Eastern NYS Field Crops Weekly Pest Report: Evaluation 2004

The purpose of the Eastern NYS Field Crops Weekly Pest Report is to provide timely pest information to field crop extension educators. The report was a compilation of pest data collected on a weekly basis by several people across Eastern NYS. The pest report was distributed in a Cornell University Field Crops Staff List Server. Extension Educator could then select the pest information that best fit their current situation and alert field crop producers in a wide array of methods. At the completion of the growing season a survey was sent electronically to the field crop extension educators to measure the impact of the weekly pest report

Human behaviour outdoors and the environmental factors

The study of human behaviour outdoors has been an area of interest examined from different perspectives. Even so, the study of human behaviour in outdoor public spaces still requires further input from the perspective of human factors. This thesis presents a literature review of behaviour in public spaces where the author evaluated the attendance to public squares, the activities performed by users, the time of permanence, the sitting preferences of users and people’s characteristics among other behaviours. Previous studies have reported a relationship between thermal comfort and human behaviour; however, there is a lack of studies approaching the study of human behaviour using observational methods which allows assessing human behaviours such as number of people, number of groups, time of permanence among others, taking into account environmental factors such as: air temperature, globe temperature, mean radiant temperature, relative humidity, wind speed, sun and shadow presence and illuminance. As part of this research, three studies were conducted in the city centre of Nottingham during summer and autumn of 2015 and winter of 2016 in order to collect data of human behaviour and find its relationship with the air and globe temperature, calculated mean radiant temperature, wind speed, relative humidity and illuminance. These studies were conducted using observational methods by creating a coding scheme after conducting video analysis of social and individual behaviours. A methodology was created to incorporate processes that allow gathering data for observational analysis, which was subsequently processed using multiple regression models and survival analyses. The overall analysis led to the identification of the main environmental factors influencing human behaviour across different environmental conditions. The studies and analyses conducted showed that various environmental factors work together to influence the decisions of the users of a public space. Accordingly, the models used to predict human behaviour should include the environmental variables that explain better its variability, based on the environmental data of the place. Moreover, this study showed that individual analysis should be performed on a seasonal basis using the environmental and human behaviour data of each season in addition to the analysis performed to the whole data set. The reason for this is that the seasonal data is better at explaining some human behaviours than the model built with the whole data set collected in various seasons. For instance, the relationship between wind speed and number of people is positive during summer and negative during autumn and winter; however, when the three seasons are analysed together, the relationship is negative, which does not explain accurately the phenomena in summer. Conversely, illuminance was found to be an important factor influencing behaviour across the seasons and also contributed to the prediction of behaviour in the all season’s analysis. Finally, this thesis presents an application of the results by presenting general recommendations of urban design based on the findings of analysing human behaviour in accordance with the thermal environment. The studies conducted during the three seasons presented a cross-internal validation of the multiple regression models. In addition, a final study which consisted of a mock scenario was conducted to perform an external validation of the previous results. A number of conclusions were drawn about the conditions required to perform further external validations, following the parameters identified that may affect the results of the validation

Variability of human behaviour in outdoor public spaces, associated with the thermal environment

This paper presents part of the outcomes of a programme of research into the influence of the thermal environment on human behaviour in an outdoor public seating area. The research was conducted during one month in summer, autumn and winter of 2015 and 2016. The data gathered consists in the conduct of people using a public square in Nottingham city centre, and measurements of the environmental conditions taken at that place. The data of Number of People and the Size of Groups of people, were analysed according with the thermal environment of the place. The results showed a strong significant correlation between Number of People and Globe Temperature_sun [r = .66, p < .001]. A multiple regression analysis found that the Number of People per minute in a public space can be predicted using the Globe Temperature_sun and the Wind Speed data of that place [R-square of .39, p < 0.001]. These prediction models can be used to forecast the occupancy of the place and the grouping of users under different environmental conditions. The results can assist the design of urban spaces by allowing testing their future use with predicted data of human behaviour. In addition, the data obtained will serve as a foundation for further research about the human behaviour in public spaces

Human behaviour outdoors and the environmental factors

The study of human behaviour outdoors has been an area of interest examined from different perspectives. Even so, the study of human behaviour in outdoor public spaces still requires further input from the perspective of human factors. This thesis presents a literature review of behaviour in public spaces where the author evaluated the attendance to public squares, the activities performed by users, the time of permanence, the sitting preferences of users and people’s characteristics among other behaviours. Previous studies have reported a relationship between thermal comfort and human behaviour; however, there is a lack of studies approaching the study of human behaviour using observational methods which allows assessing human behaviours such as number of people, number of groups, time of permanence among others, taking into account environmental factors such as: air temperature, globe temperature, mean radiant temperature, relative humidity, wind speed, sun and shadow presence and illuminance. As part of this research, three studies were conducted in the city centre of Nottingham during summer and autumn of 2015 and winter of 2016 in order to collect data of human behaviour and find its relationship with the air and globe temperature, calculated mean radiant temperature, wind speed, relative humidity and illuminance. These studies were conducted using observational methods by creating a coding scheme after conducting video analysis of social and individual behaviours. A methodology was created to incorporate processes that allow gathering data for observational analysis, which was subsequently processed using multiple regression models and survival analyses. The overall analysis led to the identification of the main environmental factors influencing human behaviour across different environmental conditions. The studies and analyses conducted showed that various environmental factors work together to influence the decisions of the users of a public space. Accordingly, the models used to predict human behaviour should include the environmental variables that explain better its variability, based on the environmental data of the place. Moreover, this study showed that individual analysis should be performed on a seasonal basis using the environmental and human behaviour data of each season in addition to the analysis performed to the whole data set. The reason for this is that the seasonal data is better at explaining some human behaviours than the model built with the whole data set collected in various seasons. For instance, the relationship between wind speed and number of people is positive during summer and negative during autumn and winter; however, when the three seasons are analysed together, the relationship is negative, which does not explain accurately the phenomena in summer. Conversely, illuminance was found to be an important factor influencing behaviour across the seasons and also contributed to the prediction of behaviour in the all season’s analysis. Finally, this thesis presents an application of the results by presenting general recommendations of urban design based on the findings of analysing human behaviour in accordance with the thermal environment. The studies conducted during the three seasons presented a cross-internal validation of the multiple regression models. In addition, a final study which consisted of a mock scenario was conducted to perform an external validation of the previous results. A number of conclusions were drawn about the conditions required to perform further external validations, following the parameters identified that may affect the results of the validation

Evaluation of a potato leafhopper (PLH) resistant alfalfa cultivar effects on PLH injury in alfalfa: grass mixed stands with and without insecticide.

The combination of a resistant cultivar and a grass resulted in significantly better PLH control than did the resistant cultivar alone or the grass alone. The resistant cultivar had 36% fewer PLH than the susceptible cultivar; however, the number of PLH was significantly higher than for the plots that were sprayed with insecticide (average less than 1 PLH per sub-plot). The untreated plot with the lowest PLH damage score was the resistant alfalfa/grass mixture (score = 1.8), whereas the resistant cultivar alone scored 2.4 and the susceptible cultivar alone and with grass averaged 3.5

The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid

Vehicle-to-grid (V2G) has been identified as a key technology to help reduce carbon emissions from the transport and energy sectors. However, the benefits of this technology are best achieved when multiple variables are considered in the process of charging and discharging an electric vehicle. These variables include vehicle behaviour, building energy demand, renewable energy generation, and grid carbon intensity. It is expected that the transition to electric mobility will add pressure to the energy grid. Using the batteries of electric vehicles as energy storage to send energy back to the grid during high-demand, carbon-intensive periods will help to reduce the impact of introducing electric vehicles and minimise carbon emissions of the system. In this paper, the authors present a method and propose a V2G control scheme integrating one year of historical vehicle and energy datasets, aiming towards carbon emissions reduction through increased local consumption of renewable energy, offset of vehicle charging demand to low carbon intensity periods, and offset of local building demand from peak and carbon-intensive periods through storage in the vehicle battery. The study included assessment of strategic location and the number of chargers to support a fleet of five vehicles to make the transition to electric mobility and integrate vehicle-to-grid without impacting current service provision. The authors found that the proposed V2G scheme helped to reduce the average carbon intensity per kilowatt (gCO2/kWh) in simulation scenarios, despite the increased energy demand from electric vehicles charging. For instance, in one of the tested scenarios V2G reduced the average carbon intensity per kilowatt from 223.8 gCO2/kWh with unmanaged charging to 218.9 gCO2/kWh using V2G

Randomly Amplified DNA Fingerprinting: A Culmination of DNA Marker Technologies Based on Arbitrarily-Primed PCR Amplification

Arbitrarily-primed DNA markers can be very useful for genetic fingerprinting and for facilitating positional cloning of genes. This class of technologies is particularly important for less studied species, for which genome sequence information is generally not known. The technologies include Randomly Amplified Polymorphic DNA (RAPD), DNA Amplification Fingerprinting (DAF), and Amplified Fragment Length Polymorphism (AFLP). We have modified the DAF protocol to produce a robust PCR-based DNA marker technology called Randomly Amplified DNA Fingerprinting (RAF). While the protocol most closely resembles DAF, it is much more robust and sensitive because amplicons are labelled with either radioactive (33)P or fluorescence in a 30-cycle PCR, and then separated and detected on large polyacrylamide sequencing gels. Highly reproducible RAF markers were readily amplified from either purified DNA or alkali-treated intact leaf tissue. RAF markers typically display dominant inheritance. However, a small but significant portion of the RAF markers exhibit codominant inheritance and represent microsatellite loci. RAF compares favorably with AFLP for efficiency and reliability on many plant genomes, including the very large and complex genomes of sugarcane and wheat. While the two technologies detect about the same number of markers per large polyacrylamide gel, advantages of RAF over AFLP include: (i) no requirement for enzymatic template preparation, (ii) one instead of two PCRs, and (iii) overall cost. RAF and AFLP were shown to differ in the selective basis of amplification of markers from genomes and could therefore be used in complementary fashion for some genetic studies

Exploring Opportunities for Vehicle-to-Grid Implementation through Demonstration Projects

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles’ batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Vehicle‐to‐grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle‐to‐grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle‐to‐grid aggregation services

Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper