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In the present research, a multi-objective model is developed for surface water resource management in the river basin area which is connected to the lake. This model considers different components of sustainable water resource management including economic, social and environmental aspects, and simultaneously tries to resolve conflicts between different stakeholders by means of non-symmetric Nash bargaining, which is linked to the multi-objective optimization method. This study proposes a new methodology to improve Nash Conflict Resolution through finding the optimum degree of the utility function. The proposed model is examined in the Zarrineh River basin in Iran. The results show that the amount of available resources or volume of reservoirs play a significant role in determining the optimal degree of the utility function and efficiency of the proposed method in such a way that the higher amount of resources or the larger reservoirs will result in the higher optimal degree of the utility function. In the proposed multi-objective model, two different amounts of surface water inflow are considered. The first assumed amount is the long-term average flow rate and the second one is equal to 80% of the first mode, which is reduced based on the estimated impacts of climate changes. This multi-objective allocation model could supply 100 and 97.5% of the environmental demand of Lake Urmia in the first and second situations, respectively.

(1) Background: Physiological responses, such as heart rate and heart rate variability, have been increasingly utilized to monitor, detect, and predict mental stress. This review summarizes and synthesizes previous studies which analyzed the impact of mental stress on heart activity as well as mathematical, statistical, and visualization methods employed in such analyses. (2) Methods: A total of 119 articles were reviewed following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Non-English documents, studies not related to mental stress, and publications on machine learning techniques were excluded. Only peer-reviewed journals and conference proceedings were considered. (3) Results: The studies revealed that heart activities and behaviors changed during stressful events. The majority of the studies utilized descriptive statistical tests, including t-tests, analysis of variance (ANOVA), and correlation analysis, to assess the statistical significance between stress and non-stress events. However, most of them were performed in controlled laboratory settings. (4) Conclusions: Heart activity shows promise as an indicator for detecting stress events. This review highlights the application of time series techniques, such as autoregressive integrated moving average (ARIMA), detrended fluctuation analysis, and autocorrelation plots, to study heart rate rhythm or patterns associated with mental stress. These models analyze physiological data over time and may help in understanding acute and chronic cardiovascular responses to stress.

The formation of modifications or conception in the landscape could possibly, be a procedure relative to its natural and non-disturbance process; and it could be hastened by the occurrence of disturbance regimes. The objective of this research is to survey the changes in a landscape structure, over a period of 30 years, to attain information, as to the current conditions of land use, utilizing landscape metrics in the watershed area of the Latian Dam, so as to analyze the results and the voids present, towards obtaining a specified sustainable regional planning for the abovementioned watershed. Land use was identified and reviewed by means of four Landsat satellite images for 1987, 1998, 2007, and 2017; and in this watershed, it was classified into four classes, (a) build-up areas, (b) vegetated areas, (c) bare lands and (d) water bodies. Subsequently, by taking advantage of 7 metrics at the landscape level and 8 metrics at the class level, the landscape structure in this watershed was quantified by utilizing the Fragstats 4.2 Software. The survey results illustrated an increment in the number of patches (NP), decrementing the mean area of the patches (AREA-MN), and increasing the Interspersion & Juxtaposition Index (IJI) signifies amplified fragmentation at the landscape level in this watershed. Similarly, the NP has also incremented at the class level, and thus, the fragmentation of patches and fragmentation in the entire three classes of land use, namely, build-up areas, bare lands, and vegetated areas has occurred. The amount of patchiness for the build-up class, with due attention to the increment in the mean area of patches (AREA-MIN), which demonstrates the fact that, this class is inclined and has a tendency towards a coarse-grained structure and a metric decrement in the AREA-MIN in the vegetated areas, illustrates that this class is prone to the fine-grained structure.

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t 1 (1987), t 2 (1998), t 3 (2007), and t 4 (2017) to authenticate the LULC predictions, so to obtain estimates for t 5 (2027) and t 6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development.

In recent years, due to a decrement in water quality and scarcity, desalination systems have gained popularity for desalination purposes. Synchronously, with the development of this system, particularly, in concern with the littoral regions, seawater concentrate disposal consisting of various pollutants was taken into consideration. In this research, two desalination plants near each other were selected and four scenarios have been foreseen, for the discharge of seawater concentrate and the desalination intake, which are taken under study in the Ramin-Chabahar region, based on dual-dimensional hydrodynamic simulation, comprising of diffusion and release, by utilizing the MIKE 21 Software. Due to the proximity of the two desalination plants, to reduce the costs of piping in the sea, the location of discharge and intake were considered common. On the grounds pertaining to the modeling results, the discharge of seawater concentrates, at a distance of 300 m (5 m of depth) from the coast and the intake point, at a distance of 800 m, in elongation, has had the minimum environmental impact; as well as having no undesirable effect on the water quality of the intake, in addition to being cost-effective, from the economic viewpoint. To dilute seawater concentrate to a standardized level, it is appropriate to discharge through a diffuser with 10 nozzles, which are spaced out at 3.25 m from each other, being positioned linearly on one side, at an angle of 60 degrees. With the optimal selection of intake and discharge points of seawater concentrate in marine desalination plants, in addition to increasing the quality of treated water and reducing adverse environmental effects, construction and operation costs are also reduced.

There is always an adamant need to comprehend and draw the complex challenges of sustainability in order to help organize studies, due to the increasing human-related pressures on coastal zones. Hence, by formulating such a comprehensive framework, it could be possible to anticipate changes and support managerial decisions, as well as the degree of resilience of the region’s environment. One of the approaches utilized in littoral or coastal zones is the conceptual framework of drivers, pressure, status, impact, and responses (DPSIR)..Qeshm Island, the largest island in the Persian Gulf, is accounted for being the most vital and strategic areas of the mentioned region. In recent decades, Qeshm has become one of the major cultural, natural, geological, and tourism hubs of the country due to its unique regional characteristics, along with its biodiversity and environmental sensitivity. Thereby, in the present research, a combined approach shall be followed to explore the resilience of the marine environment on the northern coast of Qeshm Island by taking advantage of the socioeconomic criterion. In this respect, the conceptual framework of the DPSIR model is utilized in combination with the structural equation model (SEM-PLS) (or partial least squares), which is one of the nonexperimental techniques, to quantify the results in the best manner possible. On the basis of the fuzzy cognitive map (FCM), the regional economic index bearing the weights of 0.62, 0.62, and 0.5, along with an institutional-managerial and biological index, respectively, denotes a two-way positive correlation, whereas this factor has a two-way, but adverse correlation, relationship with a weight of 0.65 in terms of the sociocultural index. Similarly, there is also a one-way and negative relationship, as to the economic index, with a weight of 0.69 which is in relevance with the physio-chemical index.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

Moisture movement in pavements and road embankments is receiving more attention in pavement and geotechnical engineering. Water from rainfall is the primary source of moisture in soils. Following a rainstorm event, large quantities of moisture can be absorbed by the soil when the water migrates into the soil mass. The passage of moisture has an impact on the mechanical performance and functionality of the pavement infrastructure. When the pavement infrastructure is built on expansive soils, water flow can cause damage to pavements due to the swelling and shrinking of expansive soils through adsorption and desorption of moisture. Such damage can result in severe financial loss; in fact, the estimated yearly cost of damage from expanding soil problems is $2.3 billion in the United States. Oklahoma contains large expanses of medium to highly expansive clays. The state's largest cities are located in these areas, and significant and costly highway systems have been built to support the population density. These areas have relatively high average annual precipitation, which worsens the expansive clay problems. The swelling or shrinking of expansive clays causes distortion and cracking in pavements, reducing pavement service life. Thus, it is critical to understand how water moves in road embankments of expansive soils subjected to seasonal rainfall and to predict the vertical movement of pavements built on expansive soils. This study used Oklahoma Mesonet measurements to develop a data-driven statistical model for estimating soil diffusivity and soil suction in order to predict the movement of expansive soils over time. The first component of the study used unsupervised learning and a nonlinear least squares model to estimate soil diffusivity. The second component of the study presents a mechanistic-numerical model for predicting equilibrium suction that considers the diffusion coefficient's effects and uses surface field suction measurements. The final component of the study utilized Interferometric Synthetic Aperture Radar (InSAR) technique for effective displacement monitoring using time series of SAR data. The study investigated the performance of moisture barriers on two state highways in Oklahoma, where expansive soils are a major problem.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes

Background Antibiotics have been increasingly used for veterinary and medical purposes. The overuse of these compounds for these purposes can pollute the environment, water resources in particular. Tetracycline, among other forms of antibiotics, is one of the most applied antibiotic in aquaculture and veterinary medicine. The present study aimed to tack the traces of tetracycline in the effluents of municipal and hospital wastewater treatment plants, surface and groundwater resources and finally the drinking water provided from these water resources. Methods The samples were taken from Fasha-Foyeh Dam, wells located at Varamin Plain, and Yaftabad; and also, wastewater samples were collected from the wastewater treatment plant effluents of Emam Khomeini Hospital and a municipal wastewater treatment plant which its effluent is being released to the surface water of the area covered in this work. 24 samples were collected in total during July 2012 to December 2012. The prepared samples were analyzed using high-performance liquid chromatography. Results Based on the results, mean tetracycline levels in surface and ground water at nearby of animal farms was found to vary from 5.4 to 8.1 ng L -1 . Furthermore, the maximum TC concentration of 9.3 ng L -1 was found to be at Yaft-Abad sampling station. Although tetracycline traces could not be detected in any investigated Hospital WWTP effluents, it was tracked in MWWTP effluent samples, in the concentration range of 280 to 540 ng l −1 . Conclusion The results showed that the concentration of TC in water resource near the animal farms is higher than the other sampling stations. This is related to the usage of antibiotic for animals. In fact, it caused the contamination of water resources and could contribute to radical changes in the ecology of these regions.