A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

Face Recognition on Partial and Holistic LBP Features

An algorithm for face description and recognition based on multi-resolution with multi-scale local binary pattern （multi-LBP） features is proposed. The facial image pyramid is constructed and each facial image is divided into various regions from which partial and holistic local binary patter （LBP） histograms are extracted. All LBP features of each image are concatenated to a single LBP eigenvector with different resolutions. The dimensionaUty of LBP features is then reduced by a local margin alignment （LMA） algorithm based on manifold, which can preserve the between-class variance. Support vector machine （SVM） is applied to classify facial images. Extensive experiments on ORL and CMU face databases clearly show the superiority of the proposed scheme over some existed algorithms, especially on the robustness of the method against different facial expressions and postures of the subjects.

The New Contribution to the Important Discourse on Human Rights by General Secretary xi Jinping

Under the background of the fact that human rights protection has become an important part of the socialist construction with Chinese characteristics in the new era, and with the international background of the strengthened trend of mainstreaming of human rights, Xi Jinping’s series of speeches and the 19 th CPC national Congress reports comprehensively explained the construction of human rights in China and the development of the international human rights. The important discourse on human rights by General Secretary Xi Jinping is people-centered: people’s yearning for a better life is our goal and reveals the source of human rights. Chinese dream is a dream of the country, of the nation, and of everyone in China. The close integration of individual and collective human rights points out that the state and people are important parts of collective human rights and it is an effective response to the "human rights over sovereignty" of Western countries. That there are not the best human rights, but the better ones; fighting for human rights is not always done, but always doing scientifically reveals the operational form of human rights. The right of survival and development is the primary human right; to attach importance to the right of peace conforms to the reality of our country and it has the support of the vast number of developing countries. Building a community with a shared future for human beings is a new vision for the development of the international human rights. only when the perfection and implementation of Constitution and law are paid attention to, and the democratization and legalization of the international human rights cause are promoted, can the guarantee be provided for the realization of human rights. The important discourse on human rights by General Secretary Xi Jinping is guided by Marxism, carries the communist party member’s original intention of serving people and is deeply rooted in the masses of the people. It inherits the theory of "benevolence" and "harmony" in Chinese culture,stands at the height of history and times, and points out the direction for the all-round development of Chinese people and the overall progress of society, and for the liberation of all mankind. This scientific theory is successfully guiding China’s human rights construction constantly towards new achievements and has had a profound and extensive impact on the international human rights cause.

Increasing power plant efficiency with clustering methods and Variable Importance Index assessment

Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.