Historical Data Research Articles

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

Understanding the Health Impacts of Vehicular Emissions in South Florida: A Comprehensive Analysis

South Florida is famous for its diverse cultural scene and year-round sunshine. This success, however, has not been without its consequences. While the region enjoys economic prosperity, the hidden cost of deteriorating air quality and adverse health effects from vehicle emissions necessitates urgent attention. Electric vehicles (EVs) have emerged as a potential solution, promising reduced emissions, and increased energy efficiency. However, the intricate life cycle emissions associated with EV energy production raise questions about their net benefits. Using predictive modeling and historical data, the study forecasts emissions trajectories and assesses their health implications. Results indicate a substantial reduction in pollutants like PM2.5 and NOx by 2050, particularly in counties with higher vehicle miles traveled (VMT). However, challenges remain, such as Broward County’s heightened dependence on polluting electricity sources for EV charging, leading to increased SO2 emissions and public health costs. The analysis underscores the importance of transitioning to cleaner energy sources, highlighting the potential benefits of renewable resources in reducing emissions and improving public health outcomes. By incorporating comprehensive data and predictive models, this study provides valuable insights for policymakers and communities, advocating for a concerted effort towards sustainable transportation solutions. Ultimately, the findings emphasize the necessity of proactive measures to mitigate the adverse effects of vehicle emissions and foster a healthier, more sustainable future for South Florida.

Cropping calendar, agroclimatic and agroecological zoning for rainfed maize (Zea mays L.) under different rainfall scenarios in a semi-arid region of Brazil

Context The productivity of crops is influenced by the available net water balance, which relies on a complex interplay of edaphic and climatic factors. In this sense, rainfall is the leading water input for crop growth and yield, especially in water-scarce regions under semi-arid climates worldwide. Aims This study aimed to define the optimum cropping calendar and the agroclimatic and agroecological zones for rainfed maize in the state of Ceará, which is predominantly located within the Brazilian semi-arid region, assuming different rainfall scenarios. Methods We considered the subdivision of the study area into eight homogeneous precipitation regions according to the regional rainfall pattern. The climatic water balance for each region over two decades was calculated from historical weather data series, assuming three rainy scenarios: dry, regular, and rainy. The agroclimatic zoning was defined through the crop water balance results, soil classification, land use and occupation, slope and temperature, weighted through a multi-criteria decision analysis based on the Analytic Hierarchy Process. Key results The recommended planting period was broader in rainy conditions. However, there may be some dry spells between two 10-day intervals of the calendar, which impair plant growth depending on their intensity. For the dry scenario, the agroclimatic zoning showed that the marginal water deficiency covered 96.55% of the study area. Conclusions Rainfed maize cultivated under the semi-arid climate of Ceará state is viable in rainy conditions, partially restricted in regular years, and not recommended in dry years. Implications This study emphasises the importance of climate monitoring in effective planning for rainfed maize cultivation in semi-arid regions.

Crop Yield Prediction using CNN

Deep learning is a branch of Machine Learning which is completely based on artificial neural networks, as neural networks are going to mimic the human brain so deep learning is also a kind of mimic of the human brain. Farming is the main occupation of India. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production . A prerequisite of intelligent systems has brought artificial neural networks to become a new technology which provides assorted solutions for complex problems in agriculture research. Performance of the agriculture sector mainly hinges on natural forces such as spatio-temporal distribution of rainfall, temperature, climate etc, with the result any deviation of monsoon from the normal pattern brings about numerous fluctuations in area and production. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production. The basic principle of ANN architecture, Data Modeling for Prediction involves four stages namely historical data analysis (Descriptive), Data preprocessing, modeling of Data and Performance Estimation. First classify data based on different attributes. Regression analysis using CNN, it observes the relation between an independent (predictor) and dependent (target) variables. Based on relation training the model will predict crop yield production. application of ANN in predicting crop yield by using various crop performance features as input parameter

Identifying the deficiencies of currently available CGM to improve uptake and benefit.

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

The impact of land use and cover changes on river flows in Wundanyi Catchment of Taita Hills, Kenya (1970–2030)

<p>Taita hills are one of the most important biodiversity hotspots of Kenya but experiencing a high rate of deforestation due conversion of its original forestland to agriculture and settlement during the last century. These landscape dynamics coupled with rainfall fluctuations in these critical ecosystems may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of land use/cover change (LUCC) in Wundanyi catchment from 1970 to 2030 and predict their specific and combined effects on surface runoff and stream flow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary data from Wundanyi catchment, and Landsat TM and ETM+ imagery of Taita Hills for 1990, 2000, and 2010. Results show highly variable mean seasonal and annual values of discharge in Wundanyi catchment probably attributed to environmental changes affecting Taita Hills in general and Wundanyi catchment in particular. Compared to 1990, major land use/cover changes in 2010 were featured by the expansion of built-up area (250%), plantation forest (23.7%), broadleaved forest (17.4%), and thicket (15.9%). Was also notable the decrease in woodland (−30.3%), cropland (−21.6%), and shrubland (−0.8%). Dynamic spatial trends by the year 2030 will be evidenced by increased thicket by 0.41% per annum (<em>R</em><sup>2</sup> = 81.6%), and by decreased plantation forests (−0.13%; <em>R</em><sup>2</sup> = 91.3%), woodland (−0.10%; <em>R</em><sup>2</sup> = 77.6%), shrubland (−0.11%; <em>R</em><sup>2</sup> = 85.2%), broadleaved forests (−0.03%; <em>R</em><sup>2</sup> = 56.6%) and cropland (−0.09%; <em>R</em><sup>2</sup> of 84.4). These changes will shape the catchment landscape and influence its hydrology, unless the existing forest and agricultural policy interventions are enforced. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi catchment.<strong></strong></p>

Understanding the social construction of juvenile delinquency: insights from semantic analysis of big-data historical newspaper collections

AbstractMassive historical newspaper collections contain rich information about the historical development of social issues and constitute a unique resource for studying the social construction of issues such as juvenile delinquency. However, manual analysis of millions of pages of newspaper articles is infeasible. In this paper, we propose a suite of computational methods, including cross-context lexical analysis, dynamic semantic analysis, and valence analysis, to facilitate the study of historical social construction. We apply these methods to ProQuest Historical Newspapers$$^{\textrm{TM}}$$ TM collection in the period of 1790–2006 to study the social construction of juvenile delinquency over this period. Our results show that the proposed methods are effective in revealing insights regarding the social construction of juvenile delinquency, leading to a better understanding of this complex issue and specific hypotheses for further study. Overall, our study shows the great promise of leveraging natural language processing techniques for analyzing historical news data to study social construction of societal issues.

Criminal Investigation System

The Criminal Investigation System with represents a significant advancement in the field of law enforcement technology. By leveraging the power of data and predictive analytics, it empowers investigators to solve cases faster, identify suspects more accurately, and ultimately, enhance public safe. Based on the historical crime data suggest that Hashing algorithm. The digital system functions by gathering and classifying different kinds of information, such as crime scene reports, witness statements, forensic evidence, and suspect profiles. It facilitates the work of an officer by allowing him to compile new, vital case details on demand and to monitor the case's progress online. Keywords: Criminal Investigation, System Law, enforcement technology ,Data Predictive, analytics, Solve cases, faster, Identify suspects public safety, Historical crime , data Hashing algorithm , Gathering information Classifying , information Crime ,scene reports, Witness statements ,Forensic evidence ,Suspect profiles , Officer.

Three-layered location recommendation algorithm using spectral clustering

Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To address this challenge, this paper proposes combining Spectral Clustering with a three-layered location recommendation model to develop a recommender system named LSC, applied to Yelp datasets. LSC leverages various information, including users’ check-in data, demographics, location demographics, and users’ friendship network data, to train the recommender system and generate recommendations. Evaluation of LSC’s performance utilizes the Yelp dataset and several comparison metrics, such as accuracy, RMSE, and F1-score. The results demonstrate that our proposed algorithm delivers reliable and significant performance improvements across various evaluation metrics compared to competing algorithms.

Seventeenth-century Seville houses: a survey by the Alarife Juan De Legarra (1637)

Despite the wealth of preserved information, research on the domestic architecture of Seville has been limited. Much of this historical data is available in survey books that were formally compiled in Seville from the Modern Age onwards. These books had detailed descriptions and sometimes illustrations provided by surveyors who were commissioned by owners or tenants.The intention of this study is to illustrate that the information contained in building surveys goes beyond simple descriptions or measurements often accompanied by drawings. They are a comprehensive account that, even centuries later, help reconstruct the architecture, material culture and everyday life that no longer exist. Our research contributes to the understanding of the domestic architectural history of Seville by focusing on one specific survey (a collection of drawings and a written description) extracted from the Hospital de las Cinco Llagas Collection, which is found in the Seville Provincial Council Archive. At least one of the drawings and the written survey can be attributed to the Alarife (architect or master builder) Juan de Legarra, who signed them in 1637.This research explores the architectural development of the city, offering fresh insights into its architecture, heritage, and history. It supplements existing historical, visual, and written records derived from prior studies with a meticulously curated bibliography. Additionally, it uses visualisation techniques to reconstruct buildings, augmenting the research.This endeavour has yielded anticipated benefits, particularly in facilitating visual analysis. The surveys have proven to be invaluable for historical inquiry, yielding unexpected insights through the thorough analysis of both written and graphic sources which serve as a basis for hypothetical construction plans due to their detailed descriptions. Furthermore, this study has contributed to enriching both historical and architectural terminology.The incorporation of digital technologies into this architectural context allows us to expand our knowledge through the exhaustive compilation of building records. It also facilitates in-depth analysis of architecture, which could result in an up-to-date architectural catalogue that would stimulate interest in preserving a legacy that, until recently, was at risk of being lost.

AN ANALYSIS OF BEST PERFORMING STOCKS OF BANKING SECTOR THROUGH TECHNICAL ANALYSIS

This research conducts an in-depth analysis of the best performing stocks within the banking sector using technical analysis methods. Through the examination of historical price data, trading volumes, and various technical indicators, the study aims to identify patterns and trends that contribute to the success of these stocks. Key factors such as moving averages, support and resistance levels, and momentum indicators are analyzed to determine the stocks' performance and potential future movements. The findings provide valuable insights into the effectiveness of technical analysis in predicting stock performance within the banking sector. Keywords: banking sector, technical analysis, stock performance, and historical price data, trading volumes, technical indicators, moving averages, support and resistance levels, momentum indicators.

Medium- and Long-Term Electric Vehicle Ownership Forecasting for Urban Residents

With the rapid development of electric vehicles (EVs) in Chinese cities, accurately forecasting the number of EVs used by urban residents in the next five years and more long term is beneficial for the government to adjust industrial policies of EVs, guide the rational planning of urban charging facilities and supporting distribution network, and achieve the rational and orderly development of the EV industry. The paper considers the advantages of using the grey GM(1,1) prediction model to predict the short-term ownership of EVs by urban residents. Then, by forecasting the number of EV users in a certain city in the future and predicting the number of private vehicles in the future, the boundary conditions for long-term year ownership of EVs by residents are determined. Combined with historical data and short-term forecast data generated by the grey prediction model, the model parameters that include the innovation coefficient and imitation coefficient of the Bass model are trained using a genetic algorithm. Finally, the Bass model is used for medium- to long-term ownership forecasting from 2023 to 2040. The prediction error for the target year is provided. The simulation results indicate that the ownership of resident EVs in this city will experience rapid growth in the next five years.

Missing Landscapes: A Geohistory of Parkland Landscapes in Northwestern Morocco

Northwestern Morocco is characterized by highly anthropized landscapes under the combined effect of agricultural intensification, resource overexploitation, urbanization, and tourism, but also local reforestation. Reconstructing the recent changes in vegetation in the region of the lower valley of the Loukkos river near Larache and their relationship to the settlement history are particularly helpful for understanding the processes at work within the landscape construction. The geohistorical approach combines paleoenvironmental, documentary, and historical data. The last few centuries have seen the emergence of wooded stands, in which cork oaks are a structural element. As forests were retreating, parklands intended for agriculture, agroforestry, and herding, like the Spanish dehesa and Portuguese montado, began to emerge. Nearly all of them have disappeared today, but we can identify their legacy and evaluate their cultural significance through comparing them with their counterparts in the Iberian Peninsula, but also in other areas of Morocco. Their deep historical roots give this landscape an evolving heritage character that is directly linked to the communities’ lifestyles, culture, and history.

Change in Marriage Behaviour in North-Central Namibia 1925–2009

Namibia is the only country in Africa for which historical data is available to describe the change in marriage behavior since the 1920s. The aim of this article is to describe and to understand how the age at first marriage changed and how it was related to cohabitation since the 1920s in Namibia. The description of changes is based on parish register material and the family reconstitution method. The mean age at first marriage was over 20 for both sexes as early as the 1920s. In 1945–49, the mean age at first marriage started to increase, reaching about 29 for men and 24 for women at the end of the 1950s. The explanation for the increase was labor migration. The new cohabitation model was introduced from the end of the 1970s to the 1980s. Features of this model were cohabitation before marriage, late marriage age, a low married proportion of the population and high proportion of premarital births.

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

Heterogeneous impact of crop diversification on farm net returns and risk exposure: Empirical evidence from Ghana

AbstractIncreasing frequency of extreme weather events threatens the livelihoods of low‐income farm households due to the heavy dependence on rain‐fed agriculture coupled with the under‐developed formal markets for risk management products. Thus, crop diversification is one of the widely used ex ante adaptation strategies to hedge against weather risk exposure. In this study, we use survey data from the northern Savanna zone of Ghana merged with historical weather data to shed light on the heterogeneous impact of crop diversification on farm net returns and risk exposure. We employ the dose response function and instrumental variable techniques to address potential endogeneity concerns. Overall, our findings show that crop diversification is a welfare‐enhancing strategy that significantly increases farm net returns, lowers the probability of crop failure, and thus decreases downside risk exposure. Notably, our dose‐response function analysis demonstrates that the positive benefits of crop diversification are particularly pronounced at lower intensities, reaching an optimal threshold. Beyond this point, the incremental advantages tend to diminish, suggesting the importance of carefully considering the optimal level of diversification for maximum benefits. The results further underscore the significant impact of both access to agricultural extension services and fertilizer usage on the adoption of crop diversification.

A multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints

AbstractThis study develops a multi‐stage stochastic model to forecast the issuance of green bonds using the Filtered Historical Simulation (FHS) method to identify the most cost‐effective conditions for issuing these bonds amid various risk factors. Drawing on historical yield data and financial metrics of corporate green bonds from December 2014 to June 2023, the model considers fluctuating elements such as risk probabilities, financial risks in worst‐case scenarios, and liquidity risks at upcoming issuance moments. Our findings reveal the model's effectiveness in pinpointing the lowest possible costs of issuing new green bond portfolios in the future, while also addressing expected financial risk, risk occurrence probability, and liquidity issues. The results provide issuers with the insights needed to accurately time the market, tailor bond maturities according to a corporation's future risk profile, and enhance liquidity management. Notably, our model indicates that refining the estimated probability of future risk occurrences can lead to significant savings in green bond issuance costs. This approach allows for adaptable bond issuance strategies, addresses inherent debt, and enables detailed risk management, offering substantial benefits for green enterprises navigating the complexities of future financial landscapes.

The impact of herbicide-resistant canola systems on the weed community dynamics in the Canadian Prairies

Increased adoption of no-till and extensive cultivation of herbicide-resistant (HR) crops in low diversity rotations are some of the transformations that occurred during the 1970s-2000s in western Canada. How these transformations in canola-based crop rotations impacted weed communities are not comprehensively studied. Historical data analysis of weed survey data ( Saskatchewan, Manitoba and Alberta) in canola crops and a canola rotation field experiment were used to understand how tillage, herbicide-resistant cropping systems, and frequency of canola in the rotation can impact weed abundance, composition, and diversity in the Canadian prairies. Weed survey data revealed that the frequency of many weed species declined in years after 1995 compared with before 1995, the year HR cultivars were introduced. Gallium spurium (false cleavers) and volunteer wheat (Triticum aestivum L.) were the only species showing a continuous increase in frequency throughout the surveyed years. Species richness showed no trajectories but varied depending on climatic conditions. Glyphosate-resistant (G.R.) systems were associated with green foxtail irrespective of their frequency in the rotation. Wild oat was predominant in the longer rotations with glufosinate and imidazolinone-resistant canola. In the field study, weeds associated with pre-herbicide weed control timing were relatively controlled when herbicides were applied (glyphosate and glufosinate), except for foxtail barley (Hordium Jubatum L), which was not controlled in most HR systems. Overall, both weed survey data and field experiment data revealed greater control of most weed species due to growing of HR canola cultivars.