scholarly journals Terminal Airspace Capacity Evaluation Model under Weather Condition from Perspective of a Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Liu
Keyword(s):  
Network Flow ◽  
Topological Structure ◽  
Evaluation Model ◽  
Weather Condition ◽  
Weather Conditions ◽  
Severe Weather ◽  
Safe Operation ◽  
Weather Factor ◽  
Route Network ◽  
Capacity Evaluation

A route network lays in the terminal airspace. The route network can be divided into multiple subnetworks according to sectors. When severe weather conditions occur, a controller takes measures to obtain safe operation of flights, such as navigation guidance or changing the availability of routes. In such circumstances, the route structure of a subnetwork is changed, and the controller’s attention paid to each route is also changed as well as the unit workload on it. As the subnetwork is handled by one controller, capacities of routes in it are associated. We find the way to determine the “related capacity” of a route in the conditions that whether topological structure of the terminal route network is changed or not. The capacity of the terminal route network calculated by network flow theory represents the capacity of terminal airspace. According to the analysis results, the weather factor reduces capacity of terminal airspace directly by reducing the capacities of routes blocked. Indirectly, it diverts controller’s attention to change capacities of other routes in the subnetwork.

Building Effective Organizations and Measures to Cope With Severe Weather in Qinshan III NPP

2010 ◽  
Author(s):  
Jun Zhou
Keyword(s):  
Management System ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Weather Conditions ◽  
Severe Weather ◽  
Effective Management ◽  
Safe Operation ◽  
Standard Response

Severe weather such as typhoon has long been a great challenge threats the safe operation of nuclear power plants. To cope with typhoon, Qinshan III NPP has developed an effective management system, including building powerful organizations, creating standard response procedures and consumable storage, which proven to be effective to ensure the safe operation of Qinshan III plant under severe weather conditions.

scholarly journals ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jingming Xia ◽  
Dawei Xuan ◽  
Ling Tan ◽  
Luping Xing
Keyword(s):  
Intelligent Transportation System ◽  
Rapid Development ◽  
Weather Condition ◽  
Network Models ◽  
Weather Conditions ◽  
Severe Weather ◽  
Urban Traffic ◽  
Deep Convolutional Neural Networks ◽  
Traffic Condition ◽  
Softmax Classifier

Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.

scholarly journals Peculiarities of the influence of agroclimatic factors of the humid subtropics of Russia on the yield of Hayward Actinidia deliciosa variety

2020 ◽  
pp. 42-46
Author(s):  
T. D. Besedina ◽  
Ts. V. Tutberidze ◽  
N. S. Kiseleva
Keyword(s):  
Climatic Factors ◽  
Weather Condition ◽  
Pollen Grains ◽  
Weather Conditions ◽  
Soil Condition ◽  
Actinidia Deliciosa ◽  
Potential Productivity ◽  
Weather Factor ◽  
Long Time ◽  
Humid Subtropics

The aim of the research is to reveal the peculiarities of the influence of agro-climatic factors of the humid subtropics of Russia on the yield of the industrial variety Hayward. The causal relationship between the value of variety Hayward yield and weather condition of the subtropics of Russia is revealed by multiple correlation-regression analysis over a 20-year period (2000-2019). Investment embedding in agrofi tocenosis creation of Actinidia allow effi ciently use the variety Hayward rather long time, considering its potential productivity, resistance to diseases and pests, quality of fruit, requirements to soil condition and pay-back period of the establishment of the planting. Long-term observations of productivity and modeling of the interaction of weather factor with yield of Hayward Actinidia deliciosa variety have shown the following: weather conditions of humid subtropics in the flowering phase of the crop are decisive for the year’s harvest size and are critical in their importance, since they refer to independent factors; unfavorable weather conditions in the Russian subtropics are repeated two years in a decade. At the same time, due to late spring frosts (-1,80 and -5 0 С) at the end of March – begin of April, young escapes perish, and against the background of a signifi cant deterioration in the conditions of pollination and fruit setting at temperatures above 30 0 C, abnormal pollen grains appear during flowering and the fertility decreases. At the late variety Hayward in such periods the yield forms 8-10 % from average (112.5 centers per hectare).

scholarly journals An investigation of the effects of environmental and ecologic factors on cutaneous leishmaniasis in the old world: a systematic review study

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolfazl Mohammadbeigi ◽  
Salman Khazaei ◽  
Hamidreza Heidari ◽  
Azadeh Asgarian ◽  
Shahram Arsangjang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Environmental Factors ◽  
Land Cover ◽  
Cutaneous Leishmaniasis ◽  
Weather Condition ◽  
Weather Conditions ◽  
Eligibility Criteria ◽  
Screening Process ◽  
Old World ◽  
Review Study

AbstractObjectivesLeishmaniasis is a neglected and widespread parasitic disease that can lead to serious health problems. The current review study aimed to synthesize the relationship between ecologic and environmental factors (e.g., weather conditions, climatology, temperature and topology) and the incidence of cutaneous leishmaniasis (CL) in the Old World.ContentA systematic review was conducted based on English, and Persian articles published from 2015 to 2020 in PubMed/Medline, Science Direct, Web of Science and Google Scholar. Keywords used to search articles were leishmaniasis, environmental factors, weather condition, soil, temperature, land cover, ecologic* and topogr*. All articles were selected and assessed for eligibility according to the titles or abstracts. The quality screening process of articles was carried out by two independent authors. The selected articles were checked according to the inclusion and exclusion criteria.Summary and outlookA total of 827 relevant records in 2015–2020 were searched and after evaluating the articles, 23 articles met the eligibility criteria; finally, 14 full-text articles were included in the systematic review. Two different categories of ecologic/environmental factors (weather conditions, temperature, rainfall/precipitation and humidity) and land characteristics (land cover, slope, elevation and altitude, earthquake and cattle sheds) were the most important factors associated with CL incidence.ConclusionsTemperature and rainfall play an important role in the seasonal cycle of CL as many CL cases occurred in arid and semiarid areas in the Old World. Moreover, given the findings of this study regarding the effect of weather conditions on CL, it can be concluded that designing an early warning system is necessary to predict the incidence of CL based on different weather conditions.

scholarly journals It’s the Weather: Quantifying the Impact of Weather on Retail Sales

2021 ◽  
Author(s):  
Natalie Rose ◽  
Les Dolega
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Influential Factor ◽  
Retail Sector ◽  
Retail Sales ◽  
Weather Impact ◽  
High Street ◽  
Sector Decision ◽  
The Impact ◽  
The Relationship

AbstractThe weather is considered as an influential factor on consumer purchasing behaviours and plays a significant role in many aspects of retail sector decision making. As a result, better understanding of the magnitude and nature of the influence of variable UK weather conditions can be beneficial to many retailers and other stakeholders. This study addresses the dearth of research in this area by quantifying the relationship between different weather conditions and trading outcomes. By employing comprehensive daily sales data for a major high street retailer with over 2000 stores across England and adopting a random forest methodology, the study quantifies the influence of various weather conditions on daily retail sales. Results indicate that weather impact is greatest in the summer and spring months and that wind is consistently found to be the most influential weather condition. The top five most weather-dependent categories cover a range of different product types, with health foods emerging as the most susceptible to the weather. Also, sales from out-of-town stores show a far more complex relationship with the weather than those from traditional high street stores with the regions London and the South East experiencing the greatest levels of influence. Various implications of these findings for retail stakeholders are discussed and the scope for further research outlined.

scholarly journals Integrated Evaluations of Resource and Environment Carrying Capacity of the Huaihe River Ecological and Economic Belt in China

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1168
Author(s):  
Wei-Ling Hsu ◽  
Xijuan Shen ◽  
Haiying Xu ◽  
Chunmei Zhang ◽  
Hsin-Lung Liu ◽  
...  
Keyword(s):  
Carrying Capacity ◽  
Evaluation System ◽  
Evaluation Model ◽  
Territorial Development ◽  
Huaihe River ◽  
Space Planning ◽  
Capacity Evaluation ◽  
The Huaihe River ◽  
Index Level ◽  
Resource And Environment

The evaluations of resource and environment carrying capacity and territorial development suitability, also referred to as “double evaluations”, have been taken by China as an important direction in territorial space planning. Based on the evaluation of resource and environment carrying capacity, the double evaluations can contribute to protecting ecological safety and territorial safety and promoting regional sustainable development. The focus of this study was to integratedly evaluate the resource and environment carrying capacity of the Huaihe River Ecological and Economic Belt. First, the overall weights of the factors at the dimension level and the index level in the established integration evaluation system were calculated with the fuzzy analytical hierarchy process (FAHP) method; and then, using the linear weighted function, the overall resource and environment carrying capacities of 25 cities in the belt were calculated. On that basis, the resource and environment carrying capacity evaluation model was established. Through model analysis, this study comprehensively investigated the resource and environment carrying capacity of the Huaihe River Eco-economic Belt and provided a foundation for the future territorial space planning and layout of the Huaihe River Eco-economic Belt.

scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

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