The effects of weather on runway use by rodents

1987 ◽  
Vol 65 (8) ◽  
pp. 2035-2038 ◽  
Author(s):  
Remi Gauthier ◽  
J. Roger Bider
Keyword(s):  
Deer Mouse ◽  
Principal Component ◽  
Weather Conditions ◽  
Microtus Pennsylvanicus ◽  
Meadow Vole ◽  
Weather Data ◽  
Weather Factors ◽  
Activity Data ◽  
Napaeozapus Insignis ◽  
Animal Activity

The effects of weather on runway use by four species of rodents were investigated using animal activity data obtained from a sand track. Species studied were meadow vole (Microtus pennsylvanicus), deer mouse (Peromyscus maniculatus), meadow jumping mouse (Zapus hudsonius), and woodland jumping mouse (Napaeozapus insignis). Runway use was quantified using the quotient of the activity inside to the activity outside the runway. The weather data were analyzed using a principal component analysis. The degree of concordance between the weather factors and runway use index was assessed by canonical correlation. Results showed that runway use decreases during warm, rainy, moonless nights. During these weather conditions, a predator's ability to find small rodents may be impaired, and the rodents could explore outside their runways to find new food patches.

Weather factors affecting the development of maize from sowing to flowering

1972 ◽  
Vol 78 (2) ◽  
pp. 325-331 ◽  
Author(s):  
M. N. Hough
Keyword(s):  
Soil Moisture ◽  
Correlation Analysis ◽  
Linear Correlation ◽  
Weather Conditions ◽  
Weather Data ◽  
Weather Factors ◽  
Mean Values ◽  
Rate Of Development ◽  
Linear Correlation Analysis ◽  
The Mean

SUMMARYThe phenological development from sowing to flowering of the eaxly maize hybrid INRA 200 is related to the weather conditions. Plot trial data from Wytham, near Oxford, England, and weather information from that and nearby sites formed the basic data.The mean rate of development per day from sowing to emergence is related by linear correlation analysis to the mean values of soil temperature at 5 cm depth and soil moisture deficit. A range of temperature thresholds for emergence development exist, which depend upon the soil moisture, and which differ from the true physiological threshold.Between omergence and flowering the mean rate of development per day is related by linear correlation analysis to mean air temperature, solar radiation and potential transpiration estimated from weather data. All correlations are significant, but the parameters which combine radiation and temperature are statistically better.

scholarly journals An Efficient Intelligent Power Detection Method for Photovoltaic System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Detection Method ◽  
Peak Load ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Weather Factors ◽  
Pv System ◽  
Intelligent Technique ◽  
Highly Nonlinear ◽  
Validation Set

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

scholarly journals Establish high-resolution hourly weather data for simulating building energy consumption in different regions

2019 ◽  
Vol 111 ◽  
pp. 04032
Author(s):  
Feng-Yi Lin ◽  
Ruey-Lung Hwang ◽  
Tzu-Ping Lin
Keyword(s):  
Energy Consumption ◽  
Urban Areas ◽  
Reference Value ◽  
Weather Conditions ◽  
Buffer Zone ◽  
Building Energy ◽  
Weather Data ◽  
Weather Factors ◽  
Set Up ◽  
The City

Due to the various local weather conditions in different regions of the city, the demand for air conditioning (AC) of housing is different, too. It happened occasionally to underestimate the energy consumption of AC in urban areas, because of using suburban/rural weather station data for building energy simulation. This study set up 34 automatic weather stations in the urban area of Tainan City, Taiwan for a year-round collection of local temperature and relative humidity data. Those weather measurement, the GIS information of a buffer zone and multiple regression analysis were used to establish the relationship between the weather factors, needed for the morphing approach, and the parameters of landscape use and cover. The buffer zone is an area of 1000×1000 m2 around the measured point, and is divided to two layers with upwind and downwind parts. Local hourly weather-year files for a whole of the city with a resolution of 200×200 m2 were generated by the morphing approach. With the different local hourly weather-year files, the AC-required hours and energy consumption from May to October for a typical residential with hybrid ventilation mode was obtained by using the EnergyPlus. And the cumulative UHI of each grid between May and October is calculated by taking the average of the five lowest temperatures as the reference value. The result shows that the number of AC hours of residential will increase by 10%, and the energy consumption increase from 1000 kWh to 2500 kWh, when long-term UHI intensity increases from 2000 °C-hour to 9000 °C-hour.

scholarly journals P134 Under the weather: The Meteorological correlation in Orthopaedic Trauma and within Hertfordshire

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
M Vella-Baldacchino ◽  
J Hanrahan ◽  
S Islam ◽  
R Sofat ◽  
Martinique Vella-Baldacchino
Keyword(s):  
Weather Conditions ◽  
Local Area ◽  
Meteorological Factor ◽  
Weather Data ◽  
Neck Of Femur ◽  
Activity Data ◽  
Femur Fractures ◽  
Trauma Service ◽  
Ankle Trauma ◽  
Neck Of Femur Fracture

Abstract Background The paper aims to understand the effect of meteorological factors on the number of referrals and volume of trauma operating cases within our local area. Method Trauma data was analysed in our database: (eTrauma), a digital clinical platform that co-ordinates all admissions and: trauma theatre activity. Data consisted of number of referrals per day, patient: age, mechanism of injury and type of orthopaedic injury. Weather data was: gathered from a local weather station which: records daily weather observations. Results 1160 consultations wereanalysed, 779 required an operative intervention. Neck of femur fractures: and ankle trauma were the two most common cause of trauma, accounting for 27% and 15% respectively. Neck of femur fracture pathology were not significantly correlated with any meteorological factor studied. On the contrary, ankle trauma were the only injuries significantly correlating with temperature (p < 0.03) and due point (p < 0.04). Conclusion Weather has no effect on neck of femur fractures, the most common trauma pathology treated in our department. In all seasons allocated specific trauma lists for the latter should be arranged irrelevant of the weather conditions. We identified the days receiving highest referral rate, using this data to shape the future on call trauma service.

scholarly journals Risk Prediction for Winter Road Accidents on Expressways

2021 ◽  
Vol 11 (20) ◽  
pp. 9534
Author(s):  
Daeseong Kim ◽  
Sangyun Jung ◽  
Sanghoo Yoon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Winter Season ◽  
Weather Conditions ◽  
Road Accident ◽  
Weather Data ◽  
Road Accidents ◽  
Weather Factors ◽  
The Road ◽  
Road Geometry

Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.

scholarly journals Influence of Weather Conditions and Climate Oscillations on the Pine Looper Bupalus piniaria (L.) Outbreaks in the Forest-Steppe of the West Siberian Plain

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Denis A. Demidko ◽  
Svetlana M. Sultson ◽  
Pavel V. Mikhaylov ◽  
Sergey V. Verkhovets
Keyword(s):  
Logistic Regression ◽  
Large Scale ◽  
Weather Conditions ◽  
Weather Data ◽  
Forest Steppe ◽  
West Siberian Plain ◽  
Weather Factors ◽  
The West ◽  
Climate Oscillations ◽  
Atmospheric Processes

The pine looper Bupalus piniaria (L.) is one of the most common pests feeding on the Scots pine Pinus sylvestris L. Pine looper outbreaks show a feature of periodicity and have significant ecological and economic impacts. Climate and weather factors play an important role in pine looper outbreak occurrence. We tried to determine what weather conditions precede B. piniaria outbreaks in the southeast of the West Siberian Plain and what climate oscillations cause them. Due to the insufficient duration and incompleteness of documented observations on outbreaks, we used the history of pine looper outbreaks reconstructed using dendrochronological data. Using logistic regression, we found that the factor influencing an outbreak the most is the weather four years before it. A combination of warm spring, dry summer, and cool autumn triggers population growth. Summer weather two years before an outbreak is also critical: humidity higher than the average annual value in summer is favorable for the pine looper. The logistic regression model predicted six out of seven outbreaks that occurred during the period for which weather data are available. We discovered a link between outbreaks and climatic oscillations (mainly for the North Atlantic oscillation, Pacific/North America index, East Atlantic/Western Russia, West Pacific, and Scandinavian patterns). However, outbreak predictions based on the teleconnection patterns turned out to be unreliable. We believe that the complexity of the interaction between large-scale atmospheric processes makes the direct influence of individual oscillations on weather conditions relatively small. Furthermore, climate changes in recent decades modulated atmospheric processes changing the pattern predicting pine looper outbreaks: Autumn became warmer four years before an outbreak, and summer two years before became drier.

Evaluation of Chemotherapeutic Activity of the Selected Bases’ Analogues of Nucleic Acids Supported by ab initio Various Quantum Chemical Calculations

2020 ◽  
Vol 16 (2) ◽  
pp. 93-103 ◽  
Author(s):  
Piotr Kawczak ◽  
Leszek Bober ◽  
Tomasz Bączek
Keyword(s):  
Biological Activity ◽  
Nucleic Acids ◽  
Ab Initio ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Structural Parameters ◽  
Principal Component ◽  
Polarizable Continuum Model ◽  
Activity Data ◽  
Qsar Models

Background: Pharmacological and physicochemical classification of bases’ selected analogues of nucleic acids is proposed in the study. Objective: Structural parameters received by the PCM (Polarizable Continuum Model) with several types of calculation methods for the structures in vacuo and in the aquatic environment together with the huge set of extra molecular descriptors obtained by the professional software and literature values of biological activity were used to search the relationships. Methods: Principal Component Analysis (PCA) together with Factor Analysis (FA) and Multiple Linear Regressions (MLR) as the types of the chemometric approach based on semi-empirical ab initio molecular modeling studies were performed. Results: The equations with statistically significant descriptors were proposed to demonstrate both the common and differentiating characteristics of the bases' analogues of nucleic acids based on the quantum chemical calculations and biological activity data. Conclusion: The obtained QSAR models can be used for predicting and explaining the activity of studied molecules.

Exploring Weather Data to Predict Activity Attendance in Event-based Social Network

2021 ◽  
Vol 15 (2) ◽  
pp. 1-25
Author(s):  
Jifeng Zhang ◽  
Wenjun Jiang ◽  
Jinrui Zhang ◽  
Jie Wu ◽  
Guojun Wang
Keyword(s):  
Real Life ◽  
Prediction Performance ◽  
Weather Data ◽  
Gradient Boosting ◽  
User Interest ◽  
Weather Factors ◽  
Event Duration ◽  
Outdoor Activities ◽  
Weather Factor ◽  
Event Based

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

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