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Mapping lithological units and conducting mineral exploration in a relatively short time and at a reduced cost, requires high-resolution satellite data and state-of-the-art image processing approaches and methods. In this regard, the current paper aims to test an integration of visible-near infrared imagery from WorldView-3 (WV-3) and object-based classification for mapping potential deposits of ocher in Hormuz Island, southern Iran. A combination of field observations, spectroscopy, microscopic mineralogy, and geochemical-based XRD and XRF analyses were conducted on the samples collected throughout the study area. The reflectance and absorption features of the ocher were extracted from spectroscopic measurements of samples from well-known ocher mines at the area, and were then convolved to the WV-3 bands. Results showed that the spectral characteristics of ocher are governed by iron oxides and clay minerals with a presence of distinct strong absorption and high reflectance features in the 510–625 nm and 630–745 nm wavelength regions, respectively. These features are complemented by molecular vibration processes of water O–H intramolecular stretching and H–O–H bending that generate absorption features in the 1440–1940 nm region. Additional absorption features in the 2210–2300 nm are most likely due to the Al-OH and CO3−2 vibrations. The absorptions centered at 480 nm, 540 nm, and 820 nm correspond to bands 2, 3, and 7 of the WV-3, respectively, whereas the high reflectance feature centered near 700 nm corresponds to band 6. These four bands, which were considered as index bands of ocher in this study, were used to assign the segmentation weights and to create the thresholds during image processing. Brightness, density, compactness, and homogeneity features were the primary factors for selecting the training areas in the index bands. The ocher-rich areas were enhanced by using a two-stepapproach of object-based image analysis (OBIA) and image classification, for suitable threshold selection. The information about the feature variables of the ocher within the image and lithological object hierarchy were obtained to evaluate the features of ocher deposits. Information extracted from the index bands provided an important description of the object features, including mean, standard deviation, minimum and maximum pixel values, hue, saturation, and intensity, while the training areas obtained information from the three known ocher mines. Considering the fixed numerical range of ocher mines as a reference, the fixed numerical range of general and specific features of ocher in index bands of WV-3 were achieved. The numerical ranges of pixels were used for creating threshold conditions when applying the "assign class" algorithm in bands 2, 3, and 7 are 815–975 nm, 930–1052 nm, and 1721–1904 nm, respectively. The presented OBIA approach shows a high potential with an overall accuracy of 88 % to discriminate deposits of ocher based on compatibility between ocher mines and identified ocher-bearing pixels. [ABSTRACT FROM AUTHOR]

• A novel approach for regional modeling and forecasting of PWV using LS-SVR is suggested. • The results of the new model are analyzed at control and radiosonde stations. • A comparative analysis between the results of new model, GPS and VBT is made. We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVR PWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting. [ABSTRACT FROM AUTHOR]

This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWD SVM) and ANN (ZWD ANN) is compared with the ZWD obtained from the GPS (ZWD GPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWV SVM , PWV ANN) is compared with the radiosonde PWV (PWV radiosonde) and voxel-based PWV (PWV VBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. [ABSTRACT FROM AUTHOR]

• Identification of important indicators for process safety. • A comparative study of process safety indicators in developing and developed countries. • Measuring the relative importance of the indicators using the Fuzzy Best Worst Method. Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators. [ABSTRACT FROM AUTHOR]

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map. [ABSTRACT FROM AUTHOR]

This research aims to improve the traditional statistical approach to evaluate the seismic resilience of concrete structures exposed to chloride attack. In the mentioned approach, the uncertain parameters are modeled using certain probability distribution functions (PDFs) selected from some standards such as fib. However, the mentioned PDFs should contain the actual conditions of the desired structure. This paper introduces a Bayesian framework for updating PDFs used in deterioration analysis, which is an important part of the life-cycle seismic resilience assessment process of corroded concrete structures. The proposed methodology was applied to a two-story reinforced concrete school in Iran that suffered from chloride corrosion for twenty years. The seismic resilience of the structure was investigated for a 50-year life cycle. The results showed that the updated model can significantly reduce the uncertainty of the influencing parameters in the seismic resilience evaluation process. Using the updated model resulted in a conservative seismic resilience index compared to the non-updated model. This research shows that the role of inspection data in periodically updating the seismic-resistance calculations of corroded concrete structures is not only necessary but also possible and beneficial. • A new methodology for assessing seismic resilience of corroded reinforced concrete systems. • The proposed algorithm integrates the structural inspection data with the seismic resistance assessment process. • The uncertainty of results is reduced using Bayesian framework. • The efficiency of the methodology is evaluated using a real-world case study. [ABSTRACT FROM AUTHOR]

Water scarcity threatens food security and leads to various economic, social, and environmental challenges, particularly in arid and semi-arid regions. As the agricultural sector consumes the most significant amount of water, conserving water resources in this sector is particularly important. Cultivation of low-water-demand (LWD) crops with high value-added is one of the effective measures to reduce agricultural water demand and improve farmers' livelihoods. However, changing water-intensive crops to LWD crops (e.g., medicinal plants) is voluntary pro-environmental behavior. Considering the role of psychological theories in understanding and explaining human behaviors, the main purpose of the current paper is to explore the socio-psychological determinants underlying farmers' intention to adopt an LWD cropping pattern using the Health Belief Model (HBM). In this regard, data were collected using face-to-face interviews with 184 farmers living in Zanjan province, Iran to examine their intention to cultivate LWD medicinal plants. Using structural equation modeling for data analysis, the HBM explained 49% of the variance in farmers' behavioral intentions. The structural equation modeling outcomes also revealed that self-efficacy, perceived barriers, cues to action, and perceived benefits are significantly related to farmers' intention to choose medicinal plants. Overall, the findings indicated the effectiveness and applicability of the HBM in illustrating farmers' intention to change their cropping pattern. Applying some strategies such as providing the necessary facilities and training programs for farmers, as well as, advertisements aimed at promoting the cultivation of medicinal plants can significantly contribute to conserving water resources in the study area. • Evaluating farmers' intention to medicinal crops using the Health Belief Model. • The HBM accounted for 49% of the variance of the farmers' behavioral intentions. • Self-efficacy, perceived expectations, and cues to action affect farmers intentions. • Self-efficacy was identified as the main predictor of farmers behavioral intentions. • Cultivating medicinal crops help conserving water resources in the study area. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of reliable and green routing in a road-rail multimodal transportation network. To enhance network reliability, types of disruptions occurring simultaneously in each node and arc are identified. Additionally, carbon emission is considered a risk caused by transportation process disruptions for achieving green routing. A combination of rating methods such as DEMATEL-based Analytic Network Process (DANP) and the Combined Compromise Solution (COCOSO), along with Failure mode and effects analysis (FMEA) Criticality reliability assignment method, is used to evaluate disruptions and select a safe route from origins to destinations. The DANP method produces a ranking of sub-criteria, and the COCOSO method produces a ranking of criteria in the network. The FMEA Criticality reliability assignment method is used to assign reliability to each option. Next, the route with the highest reliability from each origin to each destination is selected. The presented model investigates a cement road-rail transportation network in Iran. After analyzing the sensitivity of criteria and sub-criteria, the results provide best routes with increased reliability and reduced emissions risk. • Identifying risks in each node and arc in the transportation network. • Prioritizing criteria, sub-criteria, and options using a mix of DANP and COCOSO ranking methods. • Assessing the reliability of each option using the FMEA Criticality method. • Selecting the best path with the highest reliability and the less carbon emission risk in each O-D pair. • Considering a case study in the cement road-rail transportation network in Iran. [ABSTRACT FROM AUTHOR]

• This paper proposes an Indexed Meta-Goal Planning Model to solve imprecision in data. • This integrated model is a novelty and has been applied to sustainable optimal cropping patterns. • It has been tested in Iran to solve conflicting uses of natural resources. • The results compared with Sen and Pal show no variation in land allocation for each crop. • the least variation in all problems is for wheat and barley production. Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning. [Display omitted] [ABSTRACT FROM AUTHOR]

Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants. [ABSTRACT FROM AUTHOR]

[Display omitted] • We considered two strategies of electricity purchasing for industries: TB and NTB. • We applied an evolutionary game theory approach to examine industries' behavior. • We studied three real-world cases of Iran: cement, steel, and paper industries. • We mainly focused on finding the population ratio convergence to TB/NTB strategies. Electricity shortages severely affect the production chain and cause damage to manufacturing productivity. This study examines how the industrial sector can transform technological innovations toward renewable electricity procurement. We consider two strategies for manufacturers: purchasing electricity from a technology-based supplier (TB strategy) and purchasing it from a non-technology-based supplier (NTB strategy). Manufacturers need technological transformation, such as upgrading their electrical system toward digitalization to manage power outages. We examine manufacturers' pricing decisions and their long-term behavior to adopt the TB and NTB strategies using a one-population evolutionary game-theoretic approach. Also, we investigate the majority of manufacturers' population that eventually choose the best strategy. Moreover, we apply the present model to real-world cases of Iran's cement, steel, and paper industries. The results show that the evolutionary behavior of sectors with high annual electricity consumption, such as steel, converges to the TB strategy faster than cement and paper industries. In contrast, the cement and paper industries are more sensitive to subsidy allocation than the steel industry with respect to electricity procurement strategies. [ABSTRACT FROM AUTHOR]

• This study applied a logistic growth model with parameters estimated by a non-linear least squares (NLS) method to daily new COVID-19 cases. • The model fitted time-series data exceedingly well for the whole of China, eleven selected Chinese provinces and municipalities, South Korea and Iran. • This study provided key estimates and a 95% confidence interval for parameters K , r, and P 0. • This study found that the growth rates of outbreaks differed between provinces in China and between South Korea and Iran. • As of March 13, 2020, this study's model suggested that countries such as the U.S.A., France, Italy, and Spain were still in the early stages of outbreaks. As the coronavirus disease 2019 (COVID-19) pandemic continues to proliferate globally, this paper shares the findings of modelling the outbreak in China at both provincial and national levels. This paper examines the applicability of the logistic growth model, with implications for the study of the COVID-19 pandemic and other infectious diseases. An NLS (Non-Linear Least Squares) method was employed to estimate the parameters of a differentiated logistic growth function using new daily COVID-19 cases in multiple regions in China and in other selected countries. The estimation was based upon training data from January 20, 2020 to March 13, 2020. A restriction test was subsequently implemented to examine whether a designated parameter was identical among regions or countries, and the diagnosis of residuals was also conducted. The model's goodness of fit was checked using testing data from March 14, 2020 to April 18, 2020. The model presented in this paper fitted time-series data exceedingly well for the whole of China, its eleven selected provinces and municipalities, and two other countries - South Korea and Iran - and provided estimates of key parameters. This study rejected the null hypothesis that the growth rates of outbreaks were the same among ten selected non-Hubei provinces in China, as well as between South Korea and Iran. The study found that the model did not provide reliable estimates for countries that were in the early stages of outbreaks. Furthermore, this study concured that the R 2 values might vary and mislead when compared between different portions of the same non-linear curve. In addition, the study identified the existence of heteroskedasticity and positive serial correlation within residuals in some provinces and countries. The findings suggest that there is potential for this model to contribute to better public health policy in combatting COVID-19. The model does so by providing a simple logistic framework for retrospectively analyzing outbreaks in regions that have already experienced a maximal proliferation in cases. Based upon statistical findings, this study also outlines certain challenges in modelling and their implications for the results. [ABSTRACT FROM AUTHOR]

• Monte Carlo simulation has been employed to assess the long-term outage data. • The CCDFs are converted into algebraic functions using the high-accuracy regression. • Outage costs are calculated through the estimated outage duration and designed CDFs. • The annual outage duration of distribution feeders is predicted using Monte Carlo. This paper introduces an effective evaluation method for customer outage costs and durations based on defining customer damage functions (CDFs) and processing the long-term interruption data. The proposed approach sorts and processes interruption data, utilizing the Monte Carlo technique to assess outage durations. Then, by weighting sector customer damage functions in each load point, composite customer damage functions (CCDF) are defined. The determined CCDFs are transformed into algebraic functions through curve fitting for any substation. Then, using the estimated outage duration and designed CCDF, the customer outage costs are calculated at any load center. The methodology introduced enhances the predicting accuracy of outage duration and cost by reducing the influence of rare lengthy outages and amplifying that of recurring events. Furthermore, this study defines fixed CCDFs independent of outage durations and could be employed for future time intervals of the proposed load points. Moreover, sorting data based on outage durations improves the accuracy of Monte Carlo outage estimation. The proposed method validation is evaluated on the sub-transmission system of Sistan and Baluchestan, Iran. [ABSTRACT FROM AUTHOR]

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices. • A novel machine learning (KELM-INFO) approach for forecasting TDS is created. • The KELM-INFO is validated by four well-known machine learning methods. • The V-KELM-INFO model is assessed against the metaheuristic-based V-KELM models. • This work provides insight into improving the accuracy of model-based methods. [ABSTRACT FROM AUTHOR]

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area. [ABSTRACT FROM AUTHOR]

• The M w = 7.3 earthquake near the Iran-Iraq border in west Iran occurred in 12.11.2017. • Median, Kalman filter and NN were implemented to investigate TEC measurements. • Swarm data analysis identifies anomalies 8–11 days prior to the earthquake. • Climatological data analysis identifies anomalies that precede ionospheric anomalies. • The sequence of different anomalies confirms a bottom-up LAIC process. The M w = 7.3 earthquake near the Iran-Iraq border in west Iran (34.911°N, 45.959°E) occurred at 18:18:17 UTC (LT = UTC + 03:30), November 12, 2017 as the result of oblique-thrust faulting at mid-crustal depth (∼19 km). Median, Kalman filter and Neural Network, as three standard, classical and intelligent methods, have been implemented to investigate three months of GPS Total Electron Content (TEC) measurements and to detect the striking anomalous variations around the time and location of the mentioned earthquake. The first method detects unusual variations, 9 days before the event, between 21:00 and 22:00 UTC. The other two methods of Kalman filter and Neural Network detect another clear anomaly on 11 days preceding the earthquake at 16:00 UTC. These findings are two of the outstanding results of GPS-TEC precursor analysis. This paper also presents the results of Swarm satellites (Alpha, Bravo and Charlie) data analysis inside the Dobrovolsky area around the Iran earthquake epicenter during the period from 1 August to 30 November 2017. The time series and orbital analysis of six measured parameters including electron density, electron temperature, magnetic scalar and vectors (X, Y, Z) components indicate irregular variations between 8 and 11 days prior to the occurrence of the earthquake. Since the variations of the solar and geomagnetic indices follow a normal behaviour during the whole period of the observed ionospheric anomalies between 8 and 11 days before the earthquake, it can be concluded that multi-precursors analysis has an important role to acknowledge the seismo-LAI (Lithospheric-Atmospheric-Ionospheric) anomalies associated to strong earthquakes such as this case. Furthermore, some physical and chemical atmospheric parameters from a climatological database are investigated and some interesting anomalies above two standard deviations prior to the earthquake are found. This paper shows not only anomalies in atmosphere and ionosphere but also a contemporary analysis of different data sources to detect the possible Lithosphere Atmosphere Ionosphere Coupling (LAIC) effects. [ABSTRACT FROM AUTHOR]

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH. [ABSTRACT FROM AUTHOR]

[Display omitted] • Energy-Water-Food security nexus of mung bean production in Iran is analyzed. • Non-renewable cumulative energy demand is estimated at 27.4 MJ/kg of beans. • Total water footprint is estimated at 1.55 m3/kg of beans. • On-farm production concentrates the largest environmental impacts (40% to 96%). • Cooking accumulates over 50% of the carbon footprint. Mung bean is a very important crop in Iran in both socio-economic and nutritional terms. However, although discussions on food and food security increasingly include sustainability issues, there are no precedents in academic literature that analyze in depth the nexus between energy, water use and food security in relation to this crop in Iran from an agri-food system approach. Therefore, our main objective is to assess the energy-water-food security (EWFs) nexus and the environmental impact of mung bean production in Iran from a "cradle to fork" approach using different nutritional units (1 kg of beans, 1 kg of proteins, and 1000 kcal) and load allocation criteria. In addition, an economic analysis of the farms is carried out. The results show that the on-farm production of mung beans is the phase where the largest environmental impacts are concentrated (between 40 % and 96 % of them, including those related to water and energy use), while cooking accounts for more than 50 % of the carbon footprint. The non-renewable cumulative energy demand (NR CED) and total water footprint (TWF) per kilogram of beans ("cradle to fork") is estimated at 27.4 MJ and 1.55 m3 and the farm Net Margin (NM) is estimated at 3,677 USD per ha. The paper discusses whether mung bean is a low-impact option for protein production, especially when compared to animal products and the importance of using different functional units and load allocation criteria to address the issue of EWFs and sustainability. In this regard, further research is needed to improve the environmental efficiency of bean production, which is critical for promoting sustainable diets in line with food security goals. [ABSTRACT FROM AUTHOR]

This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city. [Display omitted] • Potential of municipal solid waste (MSW) gasification for all Iranian cities. • Comparison of using air, steam, and air-steam agents for MSW gasification. • The payback period of air agent MSW gasification is lower than other agents. • Machine Learning used for prediction of the behavior of MSW gasification. [ABSTRACT FROM AUTHOR]

One of the most important features of a reliable power system is its capability to supply the demand continuously. This continuous supply has been maintained by the transient stability of the system against large disturbances. The study of this type of stability is examined through different indicators. One of the common indicators to evaluate the transient stability of the power system is the well-known Critical Clearing Time (CCT) index. Conventional methods for calculating CCT have presented good accuracy, however, their computational cost is very high which makes them not suitable for real-time applications and real large-scale networks. Considering their ability to feature extraction of big data, deep neural networks can be utilized as reliable tools to cover these deficiencies. In this regard, to cover the shortcomings of the conventional methods, this paper proposes a method based on deep Convolutional Neural Networks (CNN) to estimate the CCT index in real-time power system applications. Moreover, to analyze a realistic case, nonlinear limiters of the excitation systems which have a considerable effect on transient stability index are considered in this study. Thanks to the using of several deep layers and the comprehensive established database, the accuracy of proposed method is appropriately high. Numerical studies on IEEE standard networks as well as a real case in Iran Power Grid (IPG) represents the advantages of the proposed method. • A deep-learning-based convolutional neural network is designed to estimate CCT index of power system. • Nonlinear limiters of the excitation system are taken into account. • A real case study in Iran grid is considered for simulation. • As a result, the transient stability of system is accurately determined in on-line manner. [ABSTRACT FROM AUTHOR]

Water impoundment combined with more frequent precipitation extremes due to climate change increases landslide hazards on the slopes surrounding dam reservoirs. In situ monitoring systems in these potential landslide-prone areas are often unavailable, making landslide failures challenging to forecast. This paper describes a multisensor and multivariate remote sensing approach using data from Envisat, Sentinel-1, Landsat and PlanetScope satellites to reconstruct the spatiotemporal evolution of the mechanism and causes of the March 2019 landslide failure backside of the dam reservoir in Hoseynabad-e Kalpush village, north–central Iran. Statistical analysis and time series clustering are performed to derive the main landslide kinematic features from multitemporal interferometric synthetic aperture radar (MT-InSAR) analysis. We also exploit GIS and wavelet analysis to correlate potential external driving factors with landslide kinematics. Envisat and Sentinel-1 MT-InSAR analyses revealed that a previously stable old landslide was reactivated following reservoir impoundment in early 2013. As the reservoir water level rose during the following years up to 34 m in 2019, the landslide displacement rate gradually increased from 3.5 cm/yr to 8.4 cm/yr, and the destabilization gradually propagated upslope. At this stage, seasonal precipitation effects were detected only in the vertical component, indicating swelling and shrinkage movements of the shallower soil layer. The reactivated landslide accelerated and catastrophically failed following the exceptional precipitation in early 2019, producing a horizontal shift of >40 m, detected with optical image digital correlation. In the aftermath, the landslide continued to move with a decreasing trend until final stabilization in October 2021. Our study demonstrates how combined observations derived from multisensor satellite remote sensing data can be used to assess landslide precursors and kinematics, as well as the influence of climatic and anthropogenic factors on the instability of slopes surrounding water reservoirs. This is especially relevant in data-scarce areas. • Deep-seated landslide pre-, co- and post-failure analysis using satellite remote sensing. • Anthropogenic, environmental and climatic interaction in slope instability in Iran. • MT-InSAR shows landslide reactivation following impoundment in a nearby reservoir. • Slow-moving landslide failure following extreme precipitations detected using DIC. • Post-failure motion with negative quadratic decay trend linked to reservoir drawdown. [ABSTRACT FROM AUTHOR]

This paper proposes a novel agent-based model (ABM) to assess actual informal water market (IWM) trade—a highly common form of reallocating water globally, but where analysis is challenging due to data paucity. Because, typically, there is not an integrated or centralized institution coordinating transactions in IWMs, social networks and communications between farmers play a key role. This makes applications of ABMs in the informal space appealing and useful. Survey and interview data are used for the model development via a sample of farmers in Mojen Area, Iran. Also, in the final model, some critical human behaviors (e.g. adaptability and self-interest) are described in the form of parameters and formulas, with optimum values calibrated based on the Mojen sample as well as making possible a detailed assessment of the effects on water transaction and farmers' profit margins. The result of the simulation reveals that IWM trade frees up water for use by other farmers in the Mojen area, especially when stronger social networks are in place where the average number of water transactions will be increased—about 50%. The presence of IWMs also contributes to a more stable cultivation area in dry years, where water trading helps protect perennial crops that have become an increased production system choice in recent years. Further, informal water markets help farmers to optimally use water supply in order to deal with lower rainfall years and fulfil water needs during higher rainfall years due to increasing cultivation areas. [Display omitted] • An agent-based social network model for informal water market (IWM) simulation is developed. • A method of trades and an updating rule for simulating profit margins are proposed. • Experience, prior knowledge, and land area are three factors affecting water exchanges. • IWM can greatly improve the situation of water scarcity. • IWM stimulates farmers to use water in the best way in both trading and irrigation. [ABSTRACT FROM AUTHOR]

There is a growing concern about the pollution of agricultural systems caused by plastic residue and debris. This pollution poses a threat to the quality of food products. To gain a deeper understanding of how to maintain cleaner farmlands and reduce plastic residue and debris pollution, this cross-sectional study investigated the intention and behavior of plastic residue management (BPRM) in farmlands. Using random sampling method, the study surveyed 338 farmers in northwestern Iran. The resulting evidence found that 37.6% of farmers perform the BPRM with a mean score above average. The path analysis cleared that among the conceptualized ecological, economic, and social connotations, only the ecological attitude had a significant and positive impact on the BPRM (β E c o A t t i → B H V = 0.28, p < 0.001). It was found that both fact feedback (β F F B s → Int = 0.76, p < 0.001) and causal feedback (β C F B s → Int = 0.22, p < 0.001) contribute to reducing plastic residue pollution and forecasting the intention to perform the BPRM. The intention, in turn, directly predicts BPRM (β Int → B H V = 0.30, p < 0.001). Using a 2 × 2 cross-tabulation of causal feedback and the scenario of landfilling, derived from perceived action-based life cycle analysis of plastic wastes, the association between the two variables was found to be statistically significant (x 2 (df) = 3.24(1), p < 0.05). The resulting evidence would provide essential information for farm management strategists and governmental authorities to reduce plastic residue pollution in farmlands. This can be achieved by implementing policy initiatives that aim to change farmers' intentions and behaviors. • Paper indicates how to keep farmlands cleaner from plastic residue pollution. • Factual feedback forecasts intention to perform plastic residue management behavior. • Cause feedback predicts intention to perform plastic residue management behavior. • Cause feedback links with scenario of landfilling from action-based life cycle. • Factual and causal feedback should be given to farmers. [ABSTRACT FROM AUTHOR]

Around 90% of fresh renewable water is being used in Iran, indicating high water stress conditions across the country. Given that agricultural irrigation accounts for the majority of water use and consumption, any efforts to alleviate water stress must focus on enhancing agricultural water management. This paper aims to exhaustively compile the existing literature on the consequences and drivers of water insecurity, and to discuss the strategies balancing food and water security in the context of agricultural water management. Severe water scarcity can be largely attributed to governance gaps in Iran. In addition, inefficient water use, population growth, and warming/drying trends in the recent half a century are other major causes of the water scarcity. Groundwater over-depletion, quality deterioration of aquatic resources, decreased environmental flows and habitat destruction, and water conflicts are the significant consequences of improper water management in Iran. Several water productivity and conservation interventions, drawn from a rich literature, were suggested to improve agricultural water management in Iran. Furthermore, some water-food-energy nexus optimization methods, including changing cropping patterns, modifying energy tariffs, manipulating food diet, and reducing food waste, were discussed. Nexus-based strategies, which aim to achieve a balance between food production and water sustainability, are of high importance in reducing water consumption in the agricultural sector. While there has been a significant focus on improving water productivity and nexus-based measures, it is essential that policy-makers prioritize enhancing the water governance dimensions to effectively address water scarcity and its consequences in Iran. In other words, improving the water governance system is a prerequisite for adopting any strategy aimed at enhancing agricultural water management and coping with water security. [Display omitted] • This study addresses water scarcity in Iran focusing on the agricultural sector. • Severe water scarcity can be mostly attributed to water governance gaps in Iran. • Groundwater degradation is the most tangible consequence of poor water management. • Improving water-food-energy nexus, water productivity, and governance is essential. • Improving water governance is a prerequisite for enhancing food-water security. [ABSTRACT FROM AUTHOR]

Wetlands exist in complex ecological conditions that are changeable in time and space in terms of function and structural diversity. In recent decades, wetlands have been exposed to a wide range of threats. Assessment of these threats is essential to develop an understanding of the state of a wetland ecosystem and to develop a suitable management strategy. This paper discusses wetland vulnerability in terms of analysis of human and environmental systems from application of the driver-pressure-state-impact-response (DPSIR) framework. This assessment presented a systematic methodology for assessment of wetland vulnerability in a social-ecological approach applying broad-scale ecosystem services and vulnerability functions. The method combined the hydro-geomorphic approach with estimations of vulnerability indicators and DPSIR analysis. The aim of this paper was to assess vulnerability of wetland ecosystem services and to characterize the threat indicators according to importance, severity, and probability of occurrence. Quantitative and qualitative methods were applied to characterize values for these three indicators. The Multi Criteria Decision Making (MCDM) method was used to prioritize threats and impacts of the wetland on the basis of experts’ opinions. The proposed methodology was applied to the Choghakhor international wetland landscape in south-western Iran. Vulnerability assessment revealed that water requirement of the lowland and the water transfer system were the most important factors threatening the wetland. Agricultural activities, settlements and urban areas, drought, tourism, population growth, and mining activities in the upland were the next most important priorities, in that order. Hydrological balance was determined as the most vulnerable function and was considered as the most important function in the Choghakhor wetland. The DPSIR model was used to determine a management strategy to reduce vulnerability of ecosystem services in response to drivers, pressures, states and impacts indicated by modelling. [ABSTRACT FROM AUTHOR]

This research considers a new reliable backup hub under the robust-possibilistic uncertainty. The considered multi-objective model aims to minimize the total transportation cost of a p -hub median protection model while maximizing the flow between each pair node and minimizing the total transportation time. Additionally, it considers the potential disruption risks in the hub links of the transportation network for which a failure mode and effect analysis (FMEA) method is used to measure the risk of network's arcs. Consequently, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the demand elasticity is considered in this paper. As the exact values of some parameters are not known in advance, a fuzzy multi-objective decision making-based approach is proposed to optimally solve small-sized problems. Furthermore, a number of sensitivity analyses are done on a real case study in the Iranian Road Transportation Sector with useful managerial insights. • Designing a new reliable p -hub protection model with elastic demand. • Using financing tools for establishing hubs. • Measuring the risk level of arcs in the hub network. • Devising a two-phase solution method. • Considering a real-case study in the Road Transportation sector of Iran. [ABSTRACT FROM AUTHOR]

• Developing a multi-objective optimization model to redesign of GSBE-SNR considering incoterms. • Integrating social objectives into multi-objective model. • Greenhouse gas emission savings are maximized in the global sustainable bioenergy network. • Using GIS software to draw precise maps of the studied countries before and after redesigning. • Iran-o-Armenia case study with taking the sustainability conditions of each country into account. In today's industrial world, depletion of fossil resources and the adverse environmental effects of consuming fossil fuels have become one of the serious challenges in sustainable development of the societies. In recent years, substantial attention has been paid to using biomass for producing bioenergy in order to increase economic performance, reduce environmental effects, and providing new opportunities in different societies in pursuit of sustainable development. The complexities related to procurement, logistics, technology selection, raw material management (biomass), and product distribution planning are the main causes of using the optimization models to design the bioenergy supply chains. On the other hand, the globalization of economy and industry increased the significance of the subjects related to global logistics and the ecological and social objectives of the countries have undeniable influences on each other. Hence, in this paper, a mathematical model has been developed to redesign a global bioenergy supply network. This model has simultaneously studied the economic, environmental, and social objectives and the environmental coefficients of the model were calculated using SimaPro software. The multi-objective model was solved by augmented ɛ-constraint method and the decision makers were informed of the obtained Pareto solutions. Data taken from the study on Iran and Armenia was used to validate the model and the Geographic information system (GIS) software was used with the goal of studying the geographical map of each country more accurately. [ABSTRACT FROM AUTHOR]

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran. [ABSTRACT FROM AUTHOR]

Growing demand for electricity has made power grid design and expansion planning one of the main challenges in power industry management. In recent years, reconfiguration of existing power grid along with the adoption of renewable power generation leads to a significant reduction in expansion costs and GHG emissions. This paper offered a novel optimization model to address the design and planning of power grid expansion in a dynamic environment. Besides capacity planning, the model also determines location and time to construct new facilities. This research aims to satisfy demand by considering reduction in net present value of costs and increase in network efficiency. Electricity tariff and cost of load shedding differ according to different power consumers (i.e., residential, commercial, industrial and agricultural). Due to inherent intermittency in renewable energy resources and their subsequent impact on the entire power grid, different scenarios are generated, and the model is solved using the sample average approximation method. Eventually, validation of the proposed model and sensitivity analysis is carried out through a real case study in Iran. Computational results demonstrate the practicality of the stochastic model and show integration of renewable power plants would decrease the transmission and sub-transmission network costs. • A novel optimization model is developed to determine the power grid's design parameters and expansion mode. • Discretization of the electricity demand in different sectors is considered. • Sub-transmission systems alongside with renewable resources are incorporated in the proposed network. • The incentive by the government is considered for private enterprise to invest in renewable energy. [ABSTRACT FROM AUTHOR]

Due to problems such as limited land area for waste disposal and waste-borne diseases, waste management organizations have increasingly been offering technologies for recovering energy from waste. These technologies can help governments, local authorities, developers and investors for mitigating climate change and building sustainable societies. The suitable waste-to-energy production technology selection is a complex issue in waste supply chain management that must not only be assessed in terms of both socioeconomic and environmental criteria. With the purpose of balance between energy trilemma issues in the context of waste-to-energy generation and develop sustainable waste management strategies in the waste chain, energy justice criteria must also be taken into consideration. The paper considers the application of an integrated multi-criteria decision-making model consisting of fuzzy decision-making trail and evaluation laboratory method, the analytic network process and the simple additive weighting approaches. The integrated method can be applied to select the suitable technology in a sustainable manner, taking into account energy justice criteria. The applicability of proposed model is demonstrated by a case study of the technology selection in the city of Behbahan, Iran. It includes various technologies for waste-to-energy generation and ranks technologies from the most to least preferred as: Anaerobic digestion, Gasification, Pyrolysis, and Incineration. Image 10859 • We focused on the case of Iran for waste-to-energy technologies selection. • Anaerobic digestion, Gasification, Pyrolysis and Incineration items are applied. • The criteria of the decision making are the energy justice principles. • The energy justice concept refers to the sustainability criteria. • The combination of decision-making methods is employed. [ABSTRACT FROM AUTHOR]

Highlights • Iran is among the top ten CO 2 emitters in the world. • Achieving sustainable development undermined by heavy reliance on fossil fuels. • Environmental indicators are not consistent with the society and the economy. • However, improving the social indices strengthens the environmental indicators. • Altering the environmental subsystem resulted in convergence of sustainable system. Abstract Iran is among the top ten CO 2 emitters in the world and has pledged to reduce such emissions by up to 12 percent by 2030 through various policies and strategies. However, achieving this is somewhat undermined by the country's heavy reliance on fossil fuels which contributes to environmental pollution and depletion of natural resources. It is therefore important to have an understanding of the economic, social, and environmental subsystems and their interactions in order to formulate a suitable development path or model. Due to the lack of comprehensive studies on the current situation and the dynamics among these variables, this paper provides a multi-stage analysis of the issues based on data from 1992 to 2015 to construct the principal component analysis (PCA) combined index for each subsystem. Then, the interactions among the subsystems are investigated over the short- and long-terms using the vector autoregressive (VAR) model. The results of the PCA show that Iran lacks a balanced sustainable development approach as improvements in environmental indicators do not match those of the society and the economy indices. Further, the associated time path for the economic and societal indices depicts an increasing trend over time, especially in the economy index and that, since 2010, this subsystem has overtaken the societal index. Estimations of the VAR model, impulse response functions, and variance decomposition analysis show that the country's economic development has seriously undermined the environment even as improving the social indices has strengthened the environmental indicators. The interactions among subsystems show that economic development of the country took little consideration of environmental issues. This is the main reason for the backwardness of the environmental indices and its divergence from economic and societal indices. It is clear that assuming equal rates of growth for all subsystems have not produced a balanced development path. For Iran to achieve her sustainable development goals there should be increased focus on the environmental subsystem with the overall system converging symmetrically when the high growth rate assumption (double) is applied for this index compared with the societal and economical indices. [ABSTRACT FROM AUTHOR]

This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit. • Develop a novel carrier collaboration routing problem model with multi-commodity service and sharing fair profit. • Extend the carrier collaboration to general problems with in many-to-one problem pickup and delivery category. • Solve the problem with genetic algorithm with a new specific chromosome representation. • Result of implementation of the model in the real-world and sample instances. • Sensitivity analysis of the model based on fair distribution between participants is conducted. [ABSTRACT FROM AUTHOR]

This paper aims to improve the spatial accuracy of urban growth simulation models and clarify any associated uncertainties. Artificial Neural Networks (ANNs), Random Forest (RF), and Logistic Regression (LR) were implemented to simulate urban growth in the megacity of Tehran, Iran, as a case study. Model calibration was performed using data between 1985 and 1999 whereas the data between 1999 and 2014 was used for model validation. First of all, Transition Index Maps (TIMs) were computed by means of each model to assess the potential of urban growth for each cell. Using the standard deviation, consensus between the TIMs was evaluated. Because the TIMs of the individual models manifested discrepancies, they were combined using a number of ensemble forecasting approaches including median, mathematical average, principle component analysis, and weighted area under the total operating characteristic. The individual and combined TIMs were then put into Cellular Automata (CA) to simulate the future pattern of urban growth in Tehran. The results were evaluated in two stages. At first, the TIMs were evaluated by means of Total Operating Characteristics (TOC), and then a set of statistical indices was used to evaluate the spatial accuracy of the simulated urban growth maps. The best result was obtained by median ensemble forecasting, whereas the LR model showed the lowest level of accuracy. In similar studies, it is recommended to implement and compare different ensemble methods when integrating individual models. • We compared the effectiveness of ANNs, RF and LR for urban growth modelling. • Standard error map indicated discrepancy among the results of individual models. • Various ensemble forecasting approaches were proposed to combine individual models. • Mean ensemble forecasting created more accurate simulated maps. [ABSTRACT FROM AUTHOR]

Providing accessibility by public transportation is one of the main concerns in sustainable transportation development. An appropriate accessibility index should not only take transportation and land use into account but also the people who want to attend activities via the transport system. Travel time as the most common variable is used to indicate the role of transportation. A trip with public transport includes different parts: walking from the origin point to the bus stop or from the bus stop to the destination point, waiting for the bus to arrive, and in-vehicle time. These different parts have different weight values for passengers which affect their tendency towards traveling by public transportation. In this paper, a GIS-based multimodal gravity model is developed based on the weighted door to door travel time to compute accessibility by public transportation. Five main distinct urban activities including employment, education, healthcare, shop, recreation opportunities, and services are considered and the accessibility of the target population at the census block level to these destinations through public transportation is computed. In the next step, in order to consider all activities together and report a single unique index, the five computed accessibility indices are integrated into a composite index using a principal component analysis (PCA). The integrated accessibility measure helps to get an insight into the relative distribution of the benefits of public transportation and its interaction with the land use. The proposed method is applied to the City of Isfahan in Iran. The results indicate where places, for each activity and in overall, would benefit from the better land use and public transportation interaction and where regions would suffer from low accessibility level. This work provides a methodological framework as a tool for measuring the performance of public transportation and its interaction with the land use pattern. • Measuring the accessibility at block polygon level based on weighted door to door travel time, demand, and attraction • Developing a multimodal network to estimate travel time of public transportation including walking, waiting, and in-vehicle • Measure accessibility to five main urban activities and determine the level of accessibility and spatial equity realistically • Applying principal components analysis to composite accessibility to different urban activities into a single unique index. [ABSTRACT FROM AUTHOR]

In the digital soil mapping framework, machine learning (ML) algorithms are currently the most popular methods for the spatial prediction of soil properties. The fast developments of easy-to-use software implementations for a large panel of ML algorithms have encouraged comparison studies between algorithms, with the goal of ranking their performances and identifying the best ones among them. However, as no firm conclusions can be drawn about the best ML algorithm to be used in general, this suggests that combining a set of them could be a better approach. Numerous methods have been proposed to do so, most of them relying on a linear weighting of the individual algorithms. However, there are almost as many methods for linearly weighting ML algorithms as there are ML algorithms, thus leaving the problem unsolved. Moreover, these weighting methods are mostly used out-of-the-box, without paying a proper attention to the associated hypotheses. In this paper, we propose to address this issue by setting the problem in a more formal framework. Starting from classical hypotheses, it is shown how the benefit of averaging various ML algorithms can be estimated from their joint performances. Relying afterwards on the most commonly used linear weighting schemes, it is reminded that, as long as the performance metrics are based on mean square errors, the best averaging method is by essence the best linear (unbiased) predictor. Using a more general Bayesian framework, it is also shown that accounting for conditional biases when weighting ML algorithms is a key issue for obtaining improved predictions, and explicit formulas are proposed for that goal. Finally, these theoretical results are illustrated and discussed using a soil data set collected over an arid and semi-arid region in Iran where clay content, calcium carbonate equivalent, soil organic carbon and electrical conductivity were measured in topsoil samples. • Averaging results of several machine learning algorithms is beneficial. • Numerous averaging methods exist but are mostly used out-of-the-box. • The benefit of averaging depends on the correlation between prediction residuals. • Conditional biases are a key issue when averaging machine learning results. • A Bayesian approach provides the best results when conditional biases are present. [ABSTRACT FROM AUTHOR]

• A Decision Support System for flash-flood risk management in the arid areas is designed. • An integrated GIS and Watershed Modelling System is applied for flood flow simulation. • AI and Classic Delphi are employed for smart sustainable management of flash-flood risks. • Flood risk is analysed using Failure Mode and Effects Analysis & Hazard & Operability techniques. This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions. [ABSTRACT FROM AUTHOR]

In this paper, synthesis, modification, and application of ZSM-5 in upgrading oil refinery residue was studied. The residue with API 1 1 American Petroleum Institute. ; 15.51 and viscosity (at 50 0C); 275 cSt was from the Abadan oil refinery in Iran. ZSM-5 was synthesized using microwaves as a new method and was utilized in upgrading oil refinery residue for the first time. The synthesized ZSM-5 was modified using five metals, including Sn, Ni, Ag, Cu and Fe. The catalysts structures were identified using XRD, FTIR, SEM, EDX, and BET. Microwaves in catalyst synthesis led to an important reduction in the process duration. The influence of ZSM-5 modification with the metals on the heavy oil residue conversion, Sp Gr, viscosity, and API were evaluated at five temperature levels (360, 380, 400, 420, and 440 °C). The catalyst-to-feed ratio, 60 wt%, and reaction time, 2 h, were constant in all the experiments. The results revealed that Sn was the most appropriate metal to modify the catalyst. The maximum conversion was obtained at temperature of 440 °C, which resulted in a product with the specifications, including Sp Gr: 0.8183, viscosity: 1.711 cSt, and API: 41.42. The product conversion was 64.6 % and 86.4 wt% for HZSM-5 and Sn-ZSM-5, respectively, representing a 33.75 % increase. The distillation curves and carbon numbers of the feed and the product were investigated. The ranges of the carbon numbers and temperature for the feed and the product were 13–50, 230–582 °C and 5–16, 49.6–293.85 °C, respectively. • Microwave synthesis and application of ZSM-5 in upgrading oil residue was studied. • ZSM-5 was modified using five metals including Fe, Sn, Cu, Ag, and Zn. • The conversion was 64.6 % and 86.4 wt% for HZSM-5 and Sn-ZSM-5. • The range of the carbon numbers in the feed and the product was 13–50 and 5–16. • The temperature range of the feed and the product was 230–582 and 49.6–293.85 °C. [ABSTRACT FROM AUTHOR]

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented ɛ -constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time. [ABSTRACT FROM AUTHOR]

[Display omitted] • New designing framework for hybrid PV/WT/Battery system considering uncertainty. • Application of Cloud theory to model the wind, solar, and load uncertainty. • Considering the effect of battery degradation cost in the hybrid system design. • Using of a new meta-heuristic algorithm named OBLGBO for sizing the hybrid system. • Evaluating the superiority of the OBLGBO compared with GBO, GWO,and PSO. This paper performs a new optimal framework for a hybrid photovoltaic-wind system design integrated with battery storage (PV/WT/Battery), considering cloud-based uncertainty modeling and battery degradation based on real meteorological data from the Sarein-Ardabil region in Iran. The objective function is presented as minimizing the total net present cost (NPC), load loss, and battery degradation cost. The decision variables include the number of PVs, WTs, batteries, inverter power, and the angle of PVs installation, which is optimally determined via a new meta-heuristic optimization algorithm named opposition-based learning and Gradient-based optimizer (OBLGBO). In the proposed framework, the cloud theory method based on combining fuzzy theory and probability statistics has been applied for modeling the energy resources and load demand uncertainties. The simulation results indicate that considering battery degradation costs increases the overall cost of designing hybrid systems. As the cost of degradation increases, reliability indices improve due to an increase in the number of wind turbines and a decrease in the number of batteries. Also, the results demonstrated that incorporating the uncertainties based on the cloud theory increases the design cost, and the system reliability is weakened. Therefore, the proposed optimal framework has presented a real and accurate approach to the optimal design of energy systems with more accurate knowledge of electricity generation costs and the cost of improving reliability in conditions of uncertainties. Moreover, the superior capability of the OBLGBO compared with traditional GBO and the well-known particle swarm optimization (PSO) and grey wolf optimizer (GWO) is proved to achieve lower design cost and better reliability indices. [ABSTRACT FROM AUTHOR]

• Propose a framework for measuring a maturity level of performance-based budgeting. • Develop a parallel network data envelopment analysis model. • Consider the hierarchical configuration of performance indicators. • Use fuzzy sets theory to deal with vagueness and ambiguity. • Present a case study to demonstrate the applicability of the developed framework. Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the full repowering of a steam Power Plant with a capacity of 320 MW in Iran was evaluated technically and economically. Implementation of this plan for the target power plant was selected because of its over 30 years of age. Due to the presence of steam flash feed water preheaters and the possibility of preheating feed water entirely or partially prior to entering heat recovery steam generator, the effect of using or not using these low and high pressure equipment was evaluated in different cases. To do this, five different cases were proposed and reviewed. Considering the available gas turbines in Iran, the gas turbines models Siemens V94.2 and V94.3A were selected. In each case, the optimum point is obtained in terms of the lowest cost of electricity generation. Finally, after reviewing the results of all cases, full repowering plan with parallel feed water heating was selected as the best alternative in terms of energy and exergy efficiencies and the price of generating electricity. If two heat recovery steam generators and two V94.3A gas turbines are used, the net exergy and energy efficiencies for the cycle can reach 54.49% and 52.98%, respectively. Moreover, the power capacity of a repowered unit is equal to 700.44 MW with electricity generation price of 0.05986 $/kWh. Graphical abstract Image 1057 Highlights • In the present study, a real power plant has been studied (A Case Study). • The five different arrangements of feed water heaters for proposed scenarios have been considered. • The merging of water-gas heat exchangers with existing preheaters has been investigated. • Optimum techno-economic case for repowering has been presented. [ABSTRACT FROM AUTHOR]

• A multi-criteria framework is proposed for bioethanol facility location. • The best worst method is used to calculate the weights of the criteria. • The provinces of Iran are evaluated as alternatives in this study. • A questionnaire answered by 41 experts from Iran is used for the BWM. • Province of Khuzestan (followed by Tehran) is selected for bioethanol production. One of the major factors in the success of renewable energy is finding a proper location for production facilities. At a national level, different parts of a country (e.g. provinces) can be seen as alternatives that can be assessed based on a set of criteria, and ranking them to identify the best location. The focus in this paper is on identifying the best location for the production of bioethanol. After a comprehensive literature review, an evaluation framework is proposed based on the three dimensions of sustainability (economic, environmental and social). Using data provided by a sample of experts in a developing country – Iran – and applying the best-worst method (BWM), a number of decision-making criteria are evaluated. Performance data involving the various provinces of Iran are collected from different sources. The performance data and the weights identified through BWM are used to calculate an overall score for each province, which is then used to rank the provinces, with the province of Khuzestan (closely followed by Tehran) being identified as the most suitable province for bioethanol production in Iran. [ABSTRACT FROM AUTHOR]

Abstract This paper designs a novel biogas fueled combined cooling, fresh water and power (CCWP) generation system, and investigates its performance under extremely-hot summer days. In this tri-generation system, biogas is used as clean resource to reduce emissions and consumption of fossil fuels. Six electrical chillers are used for cooling demand supply of a benchmark building located in Ahwaz, Iran. In addition, expected required power of chillers and building is supplied either by gas turbine cycle or by main grid (If total power consumed by chillers and electrical equipment is larger than output power of gas turbine, building owner pays for purchasing electricity from local power system to satisfy not-supplied demand). A low-grade waste heat is extracted from the flue gases for preheating purpose and closed-air open-water humidification dehumidification (HDH) desalination process. In addition, a mixed integer non-linear programming problem is solved to minimize an objective function composed of daily electricity cost, total capital investment, operation and maintenance cost of CCWP generation system considering all operational limitations of gas turbine cycle, chillers, HDH process, and strategic cooling and electrical demand response programs (DRPs). If ambient air decreases, gas turbine power and mas flow rate of potable water will be increased due to lower compressor power and higher absolute humidity of air, respectively. Application of DRPs reduces total electricity cost from 31.2$ to 16.4$. Utilization of biogas and waste heat recovery of flue gases with no need to combustion of fossil fuels results in emission reduction. Hence, this tri-generation plant is called a net-zero emission system. [ABSTRACT FROM AUTHOR]

Highlights • Developing a possibilistic MILP model to design a second-generation biodiesel supply chain network under epistemic uncertainty. • Employing a credibility-based possibilistic programming approach to convert the original possibilistic programming model into a crisp counterpart. • Proposing an accelerated benders decomposition algorithm using efficient acceleration mechanisms to deal with the computational complexity of solving the proposed model. • Verification and validation of the proposed approach through investigating a real case study in Iran. Abstract This paper proposes a possibilistic programming model in order to design a second-generation biodiesel supply chain network under epistemic uncertainty of input data. The developed model minimizes the total cost of the supply chain from supply centers to the biodiesel and glycerin consumer centers. Waste cooking oil and Jatropha plants, as non-edible feedstocks, are considered for biodiesel production. To cope with the epistemic uncertainty of the parameters, a credibility-based possibilistic programming approach is employed to convert the original possibilistic programming model into a crisp counterpart. An accelerated benders decomposition algorithm using efficient acceleration mechanisms is devised to deal with the computational complexity of solving the proposed model in an efficient manner. The performance of the proposed possibilistic programming model and the efficiency of the developed accelerated benders decomposition algorithm are validated by performing a computational analysis using a real case study in Iran. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The current paper presents a method for determining land suitability for agricultural assessment in arid regions. • The present research has addressed the fundamental problem of environmental planning, which is soil erosion. • The land suitability evaluations presented in the current work decrease risks and consequently reduce financial losses for spatial and economic capabilities. • Integrating MCE-GIS and fuzzy logic has advanced the conventional map overlay techniques in land use suitability. Abstract The accurate evaluation of ecological resources plays a critical role in optimal planning for spatial development. For its study of an area of about 11.6 Mha, the present research aimed to assess land suitability in arid regions of eastern Iran by employing a fuzzy inference system in a GIS environment. Primary data, such as the physical properties of soil, climatic factors, elevation, land type, vegetation cover, and faults, were investigated and a geomorphologic map was drawn for working units and planning. In addition, a layer of soil erosion potential was extracted according to the CORINE model. As each environmental element poses a different effect on other parameters, the significance and impact of each factor were first prioritized by a multi-criteria decision-making method. Then, a fuzzy inference system was employed to create a land suitability map for the study area. With the Mann-Whitney non-parametric test, the achieved map was adapted to the land reality. Without considering the potential of soil erosion in areas where agricultural and residential land have currently been developing and expanding, the results showed that locational development is possible in 70% of the region (an area of about 8 million hectares). However, when considering the assessment of the soil erosion potential and inappropriate climate and environmental conditions in 81% of the study area, only 10% of the area allows for the development of spatial-economics. Given that the purpose of this research is founded on risk management, the results can be considered by developers for future ventures. [ABSTRACT FROM AUTHOR]

Abstract Presently, the ionosphere effect is the main source of the error in the Global Positioning System (GPS) observations. This effect can largely be removed by using the two-frequency measurements, while to obtain the reasonable results in the single-frequency applications, an accurate ionosphere model is required. Since the global ionosphere models do not meet our needs everywhere, the local ionosphere models are developed. In this paper, a rapid local ionosphere model over Iran is presented. For this purpose, the GPS observations obtained from 40 GPS stations of the Iranian Permanent GPS Network (IPGN) and 16 other GPS stations around Iran have been used. The observations have been selected under 2014 solar maximum, from the days 058, 107, 188 and 271 of the year 2014 with different geomagnetic activities. Moreover, ionospheric observables based on the precise point positioning (PPP) have been applied to model the ionosphere. To represent our ionosphere model, the B-spline basis functions have been employed and the variance component estimation (VCE) method has been used to regularize the problem. To show the efficiency our PPP-derived local ionosphere model with respect to the International GNSS Service (IGS) global models, these models are applied on the single point positioning using single-frequency observations and their results are compared with the precise coordinates obtained from the double-differenced solution using dual-frequency observations. The results show that the 95th percentile of horizontal and vertical positioning errors of the single-frequency point positioning are about 3.1 and 13.6 m, respectively, when any ionosphere model are not applied. These values significantly improve when the ionosphere models are applied in the solutions. Applying CODE's Rapid Global ionosphere map (CORG), improvements of 59% and 81% in horizontal and vertical components are observed. These values for the IGS Global ionosphere map (IGSG) are 70% and 82%, respectively. The best results are obtained from our local ionosphere model, where 84% and 87% improvements in horizontal and vertical components are observed. These results confirm the efficiency of our local ionosphere model over Iran with respect to the global models. As a by-product, the Differential Code Biases (DCBs) of the receivers are also estimated. In this line, we found that the intra-day variations of the receiver DCBs could be significant. Therefore, these variations must be taken into account for the precise ionosphere modeling. [ABSTRACT FROM AUTHOR]

Human consumption of fish protein, including canned tuna fish, is increasing steadily worldwide. However, there are some concerns about the potential exposure to elevated concentrations of metals in canned tuna fish. Several studies have been conducted in Iran regarding the concentration of metals in seafood, including copper (Cu), selenium (Se), iron (Fe), zinc (Zn), mercury (Hg), lead (Pb), chromium (Cr), arsenic (As), nickel (Ni), tin (Sn), and cadmium (Cd) in canned tuna fish. The main aim of this study was to gather data from existing papers and to perform a meta-analysis of the pooled concentrations of metals to evaluate their non-carcinogenic and carcinogenic risks in children and adults consumers. Search was conducted retrieving data from the international biomedical databases with highly public access and consultation, e.g., Web of Science, PubMed, Science Direct, and Scopus, and national database (SID and Irandoc) between 1983 and November of 2017. Data from 23 articles and 1295 samples were assessed and extracted. The ranking order of metals based on mean concentrations (μg/g wet weight) were Fe (13.17) > Zn (9.31) > Se (2.23) > Al (1.8) > Cr (1.63) > Cu (1.52) > As (0.38) > Ni (0.33) > Pb (0.24) > Cd (0.14) > Hg (0.11) > Sn (0.1). Except for Cd and Se, concentrations of other metals in the canned tuna fish were lower than the limits recommended by the United States Environmental Protection Agency (USEPA), World Health Organization (WHO), Food and Agriculture Organization (FAO) and Iran National Standards Organization (INSO). The minimum and maximum target hazard quotient (THQ) for adults were 5.55E-5 for Al and 2.23E-08 for Cr. For children, they were 7.23E-05 for Al and 2.91E-08 for Cr. THQ, and total target hazard quotient (TTHQ) were ≤1.0 for adult and children consumers. The Incremental Lifetime Cancer Risk (ILCR) of As was 3.21E-5 in adults and 4.18E-5 in children. Adults and children that consume canned tuna fish in Iran are not at non-carcinogenic risk but have a carcinogenic risk due to As. [ABSTRACT FROM AUTHOR]

This paper examines the role of bank-firm relationships in transmitting credit supply shocks to the real side of the economy in an emerging market. Using a hand-collected dataset for Iranian public companies, we exploit firms' exposure to a bank involved in a massive Ponzi scheme in 2011. We document a nearly 8 percentage point drop in annual employment growth rate for firms connected to the troubled bank following the credit dry-up caused by the scandal. We show that the magnitude of the effect on employment and investment is amplified by bank-firm relationship at least as much as by the financial constraint status found in previous studies. The results highlight the role of bank-firm relationships and the importance of access to multiple creditors in alleviating the consequences of credit supply disruptions. • We evaluate the impact of a credit supply shock on employment and investment. • We exploit a credit dry-up caused by a banking scandal in Iran in 2011. • Firms connected to the troubled bank experienced 8% slower employment growth rate. • The effect is amplified by both financial constraint and bank-firm relationship. • In the cross section, the impact is concentrated in firms with fewer than 4 creditors. [ABSTRACT FROM AUTHOR]

Conventional Data Envelopment Analysis (DEA) assesses the performance of Decision-Making Units (DMUs) by measuring only one type of efficiency using definite data. However, due to the vastness of some industries, such as banks, calculating only one type of efficiency cannot confirm DMUs as efficient or inefficient. Also, real-world cases are often faced with uncertain data, and conventional DEA lacks the power to consider these uncertainties in evaluation. In this paper, we develop a multi-objective DEA model to calculate three types of efficiencies, including profitability, operational, and transactional for bank branches when there are uncertain data. First, we apply a modified DEA model which is capable of calculating the common weight of all inputs and outputs by solving only one linear programming model. Then, we employ a robust approach to handle the uncertainty in data. The uncertainty in data is described with discrete scenarios. Discrete scenarios require a set of possible values for each parameter with uncertain nature. Finally, we apply a fuzzy programming method to convert the proposed multi-objective model into a single-objective one. Our main goal is to calculate three types of efficiencies for bank branches under four different scenarios. To validate the accuracy of the proposed model, a real case of 45 Agriculture bank branches located in West Azerbaijan in Iran is examined. The results show that the proposed model can produce accurate results under different scenarios. We also perform a comparative analysis on each efficiency aspect to specify the benchmark branches and also inefficient branches. Comparative analysis can help managers recognize where improvement should be prioritized. [ABSTRACT FROM AUTHOR]

Due to the adverse impacts of severe droughts on various aspects of human life and ecosystem services (ESs), the spatio-temporal assessment of the resilience of ESs under droughts is essential. In this paper, we propose a new methodology for assessing ESs-based resilience, taking into account the seven resilience principles: a) redundancy and diversity, b) managing connectivity, c) managing slow variables and their feedback, d) complex adaptive system (CAS) thinking, e) experimentation and learning, f) broadening participation, and g) polycentric governance. These principles enable us to consider the main social, political, hydrological, economic, and environmental aspects concerning resilience which have been overlooked in previous studies. The methodology is evaluated by applying it to Zarrinehroud River Basin (ZRB) in north-western Iran. A set of qualitative and quantitative criteria and their sub-criteria are proposed for quantifying the ES-based resilience and generating time series of resilience against severe droughts in several sub-basins in the study area. To evaluate the criteria and sub-criteria, the required data are derived from calibrated SWAT and MODSIM models as well as experts' judjments. The time series of ES-based resilience under 128 Water and Environmental Resources Management (WERM) scenarios (for enhancing agricultural practices, altering and modernizing irrigation methods, improving irrigation network and drainage facilities) and Climate Change (CC) scenarios (RCP 4.5, RCP6.0, and RCP 8.5) are derived for each sub-basin based on short-term (2020–2049) and long-term (2020–2098) periods. The low resilience values (0.53–0.6) of all sub-basins under the base management scenario (the status quo scenario (SC 0)) illustrate the need for implementing some projects to enhance the ESs in the study area. The results show that the WERM scenario SC 12346 can improve the values of the resilience criterion in sub-basins up to 0.85 and reduce the vulnerability of the study area to droughts. By evaluating all management scenarios, scenario SC 12346 is reported as the best scenario, since it can significantly increase the resilience of all sub-basins against extreme droughts with an acceptable cost of 636 million US dollars compared to other scenarios. Under this WERM scenario, the resilience values of sub-basins increase up to 40%. This scenario suggests implementing a set of projects such as improving irrigation networks and drainage facilities. [Display omitted] • A new methodology is proposed to assess basin-wide spatio-temporal resilience. • 128 scenarios are evaluated using a novel two-point evidential reasoning approach. • The best management scenario enhances the resilience value from 0.53 up to 0.85. • The selected scenario suggests improving irrigation networks and drainage facilities. • The seven resilience principles decrease the width of uncertainty band by 0.44%. [ABSTRACT FROM AUTHOR]