ states and for p>0.5 for six qubit GHZ states. This implies that multipartite states are more fragile to decoherence for higher values of N. Surprisingly, a rapid sudden death of discord occurs in case of phase flip channel. However, for bit flip channel, no sudden death happens for the six-qubit states. On the other hand, depolarizing channel heavily influences the QD and GQD as compared to the amplitude damping channel. It means that the depolarizing channel has the most destructive influence on the discords for multipartite states. From the perspective of accelerated observers, it is seen that effect of environment on QD and GQD is much stronger than that of the acceleration of non-inertial frames. The degradation of QD and GQD happens due to Unruh effect. Furthermore, QD exhibits more robustness than GQD when the multipartite systems are exposed to environment.Comment: 15 pages, 4 figures, 4 table

Cumulative genetic score of KIAA0319 affects reading ability in Chinese children: moderation by parental education and mediation by rapid automatized naming

Abstract KIAA0319, a well-studied candidate gene, has been shown to be associated with reading ability and developmental dyslexia. In the present study, we investigated whether KIAA0319 affects reading ability by interacting with the parental education level and whether rapid automatized naming (RAN), phonological awareness and morphological awareness mediate the relationship between KIAA0319 and reading ability. A total of 2284 Chinese children from primary school grades 3 and 6 participated in this study. Chinese character reading accuracy and word reading fluency were used as measures of reading abilities. The cumulative genetic risk score (CGS) of 13 SNPs in KIAA0319 was calculated. Results revealed interaction effect between CGS of KIAA0319 and parental education level on reading fluency. The interaction effect suggested that individuals with a low CGS of KIAA0319 were better at reading fluency in a positive environment (higher parental educational level) than individuals with a high CGS. Moreover, the interaction effect coincided with the differential susceptibility model. The results of the multiple mediator model revealed that RAN mediates the impact of the genetic cumulative effect of KIAA0319 on reading abilities. These findings provide evidence that KIAA0319 is a risk vulnerability gene that interacts with environmental factor to impact reading abilities and demonstrate the reliability of RAN as an endophenotype between genes and reading associations

Data of fluorescence, UV–vis absorption and FTIR spectra for the study of interaction between two food colourants and BSA

In this data article, the fluorescence, UV–vis absorption and FTIR spectra data of BSA-AR1/AG50 system were presented, which were used for obtaining the binding characterization (such as binding constant, binding distance, binding site, thermodynamics, and structural stability of protein) between BSA and AR1/AG50. Keywords: Bovine serum albumin, Acid red 1, Acid green 50, Dat