Maize kernel weight responses to sowing dateassociated variation in weather conditions

Variation in weather conditions during grain filling has substantial effects on maize kernel weight(KW). The objective of this work was to characterize variation in KW with sowing date-associated weather conditions and examine the relationship between KW, grain filling parameters, and weather factors. Maize was sown on eight sowing dates(SD) at 15–20-day intervals from mid-March to mid-July during 2012 and 2013 in the North China Plain. With sowing date delay, KW increased initially and later declined, and the greatest KW was obtained at SD6 in both years. The increased KW at SD6 was attributed mainly to kernel growth rate(Gmean), and effective grain-filling period(P). Variations in temperature and radiation were the primary factors that influenced KW and grain-filling parameters. When the effective cumulative temperature(AT) and radiation(Ra)during grain filling were 950 °C and 1005.4 MJ m-2, respectively, P and KW were greatest. High temperatures(daily maximum temperature [Tmax] > 30.2 °C) during grain filling under early sowing conditions, or low temperatures(daily minimum temperature [Tmin] < 20.7 °C) under late sowing conditions combined with high diurnal temperature range(Tmax-min> 7.1 °C) decreased kernel growth rate and ultimately final KW. When sowing was performed from May 25 through June 27, higher KW and yield of maize were obtained. We conclude that variations in environmental conditions(temperature and radiation) during grain filling markedly affect growth rate and duration of grain filling and eventually affect kernel weight and yield of maize.

Innovation of the double-maize cropping system based on cultivar growing degree days for adapting to changing weather conditions in the North China Plain

Double-maize cropping system is an effective option for coping with climate change in the North China Plain. However, the effects of changes in climate on the growth and yield of maize in the two seasons are poorly understood. Forty-six cultivars of maize with different requirements for growing degree days (GDD), categorized as high (H), medium (M) or low (L), and three cultivar combinations for two seasons as LH (using JD27 and DMY1 from category L in the first season;and YD629 and XD22 from category H in the second season), MM (using JX1 and LC3 from category M in the first season;and ZD958 and JX1 from category M in the second season) and HL (using CD30 and QY9 from category H in the first season;and XK10 and DMY3 from category L in the second season) were tested to examine the eco-physiological determinants of maize yield from 2015 to 2017. The correlations between the combinations of cultivars and grain yield were examined. The combination LH produced the highest annual grain yield and total biomass, regardless of the year. It was followed, in decreasing order, by MM and HL. Higher grain yield and biomass in LH were mainly due to the greater grain yield and biomass in the second season, which were influenced mainly by the lengths of the pre- and post-silking periods and the rate of plant growth (PGR). Temperature was the primary factor that influenced dry matter accumulation. In the first season, low temperatures during pre-silking decreased both the duration and PGR in LH, whereas high temperatures during post-silking decreased the PGR in MM and HL, resulting in no significant differences in biomass being observed among the three combinations. In the second season, high temperatures decreased both the PGR and pre- and post-silking duration in MM and HL, and consequently, the biomass of those two combinations were lower than that in LH. Moreover, because of lower GDD and radiation in the first season and higher grain yield in the second season, production efficiency of temperature and radiation (Ra) was the highest in LH. More importantly, differences in temperature and radiation in the two seasons significantly affected the rate and duration of growth in maize, and thereby affecting both dry matter and grain yield. Our study indicated that the combination of LH is the best for optimizing the double-maize system under changing climatic conditions in the North China Plain.

The relationship between ischemic stroke and weather conditions in Duzce, Turkey

BACKGROUND: Weather conditions are thought to increase the risk of stroke occurrence. But their mechanism has not yet been clarified. We investigated possible relationships between ischemic stroke and weather conditions including atmospheric pressure, temperature, relative humidity, and wind speed.METHODS: One hundred and twenty-eight patients with ischemic stroke who had been admitted to our hospital between January 1 and December 31, 2010 were enrolled in this study. We investigated the relationship between daily cases and weather conditions the same day or 1, 2, and 3 days before stroke.RESULTS: A negative correlation was found between maximum wind speed and daily cases 3 days before stroke. As the relationship between daily cases and changes of weather conditions in consecutive days was evaluated, a negative correlation was found between daily cases and change of atmospheric pressure in the last 24 hours.CONCLUSIONS: The maximum wind speed 3 days before stroke and change of atmospheric pressure in the last 24 hours were found to increase the cases of ischemic stroke. We recommend that individuals at risk of ischemic stroke should pay more attention to preventive measures, especially on days with low maximum wind speed, on subsequent 3 days, and on days with low atmospheric pressure in the last 24 hours.

The Effect of Weather Conditions on Pediatric Supracondylar Humerus Fractures

Background: Environmental conditions are effective on childhood supracondylar humerus fractures. In this study, we evaluated the relationship between weather conditions and these fractures. Materials and Methods: We started this study following the approval of a non-interventional research ethics committee. Patients (<16 years) who applied to the hospital between January 2013 and July 2018 with supracondylar humerus fractures were included in the study. The hospital’s PROBEL system was used to diagnose (International Classification of the Diseases, ICD, S42.4) radiological images and patient information. Information on the weather conditions in the day and time period, in which supracondylar humerus fracture occurred, was obtained from TR (Turkey) Ministry of Agriculture and Forestry General Directorate of Meteorology. The relationship between supracondylar fracture and daily weather conditions (wind, air temperature, cloudiness, and season) was analyzed. Results: Most of the patients (n = 76, 40.6%, p < 0.05) were fractures occurring under cloudless weather conditions. Fractures were concentrated with a second frequency in cloudy weather over 60%, except for cloudless weather conditions (n = 51, 27.3%, p < 0.05). In the summer months when the air temperature was high, the number of fractures was higher (n = 62, 33.2%, p < 0.05) and less in the winter months (n = 24, 12.8%, p < 0.05). In hot weather conditions where the daily average temperature was above 20°C, supracondylar humerus fractures were observed more (n = 101, 54%) and less than 10°C (n = 43, 23%, p < 0.05). Increased incidence of fractures was observed in the presence of wind (n = 4, 2.1%, p < 0.05). Conclusion: Supracondylar humerus fractures are common in children. Environmental conditions (temperature, wind, and season) can be effective in the occurrence of these fractures.

Analysis of Weather Conditions for Aerial Security and Optimization in Maroua-Salak

In this study, the weather conditions of the accident at Maroua-Salak airport on 02 August 2020 are analyzed. This C130 aircraft accident took place under bad weather conditions. This bad weather is diagnosed using the models as CFS, GFS and GEFS that reanalyze outputs of the results. At the end of this re-analysis, the result obtained shows that the event which took place at the Maroua-Salak airport was predictable. However, the analysis of these results has made it possible to present the rainfall cumul between 05 and 10 mm for this day. In addition, a comparative study of the rainfall cumul obtained by the CFS, GFS and GEFS models and that estimated by the satellites has been carried out. These models can significantly improve the prevention of such disasters in the region.

A Trend towards a Stable Warm and Windless State of the Surface Weather Conditions in Northern and Northeastern China during 1961–2014

This study investigates the trends in the mean state and the day-to-day variability （DDV） of the surface weather conditions over northern and northeastern China （NNEC） during 1961-2014 using CN05.1 observational data. In this study, we show that the surface temperature （wind speed） has increased （decreased） over NNEC and that the DDV of the surface temperatures and wind speeds has decreased, indicating a trend towards a stable warm and windless state of the surface weather conditions over NNEC. This finding implies a trend towards more persistent hot and windless episodes, which threaten human health and aggravate environmental problems. The trends are also examined in reanalysis data. Both the ERA-40 and the NCEP data show an increasing （decreasing） trend in the mean state of the surface temperatures （wind speeds）. However, the reanalysis data show a consistent decreasing trend in the DDV of the surface weather conditions only in the spring. The underlying reason for the decreased DDV of the surface weather conditions is further analyzed, focusing on the spring season. Essentially, the decreased DDV of the surface weather conditions can be attributed to a decrease in synoptic-scale wave activity, which is caused by a decrease in the baroclinic instability. There is a contrasting change in the baroclinic instability over East Asia, showing a decreasing （increasing） trend north （south） of 40°N. This contrasting change in the baroclinic instability is primarily caused by a tropospheric cooling zone over East Asia at approximately 40°N, which influences the meridional temperature gradient over East Asia.

Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

Modeling of the Photovoltaic Module Operating Temperature for Various Weather Conditions in the Tropical Region

The operating temperature is a critical factor affecting the performances of photovoltaic(PV)modules.In this work,relevant models are proposed for the prediction of this operating temperature using data(ambient temperature and solar irradiance)based on real measurements conducted in the tropical region.For each weather condition(categorized according to irradiance and temperature levels),the temperatures of the PV modules obtained using the proposed approach is compared with the corresponding experimentally measured value.The results show that the proposed models have a smaller Root Mean Squared Error than other models developed in the literature for all weather conditions,which confirms the reliability of the proposed framework.

Influences of harvester and weather conditions on field loss and milling quality of rough rice

A better understanding of factors affecting field losses and milling quality during harvest is needed to improve the economic value of rice.The main objective of this study was to evaluate the impact of harvester header,harvester type,and weather conditions on field loss and milling quality of rough rice.Four popular harvesters equipped with different headers:John Deere 9870 with header D60-S,John Deere 9860 with header D972-S,Hardy,and John Deere 9770 with stripper header were evaluated in this study.The header and total field losses of rice were evaluated for each harvester under regular,rainy and windy weather conditions.Tests were conducted in a rice field cultivated with medium grain rice,variety M206,at Grimes,CA.Total rice yield(TRY),head rice yield(HRY)and whiteness index(WI)were examined to evaluate the effect of weather conditions on milling quality.The obtained results revealed that the rice field loss was affected by harvester header and type,and weather conditions.Among the conventional headers,D60-S significantly reduced the rice loss compared to the D972-S and the Hardy.Harvesting with John Deere 9860 resulted in significant header and field loss compared to other tested harvesters.The average loss increased from 236.2 kg/hm^(2)and 445.2 kg/hm^(2)under regular condition to 711.1 kg/hm^(2)and 907.9 kg/hm^(2)under windy condition for John Deere 9870 and John Deere 9860,respectively.The corresponding loss percentages increased from 2.6%and 4.9%to 7.9%and 10.1%.HRY was significantly affected by weather condition at harvest time.However,the weather condition had no significant effect on TRY and WI.To minimize the rice field loss and obtain good milling quality,commercial rice harvesting during regular weather conditions is important.A proper combination of harvester type and header could be vital for reducing the rice loss.

Problems and Opportunities for Biometeorological Assessment of Conditions in Cold Season

The article is devoted to a discussion of the possibilities of biometeorological assessment of the severity of weather conditions during the cold season.The relevance of the study is ensured not only by the fact that residents of a number of states,whose total number is more than 27 million people,live in these extreme climatic conditions,but also by the need to improve biometeorological approaches to assessing the impact of these conditions on the body and health of the population.This study examined biometeorological characteristics that illustrate a measure of cold stress.These include the Siple wind-chill index;Bodman winter severity index;Arnoldi weather hardness coefficient;Mountain wind chill index;weather hardness coefficient according to I.M.Osokin.The results of a comparison of winter severity assessments based on the values of the calculated Siple and Bodman indices made it possible to establish that the Bodman index is more acceptable when assessing mildly severe winters.The most adequate for assessing the“severity”of the cold period against the background of a decrease in air temperature and an increase in wind speed is the Siple index.The need to provide the countries of the world with high-quality hydrometeorological and biometeorological forecast information is justified and relevant.In this regard,these studies are very promising.

Feature extraction for classification of different weather conditions

Classification of different weather conditions provides a first step support for outdoor scene modeling,which is a core component in many different applications of outdoor video analysis and computer vision.Features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification.In this paper,features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed.Based on the autocorrelation of each pixel’s intensities over time,two temporal features are used for coarse classification of weather conditions according to their visual effects.On the other hand,features are extracted for fine classification of video clips with rain and snow.The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features,by using C-SVM as the classifier.

A Study on Palaeogeography and Metallogenic Conditions of Early Carboniferous Bauxite Deposits of Old Weathered Crust in Guizhou, Sichuan, Hunan and Hubei Provinces

There are four subtypes. namely, Ia, Ib, Ic and Id of the Early Carboniferous bauxite deposits of the old weathered crust type in Guizhou, Sichuan, Hunan and Hubei provinces. They are all distributed in the Upper Yangtze old land. As a result of the Hunan-Guizhou palaeo-faulting, the crust on the northern side of the fault was uplifted and became land, thus suffering denudation. The bauxitic substances left in the weathered crust evolved and were reworked into bauxite. On the other hand, the crust on the southern side of the fault sank and remained to be part of an ocean. The bauxite deposits of Ia and Ib subtypes were formed by in-situ enrichment of residual bauxitic substances in the weathered crust of the old land, but bauxite deposits of Ic and Id subtypes resulted from transportation, accumulation and sedimentation of allothogenous bauxitic substances on the weathered crust of the old land. The processes of transportation may be distinguished as dry transportation and wet transportation, both of which proceeded under atmospheric conditions.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

A PRELIMINARY STUDY ON THE VARIATION FEATURES OF GRAIN YIELD OF CHINA IN RECENT 40 YEARS

Based on grain yield data of China, the vanation features of grain yield in China in recent 40 years and their relationships with the associated factors were analyzed. Results show that the total grain yield increased gradually, and its interannual variation is influenced principally by unit area crop yield (about 70 percent in average). The influences of agricultural technology, social factors and weather conditions on thet area crop yield can be separated because of their unique variation trends and time scales. The influence of agricultural technology is in a smoothly and gradually incremental trend, and the influence of the social factors is oscillated with three waves in recent 40 years, and the influence of weather conditions is fiuctuated sharply from year to year. Their mean effects on the inter-annual variation of unit area crop yield are about 35 to 40 percent, 10 to 15 percent and 50 percent respectively. In the view point of predictions, the effects of weather conditions are much more important.

Correlation of icing events with meteorological variables,and two statistical models to discriminate freezing days

In this paper, the characteristics of meteorological variables are statistically correlated with icing events （i.e., glaze and rime） in China, using daily observations of air temperature, relative humidity, wind speed, and weather phenomena from 700 stations in China from 1954 to 2008. The weather conditions most favorable for icing events are investigated and two statistical models are developed to discriminate potential freezing days. Low air temperature, high relative humidity, and low wind speed are shown to be important conditions for occurrence of icing events; also, the favorable daily mean air temperature is shown to have a decreasing trend from north to south in China, while the favorable relative humidity and wind speed varies little across the country. The statistical model developed with the daily mean temperature combined with precipitation, fog, and mist weather phenomena proved to be well able to determine the possible occurrence of freezing days. The accuracy of model outputs is well above 60% for northwestem Yun- nan, Guizhou, northern Guangxi, southern Hunan, and southern Jiangxi, among other regions where icing events are more fre- quent, and the average false alarms are few. Using observations or forecast products of conventional meteorological variables, this model has high performance and is practical and applicable for early warning and monitoring of icing events.

Coping with drought?Effects of extended drought conditions on soil invertebrate prey and diet selection by a fossorial amphisbaenian reptile

Arid climates are characterized by a summer drought period to which animals seem adapted.However,in some years,the drought can extend for unusually longer periods.Examining the effects of these current extreme weather events on biodiversity can help to understand the effects of climate change,as models predict an increase in drought severity.Here,we examined the effects of“unusual”extended drought on soil invertebrate prey availability and on diet composition(based on fecal contents)and diet selection of a fossorial amphisbaenian,the checkerboard worm lizard Trogonophis wiegmanni.Weather data show interannual variations in summer drought duration.The abundance and diversity of soil invertebrates in spring were high,and similar to those found in a“normal”early autumn,after some rain had ended with the summer drought.In contrast,in years with“unusual”extended drought,abundance,and diversity of soil invertebrates in early autumn were very low.Also,there were seasonal changes in amphisbaenians’diet;in autumn with drought,prey diversity,and niche breadth decreased with respect to spring and autumns after some rain had fallen.Amphisbaenians did not eat prey at random in any season,but made some changes in prey selection that may result from drought-related restrictions in prey availability.Finally,in spite that amphisbaenians showed some feeding flexibility,their body condition was lower in autumn than in spring,and much lower in autumn with drought.If extended drought became the norm in the future,amphisbaenians might suffer important negative effects for their health state.

Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions

With the growing rate of automated vehicles(AVs)at the lower level of automation,the experimental tests are also in progress with vehicles at higher levels.In the absence of extended digital infrastructures and deployment of level 5 full automated vehicles,the physical infrastructure is required to maintain a fundamental role to enable their introduction in public roads.This paper focuses on lane support system(LSS)whose operational design domain(ODD)is strongly connected to the road characteristics and conditions.An experimental test was carried out with a state of the art,and LSS and advanced technologies were used for road monitoring on different roads under various environmental conditions including dry,wet pavements and rain.We applied the generalized estimation equation for logistic regression to account within-cluster homogeneity which is induced by repeated measures on the same road sections.Statistical models allow the identification of variables that are significant for the LSS fault probability among various effects of road features including marking,pavement distress,weather conditions,horizontal curvature,and cross section.Results pointed out the relevance of the wet retro-reflection of marking(RLw)and the horizontal curvature in the definition of ODD for LSS.Threshold values have been proposed for the tested LSS.Wet pavement doesn’t affect the LSS performance when compared to the dry condition.Rain was shown to be critical even with very good road characteristics.

Weather-based interruption prediction in the smart grid utilizing chronological data

This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region.Based on common weather conditions,a theoretical model can predict interruptions and risk assessment with immediate weather conditions.Using daily and hourly weather data,the created models will predict the number of daily or by-shift interruptions.The weather and environmental conditions to be addressed will include rain,wind,temperature,lightning density,humidity,barometric pressure,snow and ice.Models will be developed to allow broad applications.Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software,and the results will be used to refine the models.Models developed in this study will be used to predict power interruptions in areas that can be readily monitored,thus validating the models.The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level.Reliability is major concern for every utility.Prediction and timely action to minimize the outage duration improves reliability.Use of this predictor model with existing smart grid self-healing technology is proposed.

Velocity Calculation by Automatic Camera Calibration Based on Homogenous Fog Weather Condition

A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Three major steps are included in our algorithm. Firstly, current video frame is recognized to discriminate current weather condition based on area search method （ASM）. If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function （ESF）. Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. Finally, scene transmission image is got by dark channel prior theory, camera s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Vehicles pixel coordinates are transformed to camera coordinates. Distance between vehicles and the camera will be calculated, and then average velocity for each vehicle is got. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithms verifies the effectiveness of our algorithm.

"Weather" transit is reliable? Using AVL data to explore tram performance in Melbourne,Australia

This paper uses automatic vehicle location （AVL） records to investigate the effect of weather conditions on the travel time reliability of on-road rail transit, through a case study of the Melbourne streetcar （tram） network. The datasets available were an extensive historica; AVL dataset as well as weather observations. The sample size used in the analysis included all trips made over a period of five years （2006-2010 inclusive）, during the morning peak （7 am-9 am） for fifteen randomly selected radial tram routes, all traveling to the Melbourne CBD create a linear model Ordinary least square （OLS） regression analysis was conducted to with tram travel time being the dependent variable. An alternative formulation of the model is also compared. Travel time was regressed on various weather effects including precipitation, air temperature, sea level pressure and wind speed; as well as indicator variables for weekends, public holidays and route numbers to investigate a correlation between weather condition and the on-time performance of the trams. The results indicate that only precipitation and air temperature are significant in their effect on tram travel time. The model demonstrates that on average, an additional millimeter of precipitation during the peak period adversely affects the average travel time during that period by approximately 8 s, that is, rainfall tends to increase the travel time. The effect of air temperature is less intuitive, with the model indicating that trams adhere more closely to schedule when the temperature is different in absolute terms to the mean operating conditions （taken as 15 ℃）.