The relationship among environmental variables,jellyfish and non‐gelatinous zooplankton: A case study in the north of the Gulf of Oman

Processes underlying the temporal and spatial variations observed in the distribution of jellyfish and non‐gelatinous zooplankton in the Gulf of Oman are not well understood. This information gap is clearly a major issue in controlling the harmful blooms of jellyfish and non‐gelatinous zooplankton. Samples of jellyfish and non‐gelatinous zooplankton were collected from six stations in Chabahar Bay and three stations in Pozm Bay within four seasons. At each station, environmental variables were also recorded from bottom and surface water. A total of 83 individuals of medusae representing four species of Scyphozoa (i.e., Cyanea nozakii, Chrysaora sp., Pelagia noctiluca, Catostylus tagi) and species of Hydrozoa (i.e., Diphyes sp., Rhacostoma sp., Aequorea spp.) were observed in the study area. A total of 70,727.25 individuals/m^?3 of non‐gelatinous zooplankton dominated by copepods and cladocerans were collected in nine stations within the four seasons. The results of a RELATE analysis yielded no significant association between species composition for jellyfish and non‐gelatinous zooplankton. Among environmental variables, water transparency, nitrite concentration, water depth and temperature were better associated with the total variation in jellyfish species composition than with that of non‐gelatinous zooplankton. Dissolved oxygen, pH, and phosphate concentration were significant environmental variables associated with the variation in the spatial and temporal distribution patterns of non‐gelatinous zooplankton assemblages. Although some jellyfish species (i.e., Rhacostoma sp., Pelagia noctiluca, Catostylus tagi) occur independently of non‐gelatinous zooplankton assemblages, other jellyfish (i.e., Chrysaora sp., Aequorea spp., Cyanea nozakii, Diphyes sp.) are strongly correlated with non‐gelatinous zooplankton assemblages.     

Chemical identification,antioxidant, cholinesterase inhibitory,and cytotoxic properties of fucoidan extracted from Persian Gulf Sargassum angustifolium

Marine macroalgal sulfated fucose-containing polysaccharides, like fucoidan, have drawn significant attention due to their biotechnological potentials, such as anti-cancer, antioxidant, and anti-cholinesterase activities. The fucoidan derived from brown macroalgae Sargassum angustifolium species (FSA) was investigated for its cytotoxic effects and alterations in cell proliferation, and cell cycle-related gene expression in the present study occurred on NB4 cell line. The results showed that FSA would induce p53, p21, pro-apoptotic genes and increase expression of the p15 gene as a cell arrest marker. Also, FSA inhibited the anti-apoptotic effect of the Bcl-2 gene and decreased dnmt-1 gene expression. FSA significantly exhibited potent 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity (p<0.05) with an IC₅₀ value of 0.157 mg/mL and showed moderate anti-acetylcholinesterase activity with an IC₅₀ value of 1.20 μg/mL. These results indicated the potential of FSA for the development of therapeutic or preventive agents of cancer and Alzheimer’s disease mainly through cytotoxic effect and AChE (acetylcholinesterase) inhibition as well as additional antioxidant capacities.     

Hydrogeologic framework of the Maku area basalts, northwestern Iran

The Maku area in northwestern Iran is characterized by young lava flows which erupted from Mount Ararat in Turkey. These fractured volcanic rocks overlie alluvium associated with pre-existing rivers and form a good basalt-alluvium aquifer over an area of 650 km². Groundwater discharge occurs from 12 large springs, ranging from 20 to 4,000 L s^?1, and from some extraction wells. Permian and Oligo-Miocene age limestones along the northern boundary of the Bazargan and Poldasht Plains basalts are intensively karstified and groundwater from these high lands easily enters the basalt-alluvium aquifers. The transmissivity of the basalt-alluvium aquifer ranges from 24 to 870 m² d^?1, indicating heterogeneity. Groundwater of the aquifer is a sodium-bicarbonate and mixed cation-bicarbonate type and the concentration of fluoride is higher than the universal maximum admissible concentrations for drinking. In order to determine the chemical composition and identify the source of the high fluoride concentrations in the groundwater of the basaltic area, water samples from the springs, wells and rivers were analyzed. The results indicate that the high fluoride water enters the study area from the Sari Su River.     

Precipitation rate climatology related to different cloud types using satellite imagery over Iran

Cloud types have a substantial influence on precipitation. This paper presents a study of the monthly variations of daytime different cloud types over Iran using data collected from Moderate-resolution Imaging Spectroradiometer (MODIS) aboard Terra during 2001–2015, MODIS aboard Aqua during 2002–2015, International Satellite Cloud Climatology Project (ISCCP) H-series cloud type data during 2001–2009 and precipitation rate associated with different cloud types using Tropical Rainfall Measuring Mission (TRMM) satellite products during 2001–2009. Different cloud types were determined using MODIS cloud optical thickness and cloud top pressure data based on ISCCP algorithm. The results showed that stratocumulus and cumulus clouds have maximum occurrence frequency over marine areas especially southern seas. The maximum frequency of nimbostratus and deep convective occurrence occurred over mountainous regions particularly at the time of Aqua overpass and cirrus and cirrostratus are observed over southeast of Iran during warm months due to monsoon system. Altostratus cloud is extended in each month except January, at the time of Terra overpass while nimbostratus is seen at the time of Aqua overpass during warm months in the study area. Cumulus and altocumulus clouds have shown remarkable frequency in all months especially over marine regions during warm and fall months. The higher value of precipitation rate is related to altostratus with a rate approximately 7 mm/h at the time of Terra overpass during April. Altostratus has the maximum recorded precipitation rate except in Nov., Dec., Sep., and Jan. at the time of Terra overpass, whereas the maximum precipitation rate is linked to nimbostratus cloud activity (up to 5 mm/h) except for March, April, and Sep. at the time of Aqua overpass. Deep convective (up to 1.32 mm/h), cirrostratus (up to 1.11 mm/h), and cirrus (0.02 mm/h) are observed only in warm months. The results were compared with ISCCP cloud types so that precipitation rate classified from low to high and Spearman rank correlation was calculated. The results showed good agreement between these two cloud type data; however, there were few notable difference between them.     

Evaluating the support vector machine for suspended sediment load forecasting based on gamma test

Due to the various influencing factors on river suspended sediment transportation, determining an appropriate input combination for developing the suspended sediment load forecasting model is very important for water resources management. The influence of pre-processing of input variables by Gamma Test (GT) was investigated on performance of Support Vector Machine (SVM) with two kernels; Radial Basis Function (RBF) and polynomial in order to forecast daily suspended sediment amount in the period between 1983 and 2014 at Korkorsar basin, northern Iran. The best input combination was identified using GT and correlation coefficient analysis. Then, the SVM model was developed and the suspended sediment amount was forecasted with RBF and polynomial kernels. The obtained results in testing phase showed that GT-SVM (RBF kernel) model can estimate suspended sediment more accurately with the lowest RMSE (14.045 ton/day), highest correlation coefficient (0.88) and highest NSEC coefficient (0.88) than SVM (RBF kernel) model (RMSE?=?18.36ton/day, \( {R}^2=0.79, \) \( NSEC=0.73 \)) and had a better performance than the other models. The results indicated that in forecasting the first nine maximum values of suspended sediment load, GT-SVM (RBF) had a higher capability than the other models and could provide a more accurate estimation from the maximum rate of suspended sediment. The results of this study showed the capability of identifying the priority of the input parameters can change GT to a useful and technical test for input variables pre-processing to forecast the amount of suspended sediments.     

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity

ABSTRACT

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.     

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area.     

Intertidal gastropod assemblages shaped by key environmental variables across the northern Persian Gulf and the Gulf of Omanss

Variations in environmental factors can alter the species distribution pattern in intertidal rocky shores. The Persian Gulf (PG) and the Gulf of Oman (GO) vary substantially with respect to environmental and oceanographic conditions. The abundance and biodiversity of intertidal rocky gastropods in five locations across the northern PG and the GO were compared, and the environmental variables underlying the distribution pattern of these organisms were investigated. A total of 67 gastropod species were identified. The largest average density (294 ind./m²) and diversity (N = 43) for gastropods occurred in the Hotel Lipar station (LIP) located in Chabahar Bay in the GO. Clypeomorus bifasciata (107.43 ind./m²) followed by Cerithium caeruleum (94.67 ind./m²) were the most abundant species. Planaxis sulcatus and Siphonaria spp. occurred in all locations during both sampling occasions. Species richness and abundance of gastropods showed significant differences between LIP and remaining locations. A significant difference was found in assemblage structure across locations. In general, the species richness and density in the locations at GO were significantly larger than those locations in the PG, suggesting that the harsh environmental condition in the PG might be the forcing factor for this diminish. Distinct grouping was observed in both assemblage structure and species composition between locations in the PG and the GO. The spatial and temporal distribution patterns of gastropods assemblages were significantly correlated with variation in salinity and substrate rugosity.     

Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study

In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.