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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]

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]

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]

[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]

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]

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]

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]

[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]

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]

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]

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]

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]

[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]

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]

The aim of this paper is to provide a solution to decrease water consumption in the slab-cooling unit of Mobarakeh Steel Complex in Iran. The plan should give an hourly decline in water consumption during a one-year operation period to calculate the annual reduction in water consumption of the proposed process. Recommended solutions for the conversion scheme of an existing wet cooling tower to a dry or hybrid cooling system require modeling of the slab cooling process. The curves of temperature drop in slabs are extracted in this paper by modeling the transient heat transfer of the slabs in the cooling process. This will reduce the computational volume. Then, the design and optimization of the hybrid cooling system done by the Genetic Algorithm (GA) is investigated. Accordingly, the objective function (reducing the costs of construction, operation, waste of water during the year) is chosen by single-objective method, and the modeling of the series hybrid system is done for the off-design mode during one year. The present design is for different scenarios and various savings of water consumption. The results demonstrate that if the control system is fitted with the sensitivities of the ambient temperature and also is based on the water consumption reduction and equipment design according to reliable standards, then the annual water consumption will be reduced by 88.3% (1.89 million m3). The payback period will be 3.63 years for the proposed system. • Numerical modeling of slab cooling process in transient cooling. • Extraction of the outlet water performance map to model the hybrid in off-design. • Modeling of off design air cooler control system and wet cooling tower. • An overview of hybrid system during the year based on an optimized design. [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]

The optimal siting of wind farms is a multi-attribute problem, and different objectives and constraints like economic, environmental, social, and technical should be taken into account. The widely-used category of site selection methods, multi-criteria decision making (MCDM), suffers from local scoring despite its numerous advantages. In this paper, support vector regression (SVR) is implemented to add the global scoring capability to MCDM by using a global-scale span of decision criteria. Here, the weighting of the decision criteria is obtained by the analytic hierarchy process (AHP), and then an SVR is utilized to appoint a score to any candidate site location. The proposed procedure has been used to study potential wind sites in Iran. In this regard, the study was conducted through three scenarios based on the natural and artificial potential of candidate sites. This study reveals that there are several vast areas, especially in eastern Iran, having high wind power density and flat terrain that are ready to be utilized as an endless source of clean energy provided that proper investment is made in power transmission lines and road networks. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy is essential for socio-economic development and due to the increase in energy consumption it is necessary to look for renewable and clean energy over the coming decades. Wind energy has received more attention than other sources due to better economic conditions as well as lack of environmental pollution and unlimited wind resources. This paper describes a practical method for measuring the potential of wind resources with the ability to generate electricity, using the geographic information system and considering the selection criteria of wind farms. The present study analyzed a multi-criteria decision support system to define wind energy resources in western Iran. In the method of calculating wind potential in this paper, the desired items are considered with equal importance and these criteria include technical, environmental, economic and geographical factors. The aims of this research are measuring the potential of areas suitable for generating electricity from wind energy for large turbines according to international standards, stating the standards and criteria for measuring the potential of wind resources and finally implementing the items mentioned in regional electricity. The results show that assuming the use of Gamesa G58 turbine, a maximum of 1897 MW of wind power can be generated in the study area, which is 26% of the region's electricity supply in 2025 horizon. The method used in this study can be applied for future wind potential measurements and it is recommended to be integrated with fuzzy AHP method to enhance the accuracy of the results. • Describing a practical method for measuring the potential of wind resources with the ability to generate electricity, using the geographic information system and considering the selection criteria of wind farms. • Measuring the potential of areas suitable for generating electricity from wind energy for large turbines according to international standards. • Obtaining the necessary factors to select suitable locations for wind farms in Iran's Western Regional Electricity in order to provide criteria based on GIS and evaluating their impact. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different indicators identifications for environmental effect evaluation under developing region. • Analysis of Tolerate level of the ecosystem in two methods (TOPSIS and the Ecosystem Quality Index (EQI). • A comprehensive model for minimizing the deviation of indicators from the standard levels. • Make the plans for regional development for stockholders considering economic growth by attention to sustainable development. There are several methods for assessing ecosystem tolerance level, each method is defined according to the target of the ecosystem development. This paper seeks a way to plan for the development of the existing ecosystems modified for human activities. For this purpose, two methods have been applied to assess the effects of local development on the local ecosystem tailored to the different demands. Thus, after gathering data in a coastal region all technologies being capable to be applied in this region regarding ecotourism development (population growth) were determined. Via different indicators, the quality of the ecosystem was evaluated. The Ecosystem Quality Index (EQI) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are methods of decision making. Both methods were applied to minimize the negative effects of the development plan. These methods have similar behavior against development. In the context of decision-making, a minimization method was used so as to minimize the distance between the values of the indicator in the current and future time horizon in the EQI method. The second method; that's to say, minimization of negative effects is the main target to choose the technology. In the present paper, Shif Island, as a certain limited region in the south of Iran, has been investigated. In this region, industrial development might directly affect the soil and water quality and it might disrupt the development of agriculture and the natural ecosystem. In this regard, local development might have restrained the availability of resources and also might have a detrimental effect on the environment. Population growth has been one of the main pressures on the ecosystem. If the economic development of the island is aimed at the expansion of ecotourism, it shall have both positive and negative effects on the local population. The development of the wastewater treatment system in Shif Island would lead to an increase in the tolerance level in 2025. Since 2018, due to the development of industrial and rural facilities, Soil erosion has been observed. After 2027, the tolerance level will be increased. Extension of the organic phase of soil and accumulation of biomass also will improve the quality of the soil. The application of two methods and analysis of variation of ecosystem tolerance has indicated that a decreasing trend of ecosystem tolerance should be expected until 2027. Then, a steady increase in ecosystem tolerance will be observed from 2028 onwards and will go on in the years following then. [ABSTRACT FROM AUTHOR]

• Detection of the internal pathways of collapsed pipes based on the UAV-drivers. • Assessment of soil properties associated with geophysical data characteristics. • Estimation of the geometric properties of potential soil pipes and collapsed pipes. • Understanding the soil erosion features in rangelands and agricultural lands. The study and mapping of evidences of soil piping using non-destructive techniques is a key issue in quantitative geomorphology. This paper attempts to combine Aerial mapping systems (Unmanned Aerial Vehicles (UAV)), soil physical and chemical attributes, and near-surface geophysical survey tools (ground penetrating radar and electrical resistivity tomography) to provide a comprehensive understanding of geometric features of soil piping in arid and semi-arid regions. The UAV Mapping System was applied to prepare ortho-photos, topography, analytical hill shading, drainage density, and land use maps of two different piping sites (site 1: rangelands, and site 2: agricultural lands) in Sarakhs plain, Razavi Khorasan Province, northeastern Iran. The physical and chemical soil attributes were analyzed in six soil profiles to check the hypothesis that these soil attributes control the occurrence of piping-related features (i.e., sinkhole, blind gully, gully), and to test if there are any differences in soil properties between the two land use types. The near surface geophysical tools were used to determine the approximate size of soil pipes, and to simulate their internal structure. The results of the derived UAV's independent variables confirmed that typical soil erosion features related to piping (i.e., sinkhole, blind gully, gully) developed exactly in adjustment with subsurface processes (i.e., drainage density). The quantitative algorithms of pedology revealed significant differences of Na+ and SAR for soil profiles with and without piping erosion, but these soil properties themselves are not enough to explain piping development at the two study sites. The volume of GPR line surveys was 1701.5 m3 in site 1 and 1203 m3 in site 2. The potential distribution of subsurface pipes revealed by applying GPR were more extensive than those deduced from soil surface observations: three-dimensional pipe number density (number of soil pipes per unit soil volume; # m−3) of potential pipes and collapsed cavities simulated with a mean pipe length and pipe depth of 143.4 cm and 88 cm in rangelands and 175.75 cm and 79.14 cm in agricultural lands, respectively. The maximum density of pipes or pipe roof collapses occurred within soil depth boundaries of 0–50 cm at site 1 and 30–100 cm at site 2. The ratio of surface erosion features to subsurface potential piping in rangeland and agricultural lands was ca. 26.1 % and 10.4 %, respectively. The electrical resistivity tomography (ERT) measurements indicated higher resistivity values in piping-prone areas, where pipes were initiated at the bedrock interface. Integrating all data obtained by different techniques in this study allowed to better understand the extent of piping erosion in both surface and subsurface soil horizons. [ABSTRACT FROM AUTHOR]

Iran's energy consumption is constantly and rapidly increasing. Due to the country's geographical placement on the Sun Belt of the Earth, there is a great potential for photovoltaic (PV) energy to be used; however, it necessitates an evidence-based selection of appropriate locations to develop solar farms. In this paper, we used a new approach to prioritize potential PV plant sites employing geographical information system (GIS)-based Fermatean Fuzzy (FF) TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). We considered economic, geographic, climatic, infrastructures, and demographic criteria in GIS-based FF-TOPSIS and compared outputs with earlier versions of the TOPSIS method (crisp and triangular fuzzy numbers (TFN)). Although all three versions indicated high correlations of >0.89, 20.88% of pixels' priority differed depending on the chosen method's logic. Regarding the sensitivity analysis, the GIS-based FF-TOPSIS in terms of criteria's weight was substantially more resistant to uncertainty than the TNF-TOPSIS. Besides, the GIS-based FF-TOPSIS has rational sensitivity (i.e., tolerance) to the accuracy of input data. The GIS-based FF-TOPSIS method demonstrated sensitivity or robustness depending on the degree of uncertainty in the numbers, which can be altered depending on the expertise and mastery of experts. Our findings indicated that approximately 13.81% of the entire country has the potential for solar power plants. According to the results of the GIS-based FF-TOPSIS approach, the provinces of Sistan and Baluchestan, Hormozgan, Fars, and Khuzestan have the largest potential for solar power farms in Iran. • Prioritizing potential areas to localize solar farms on a national scale. • Evaluating GIS-based TOPSIS with and without considering fuzzy principles. • Integrating Fermatean fuzzy sets (FFs) concept with spatial data and GIS concepts. • Proposing a GIS-based FF-TOPSIS as a practical method for uncertainty conditions. [ABSTRACT FROM AUTHOR]

• The effect of 4 other track parameters on twist is examined for the first time. • Alignment of the rail (AL) and the super elevation (XLV) have the greatest impact on twist. • Polynomial regression and association rules have been used to analyze failures. • Heavy and expensive machinery can be utilized less frequently for track inspections. • This study paves the way toward condition-based maintenance. Maintaining railway tracks is capital intensive, time consuming and safety-critical. Novel maintenance methods can decrease these costs and improve efficiency by analysis of collected data from tracks. Twist is one of the common track failures, which poses the risk of derailment, fatalities, injuries and financial loss. In this paper, track parameters are studied for part of a major railway route in Iran. Polynomial regression and association rules, which are popular data mining approaches are used to discover relationship between twist failure with failures of other track parameters for the period between 2018 and 2020. The results show that alignment and super elevation have the highest impact on twist and most of the times these failures occur simultaneously. By adopting this approach twist failure can be identified in order to avoid chain failure and move toward condition-based maintenance. [ABSTRACT FROM AUTHOR]

• Detection of the internal pathways of collapsed pipes based on the UAV-drivers. • Assessment of soil properties associated with geophysical data characteristics. • Estimation of the geometric properties of potential soil pipes and collapsed pipes. • Understanding the soil erosion features in rangelands and agricultural lands. The study and mapping of evidences of soil piping using non-destructive techniques is a key issue in quantitative geomorphology. This paper attempts to combine Aerial mapping systems (Unmanned Aerial Vehicles (UAV)), soil physical and chemical attributes, and near-surface geophysical survey tools (ground penetrating radar and electrical resistivity tomography) to provide a comprehensive understanding of geometric features of soil piping in arid and semi-arid regions. The UAV Mapping System was applied to prepare ortho-photos, topography, analytical hill shading, drainage density, and land use maps of two different piping sites (site 1: rangelands, and site 2: agricultural lands) in Sarakhs plain, Razavi Khorasan Province, northeastern Iran. The physical and chemical soil attributes were analyzed in six soil profiles to check the hypothesis that these soil attributes control the occurrence of piping-related features (i.e., sinkhole, blind gully, gully), and to test if there are any differences in soil properties between the two land use types. The near surface geophysical tools were used to determine the approximate size of soil pipes, and to simulate their internal structure. The results of the derived UAV's independent variables confirmed that typical soil erosion features related to piping (i.e., sinkhole, blind gully, gully) developed exactly in adjustment with subsurface processes (i.e., drainage density). The quantitative algorithms of pedology revealed significant differences of Na+ and SAR for soil profiles with and without piping erosion, but these soil properties themselves are not enough to explain piping development at the two study sites. The volume of GPR line surveys was 1701.5 m3 in site 1 and 1203 m3 in site 2. The potential distribution of subsurface pipes revealed by applying GPR were more extensive than those deduced from soil surface observations: three-dimensional pipe number density (number of soil pipes per unit soil volume; # m−3) of potential pipes and collapsed cavities simulated with a mean pipe length and pipe depth of 143.4 cm and 88 cm in rangelands and 175.75 cm and 79.14 cm in agricultural lands, respectively. The maximum density of pipes or pipe roof collapses occurred within soil depth boundaries of 0–50 cm at site 1 and 30–100 cm at site 2. The ratio of surface erosion features to subsurface potential piping in rangeland and agricultural lands was ca. 26.1 % and 10.4 %, respectively. The electrical resistivity tomography (ERT) measurements indicated higher resistivity values in piping-prone areas, where pipes were initiated at the bedrock interface. Integrating all data obtained by different techniques in this study allowed to better understand the extent of piping erosion in both surface and subsurface soil horizons. [ABSTRACT FROM AUTHOR]

• Developing a framework for the wellness tourism supply chain. • Designing a mathematical model for the wellness tourism supply chain. • Observing the concept of sustainability in wellness tourism. • Using the 3 V big data concept in data collection. • Considering a real case study from Sarein spa in Iran. Nowadays, the most successful and prominent wellness tourism centers are those that, consider the aspects of sustainable development, while providing memorable services with the best quality and reasonable prices. A wellness tourism center is seeking to meet the purposeful journey of wellness tourists by improving their mental, physical, and spiritual happiness. This paper presents a three-objective mathematical model to formulate the supply chain of services provided by a wellness tourism center. The purpose of this model is to establish a balance between sustainability aspects in this supply chain. The first objective seeks to maximize profits from the supply chain and the second and third objectives are to maximize customer satisfaction concerning environmental and social impacts. The proposed model includes quantitative parameters of operating costs, capacity increase cost, service capacity, and service demand. Due to the multi-objective nature of the model, the improved multi-choice goal programming method is used. Real data are collected from the Ardabil-Sarein wellness tourism center in Iran and used as a case study to validate and analyze the model. In collecting the dataset for the parameters, the three aspects of big data (3 V's) are considered. Findings show that the model offers different types of discounts for various periods following the tourist demand. Additionally, the wellness tourism center can use the original cost to expand its range of services by offering different tours. Finally, the limitations and managerial suggestions are discussed. [ABSTRACT FROM AUTHOR]

Rapid changes and evolution in technological innovation have spilled over many industries and compelled firms to release new products periodically. The advent of new products intensifies the importance of analyzing sales data trends and finding the most profitable portfolio that can no longer be ignored in production planning. The topic of diffusion processes has evoked considerable interest from academics and practitioners to forecast sales patterns of two or three competitive generations. However, there is still a lack of in-depth discussion on the effects of core determinants on customers' preferences and choices. This paper bridges the gaps in the literature by conceptualizing the interaction effects among current and new products on diffusion processes that could affect supply chain operations. Specifically, the Bass diffusion model (BDM) is modified to estimate market volume, advertisement effects, and sales data trends, then combined with a choice model to examine how consumers' propensities affect the choice probability of alternatives. The proposed choice-based diffusion model is incorporated into a supply chain problem for joint planning on a production-location-inventory problem concerning the economic and social aspects of designing multi-generation production lines. The model formulation is a bi-objective mixed-integer nonlinear problem with a computation burden. Thus an outer approximation-based algorithm is proposed to reduce computational complexity and combined with an augmented ε -constraint to generate efficient Pareto sets. The model is validated by conducting a case study in Iran and compared to other established models. The computational results demonstrate the proposed model outperforms the expert forecasts, and there are no significant differences between the total sales estimated by the proposed model and the actual sales. The manager is advised to adopt a conscious policy for adoption engagement under supply constraints. It should price the products in a way be more tightly aligned with customers' desirability and overcome the supply constraints by increasing production or storage capacities. • A bi-objective optimization approach is applied to design and plan multi-generational products. • A novel choice-based diffusion model is proposed for demand forecasting. • The proposed non-convex model is transformed into a convex problem and solved by an outer approximation-based algorithm. • Model verification is done by conducting a case study in Iran. [ABSTRACT FROM AUTHOR]

Recently, the closed-loop supply chain (CLSC) domain has garnered considerable attention from scholars and practitioners. The increasing number of publications makes it necessary to study this domain comprehensively. To achieve this goal, this paper makes the first attempt to investigate the knowledge base and knowledge diffusion paths of the whole area systematically from both static and dynamic perspectives. Based on 1041 papers published in 2001–2020 from Web of Science, the characteristics of publications, productive authors, institutions, countries/territories and journals, as well as the co-authorship relationships are investigated by using bibliometrics. In addition, the evolution of hot topics and the historical development of CLSC domain are vividly described by keyword analysis and main path analysis respectively. The results show that: (1) Van Wassenhove, LN, Guide, VDR and Govindan, K play an important role in this domain and in addition Govindan, K has established extensive cooperative relationships; (2) INSEAD and Penn State Univ are more influential and they cooperate closely; (3) Several countries such as Italy, UK, Iran and China have begun to pay more attention to CLSC research in the past five years; (4) the hot topics revolve around the three aspects of network design, coordination and circular economy, and the trajectories of knowledge diffusion of this domain show that the focus has shifted to the green and sustainable CLSC recently. This study is able for researchers to grasp the general information, identify hot topics and capture history, development status and possible directions for the future research in the CLSC domain. • 1041 papers from Web of Science in closed loop supply chain (CLSC) domain are analyzed. • Bibliometrics and main path analysis are adopted to study the overall development of the CLSC domain. • The hot topics revolve around network design, coordination and circular economy. • The network design of CLSC is the main concern in the process of knowledge dissemination. • The influence of government policies on the CLSC in the e-commerce environment is attractive recently. [ABSTRACT FROM AUTHOR]

Globally, most industrial parks that have not gone through a proper site selection process have been neglected or underutilised and such active sites tend to burn a lot of fuels and generate greenhouse gases. Technology must be used to conduct on-site investigations of the spatial and non-spatial factors of industrial parks, especially the emerging Eco-Industrial Park (EIP). The paper seeks to appraise the global application, capability and acceptability of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) tools in the EIP sites selection and planning. Also, to identify the benefits of mixing MCDM tools when integrating with GIS. There have been some reviews on the use of GIS and MCDM methods, but so far, there is none on the role of GIS and MCDM methods in EIP site selection. Geospatial and MCDM tools can be used to carefully select EIP sites, where materials and energy can be shared to achieve cleaner production, reduce environmental pollution and promote sustainable development. To understand the usage of these tools and their role in the EIP site selection process, a critical review of twenty-two papers published from 2007 to 2020 indexed by Google Scholar, Springer, IEEE Xplore and Web of Science have been developed for the first time. The articles are classified based on country/region, journal of publication, year and citations of publication, the authors and technologies applied. The results show that most studies on EIP site selection come from Iran and China. The Journal of Environmental Monitoring and Assessment has the highest number of articles published at 28%. The highest number of articles was published in 2014 with 312 citations. GIS and Analytic Hierarchy Process (AHP) topped the technologies used, accounting for 42% and 24% respectively, and hybrid tools 34%. As categorised by criteria content, there was an overlap of criteria in most journals. Though, water bodies, utilities, infrastructure, and natural characteristics were prominent. Renewable energy resources were not given attention by most researchers, and risk assessment was absent from the site selection literature. A blend of traditional MCDM tools constitutes the state-of-the-art tools, and when these are combined with GIS in EIP site surveys, they execute to obtain actual results. Future EIP site selection that is integrated with risk assessment is envisioned to be industry 4.0 (4IR)-driven through a combination of GIS and artificial intelligence, and the recently developed MCDM hybrid modules. [Display omitted] • Poor site selection has led to industrial park being neglected or underutilised. • Abandoned/underutilised active industrial parks can burn fuels to cause GHG. • First time of critical review of role of GIS and MCDM tools in EIP site selection. • GIS and AHP currently mainly used for large-scale EIP site criteria selection. • EIP site choice risk study using GIS, AI and MCDM hybrid can sustain industry 4.0 [ABSTRACT FROM AUTHOR]

Anti-quorum sensing (QS) or quorum quenching (QQ) is known as a new anti-bacterial strategy to combat bacterial infection. One of the best candidates for this strategy is a natural plant or traditional herbal medicine. This review aimed to summarize and introduce Iranian medicinal plants with anti-QS properties. Biomedical databases (PubMed, Scopus, Google Scholar and Web of sciences) were investigated to retrieve all related manuscripts published in English and Persian. Out of 65 documents, 47 papers were published during 2010–2020. We categorized and summarized 19 papers that particularly presented the anti-QS activity of Iranian medicinal plants. Based on our results, different studies have been completed on the QQ effects of medicinal plants. We identified 106 plant species with different properties in medicine that have been evaluated for anti-QS activities in Iran. The QQ effects of herbal extracts were identified through different in vitro examinations on biosensor and clinical bacterial strains. Only 35 medicinal plants have shown these effects at sub-MICs. Our review summarizes Iranian medicinal plants with anti-QS properties. Some of these herbal extracts showed anti-QS activity against biosensors, standard and clinical bacterial strains. This result is very important because QS systems can be considered as a new target for the development of new remedial strategies and it is a good opportunity to perform QQ studies to effectively combat bacterial infections in the future. [ABSTRACT FROM AUTHOR]

Proposed closed-loop supply chain network for the olive industry. [Display omitted] • Designing a sustainable closed-loop supply chain network for the olive industry. • Developing a multi-objective optimization framework for analyzing economic, environmental and social factors. • Proposing two hybrid optimization algorithms consisting of four meta-heuristics for the first time. • Studying a real-case study to implement the sustainability goals. A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO 2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran. [ABSTRACT FROM AUTHOR]

The significance results of the paper to the readers of the Ecological modeling is: • This is the first report to analysis of potato potential yield, yield gap based on GYGA protocol in Iran. • According to the results, the average Y p was 67.3 t. ha-1 and actual yield (Ya) was 30 t ha-1. • Iranian farmers produced 5 million tons of potato from about 164000 ha. If they can obtain only 80% of Yp (53.8 t. ha-1), amount of potato production will be 8.8 million tones. • As result, they can produce 5.2 million tons tuber yield of potato in 97000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands. Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Y p was 67.3 t ha–1 and actual yield (Y a) was 30 t ha–1. Therefore, the average tuber yield gap was 37.3 t ha–1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Y p (53.8 t ha–1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands. [ABSTRACT FROM AUTHOR]

In this study, the efficiency of air cleaners in removal of chemical components of PM 2.5 (PAHs, heavy metals, and ions) in the indoor air of dormitories of Tehran University of Medical Sciences, Iran were assessed. 12 rooms were selected and randomly divided into two groups: sham air purifier system deployed room (SR) vs. true air purifier system deployed room (TR). 24-h PM 2.5 samples were simultaneously collected in both TR and SR on PTFE filter papers at a flow rate of 5 L/min. The results showed overall removal efficiency of 76.5 ± 15.5%, 63.1 ± 22.5%, and 66.2 ± 24.8% for PAHs, heavy metals and ions, respectively. Moreover, the fraction of positive ions decreased when air purifiers were used in the rooms. The results showed that the smoking is an important influencing factor on the indoor air quality; smoking can leads to reduction in removal efficiency of PAHs, heavy metals and ions by 26%, 40%, and 51%, respectively. An air purifier could decreased the PM 2.5 even lower than the WHO annual guideline level in non-smoker rooms. The present study revealed that use of indoor air purifiers can significantly reduce the risk of carcinogenicity and non-carcinogenicity attributed to heavy metals and PAHs, especially in places without a smoker. [Display omitted] • Efficiency of air purifiers (AP) in removal of components of PM 2.5 was investigated. • The results showed removal of 76.5%, 63.1%, and 66.2% for PAHs, metals and ions, respectively. • Smoking leads to efficiency reduction of PAHs, metals and ions by 26%, 40%, and 51%, respectively. • AP could decline the PM 2.5 even lower than the WHO guideline in non-smoker rooms. • AP can significantly reduce the health risks attributed to metals and PAHs. [ABSTRACT FROM AUTHOR]

• Two novel multi-frequency LSTM models were proposed for solar irradiance modeling. • Performance of general LSTM network was enhanced using frequency sequences. • Sequenced-LSTM model showed a bit more accurate results than wavelet-LSTM model. • The suggested multi-frequency LSTM network outperformed classic FFNN model. • Solar irradiance modeling led to higher accuracy in regions with higher irradiance. In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively. [ABSTRACT FROM AUTHOR]

• Developing a data-driven knowledge-based system for long term load forecasting. • Applying a machine learning-based decomposition method in the knowledge base. • Representing a reasoning method based on fuzzy logic to consider uncertain evidence. Reliable long-term hourly load forecasting is imperative for electricity utilities and planners' decision-making, especially in generation-transmission expansion planning. This paper develops a data-driven knowledge-based system for regional hourly load forecasting in the long-term horizon with contributions in both knowledge base and inference engine. In this regard, in the knowledge base, two types of driving factors are taken into account to determine the forecasting model: long-term trend-related features such as macro-economic factors and short-term factors such as temperature. The knowledge base is decomposed into three-step-related parts to deal with these groups' substantial differences in frequency. Firstly, the long-term trends are identified by the tendency variables. Then, the short-term variability is considered by temperature-related variables in addition to day type. Machine learning regression methods, support vector machines, random forest, and artificial neural networks are compared to determine the non-linear relationship of variables in these steps. The results of these steps are combined in the third step. Then, in the inference engine, a new reasoning method based on fuzzy logic is represented to forecast the regional long-term hourly load under uncertain evidence of temperature as an effective non-predictable feature in the long run. To evaluate the performance of the proposed system, a comparison against five systems is conducted. The results show the superiority of this system compared to the other systems for a publicly available dataset (ISO New England electricity market) as well as Iran's residential, commercial and agricultural electricity load. [ABSTRACT FROM AUTHOR]

• Heating and cooling setpoint temperature of thermostats were optimized in a residential building for different climates of Iran. • GMDH-type neural network and NSGA-II algorithm were used for multi-optimization. • XPS is the optimum insulation for all the climates in Iran. • PPD reduces with thickness of insulation for all the cities, except Bandar Abbas. Determination of the optimum setpoint temperature of thermostats in various climates is a problem in air conditioning of residential buildings. In this paper, a new method is developed to optimize the setpoint temperature of thermostats in different climates of Iran. Design variables in the optimization process are heating setpoint, cooling setpoint, thickness, and thermal conductivity of insulations in the building envelopes. The optimization goals are minimizing energy consumption and cost of insulations in addition to maximizing thermal comfort of occupants. Thus, the static payback period (SPP) and the predicted percentage dissatisfied (PPD) indices are selected as objective functions which should be minimized in the optimization process. The methods applied to attain these objectives are numerical modeling by EnergyPlus software, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Therefore in this process, first, EnergyPlus is used to train the neural network. Afterward, the GMDH-type neural network is applied to derive polynomials computing the objective functions from the design variables. Then, Pareto optimal points for the objective functions are obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum design point is selected for different cities. According to the results, type and thickness of insulation integrated in the building envelopes affect the static payback period and thermal comfort of occupants. For all the climates of Iran, the most appropriate insulation is XPS and the optimum heating setpoint of thermostat is 22 °C. Also, the optimum value for the cooling setpoint pertains to the type of climate, so that this value for Bandar Abbas, Yazd, Tehran, Rasht and, Tabriz is, respectively, equal to 24.5, 24.7, 25.2, 25.3, and 25.6 °C. Moreover, thermal comfort of occupants increases with thickness of insulation, except for Bandar Abbas whose PPD is almost constant. The most value of PPD reduction with insulation thickness is related to Tehran where by increasing the insulation thickness from 1 cm to 5 cm, PPD decreases up to 53%. [ABSTRACT FROM AUTHOR]

• Machine-learning algorithms were adopted to predict the cost of maintenance policies. • The Markov Chain model was used to predict the post-maintenance network condition. • Optimal policies were found based on the optimization of cost and network condition. • The paper contributes to the sustainable development goals #9, #11, and #13. A framework was proposed to find optimal maintenance policies in a road network. The framework included: identifying factors that contribute to policy-making; clustering the network based on these factors; identifying criteria that impact optimal policies; and determining optimal policies and periods using these criteria. To test the framework applicability, it was applied to Iran roads network step-by-step. First, road functional class and climate were identified as factors that contribute to policy-making. Second, the network was clustered into six sub-networks based on two road functional classes and three climates. Third; maintenance cost, network condition, and maintenance period were identified as criteria that impact optimal policies. Fourth, 96 maintenance policies were applied to each sub-network considering two-year, four-year, six-year, and twelve-year maintenance periods. To quantify policies cost, seven machine-learning algorithms including Gradient Boosting Regression, Lasso, Ridge, Random Forest Regression, Elastic Net, Neural Network, and Multiple Linear Regression were tested. Using the coefficient of determination (R2) as the accuracy metric, it was found that in all sub-networks the Gradient Boosting Regression had the highest accuracy on testing set (greater than 90%) while that of other algorithms was between 50% and 90%. Sub-networks condition was modeled using the Markov Chain model and was measured by the average Pavement Condition Index (PCI). Having policies cost and sub-networks PCI, the optimal policy was selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). It was concluded that in all sub-networks, the four-year maintenance period was optimal. Roads in warm zone demanded the most intense policies, followed by those in cold and humid zones. The same applied to arterial roads followed by local ones. [ABSTRACT FROM AUTHOR]

Much attention has been paid to the deployment of Hydrogen storage systems (HSSs) and Hydrogen vehicles (HVs) in the modernized energy system. However, a research gap exists in the literature about optimal probabilistic planning of microgrids (MGs) equipped with HSS, considering the uncertainties of renewable energy resources and electric vehicle (EV) and HV owners' behaviors. The main purpose of this research is to fill such a gap by developing a new probabilistic optimization problem to determine the capacity of Hydrogen-based MGs' sub-systems. Another contribution is to consider the reliability constraints and loss of energy cost (LOEC) in the MGs' total net present cost (TNPC). The Monte Carlo simulation (MCS) and Flower Pollination Algorithm (FPA) are used to model stochastic behaviors and solve the proposed probabilistic optimization problem. This paper studies different actual climates of Iran based on historical data, while various coordinated/uncoordinated charging modes of EVs and HVs are examined. Test results infer that a significant inaccuracy (more than 4.66% depends on the climate conditions and vehicle scenarios) occurs due to neglecting the uncertainties. The sensitivity analyses imply that the reliability constraints, LOEC, and their interactions might affect the MGs' optimal design. [Display omitted] • A novel optimal probabilistic planning for hydrogen-based microgrids. • Considering the reliability constraints in the proposed stochastic method. • Studying the proposed method in various climates. • Investigating the EVs and HVs impacts on the optimal planning of MGs. • More than 4.66% accuracy improvement by applying the proposed method. [ABSTRACT FROM AUTHOR]