ALBERT

Description: Potato late blight (Phytophtora infestans) is among the most severely damaging diseases of potato (Solanum tuberusom L.) worldwide, causing serious damages in potato leaves and tubers. In the present study, the effects of potassium phosphite (KPhi) applications on photosynthetic parameters, enzymatic and non-enzymatic antioxidant properties, hydrogen peroxide (H2O2) and malondialdehyde (MDA), total protein and total carbohydrate of potato leaves challenged with P. infestans pathogen were investigated. Potato leaves were sprayed five times with KPhi (0.5%) during the growing season prior to inoculation with P. infestans. The potato leaves were artificially infected by the LC06-44 pathogen isolate. The leaves were sampled at 0, 24, 48, 72 and 96 h after the infection for evaluations. P. infestans infection reduced chlorophyll (Chl) pigments contents, chlorophyll fluorescence, carotenoid (Car) and anthocyanin contents and increased the accumulation of H2O2 and MDA. Meanwhile, our result showed that KPhi treatment alleviated adverse effect of late blight in potato leaves. KPhi application also increased plant tolerance to the pathogen with improved photosynthetic parameters Chl a, b, total Chl, Car, and anthocyanin compare to controls. Moreover, the increased oxidative enzymes activity of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APx), and non-enzymatic substances such as phenolics, flavonoids and proline were found in KPhi treated plants, compared to untreated plants after inoculation. In addition, KPhi application followed by P. infestans infection also decreased the content of H2O2 and MDA, but increased the total protein and total carbohydrate contents in potato leaves. The consequence of current research indicated that KPhi played a vital role in pathogen tolerance, protecting the functions of photosynthetic apparatus by improved oxidative levels and physio-biochemical compounds in potato leaves.

Description: Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

Description: One of the main objectives of human society in the present century is to achieve clean and sustainable energy through utilization of renewable energy sources (RESs). In this paper, the main purpose is to identify the locations that are suitable for solar energy in the Kurdistan province of Iran. Initially, solar-related data are collected, and suitable criterion and assessment methods are chosen according to the available data. Then, the theoretical potential of solar energy is assessed and the solar radiation map is prepared. Moreover, the technical potential of various solar technologies is evaluated in that study area. These technologies include concentrating solar power (CSP) and photovoltaic (PV) in power plant applications, and rooftop PV panels and solar water heaters in general applications. The results show that the Kurdistan province has the potential capacity for 691 MW of solar photovoltaic power plants and 645 MW of CSP plants. In the case of using solar water heaters, 283 million cubic meters of natural gas and 1.2 million liters of gasoline could be saved in fuel consumption. The savings in the application of domestic PV will be 10.2 MW in power generation.

Description: The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.