Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

The Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m^?2 µm^?1 sr^?1) data showed low correlation (coefficient of determination: R² ~0.46) with near-concurrent in situ Chl-a (mg m^?3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m^?3 or >2 mg m^?3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, ?0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R² of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.     

An intelligent based-model role to simulate the factor of safe slope by support vector regression

An infrastructure development in landscape and clearing of more vegetated areas have provided huge changes in Malaysia gradually leading to slope instabilities accompanied by enormous environmental effects such as properties and destructions. Thus, prudent practices through vegetation incorporating to use slope stability is an option to the general stabilized technique. Few researches have investigated the effectiveness of vegetative coverings related to slope and soil parameters. The main goal of this study is to provide an intelligent soft computing model to predict the safety factor (FOS) of a slope using support vector regression (SVR). In the other words, SVR has investigated the surface eco-protection techniques for cohesive soil slopes in Guthrie Corridor Expressway stretch through the probabilistic models analysis to highlight the main parameters. The aforementioned analysis has been performed to predict the FOS of a slope, also the estimator’s function has been confirmed by the simulative outcome compared to artificial neural network and genetic programing resulting in a drastic accurate estimation by SVR. Using new analyzing methods like SVR are more purposeful than achieving a starting point by trial and error embedding multiple factors into one in ordinary low-technique software.

     

New evolution in two-way relay networks based on physical layer network coding

Telecommunication Systems - In this paper, a physical-layer network coding (PNC) method is offered for a two-way relay network with spatial modulation (SM) for source node and relay node. For this...     

Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold,sensing time and user selection

Wireless Networks - Cooperative spectrum sensing schemes proposed to solve the hidden terminal problem and mitigate multipath fading and shadowing effects, which enhance the sensing performance and...     

Microstructure and Optical Bandgap of Cobalt Selenide Nanofilms

Semiconductors - The purpose of this research is to explore the properties of CoSe nanostructured thin films on glass substrates prepared by a chemical solution deposition method. Special attention...     

Determining Honesty of Accuser Nodes in Key Revocation Procedure for MANETs

Mobile Networks and Applications - Management of encryption keys is an essential task for establishing secure communication in a mobile ad hoc network (MANET). Any key management scheme must be...     

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.     

A wrapper‐based feature selection for improving performance of intrusion detection systems

Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.     

Quality Evaluation of Groundwater Resources using Geostatistical Methods (Case Study: Central Lorestan Plain,Iran)

In this research, based on the qualitative data of 40 wells, variations of water quality parameters of the Central Plain Aquifer were evaluated using kriging and IDW (Inverse Distance Weighting) methods. Owing to the normal distribution of the studied parameters (except Na⁺, SO₄^2?, and TH: total hardness), ordinary kriging was used for modeling. The analysis of the data trends indicated that all the variables were influenced by in two general trends, i.e., NW–SE and NE–SW. In fact, these trends were a result of the effect of the structural conditions on aquifer properties such as transmissivity and flow direction. Variogram analysis (based on C₀ near zero and C₀/σ² ratio between 0.0–0.5) showed that the Na⁺, TDS (total dissolved solids), Ca²⁺, and TH variables have a good spatial structure and the BOD (biochemical oxygen demand), COD (chemical oxygen demand), NO₃^?, and EC variables have poor spatial structure. The BOD, COD, NO₃^?, and EC (electrical conductivity) variables have the smallest range and isotropic distribution. On the other hand, the Ca²⁺, Mg²⁺, Na⁺, SO₄^2?, Cl^?, HCO₃^?, pH, TDS, TH and Alk (alkalinity) parameters are characterized by anisotropic distributions. The Na⁺, TDS, Ca²⁺, and TH variables have the largest range. The results showed that both the IDW and kriging methods have close estimates to one another. The pH variable decreases toward the outlet whereas the EC and TDS variables increase along the direction of water flow and toward the outlet. The distributions of the BOD and COD variables do not perfectly match with the aggregation of industrial activities in the central part as well as the agricultural activities in the southeastern and central parts of the aquifer. The distributions of the Ca²⁺, Mg²⁺, and Alk variables completely follow the geology condition and regional spread of carbonate formations. The Na⁺ concentration increases from the center toward the outlet. The concentration of the Cl^? variable is the highest in the central part of the plain due to the concentration of agricultural and industrial activities. The distribution of the SO₄^2? variable is influenced by a natural factor (lithology), especially in the southeastern parts and the outlet as well as artificial factors (agricultural and industrial activities) in the central and southeastern parts of the aquifer. The NO₃^? variable, which is directly influenced by agricultural and livestock-farming activities, has its maximum concentration in the southeastern areas.     

Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning

DRASTIC-based vulnerability indices and their variations for an aquifer are investigated in this paper, each of which is regarded as a framework since their rationale of using seven DRASTIC data layers is consensual and lacks empirical or theoretical formulations. The Basic DRASTIC framework (BDF) is implemented by a set of prescribed rules; whereas its three variations involve unsupervised learning from the data, which comprise: (i) learning the rates by the Wilcoxon test (WDF) but using BDF weights; (ii) using BDF rates but learning the weights by Genetic Algorithm (BDF-GA); and (iii) learning rates as in WDF and the weights as in BDF-GA (WDF-GA). These four frameworks are not supervised, but the novelty of the paper is to introduce supervised learning at the second stage by Artificial Intelligence to run Multiple Frameworks (AIMF), for which the paper uses Support Vector Machine (SVM). AIMF uses the outputs of the four frameworks as its input data and a function of observed nitrate-N values as its target data. The AIMF strategy is evaluated in the aquifer of Ardabil plain, which is exposed to anthropogenic contamination such as nitrate-N. The coefficient of correlation (r-values) between the results and nitrate-N values for the above frameworks are: 0.2, 0.37, 0.38 and 0.45; whereas AIMF enhances it to 0.84; attributable to the supervised learning.