University of Kurdistan

In this paper we give an account of the genera and species in the Botryosphaeriaceae. We consider morphological characters alone as inadequate to define genera or identify species, given the confusion it has repeatedly introduced in the past, their variation during development, and inevitable overlap as representation grows. Thus it seems likely that all of the older taxa linked to the Botryosphaeriaceae, and for which cultures or DNA sequence data are not available, cannot be linked to the species in this family that are known from culture. Such older taxa will have to be disregarded for future use unless they are epitypified. We therefore focus this paper on the 17 genera that can now be recognised phylogenetically, which concentrates on the species that are presently known from culture. Included is a historical overview of the family, the morphological features that define the genera and species and detailed descriptions of the 17 genera and 110 species. Keys to the genera and species are also provided. Phylogenetic relationships of the genera are given in a multi-locus tree based on combined SSU, ITS, LSU, EF1-α and β-tubulin sequences. The morphological descriptions are supplemented by phylogenetic trees (ITS alone or ITS + EF1-α) for the species in each genus. TAXONOMIC NOVELTIES: New species - Neofusicoccum batangarum Begoude, Jol. Roux & Slippers. New combinations - Botryosphaeria fabicerciana (S.F. Chen, D. Pavlic, M.J. Wingf. & X.D. Zhou) A.J.L. Phillips & A. Alves, Botryosphaeria ramosa (Pavlic, T.I. Burgess, M.J. Wingf.) A.J.L. Phillips & A. Alves, Cophinforma atrovirens (Mehl & Slippers) A. Alves & A.J.L. Phillips, Cophinforma mamane (D.E. Gardner) A.J.L. Phillips & A. Alves, Dothiorella pretoriensis (Jami, Gryzenh., Slippers & M.J. Wingf.) Abdollahz. & A.J.L. Phillips, Dothiorella thailandica (D.Q. Dai., J.K. Liu & K.D. Hyde) Abdollahz., A.J.L. Phillips & A. Alves, Dothiorella uruguayensis (C.A. Pérez, Blanchette, Slippers & M.J. Wingf.) Abdollahz. & A.J.L. Phillips, Lasiodiplodia lignicola (Ariyawansa, J.K. Liu & K.D. Hyde) A.J.L. Phillips, A. Alves & Abdollahz., Neoscytalidium hyalinum (C.K. Campb. & J.L. Mulder) A.J.L. Phillips, Groenewald & Crous, Sphaeropsis citrigena (A.J.L. Phillips, P.R. Johnst. & Pennycook) A.J.L. Phillips & A. Alves, Sphaeropsis eucalypticola (Doilom, J.K. Liu, & K.D. Hyde) A.J.L. Phillips, Sphaeropsis porosa (Van Niekerk & Crous) A.J.L. Phillips & A. Alves. Epitypification (basionym) - Sphaeria sapinea Fries. Neotypifications (basionyms) - Botryodiplodia theobromae Pat., Physalospora agaves Henn, Sphaeria atrovirens var. visci Alb. & Schwein.

Drought stress is one of the major abiotic stresses in agriculture worldwide. This study was carried out to investigate the effect of drought stress on proline content, chlorophyll content, photosynthesis and transpiration, stomatal conductance and yield characteristics in three varieties of chickpea (drought tolerant Bivaniej and ILC482 and drought sensitive Pirouz). A field experiment with four irrigation regimes was carried out in a randomized complete block design with three replications. Treatments included control (no drought), drought stress imposed during the vegetative phase, drought stress imposed during anthesis and drought stress during the vegetative phase and during anthesis. All physiological parameters were affected by drought stress. Drought stress imposed during vegetative growth or anthesis significantly decreased chlorophyll a, chlorophyll b and total chlorophyll content. Proline accumulation was higher in ‘ILC482 ’ than in ‘Pirouz ’ both under control and drought stress conditions. Photosynthesis, transpiration, stomatal conductance and yield were higher but sub-stomatal CO2 concentration was lower under drought stress conditions than under control conditions. The results showed that mesophyll resistance is the basic determinate of rate of phototosynthesis under drought stress conditions. Under drought conditions the drought tolerant variety ‘Bivaniej ’ gave the highest yield whereas the drought sensitive variety ‘Pirouz ’ gave the lowest yield. Drought stress at anthesis phase reduced seed yield more severe than that on vegetative stage.

Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods.

As the use of renewable energy sources (RESs) increases worldwide, there is a rising interest on their impacts on power system operation and control. An overview of the key issues and new challenges on frequency regulation concerning the integration of renewable energy units into the power systems is presented. Following a brief survey on the existing challenges and recent developments, the impact of power fluctuation produced by variable renewable sources (such as wind and solar units) on system frequency performance is also presented. An updated LFC model is introduced, and power system frequency response in the presence of RESs and associated issues is analysed. The need for the revising of frequency performance standards is emphasised. Finally, non-linear time-domain simulations on the standard 39-bus and 24-bus test systems show that the simulated results agree with those predicted analytically.

Virtual synchronous generator (VSG) control is a promising communication-less control method in a microgrid for its inertia support feature. However, active power oscillation and improper transient active power sharing are observed when basic VSG control is applied. Moreover, the problem of reactive power sharing error, inherited from conventional droop control, should also be addressed to obtain desirable stable state performance. In this paper, an enhanced VSG control is proposed, with which oscillation damping and proper transient active power sharing are achieved by adjusting the virtual stator reactance based on state-space analyses. Furthermore, communication-less accurate reactive power sharing is achieved based on inversed voltage droop control feature (V-Q droop control) and common ac bus voltage estimation. Simulation and experimental results verify the improvement introduced by the proposed enhanced VSG control strategy.

We have developed Fe3O4 magnetic nanoparticles/reduced graphene oxide nanosheets modified glassy carbon (Fe3O4/r-GO/GC) electrode as a novel system for the preparation of electrochemical sensing platform. Decorating Fe3O4 nanoparticles on graphene sheets was performed via a facile one-step chemical reaction strategy, where the reduction of GO and the in-situ generation of Fe3O4 nanoparticles occurred simultaneously. Characterization of as-made nanocomposite using X-ray diffraction (XRD), transmission electron microscopy (TEM) and alternative gradient force magnetometry (AGFM) clearly demonstrate the successful attachment of monodisperse Fe3O4 nanoparticles to graphene sheets. Electrochemical studies revealed that the Fe3O4/r-GO/GC electrode possess excellent electrocatalytic activities toward the low potential oxidation of NADH (0.05 V vs. Ag/AgCl) as well as the catalytic reduction of O2 and H2O2 at reduced overpotentials. Via immobilization of lactate dehydrogenase (LDH) as a model dehydrogenase enzyme onto the Fe3O4/r-GO/GC electrode surface, the ability of modified electrode for biosensing lactate was demonstrated. In addition, using differential pulse voltammetry (DPV) to investigate the electrochemical oxidation behavior of ascorbic acid (AA), dopamine (DA) and uric acid (UA) at Fe3O4/r-GO/GC electrode, the high electrocatalytic activity of the modified electrode toward simultaneous detection of these compounds was indicated. Finally, based on the strong electrocatalytic action of Fe3O4/r-GO/GC electrode toward both oxidation and reduction of nitrite, a sensitive amperometric sensor for nitrite determination was proposed. The Fe3O4/r-GO hybrid presented here showing favorable electrochemical features may hold great promise to the development of electrochemical sensors, molecular bioelectronic devices, biosensors and biofuel cells.

Abstract Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors' point of view, are also provided in the final concluding part.

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Communication infrastructure (CI) in microgrids (MGs) allows for the application of different control architectures for the secondary control (SC) layer. The use of new SC architectures involving CI is motivated by the need to increase MG resilience and handle the intermittent nature of distributed generation units. The structure of SC is classified into three main categories, including centralized SC (CSC) with a CI, distributed SC (DISC) generally with a low-data-rate CI, and decentralized SC (DESC) with communication-free infrastructure. To meet the MGs' operational constraints and optimize performance, control and communication must be utilized simultaneously in different control layers. In this survey, we review and classify all types of SC policies from CI-based methods to communication-free policies, including CSC, averaging-based DISC, consensus-based DISC methods, containment pinning consensus, event-triggered DISC, washout-filter-based DESC, and state-estimation-based DESC. Each structure is scrutinized from the viewpoint of the relevant literature. Challenges such as clock drifts, cyber-security threats, and the advantage of event-triggered approaches are presented. Fully decentralized approaches based on state-estimation and observation methods are also addressed. Although these approaches eliminate the need of any CI for the voltage and frequency restoration, during black start process or other functionalities related to the tertiary layer, a CI is required. Power hardware-in-the-loop experimental tests are carried out to compare the merits and applicability of different SC structures.

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

and allied fusarioid genera (www.fusarium.org).

This paper addresses robust frequency control in an islanded ac microgrid (MG). In an islanded MG with renewable sources, load change, wind power fluctuation, and sun irradiation power disturbance as well as dynamical perturbation, such as damping coefficient and inertia constants, can significantly influence the system frequency, and hence the MG frequency control problem faces some new challenges. In response to these challenges, in this paper, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and μ-synthesis robust control techniques are used to develop the secondary frequency control loop. In the proposed control scheme, some microsources (diesel engine generator, micro turbine, and fuel cell) are assumed to be responsible for balancing the load and power in the MG system. The synthesized H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and μ-controllers are examined on an MG test platform, and the controllers' robustness and performance are evaluated in the presence of various disturbances and parametric uncertainties. The results are compared with an optimal control design. It is shown that the μ-synthesis approach due to considering structured/parametric uncertainties provides better performance than the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control method.

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Voltage and frequency of microgrids (MGs) are strongly impressionable from the active and reactive load fluctuations. Often, there are several voltage source inverters (VSIs) based distributed generations (DGs) with a specific local droop characteristic for each DG in a MG. A load change in a MG may lead to imbalance between generation and consumption and it changes the output voltage and frequency of the VSIs according to the droop characteristics. If the load change is adequately large, the DGs may be unable to stabilize the MG. In the present paper, following a brief survey on the conventional voltage/frequency droop control, a generalized droop control (GDC) scheme for a wide range of load change scenarios is developed. Then to remove its dependency to the line parameters and to propose a model-free based GDC, a new framework based on adaptive neuro-fuzzy inference system (ANFIS) is developed. It is shown that the proposed intelligent control structure carefully tracks the GDC dynamic behavior, and exhibits high performance and desirable response for different load change scenarios. It is also shown that the ANFIS controller can be effectively used instead of the GDC. The proposed methodology is examined on several MG test systems.

Environmental quality strongly depends on the human behavior patterns. University students as a part of the young people of the community endure the burden of the past and current carelessness towards the environment. At the same time, they are the significant people who gain technical knowledge essential for advancing suitable solutions to change environmental behavior. Therefore, developing scientific knowledge on what inspire them to behave pro-environmentally is a significant area of concern that has practicable usages for moving on the way a sustainable future. In an examination of pro-environmentally related behavior, we used the protection motivation theory as a framework for explaining pro-environmental behavior of a sample of 310 Iranian students. Analysis indicated that the protection motivation theory constructs along with environmental attitude are able for explaining a significant portion of the variance in pro-environmental behavior. Based on the results, environmental attitude, self-efficacy, perceived costs of pro-environmental behavior and perceived intrinsic and extrinsic rewards of current environmentally unfriendly behaviors were the direct determinants of pro-environmental behavior, while rewards indirectly influenced pro-environmental behavior via environmental attitude and response costs. Also, response efficacy through self-efficacy had an indirect influence on pro-environmental behavior. Overall, considering the importance of environmental attitudes and self-efficacy, using measures and incentives to improve students’ attitude on the necessity of environmental protection and improving their sense of self-efficacy can help increase the likelihood of pro-environmental behaviors in community.

Integration of inverter-interfaced distributed generators (DGs) via microgrids into traditional power systems reduces the total inertia and damping properties, while increasing uncertainty and sensitivity to the fault in the system. The concept of the virtual synchronous generator (VSG) has been recently introduced in the literature as a solution, which mimics the behavior of conventional synchronous generators in large power systems. Parameters of the VSGs are normally determined comparing the conventional synchronous generators. This paper introduces an extended VSG for microgrids by combining the concept of virtual rotor, virtual primary, and virtual secondary control as a virtual controller to stabilize/regulate the system frequency. An H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> robust control method is proposed for robust\optimal tuning of the virtual parameters. The effectiveness of the proposed control methodology is verified via a microgrid test bench.

This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Load-frequency control (LFC) in interconnected power systems is undergoing fundamental changes due to rapidly growing amount of wind turbines, and emerging of new types of power generation/consumption technologies. The infrastructure of modern LFC systems should be able to handle complex multiobjective regulation optimization problems characterized by a high degree of diversification in policies, and widely distribution in demand and supply sources to ensure that the LFC systems are capable to maintain generation-load balance, following serious disturbances. Wind power fluctuations impose additional power imbalance to the power system and cause frequency deviation from the nominal value. This paper addresses a new decentralized fuzzy logic-based LFC schemes for simultaneous minimization of system frequency deviation and tie-line power changes, which is required for successful operation of interconnected power systems in the presence of high-penetration wind power. In order to obtain an optimal performance, the particle swarm optimization technique is used to determine membership functions parameters. The physical and engineering aspects have been fully considered, and to demonstrate effectiveness of the proposed control scheme, a time domain simulation is performed on the standard 39-bus test system. The results are compared with conventional LFC design for serious load disturbance and various rates of wind power penetrations.