Retracted: Integrated modeling of food–water–energy nexus for maximizing water
productivity

Khayatnezhad, Majid; Fataei, Ebrahim; Imani, Aliakbar

doi:10.2166/ws.2023.038

Cited by 10 publications

(1 citation statement)

References 30 publications

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“…In recent decades, an increase in the level of mangroves has been observed in various studies conducted in different regions. For instance, research conducted in mangroves along the coast of Texas (USA) in the western Gulf of Mexico (Armitage et al, 2015), the Red Sea (Almahasheer et al, 2016), Northern Australia (Asbridge et al, 2016;Khayatnezhad et al, 2023), and Southern Iran (Rostami et al, 2022) have all reported a rise in mangrove levels.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the effects of climate change on the mid-elevation pasture vegetation of mountainous areas; A Case Study of the Sablan Mountainside, Iran

Hashemian,

Fataei,

Mosayebi

et al. 2023

Preprint

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The impact of global warming and climate change on vegetation is a significant challenge in the present day. The pastures of the Sablan Mountains, located in Northwestern Iran, in Azerbaijan, are renowned for their richness and pivotal role in supporting the livelihood of residents. Additionally, these meadows serve as an alluring destination for climbers and tourists from around the world, offering a captivating recreational experience In this study, the trends of climate parameter changes and vegetation cover over a period of 17 years (2019–2003) were investigated using meteorological data from two range stations and the NDVI index derived from Landsat satellite images. The Mann-Kendall test was employed to analyze the trends, and the relationship between the simple and multiple linear regression of climate parameters and the NDVI of the entire pasture was examined. Specifically, the analysis focused on pastures at different elevations, including low elevations(2500–1500 m), middle elevations (3500–2500 m), and high elevations (above 3500 m). Additionally, a map illustrating the changes in NDVI over the 17–year period was created. According to the findings, it was observed that among the various climatic parameters examined, only the average temperature in June exhibited a significant increasing trend. The rate of increase was measured at 0.071 degrees Celsius per year, with a significance level of 5 percent Based on the analysis of NDVI trends during the first month of summer, it was observed that the NDVI of the total land use classification of pasture and the mid-elevation pasture land use classification (3500–2500 m) exhibited significant increases at a significance level of 5 percent. Precisely, the NDVI of the total land use classification of pasture showed an annual increase of 0.004, while the mid-elevation pasture land use classification demonstrated an annual increase of 0.006. The simple linear regression analysis revealed a significant direct linear correlation (R) between the NDVI of high-elevation pastures (3500–2500 meters) and the average and maximum temperatures in July: The NDVI exhibited a correlation coefficient of 0.567 with the average temperature and 0.527 with the maximum temperature, both at a significance level of 1 percent. The NDVI of pasture land use exhibited a significant straight simple linear correlation of 0.554 with the average temperature in June, at a significance level of 1 percent. The multiple linear regression analysis revealed that when considering both the average and maximum temperature of the spring as simultaneous predictors, the direct correlation between the NDVI of the 3500–2500 m class of pasture and these parameters increased from 0.567 (in the simple linear model) to 0.727 (in the multiple linear model). The results of this study suggest that the rise in temperature within the pastures of the Sablan Mountains has created favorable circumstances for the growth and expansion of vegetation, particularly in middle elevations.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the effects of climate change on the mid-elevation pasture vegetation of mountainous areas; A Case Study of the Sablan Mountainside, Iran

Hashemian,

Fataei,

Mosayebi

et al. 2023

Preprint

Self Cite

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Link prediction in social networks using hyper-motif representation on hypergraph

Meng,

Motevalli

2024

Multimedia Systems

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Reliable multiplex semi-local random walk based on influential nodes to improve link prediction in complex networks

Li,

Tang,

Zhou

et al. 2024

Artif Intell Rev

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In recent years, the exponential growth of online social networks as complex networks has presented challenges in expanding networks and forging new connections. Link prediction emerges as a crucial technique to anticipate future relationships among users, leveraging the current network state to address this challenge effectively. While link prediction models on monoplex networks have a well-established history, the exploration of similar tasks on multilayer networks has garnered considerable attention. Extracting topological and multimodal features for weighting links can improve link prediction in weighted complex networks. Meanwhile, establishing reliable and trustworthy paths between users is a useful way to create metrics that convert unweighted to weighted similarity. The local random walk is a widely used technique for predicting links in weighted monoplex networks. The aim of this paper is to develop a semi-local random walk over reliable paths to improve link prediction on a multilayer social network as a complex network, which is denoted as Reliable Multiplex semi-Local Random Walk (RMLRW). RMLRW leverages the semi-local random walk technique over reliable paths, integrating intra-layer and inter-layer information from multiplex features to conduct a trustworthy biased random walk for predicting new links within a target layer of multilayer networks. In order to make RMLRW scalable, we develop a semi-local random walk-based network embedding to represent the network in a lower-dimensional space while preserving its original characteristics. Extensive experimental studies on several real-world multilayer networks demonstrate the performance assurance of RMLRW compared to equivalent methods. Specifically, RMLRW improves the average f-measure of the link prediction by 3.2% and 2.5% compared to SEM-Path and MLRW, respectively.

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Retracted: Integrated modeling of food–water–energy nexus for maximizing water productivity

Cited by 10 publications

References 30 publications

Evaluation of the effects of climate change on the mid-elevation pasture vegetation of mountainous areas; A Case Study of the Sablan Mountainside, Iran

Evaluation of the effects of climate change on the mid-elevation pasture vegetation of mountainous areas; A Case Study of the Sablan Mountainside, Iran

Link prediction in social networks using hyper-motif representation on hypergraph

Reliable multiplex semi-local random walk based on influential nodes to improve link prediction in complex networks

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