Lorestan University

Flood is one of the most devastating natural disasters with socio-economic and environmental consequences. Thus, comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main goal of this study was to investigate the application of the frequency ratio (FR) and weights-of-evidence (WofE) models for flood susceptibility mapping in the Golestan Province, Iran. At first, a flood inventory map was prepared using Iranian Water Resources Department and extensive field surveys. In total, 144 flood locations were identified in the study area. Of these, 101 (70%) floods were randomly selected as training data and the remaining 43 (30%) cases were used for the validation purposes. In the next step, flood conditioning factors such as lithology, land-use, distance from rivers, soil texture, slope angle, slope aspect, plan curvature, topographic wetness index (TWI) and altitude were prepared from the spatial database. Subsequently, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps and the area under the curves (AUCs) was computed. The final results indicated that the FR (AUC = 76.47%) and WofE (AUC = 74.74%) models have almost similar and reasonable results. Therefore, these flood susceptibility maps can be useful for researchers and planner in flood mitigation strategies.

Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50- and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.

Abstract This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.

Chelates are compounds that are applied to improve nutrition, especially the micronutrients status of plant tissues. During past decades, various chelating agents have been synthesized and introduced to agricultural systems. The recent formulas are aminochelates that are synthesized using various amino acids and a single or several nutrient ions aimed at improving fertilizer use efficiency and more adaptation to environment protection. Apart from their primary use as a micronutrient source, aminochelates represent an effective nitrogen (N) fertilizer in plant nutrition that can avoid negative effects from simple N fertilizers, such as urea. In various studies, higher yield and quality as well as higher concentration of nutrient elements have been obtained by application of aminochelates rather than simple chemical fertilizers. These compounds claimed to be more natural and safer forms of chelating agents, with higher use efficiency and without environmental side effects. However, there is lack of sufficient knowledge especially regarding their detailed impacts and their fate within the soil and plant system. This review provides information concerning the role of aminochelates in plant nutrition and to summarize the previous recent studies that have been done using these fertilizers.

A major challenge in educational technology integration is to engage students with different affective characteristics. Also, how technology shapes attitude and learning behavior is still lacking. Findings from educational psychology and learning sciences have gained less traction in research. The present study was conducted to examine the efficacy of a group format of an Artificial Intelligence (AI) powered writing tool for English second postgraduate students in the English academic writing context. In the present study, (N = 120) students were randomly allocated to either the equipped AI (n = 60) or non-equipped AI (NEAI). The results of the parametric test of analyzing of covariance revealed that at post-intervention, students who participated in the AI intervention group demonstrated statistically significant improvement in the scores, of the behavioral engagement (Cohen's d = .75, 95% CI [0.38, 1.12]), of the emotional engagement Cohen's d = .82, 95% CI [0.45, 1.25], of the cognitive engagement, Cohen's d = .39,95% CI [0.04, .76], of the self-efficacy for writing, Cohen's d = .54, 95% CI [0.18, 0.91], of the positive emotions Cohen's d = . 44, 95% CI [0.08, 0.80], and of the negative emotions, Cohen's d = −.98, 95% CI [−1.36, −0.60], compared with NEAI. The results suggest that AI-powered writing tools could be an efficient tool to promote learning behavior and attitudinal technology acceptance through formative feedback and assessment for non-native postgraduate students in English academic writing.

Over the past decades, organic-inorganic hybrid polymers have been applied in different fields, including the adsorption of pollutants from wastewater and solid-state separations. In this review, firstly, these compounds are classified. These compounds are prepared by sol-gel method, self-assembly process (mesopores), assembling of nanobuilding blocks (e.g., layered or core-shell compounds) and as interpenetrating networks and hierarchically structures. Lastly, the adsorption characteristics of heavy metals of these materials, including different kinds of functional groups, selectivity of them for heavy metals, effect of pH and synthesis conditions on adsorption capacity, are studied.

Due to global climate change, abiotic stresses are affecting plant growth, productivity, and the quality of cultivated crops. Stressful conditions disrupt physiological activities and suppress defensive mechanisms, resulting in stress-sensitive plants. Consequently, plants implement various endogenous strategies, including plant hormone biosynthesis (e.g., abscisic acid, jasmonic acid, salicylic acid, brassinosteroids, indole-3-acetic acid, cytokinins, ethylene, gibberellic acid, and strigolactones) to withstand stress conditions. Combined or single abiotic stress disrupts the normal transportation of solutes, causes electron leakage, and triggers reactive oxygen species (ROS) production, creating oxidative stress in plants. Several enzymatic and non-enzymatic defense systems marshal a plant's antioxidant defenses. While stress responses and the protective role of the antioxidant defense system have been well-documented in recent investigations, the interrelationships among plant hormones, plant neurotransmitters (NTs, such as serotonin, melatonin, dopamine, acetylcholine, and γ-aminobutyric acid), and antioxidant defenses are not well explained. Thus, this review discusses recent advances in plant hormones, transgenic and metabolic developments, and the potential interaction of plant hormones with NTs in plant stress response and tolerance mechanisms. Furthermore, we discuss current challenges and future directions (transgenic breeding and genome editing) for metabolic improvement in plants using modern molecular tools. The interaction of plant hormones and NTs involved in regulating antioxidant defense systems, molecular hormone networks, and abiotic-induced oxidative stress tolerance in plants are also discussed.

The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

The biological effects of oxidative stress and associated free radicals on farm animal performance, productivity, and product quality may be managed via dietary interventions-specifically, the provision of feeds, supplements, and forages rich in antioxidants. To optimize this approach, it is important first to understand the development of free radicals and their contributions to oxidative stress in tissue systems of farm animals or the human body. The interactions between prooxidants and antioxidants will impact redox homeostasis and, therefore, the well-being of farm animals. The impact of free radical formation on the oxidation of lipids, proteins, DNA, and biologically important macromolecules will likewise impact animal performance, meat and milk quality, nutritional value, and longevity. Dietary antioxidants, endogenous antioxidants, and metal-binding proteins contribute to the 'antioxidant defenses' that control free radical formation within the biological systems. Different bioactive compounds of varying antioxidant potential and bio-accessibility may be sourced from tailored feeding systems. Informed and successful provision of dietary antioxidants can help alleviate oxidative stress. However, knowledge pertaining to farm animals, their unique biological systems, and the applications of novel feeds, specialized forages, bioactive compounds, etc., must be established. This review summarized current research to direct future studies towards more effective controls for free radical formation/oxidative stress in farm animals so that productivity and quality of meat and milk can be optimized.

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.

Spinel-type cobalt oxide (Co3O4) nanoparticles have been easily prepared through a simple thermal decomposition route at low temperature (175°C) using carbonatotetra(ammine)cobalt(III) nitrate complex, [Co(NH3)4CO3]NO3·H2O, as a new precursor. The structure and morphology of as-prepared Co3O4 nanoparticles were characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), UV–vis spectroscopy, Brunauer-Emmett-Teller specific surface area measurement and magnetic measurements, and thermogravimetry/differential thermal analysis. The FT-IR, XRD, and EDS results indicated that the product was highly pure well-crystallized cubic phase of Co3O4. The TEM images showed that the product powder consisted of dispersive quasi-spherical particles with a narrow size distribution ranged from 6 to 16 nm and an average size around 11 nm. The magnetic measurements confirmed that the Co3O4 nanoparticles show a little ferromagnetic behavior which could be attributed to the uncompensated surface spins and/or finite size effects. The ferromagnetic order of the Co3O4 nanoparticles is raised with increasing the decomposition temperature. Using the present method, Co3O4 nanoparticles can be produced without the need of expensive organic solvents and complicated equipments.

Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood hazard mapping. A flood-inundation inventory (including 295 flooded sites) was used as the response variable and 10 flood-affecting factors were considered as the predictor variables. Flooded sites were then spatially randomly split in a 70:30 ratio for building flood models and for validation purposes. The prediction quality of the models was validated using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The validation results indicated that prediction performance of the NNETC model (AUC = 84%, RMSE = 0.163) was slightly better than that of the NNETR model (AUC = 82%, RMSE = 0.186). Both models indicated that terrain ruggedness index was the most important predictor, followed by slope and elevation. Although the model output had a relative error of up to 20% (based on AUC), this modeling approach could still be used as a reliable and rapid tool to generate a flood hazard map for urban areas, provided that a flood inundation inventory is available.

In this work, thermal decomposition of the [Co(NH3)5(H2O)](NO3)3 precursor complex was investigated under solid state conditions. Thermal analysis (TG/DTA) showed that the complexwas easily decomposed into the Co3O4 nanoparticles at low temperature (175 °C) without using any expensive and toxic solvent or a complicated equipment. The obtained product was identified by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX). Optical and magnetic properties of the products were studied by UV-visible spectroscopy and a vibrating sample magnetometer (VSM), respectively. FT-IR, XRD and EDX analyses confirmed the formation of highly pure spinel-type Co3O4 phase with cubic structure. SEM and TEM images showed that the Co3O4 nanoparticles have a sphere-like morphology with an average size of 17.5 nm. The optical absorption spectrum of the Co3O4 nanoparticles showed two band gaps of 2.20 and 3.45 eV, which in turn confirmed the semiconducting properties. The magnetic measurement showed a weak ferromagnetic order at room temperature. Photocatalytic degradation of methylene blue (MB) demonstrated that the as-prepared Co3O4 nanoparticles have good photocatalytic activity under visible-light irradiation.

Power systems are evolving to the networks with proliferated penetration of renewable energy resources to leverage their environmental and economic advantages. However, due to the stochastic nature of renewables, the management of the rapidly increasing uncertainty and variability in power system planning and operation is of crucial significance. This paper represents a comprehensive overview of power system flexibility as an effective way to maintain the power balance at every moment. Definitions of power system flexibility from various aspects are explained to reach the reliable and economic planning and operation of the power system. The effects of the high penetration of variable energy resources on power systems and the evolution of flexibility in response to renewables are studied. A variety of resources during the flexibility evolutionary transition are introduced and discussed. As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.

Modelling the flood in watersheds and reducing the damages caused by this natural disaster is one of the primary objectives of watershed management. This study aims to investigate the application of the frequency ratio and maximum entropy models for flood susceptibility mapping in the Madarsoo watershed, Golestan Province, Iran. Based on the maximum entropy and frequency ratio methods as well as analysis of the relationship between the flood events belonging to training group and the factors affecting on the risk of flooding, the weight of classes of each factor was determined in a GIS environment. Finally, prediction map of flooding potential was validated using receiver operating characteristic (ROC) curve method. ROC curve estimated the area under the curve for frequency ratio and the maximum entropy models as 74.3% and 92.6%, respectively, indicating that the maximum entropy model led to better results for evaluating flooding potential in the study area.

The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m3s−1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.

Abstract Covalent functionalization tailors carbon nanotubes for a wide range of applications in varying environments. Its strength and stability of attachment come at the price of degrading the carbon nanotubes sp 2 network and destroying the tubes electronic and optoelectronic features. Here we present a non-destructive, covalent, gram-scale functionalization of single-walled carbon nanotubes by a new [2+1] cycloaddition. The reaction rebuilds the extended π -network, thereby retaining the outstanding quantum optoelectronic properties of carbon nanotubes, including bright light emission at high degree of functionalization (1 group per 25 carbon atoms). The conjugation method described here opens the way for advanced tailoring nanotubes as demonstrated for light-triggered reversible doping through photochromic molecular switches and nanoplasmonic gold-nanotube hybrids with enhanced infrared light emission.

reduction. The review underscores the importance of defect manipulation, including control over their distribution and type, to optimize the performance of Zr-MOFs. Through tailored defect engineering and precise selection of functional groups, researchers can enhance the selectivity and efficiency of Zr-MOFs for specific applications. Additionally, pore size manipulation influences the adsorption capacity and transport properties of Zr-MOFs, further expanding their potential in environmental remediation and energy conversion. Defective Zr-MOFs exhibit remarkable stability and synthetic versatility, making them suitable for diverse environmental conditions and allowing for the introduction of missing linkers, cluster defects, or post-synthetic modifications to precisely tailor their properties. Overall, this review highlights the promising prospects of defective Zr-MOFs in addressing energy and environmental challenges, positioning them as versatile tools for sustainable solutions and paving the way for advancements in various sectors toward a cleaner and more sustainable future.

Polymerization of citric acid onto the surface of oxidized multiwall carbon nanotubes led to MWCNT-graft-poly(citric acid) (MWCNT-g-PCA) hybrid materials. Because of the presence of conjugated citric acid branches, synthesized MWCNT-g-PCA hybrid materials were not only soluble in water but also able to trap water-soluble chemical species and metal ions. Trapping of pesticides such as zineb and mancozeb in aqueous solution by MWCNT-g-PCA hybrid materials led to encapsulated pesticide (EP) in the polycitric acid shell. Optimum conditions for encapsulation of zineb and mancozeb in hyperbranched polycitric acid such as pH, time of stirring, and temperature were investigated by the UV-vis spectroscopy method. Encapsulation of pesticides on CNT-g-PCA hybrid material was confirmed via TEM analysis. Experiments indicated that new the CNT-g-PCA-EP hybrid material in comparison with bulk pesticide had a superior toxic influence on Alternaria alternata fungi.