Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm(EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system(ANFIS) and artificial neural networks(ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization(PSO)and ant colony optimization for continuous domains(ACO_R) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACO_R methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity(EC), total dissolved solids(TDS), the sodium adsorption ratio(SAR), carbonate hardness(CH), and total hardness(TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACO_R. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.     

Semiempirical thermodynamic modeling of a direct methanol fuel cell system

In this study, a thermodynamic model of an active direct methanol fuel cell (DMFC) system, which couples in‐house experimental data for the DMFC with the mass and energy balances for the system components (condenser, mixing vessel, blower, and pumps), is formed. The modeling equations are solved using the Engineering Equation Solver (EES) program. This model gives the mass fluxes and thermodynamic properties of fluids for each state, heat and work transfer between the components and their surroundings, and electrical efficiency of the system. The effect of the methanol concentration (between 0.5 and 1.25 M) and air flow rate (between 20 and 30 mL cm^?2 min^?1) on the net power output and electrical efficiency of the system and the condenser outlet temperature is investigated. The results essentially showed that the highest value for the electrical efficiency of the system is 23.6% when the current density, methanol concentration, and air flow rate are taken as 0.2 A cm^?2, 0.75 M, and 20 mL cm^?2 min^?1, respectively. In addition, the air flow rate was found to be the most significant parameter affecting the condenser outlet temperature.     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

Synthesis and characterization of 2‐hydroxyethylmethacrylate/2‐(3‐indol‐yl)ethylmethacrylamide‐based novel hydrogels as drug carrier with in vitro antibacterial properties

In this study, a new cationic monomer 2‐(3‐indol‐yl)ethylmethacrylamide (IEMA) derived from tryptamine was synthesized in a single step and characterized by Fourier transform infrared (FTIR), ¹H‐NMR, and ¹³C‐NMR. Then, one‐step preparation of novel poly[2‐hydroxyethylmethacrylate‐c‐2‐(3‐indol‐yl)ethylmethacrylamide], or p(HEMA‐c‐IEMA), copolymeric hydrogels has been performed successfully with IEMA and 2‐hydroxyethylmethacrylate (HEMA) as monomers using free radical aqueous polymerization. The hydrogels were characterized with scanning electron microscopy, FTIR, elemental analysis, thermogravimetric analysis, and texture profile analysis instruments. p(HEMA‐c‐IEMA) hydrogels were used for swelling, diffusion, drug release, and antibacterial activity studies. The drug‐release behavior of the hydrogels was determined as a function of time at 37 °C in pH 1.2 and 7.2. The swelling and drug‐release studies showed that an increased IEMA amount caused a higher increase in swelling and drug‐release values. Additionally, zero‐order, first‐order, and Higuchi equation kinetic models were applied to the drug‐release data, and the data fit well in the Higuchi model, and the Peppas power‐law model was applied to the release mechanism. Finally, the antibacterial activities of the hydrogels were screened against Gram‐positive bacteria (Bacillus cereus and Staphylococcus aureus) and Gram‐negative bacteria (Escherichia coli and Salmonella typhimurium). © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45550.     

Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks

This study investigates the efficiency of artificial neural networks (ANNs) in health monitoring of pristine and damaged beam-like structures. Beam modeling is based on Timoshenko theory. Two commonly used network models, multilayer perceptron (MLP) and radial basis neural network (RBNN), are used. Beam material and geometrical properties, beam end conditions and dynamically obtained data are used as input to the neural networks. The combinations of these parameters yield umpteenth input data. Therefore, to examine the effectiveness of ANNs, the frequency of intact beams is first tried to be determined by the network models, given the material and geometrical characteristics of beam elements and support conditions. The methodology to compute the vibrational data utilized in training the networks is provided. Showing the robustness of network models, the second stage of the study is carried out. At this stage, the crack parameters (e.g. the location and severity of crack) are estimated by the ANNs using the beam properties, beam end conditions and vibrational data, which consist of natural frequencies and mode shape rotation values. Despite the multiplexed input data, no data reduction schemes or multistage computations are executed in training and validation of neural network models. As a result of analysis runs, the optimal MLP and RBNN models are determined. Comparison of these models shows that the optimal RBNN algorithm performs better. The effectiveness of optimal ANN models in the presence of noise is also presented. As a conclusion, the trained network can be used as a diagnosis method in structural health monitoring of beam-like structures.     

Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges

In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.     

Pre-processing data to predict groundwater levels using the fuzzy standardized evapotranspiration and precipitation index (SEPI)

Due to sudden declines in groundwater levels in Neyshabur Plain, one of the most important parts of water supply management programs at the catchment scale is to accurately predict the groundwater level fluctuations. In this paper, the rainfall data from 22 rain gauges and evapotranspiration stations during the period of 1974–2015 were used to find the cumulative effects of rainfall and evapotranspiration on fluctuations in groundwater levels. First, using the Hargreaves-Samani method, the modified evapotranspiration was calculated on the plain. Using the Kriging method, the average amount of precipitation and evapotranspiration of the reference plant was also calculated. Then, employing the fuzzy logic, the fuzzy standardized evapotranspiration and precipitation index (SEPI) was produced. The correlation results between SEPI indicator and fluctuations in groundwater levels showed that the long-term time scales had greater correlations. Thus, the correlations for the time scales of 30, 36, 42, 48, 54 and 60 months were respectively obtained as 0.56, 0.68, 0.71, 0.69, 0.59 and 046. These six parameters were used for principal components analysis (PCA) and the selection criteria (SC) index was used to select the properties affecting every component. The ranking results of testing local linear regression with PCA (LLR-PCA) and dynamic local linear regression with PCA (DLLR-PCA) models, Broyden, Fletcher, Goldfarb, Shanno algorithm with PCA (BFGS-PCA) neural network and Conjugate Gradient-PCA indicated that the DLLR model with three main components had the best performance so that the values of R², RMSE, MBE and MAE were obtained as 0.84, 0.215, 0.028 and 0.162, respectively. The results generally showed that due to severe linearity between SEPI indicator and its time scales, the use of PCA is essential for simulating fluctuations of the groundwater levels.     

Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R²) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.     

Viscous flow of medieval cathedral glass

A popular urban legend concerns the apparent flow of stained glass windows in medieval cathedrals, where the glass windows are commonly observed to be thicker at the bottom than they are at the top. Advances in glass transition theory and experimental characterization techniques now allow for us to address this urban legend directly. In this work, we investigate the dynamics of a typical medieval glass composition used in Westminster Abbey. Depending on the thermal history of the glass, the room temperature viscosity is on the order of 10²⁴ to 10²⁵ Pa·s, about 16 orders of magnitude lower than found in a previous study of soda lime silicate glass. This measurement is in quantitative agreement with a newly derived model for the composition dependence of the nonequilibrium viscosity of glass. Despite this significantly lower value of the room temperature viscosity, the viscosity of the glass is much too high to observe measurable viscous flow on a human time scale. Using analytical expressions to describe the glass flow over a wall, we calculate a maximum flow of ~1 nm over a billion years.     

River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches

This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting.