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Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

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Journal of Arid Land 2017年第4期9卷 489-503页

作者： Zahra JABERALANSAR Mostafa TARKESH mehdi bassiri Saeid POURMANAFI Department of Natural Resources Isfahan University of TechnologyIsfahan 84156-83111Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially

关键词： rangelands forage production climate change scenario generalized regression neural network Central Iran

Potential habitat modeling for reintroduction of three native plant species in central Iran

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Journal of Arid Land 2015年第3期7卷 381-390页

作者： Elham G ARDESTANI Mostafa TARKESH mehdi bassiri Mohammad R VAHABI Department of Natural Resources Isfahan University of Technology

Potential habitat modeling of endemic species is an appropriate method to maintain biodiversity,ecosystem function and rehabilitation of rangeland *** caragana,*** and *** are endemic in Iran＇s rangelands and some neighboring *** three native species could endure environmental stresses due to their distinctive ecophysiological *** play important roles in sustainable pastures production,recreation and *** suffer severe threat from many factors including;grazing,agriculture and invasive exotic *** analyzed the potential habitat of three native plant species in central Iran basing on the grid map with the resolution of *** used inventory records from field surveys,herbarium collections and 22 environmental factors to explore the environmental influences on given species distribution by Maximum entropy（Maxent） *** is a species distribution model that uses species occurrence and environmental data for predicting potential *** results of our study indicated species occurrence has strong correlation with environmental factors such as mean temperature of wettest season,elevation and precipitation of coldest *** evaluated the model accuracy by AUC（area under the receiver operating characteristic curve） based on an independent test data *** values indicated the high power of Maxent to create potential habitat map（***=0.988,***=0.927,***=0.923）.It is important to consider that AUC values tend to be lower for species that have broad distribution scope,such as *** *** suitable potential habitat distributions of the three species were predicted in the western and southwestern parts of rangelands in Isfahan *** comparisons of the actual distribution map of the three species with produced Maxent maps represent a good *** general,the model demonstrated that the occurrence of the given species

关键词： Astragalus caragana Astragalus cyclophyllon Astragalus podolobus environmental factors Maxent

Configuration Reusing in On-Line Task Scheduling for Reconfigurable Computing Systems

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Journal of Computer Science & Technology 2011年第3期26卷 463-473页

作者： Maisam Mansub bassiri Hadi Shahriar Shahhoseini Electrical Engineering Department Iran University of Science and Technology

Reconfigurable computing systems can be reconfigured at runtime and support partial reconfigurability which makes us able to execute tasks in a true multitasking manner. To manage such systems at runtime, a reconfigurable operating system is needed. The main part of this operating system is resource management unit which performs on-line scheduling and placement of hardware tasks at runtime. Reconfiguration overhead is an important obstacle that limits the performance of on-line scheduling algorithms in reconfigurable computing systems and increases the overall execution time. Configuration reusing （task reusing） can decrease reconfiguration overhead considerably, particularly in periodic applications or the applications in which the probability of tasks recurrence is high. In this paper, we present a technique called reusing-based scheduling （RBS）, for on-line scheduling and placement in which configuration reusing is considered as a main characteristic in order to reduce reconfiguration overhead and decrease total execution time of the tasks. Several experiments have been conducted on the proposed algorithm. Obtained results show considerable improvement in overall execution time of the tasks.

关键词： reconfigurable computing on-line scheduling configuration reusing RPU partitioning replacement manage-ment probability of recurrence

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking

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npj Computational Materials 2019年第1期5卷 379-386页

作者： Lee A.Griffin Iaroslav Gaponenko Shujun Zhang Nazanin bassiri-Gharb School of Electrical Engineering Georgia Institute of TechnologyAtlantaGAUSA G.W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGAUSA Department of Quantum Matter Physics University of GenevaGenevaSwitzerland ISEM Australian Institute for Innovative MaterialsUniversity of WollongongWollongongAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA

Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data,the interplay between the physical and chemical phenomena underpinning materials’functionalities is still often poorly *** reduction techniques have been used to tackle the challenge of understanding materials’behavior,leveraging the very large amount of data ***,we present a method for applying physical and chemical constraints to dimensional reduction analysis,through dimensional *** to traditional,uncorrelated techniques,this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters,through stacking of data along specific dimensions as required by physical or chemical *** proposed method is applied to the nanoscale electromechanical relaxation response in(1−x)PMN-xPT solid solutions,enabling a direct comparison of electric field-and chemical composition-dependent contributors.A poling-like,and a relaxation-like behavior with a domain glass state are identified,and their evolution is tracked across the phase *** proposed dimensional stacking technique,guided by the knowledge of the underlying physics of correlated systems,is valid for the analysis of any multidimensional dataset,opening a spectrum of possibilities for multidisciplinary use.

关键词： stacking chemical relaxation

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npj Computational Materials 2021年第1期7卷 1474-1480页

作者： Iaroslav Gaponenko Loïc Musy Neus Domingo Nicolas Stucki Albert Verdaguer Nazanin bassiri-Gharb Patrycja Paruch Department of Quantum Matter Physics University of GenevaGenevaSwitzerland G.W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGAUSA Catalan Institute of Nanoscience and Nanotechnology(ICN2) CSIC and BISTCampus UABBarcelonaSpain University of Applied Sciences and Arts Western Switzerland(HES-SO/HEPIA) GenevaSwitzerland Institut de Ciència de Materials de Barcelona(ICMAB) CSICCampus UABBarcelonaSpain School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA

Electrochemical phenomena in ferroelectrics are of particular interest for catalysis and sensing applications,with recent studies highlighting the combined role of the ferroelectric polarisation,applied surface voltage and overall switching ***,we present a systematic Kelvin probe microscopy study of the effect of relative humidity and polarisation switching history on the surface charge dissipation in ferroelectric Pb(Zr_(0.2)Ti_(0.8))O_(3)thin *** analyse the interaction of surface charges with ferroelectric domains through the framework of physically constrained unsupervised machine learning matrix factorisation,Dictionary Learning,and reveal a complex interplay of voltage-mediated physical processes underlying the observed signal *** insight into the observed behaviours is given by a Fitzhugh–Nagumo reaction–diffusion model,highlighting the lateral spread and charge passivation process contributors within the Dictionary Learning analysis.

关键词： ferroelectric charge film