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Significance Forest fires of the western United States have advanced upslope over the past few decades, scorching territories previously too wet to burn. We document an upslope advancement of high-elevation fires of 7.6 m/y, a rate comparable to the elevational velocity of vapor pressure deficit of 8.9 m/y. Strong interannual links between aridity and high-elevation forest fires and reduced influence of fire exclusion policies in montane mesic forests imply such changes are a byproduct of climate warming. We estimate that increased aridity between 1984 and 2017 exposed an additional 81,500 km2 of western US montane forests to fires. These changes have significant implications for terrestrial carbon storage, snowpack, and water quantity and quality., Increases in burned area and large fire occurrence are widely documented over the western United States over the past half century. Here, we focus on the elevational distribution of forest fires in mountainous ecoregions of the western United States and show the largest increase rates in burned area above 2,500 m during 1984 to 2017. Furthermore, we show that high-elevation fires advanced upslope with a median cumulative change of 252 m (−107 to 656 m; 95% CI) in 34 y across studied ecoregions. We also document a strong interannual relationship between high-elevation fires and warm season vapor pressure deficit (VPD). The upslope advance of fires is consistent with observed warming reflected by a median upslope drift of VPD isolines of 295 m (59 to 704 m; 95% CI) during 1984 to 2017. These findings allow us to estimate that recent climate trends reduced the high-elevation flammability barrier and enabled fires in an additional 11% of western forests. Limited influences of fire management practices and longer fire-return intervals in these montane mesic systems suggest these changes are largely a byproduct of climate warming. Further weakening in the high-elevation flammability barrier with continued warming has the potential to transform montane fire regimes with numerous implications for ecosystems and watersheds.

Climate change has caused many environmental problems, as well as water and food insecurity, and health and social impacts in many parts of the world, and especially in the world’s vulnerable regions such as developing countries. Studies have demonstrated the impacts of socio-economic and climate changes and how they result in water and environmental problems at global and regional scales. Socio-economic variation and climatic change influence the dynamic interaction of human and water systems, and our ability to address environmental problems at sub-regional scales. From this perspective, the Shared Socio-economic Pathways (SSPs), as a form of alternative development scenarios, were recently introduced to help decision-makers to cope with uncertain futures and improve their policies for mitigation and adaptation to climate change. To take advantage of SSP scenarios for policy guidance at regional and national scales, it is necessary to explore the socio-economic feedbacks and water management policies informed by different sub-regional knowledge sharing through stakeholders’ narratives. In this study, we link SSP scenarios developed with regional stakeholders using a coupled socio-economic and environmental model, in conjunction with stakeholder-generated narrative storylines for a sub-region of Pakistan. The framework allows for linking corresponding scenarios across different uncertainty levels to improve regional scale policy making, while providing knowledge regarding the future of human-water systems under a range of plausible future climate and socio-economic scenarios.

The assessment of climate change impacts requires downscaled climate projections and context-specific socioeconomic scenarios. The development of practical climate change adaptation for environmental sustainability at regional and local scales is predicated on a strong understanding of future socio-economic dynamics under a range of potential climate projections. We have addressed this need using integrated assessment of a localized hybrid Shared Socio-economic Pathway - Representative Concentration Pathway (SSP-RCP) framework, through an interdisciplinary and participatory storyline development process that integrates bottom-up local expert-stakeholder knowledge with top-down insights from global SSPs. We use the global SSPs (SSP1 to SSP5) as boundary conditions in conjunction with climate change pathways (RCP4.5, RCP8.5) to create localized SSP narratives in an iterative participatory process, using a storytelling method. By using an integrated socio-economic and environmental system dynamics model developed in collaboration with local stakeholders, we explore the potential impacts of plausible local SSP-RCP narratives and quantify important socio-environmental vulnerabilities of a human-water system (e.g., crop yields, farm income, water security and groundwater depletion) by the mid-century period (i.e., by 2050). The framework is developed to inform climate adaptation for Pakistan's Rechna Doab region, which serves as a representative case of a multi-stakeholder coupled human-water system operating in a developing country. Our results suggest that even under limited socio-economic improvements (e.g., technology, policies, institutions, environmental awareness) water security would be expected to decline and environmental degradation (e.g., groundwater depletion) to worsen. Under RCP 4.5, the average projected increase in water demand in 2030 will be about 7.32% for all SSP scenario narratives, and 10.82% by mid-century. Groundwater use varies significantly across SSPs which results in an average increase of about 29.06% for all SSPs. The proposed framework facilitates the development of future adaptation policies that should consider regional and local planning as well as socio-economic conditions.

In this study, two high voltage gain active Z-source dc/dc converters are proposed, which are suitable for renewable energy applications. The complete analyses of the proposed structures are presented and the suitable control method is proposed. The voltage and current stresses of all elements are calculated. Moreover, in order to recognise the advantages and disadvantages of the proposed converters, a comprehensive comparison between the proposed dc/dc Z-source converters with conventional converters are made. The efficiency of both proposed converters and the conventional ones are also compared. In addition, the application of the proposed topologies in fuel cell vehicle is proposed. Finally, in order to verify the theoretical analyses, simulation results in PSCAD/EMTDS software and experimental results of the prototype are presented.

Water and environmental resources exist in complex and deeply uncertain systems of social-economic and environmental components. As such, natural resource systems are impacted simultaneously by the diverse effects of many interacting human-environmental components. While conventional environmental planning commonly stresses estimation and prediction, preferring top-down initiatives and technocratic solutions, this approach often overlooks socio-economic impacts and interactions, leading to unexpected long-term outcomes. In response, it is now widely acknowledged that frameworks capturing the complex dynamics of society and the environment are needed to develop more sustainable environmental and water resources management strategies. Moreover, for robust policy-making, the performances of potential policies must be considered under multiple plausible conditions to enhance the chances of desired outcomes and limit the risk of undesirable results. This research addresses these challenges by considering deep uncertainty in coupled socio-economic and environmental systems. In this study, a computational model-based approach to support adaptive decision-making under deep uncertainty is developed and applied to adaptive policy-making of sustainable water resources management for human-water systems in developing countries. The Rechna Doab region of Pakistan is considered as a case study. Qualitative-quantitative participatory exploratory modeling is performed by incorporating a physical-socioeconomic system dynamics model, a systematic scenario selection method and a scenario discovery approach. The Driver-Pressure-State-Impact-Response (DPSIR) model is used through storytelling approaches to identify vulnerabilities in policy options in the coupled socio-economic and environmental system by considering its response to drivers, pressures, states, and impacts. Storytelling methods are used to develop qualitative storylines in order to support a detailed and stakeholder-led description of future adaptive management policies. The proposed methodology is used for systematic scenario discovery to uncover vulnerabilities across a range of possible futures and test the performance of stakeholder proposed policies. Also, the tradeoffs between water resources management alternatives, in terms of stakeholder objectives, and their robustness to deep uncertainty are assessed. The proposed approach simulates qualitative and quantitative cause-effect relationships between the environmental system and socio-economic interactions to assess candidate policies, their vulnerabilities and associated adaptive strategies.

In this study, the effect of the channel dimensions on the performance of a marine MHD thruster is investigated using a fully three-dimensional numerical model. In this model, all electric,...

A magnetohydrodynamic (MHD) thruster is a type of electric motors which does not have mechanical moving parts and directly converts electrical energy into mechanical energy. In this study, the effect of magnetic field intensity and seawater electrical conductivity on the performance of a marine MHD thruster model is investigated using fully three-dimensional numerical simulations. For the first time, all electric, magnetic and fluid flow fields are considered in three dimensions. The effects of seawater electrolysis and end loss are taken into account in all simulations and a simple analytical model is developed to verify the numerical results. It is shown that increasing the magnetic field intensity or the electrical conductivity of the working fluid decreases the electrochemical and ohmic losses of the thruster at a specific velocity. Therefore, a higher efficiency can be achieved at higher magnetic field strengths and higher seawater electrical conductivities. Also, it is revealed the end loss of the channel increases with an increase in the electrical conductivity of the working fluid and decreases with an increase in the magnetic field intensity.

An effective planning and management to deal with potential impacts of drought requires accurate estimation and analysis of this natural complex phenomenon. Application of new fusion approaches using high-resolution satellite-based products, unlike ground-based observations, can provide accurate drought analysis. This study examines three advanced fusion-based methodologies including Ordered Weighted Averaged (OWA) approach based on ORNESS weighting method (ORNESS-OWA) and ORLIKE weighting method (ORLIKE-OWA) as well as K-nearest neighbors algorithm (KNN) to fuse estimations by five individual estimator models using different remotely sensed data products. The precipitation data from Global Precipitation Climatology Project (GPCP), CPC Merged Analysis of Precipitation (CMAP), CICS High-Resolution Optimal Interpolation Microwave Precipitation from Satellites (CHOMPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM), The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) and Global Land Data Assimilation System Version-2 (GLDAS-2) products is utilized in estimating nonparametric-SPI as a meteorological drought index versus ground-based observations analysis. To achieve more accurate drought estimation, ground-based observations are classified in different clusters based on K-means clustering algorithm. Five individual Artificial Intelligence (AI) models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), M5P model tree, Group Method of Data Handling (GMDH) and Support Vector Regression (SVR) are developed for each cluster and their best results are used in fusion process. In addition, the Genetic Algorithm (GA) optimization model is utilized to determine optimal weights in weighting methods. Estimation performance of all models are evaluated using statistical error indices of Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2). Application of proposed methodology is verified over Fars province in Iran and the results are compared. Results showed that ORNESS-OWA method with lowest estimation error (MARE of 2.51% and R2 of 95%) had the superb performance in comparison with all other individual AI and fusion-based models. Also, the proposed framework based on remotely sensed precipitation data and fusion-based models demonstrated an effective proficiency in drought estimation.

This study describes the systematic signal flow graph (SFG) modelling of a single inductor triple output (SITO) DC/DC converters. The multi-stages operation of SITO converter and the parasitic elements cause obtaining a model to predict the all behaviours of converter be more difficult than typical converters. By SFG method, all small-signal transfer functions can be derived. Derivations of large-signal, small-signal and steady-state models are demonstrated by considering a single inductor triple output boost/boost convert. The effect of inductor ripple current has been considered to determine the acceptable region of SFG modelling method. Simulation and experimental results are included to show the validity of the obtained model.

This study is trying to develop an alternative approach to the issues of sediment transport simulation. A machine learning method, named least square support vector regression (LSSVR) and a meta-heuristic approach, called particle swarm optimization (PSO) algorithm are used to estimate bed material load transport rate. PSO algorithm is utilized to calibrate the parameters involved in the model to facilitate a desirable simulation by LSSVR. Implementing on a set of laboratory and field data, the model is capable of performing more satisfactorily in comparison to candidate traditional methods. Similarly, the proposed method has a better performance than a specific version of decision tree method. To enhance the model, the variables are scaled in logarithmic form, leading to an improvement in the results. Thus, the proposed model can be an efficient alternative to conventional approaches for the simulation of bed material load transport rates providing comparable accuracy.

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

A rib waveguide structure with a core of As2Se3, upper cladding of air and lower cladding of MgF2 is proposed for generation of a very broad and flat supercontinuum spectrum. Generation of a flat and broad supercontinuum requires high pumping power, but increasing the power leads to material failure in the waveguide. Here we have designed a structure with a high nonlinear coefficient, which can generate a very broad and flat supercontinuum with a width of more than 11 μm by using a pumping power of only 100 W. To the extent that we have investigated the literature, there is no report so far on such a supercontinuum with only an input power of 100 W in waveguides. The appropriate dispersion profile of the structure has resulted in zero dispersion wavelengths (ZDW) at two wavelengths of 2.1 μm and 2.8 μm. Therefore, this makes the laser pump suitable for the designed structure to be available commercially. The high refractive index difference between the core and the cladding, as well as the decrease of the pumping wavelength have led to the obtained small effective mode area. As a result, the high nonlinear coefficient of 65.4 W −1 m −1 is obtained at the central wavelength of 2.4 μm. Our numerical simulations and analyses indicate that by applying a laser pump with a pulse duration of 85 fs and an input power of 100W to the proposed waveguide having only a length of 1 cm, the output supercontinuum is achieved covering the wavelength range of 1.5 μm to more than12 μm, (at the −30 dB level from the peak).

This study presents a new three-dimensional (3D) analytical method for no-load magnetic flux density computation of axial-flux permanent-magnet synchronous machines. In the method suggested, at first the machine's cylindrical coordinates are converted to the Cartesian coordinates using conformal mapping, and then Laplace's equation is solved in Cartesian coordinates in 3D to give the scalar magnetic potential. Finally, the magnetic flux density is computed using the scalar magnetic potential. Use of the analytical method takes much less computational time than 3D finite element method does, and is, thus, useful for designing and optimisation purposes. Validation of this method is performed through the 3D finite element analysis and experimental results.

Grain loss is inevitable during harvesting operations and attempts are made to identify and minimize that. In this study, field performance of five different harvesting methods were assessed which included three indirect harvesting methods of (i) manual cutting + threshing by a tractor driven thresher (T1), (ii) rice reaper + threshing by a tractor driven thresher (T2), (iii) rice reaper + threshing by universal combine harvester equipped with pickup type header (T3), and two direct harvesting methods of (iv) head-feed rice combine (T4), and (v) whole-crop rice combine (T5). The results revealed that the maximum and minimum effective field capacity related to whole-crop combine (0.361 ha h-1) and manual cutting (0.009 ha h-1), respectively. Quantitative losses (grain and panicle shattering) in harvesting and threshing obtained to be 2.58% and 2.33% in average on indirect harvesting (T1, T2 and T3) and direct harvesting (T4 and T5), respectively which were not significant statistically. The average qualitative losses (broken, husked and cracked grains) were 2.30% for indirect harvesting and 0.61% for direct harvesting that showed a decline of 63.3% compared to indirect harvesting. Total harvesting losses were 5.07% for T3 (maximum) and 2.74% for T4 (minimum). The harvesting method affected the percentage of broken rice after milling significantly. The average broken rice for T1, T2 and T3 was 23.72, 23.28 and 24.56% respectively which were significantly higher than T4 (21.05%) and T5 (20.87%). Also, in the view of loss reduction, applying rice combine harvesters had priority respect to indirect harvesting methods.

This paper proposes a fast and optimal model predictive control method to control a three-phase four-leg quasi Z-source inverter. The four-leg predictive control results in the fast performance of the converter control by changing the control flowchart, cost function, and also reducing the volume of computations in the sampling periods. By using the flowchart presented in this paper, if the optimal switching state relates to the shoot-through state, this mode will specify at the beginning of the flowchart without checking the other states. The result of this process is speeding up and improve computing power. The proposed predictive control has the capability of separately controlling each phase, which confirms the simulations performed in MATLAB/Simulink software.

In this paper, a 12-pulse rectifier topology with hysteresis controller is presented that has a harmonic of less than 3% input current total harmonic distortion. The designed rectifier has two diode bridges each connected to a Boost converter. The autotransformer is used as a phase shift transformer, which leads to a noticeable decrease in transformer volume. The Boost converter inductor current is computed from the theoretical equations for conditions where the input current is sinusoidal. Then, this current is implemented by the hysteresis controller. The designed controller has good flexibility and can be used in a different operating conditions. The simulation results show that the THD of the input current using the proposed rectifier is lower than 3% with hysteresis controller.

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca2+,Mg2+,Na+,K+,HCO3-,CO32-,SO42-,andCl- concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and -0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

Compound dry-hot events enlarge homogenously due to teleconnected land-atmosphere feedbacks., Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.

Terrestrial evapotranspiration (ETa) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ETa remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ETa data, the interannual variability of ETa and its ties to precipitation (P), potential evaporation (ETp) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ETa to climate change's direct (P and ETp) and indirect (NDVI) impacts. The ETa, P, ETp and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr−1, 3.18 mm yr−1, 0.89 mm yr−1 and 4.0 × 10−4 yr−1, respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ETp to the change in ETa (∆ETa). The positive ∆ETa between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ETa differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ETa, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ETp was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ETa are modulated by both the sensitivity of impact factors acting on ETa as well as the magnitudes of factor changes. The greening of vegetation can influence ETa by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions.

Conflict-resolution models can be used as practical approaches to consider the contradictions and trade-offs between the involved stakeholders in integrated water resource management. These models are utilized to reach an optimal solution considering agents interactions. In this paper, a new methodology is developed based on multi-objective optimization model (NSGA-II), groundwater simulation model, M5P model tree, fallback bargaining procedures and social choice rules to determine the optimal groundwater management policies with an emphasis on resolving conflicts between stakeholders. By incorporating the multi-objective simulation-optimization model and bargaining methods, the optimal groundwater allocation policies are determined and the preferences of the stakeholders as well as social criteria such as justice are also considered. The obtained data set, based on Monte Carlo analysis of calibrated MODFLOW model, is used for training and validating the M5P meta-models. The validated M5P meta-models are linked with NSGA-II to determine the trade-off curve (Pareto front) for the objectives. Social choice rule and fallback bargaining methods, as conflict-resolution models, are applied to determine the best socio-optimal solution among stakeholders, and their results are compared. The effectiveness of the proposed methodology is verified in a case study of Darian aquifer, Fars province, Iran. Results indicated that the solutions obtained by the proposed conflict-resolution approaches have an appropriate applicability. Total groundwater withdrawal, after applying the optimal groundwater allocations, reduced to 20.85 MCM, resulting in a 4.62 m increase in the mean groundwater level throughout the aquifer.

Physical, milling and cooking attributes of the main and ratoon crops in three rice cultivars, namely Hashemi, Alikazemi and Tarom, were compared in a factorial layout based on randomized complete block design in five replications. The results revealed that for each tested cultivar, the length and slenderness ratio of the main rice grains was significantly (P

An agent-based-Nash modeling framework has been developed to find a sustainable solution for groundwater management in Daryan Aquifer, Fars Province, Iran. This framework also includes a MODFLOW simulation model, an Artificial Neural Network (ANN), and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization model. Groundwater state was simulated using MODFLOW and it was calibrated based on the measured data provided by Regional Water Organization (RWO) of Fars Province. In order to reduce the computational time, an ANN was trained and validated based on the input-output data of the MODFLOW model to estimate groundwater level. The validated ANN was linked to a nonhomogeneous elitist NSGA-II multi-objective optimization model to find a Pareto optimal front among the three objectives of reducing irrigation water deficit, increasing equity in water allocation, and reducing groundwater drawdown, as the objectives of the three main groundwater resource stakeholders; farmers, the government executive sector, and the environmental protection institutes. The Nash bargaining model was applied to the optimal solutions in order to find a compromise among the stakeholders. Social influential factors in the study environment, and policy mechanisms to encourage agents to cooperate with the management decisions were implemented in the agent-based model. These factors include training, incentives, penalties, and social norming (neighbors' impacts), as well as considering the executive and judicial systems. After application of the agent-based model, computed optimum solutions were modified according to social conditions. Finally, the Nash bargaining model was used again to find a compromise among modified optimal objectives of the stakeholders. Implementation of this solution led to 58.3% less water extraction and approximately 3 m water level uplift.

This study aimed to investigate the effects of nitrogen rates, tillage, and planting methods on the energy use efficiency (EUE) and greenhouse gas emissions (GHG) of rapeseed (Brassica napus L.) cultivation after rice harvesting in paddy fields. A split factorial experiment in a complete randomized block design was conducted at the Rice Research Institute of Iran during 2016–2018. The conventional tillage, minimum tillage, and no-tillage methods were considered as the main plots and two planting methods of direct seeding and transplanting, and the amounts of 0, 100, 200, and 300 kg ha−1 of pure nitrogen (N) as factorial in subplots. The results showed that the treatments of CTN3 (conventional tillage × transplanting × 300 kg of N fertilizer) with the averages of 39174.8 MJ ha−1 and 1108.36 kg CO2 eq. ha−1, and CDN3 (conventional tillage × direct seeding × 300 kg of N fertilizer) with 38386.0 MJ ha−1 and 1138.36 kg CO2 eq. ha−1 had the highest energy input and GHG emissions, respectively. Maximum EUE and energy productivity and low GHG were obtained using the treatment combinations of no-tillage and minimum tillage at 100 kg ha−1 of nitrogen in both planting methods, suggesting them as the promising choices for planting rapeseed in Guilan province.

Energy, water, and food resources are highly interdependent. Agricultural irrigation accounts for 84% of global consumptive freshwater use, the food supply chain demands up to 30% of global primary energy use, and roughly 80% of global electricity generation depends on water for cooling (an average of nearly 100 L of water withdrawn per kWh). Improving understanding of the complex interactions of this resource nexus is, therefore, a top priority for human well-being, sustainable development, and policymaking. Here, we present an interactive analysis toolbox, Nexus of Food, Energy, and Water (NeFEW), that synthesizes available global data to enable modeling and analysis of these resources and their interdependencies at the country-level and for user-specified categories and quantities. Sample analyses also presented here include country-specific estimates of water resources required to produce different types of food and energy, energy required per quantity of water or agricultural product supplied, and C O 2 -equivalent emissions associated with water and energy provision.

Dynamically Coupled Socioeconomic system dynamics models integrated with physically-based Environmental Models (DCSEM) can capture relationships between complex environmental and socioeconomic systems, and are promising tools for participatory environmental management involving the integration of various viewpoints, disciplines and processes for sustainable water resources management. However, the application of DCSEMs has been limited for many reasons, including the complexity of the model coupling process (i.e., the lack of a flexible model coupling approach), issues of over-parameterization, high parameter uncertainty, intensive computational requirements (due to many interacting parameters), and the possibility of eliciting behaviour-pattern oriented model outputs. As such, most conventional (numerical) procedures for model evaluation (i.e., analysis of point values of modelled results) that have been implemented for physically-based models are not applicable to DCSEMs. In order to address these challenges, this study developed a novel automatic behaviour-pattern global sensitivity analysis (GSA) procedure to determine the influence of input parameters on the general behaviour trends (rather than numerical point values) of coupled model outputs. The developed behaviour-pattern GSA procedure was implemented in an existing software (Tinamit), developed previously by the authors, to ensure ease-of-use. This study investigates the suitability of the proposed behaviour-pattern GSA procedure for the analysis of DCSEMs by comparing the proposed procedure with the conventional numerical procedure. The numerical and proposed behaviour-pattern procedures, coupled with the Morris (qualitative) and EFAST (quantitative) GSA methods, were applied to a DCSEM to rank and screen parameters in a water table depth simulation in Pakistan. The determination of important parameters facilitates subsequent model calibration and groundwater management. It was found that the Morris and EFAST methods achieved similar parameter ranking results in the numerical and behavioural procedures, respectively. The results also indicated that the behaviour-pattern GSA procedure offers more information about several important parameters, and a variety of parameter ranking orders, compared to the numerical GSA procedure, regardless of which GSA method (Morris or EFAST) was used. The increased information obtained through the application of the developed behaviour-pattern procedure confirms that the DCSEM model outputs are behaviour-oriented. It is recommended that the proposed behaviour-pattern GSA procedure be used with the Morris method, which has a higher computational efficiency (>150 times) than the EFAST method, to detect important parameters in DCSEMs.

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin-Shahr plain, Iran. Twenty-two groundwater samples were collected from wells in the study area, and their respective NO3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon-GA-DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R2 of 0.22, the Wilcoxon-GA-DRASTIC obtained a maximum R2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R2 value to the highest degree.

In this research, a fuzzy inference system (FIS) coupled with image processing technique was developed as a decision-support system for qualitative grading of milled rice. Two quality indices, namely degree of milling (DOM) and percentage of broken kernels (PBK) were first graded by rice processing experts into five classes. Then, images of the same samples were captured using a machine vision system. The information obtained from the sample image processing was transferred to FIS. The FIS classifier consisted of two input linguistic variables, namely, DOM and PBK, and one output variable (Quality), all in the form of triangle membership functions. Altogether, 25 rules were considered in the FIS rule base using the AND operator and Mamdani inference system. In order to evaluate the developed system, statistical performance of the FIS classifier was compared with the experts’ judgments. Results of analysis showed a 89.8% agreement between the grading results obtained from the developed system and those determined by the experts.

Among the cereals, rice is the major foodstuff for a large part of the world’s population. Due to its tremendous importance in the global market, its qualitative economic aspects during processing have always been attended by producers. As the most delicate of the cereals, rice needs the utmost care during post-harvest handling and processing, because in most cases, it is consumed as whole kernel. The growing demand for production of rice with high-quality and safety standards has increased the need for its accurate, fast and objective quality monitoring. Computer vision techniques, as novel technologies, can provide an automated, nondestructive and cost-effective way to achieve these requirements. In recent years, various studies have been conducted to evaluate rice qualitative features based on computer vision techniques. This paper presents the theoretical and technical principles of computer vision for nondestructive quality assessment of rice combined with a review of the recent achievements and applications for quality inspection and monitoring of the product.

A two-year research was conducted to investigate the effect of different tillage methods on some soil physical characteristics and crop yield in rapeseed cultivation after rice harvesting. Five tillage treatments including: (i) using rotavator, once to depth of 10-15 cm (T1), (ii) using rotavator, twice to depth of 10-15 cm (T2), (iii) using moldboard plow to depth of 25 cm + rotavator, once to depth of 10-15 cm (T3), (iv) no-till planting through removing rice stubbles from plots (T4), and (v) no-till planting without removing rice stubbles from plots (T5), were evaluated under randomized complete block design (RCBD) in three replications. The biannual results revealed that the effect of tillage methods was significant (p

So far, several methods to reduce the cogging torque of permanent magnet motors have been introduced. Implementation and evaluation of these methods have usually been done on radial flux types of motors. Nowadays, as axial flux permanent magnet motors have more advantages over radial ones, they are more attractive. Therefore, in this paper analytical modeling and calculation of the most effective method impact in reducing the cogging torque in axial flux permanent magnet motors will be studied. In fact, in this method the radial edges of the magnets will be curved to have a significant impact on reducing this unwanted component. This paper introduces a new concept to model this method. Finally, the accuracy of the proposed method will be verified by finite element analysis.

The Super conducting magnetic energy storage (SMES), owing to high energy density and capacity, has been widely applied in different stages of power systems. One of these applications is the frequency control of the electric power systems. Frequency of a power system depends on the balance of produced and demanded energy in any instant of time. Subsequent to a sudden change in the system, which causes produced and demanded energy mismatch, frequency oscillates. According to standards, the permissible variation band of the frequency is very restricted. Larger swings of frequency may result in instability and undesirable trips. As a result, suitable frequency control mechanisms should be implemented in the system. SMES is well-suited for this application because of high energy density and fast response. SMES is attached to system by a power conditioning system (PCS) which include power electronic converters, mainly a dc–dc chopper and an inverter. This paper, studies the application of a cascaded H-bridge (CHB) multilevel converter for frequency control. As far as the authors' knowledge, such a study has not been done before. The design procedure of the converter is presented. Simulation results on a sample system are presented to verify the performance of the proposed PCS.

Sustainable urban surface runoff management is receiving increased attention due to environmental and ecological consequences related to urbanization. This study presents a useful new framework for green infrastructure (GI) planning for the management of urban runoff quality and quantity. The proposed framework considers physical, technical, economic, and multi-stakeholder aspects related to urban runoff management while simultaneously addressing the uncertainties of the decision-making process and model parameters. The methodology is applied to regionally locating and sizing low impact development-best management practices (LID-BMPs) while considering the hydrologic and hydraulic impacts. A decision-making multi-objective optimization framework is developed by integrating: 1) a Multi-Layer Perceptron neural network founded on a Storm Water Management Model (SWMM-MLP meta-model), 2) NSGA-II multi-objective optimization, 3) fuzzy α-cut technique, and 4) a decision-making support model based on social choice theory to elicit trade-offs among system cost and LID-BMP performance indicators. The decision-making model, based on Fuzzy Social Choice (FSC) theory, is applied to simulate consensus between stakeholders for a partially cooperative group decision-making problem. The proposed methodology is explored in a catchment located in the northeastern part of Tehran, Iran. Results showed that the SWMM could be effectively replaced with a MLP-based meta-model in simulation-optimization problems for urban runoff management. In the application of FSC methods, the optimal scenarios were effective in reducing the volume of urban runoff and contamination loads while maintaining the optimality of the operation costs. Considering the optimal LID scenario, a reduction of more than 99% in runoff volume and biochemical oxygen demand (BOD), and a decrease of more than 92% of total suspended solids (TSS), occurred for the lower bound of uncertainty (lower (left) end of the α-cut = 0.3). For the upper bound of uncertainty (upper (right) end of the α-cut level = 0.3), a maximum reduction of 57% was obtained. In applying FSC methods through the decision-making process, the Borda Counting method considered the preferences of all stakeholders best. Moreover, the proposed framework allows decision-makers to decide on the acceptability and reliability of the optimal management scenarios considering their preferences and uncertainties.

The multilevel inverters have been in the center of focus of many researcher in recent years. This is partly because of some advantages such as improved output power quality. The multilevel inverters can generate a near sinusoidal output voltage. The quality of output voltage depends on the number of voltage levels of inverter. This paper proposes and analyses a generalized cascaded multilevel inverter topology with reduced switching devices. As the proposed topology is a general topology, it offers provisions to design a desired multilevel inverter. Also, the topology optimization of the proposed cascaded multilevel considering some factors such as number of switching devices, number of output voltage levels and the standing voltage on the switches is given. Also, considering the generality of the proposed multilevel inverter, the conventional cascaded H-bridge (CHB) multilevel inverters (both symmetric and asymmetric) can be derived form the proposed topology. In addition, some facts related to the conventional CHB topologies are proved mathematically. For instance it is proved that the trinary CHB topology is the best topology in the case of asymmetric condition. In order to verify the proposed topology, a 13-level inverter has been simulated and experimentally implemented.

The aims of this paper are the simple comparison of the axial flux (AF) structures versus the radial flux (RF) structures for brushless permanent-magnet (PM) synchronous motors and proposing a cost effective position sensorless control strategy for AFPM Brushless DC Motor. The comparison is developed for different motor dimensions. Moreover, a cost effective and position sensorless control strategy proposed for AFPM brushless DC (BLDC) motor drive using single current sensor. It is based on the generation of quasi-square current waveforms, using only one current controller for the three phases. Unlike the conventional methods, the proposed method presents advantages such as very simple control scheme, without needing to triangular carrier modulation, and balanced phase currents. The proposed position sensorless technique is based on the detection of zero crossing points of three voltage functions that are derived from the difference between the lines’ voltage measured at the terminals of the motor. This control algorithm is particularly suitable for cost sensitive product such as air purifiers, air blowers, cooling fan, and related home applications. The effectiveness of the proposed control system have been validated by simulation results.

In this paper we assess the effects of nonlinear optical subsystem and linear time variant RF channel of Radio-over-Fiber (RoF) in Wideband Code Division Multiple Access (WCDMA) on the system bit error rate (BER) performance by computer simulations. These effects are compensated by pilot aided estimation and equalization approach at different Output Back-Off (OBOs) levels. BER plots show that this approach is an effective strategy even at maximum nonlinearity i.e. 0.0 dB OBO.

In this paper the impact of a Radio-over-Fiber (RoF) optical subsystem on the sensitivity to the phase noise of Orthogonal Frequency Division Multiplexing (OFDM) in a multiple-input multiple-output (MIMO) system is evaluated and a method for reducing the effect of this phase noise is presented using computer simulation. The study investigates the effect of phase noise on the system Bit Error Rate (BER) for different Output-Back-Off levels in the presence of optical subsystem's nonlinearity in an AWGN channel. Hence, a way to reduce its effect is presented by estimating this phase offset that it is called synchronization of phase noise.

Radio-over-fiber (RoF) is a technology in which a radio system seeking to access a wider bandwidth, uses optical methods and tools to distribute information. This research is an attempt to assess and compensate for the nonlinearity of an optical subsystem in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based RoF system with a radio subsystem in an additive white Gaussian noise (AWGN) channel. By employing Alamouti code as a kind of orthogonal space-time block coding (OSTBC), we demonstrated improvement some systems performance.

Multilevel power converters are key elements in the electrical grid-tied energy systems. This paper presents a novel topology for stacked multicell (SM) multilevel converters. The suggested SM converter has considerable interests of applications for interconnection of energy systems (renewable energy generation systems, energy storage systems, HVDC (high voltage direct current) systems, FACTS (flexible alternating current transmission system) devices, and custom power devices) to the electric power systems. The main advantages of the proposed converter, compared to the conventional SM converter, for attaining the aforementioned purpose are the reduction of the DC voltage energy sources from two to one, power switches (IGBT's) voltage rating, converter cost and size, losses, and installation area. The proposed SM converter has been analytically modeled by obtaining the switching instants of the PWM in terms of the Kapteyn series. Numerical solution results for the derived model of the proposed converter, simulation results, and measurements taken from an experimental set-up are presented in order to validate the operation and performance of the suggested multilevel topology. The proposed SM converter has been exploited in a dynamic voltage restorer (DVR). This proposed custom power device is based on the one energy storage device (battery, capacitor, super capacitor) with halved voltage rating owing to utilizing the suggested SM multilevel inverter (DC/AC converter) as an interface between its energy storage component and the electrical grid. Simulation results confirm the capability of the suggested energy storage based grid-tied custom power device (DVR) in solving power quality problems.

This paper proposes an improved configuration of double flying capacitor multicell (DFCM) converter. The main advantages of the proposed converter, compared to the conventional DFCM converter, are the doubling of the number of output voltage levels and improvement of the output voltage frequency spectrum. This progress is achieved by adding only two low-power switches and one dc voltage source, whose voltage rating is a small fraction of the main dc-link voltage rating, to the conventional configuration of the DFCM converter. However, the number and voltage rating of high-frequency switches and capacitors and the number of high-frequency switchings during a full cycle are kept constant. The doubling of the number of output voltage levels in the proposed converter makes it possible to decrease the number of cells, the number of flying capacitors, as well as their voltage rating and the amount of stored energy in flying capacitors. Moreover, a modulation method based on phase-shifted carrier pulsewidth modulation is proposed to control the new converter. Simulation and measured experimental results are presented to illustrate the performance of the proposed configuration and its control strategy.