Corrosion inhibition of mild steel by Nettle (Urtica dioica L.) extract: polarization,EIS, AFM,SEM and EDS studies

Inhibitory action of Nettle (Urtica dioica L.) extract was investigated in hydrochloric acid solution through electrochemical (polarization, EIS) and surface analysis (optical microscopy/AFM/SEM/EDS) techniques. Effect of inhibitor concentration and temperature were evaluated. Thermodynamic parameters have been calculated and adsorption studies have been investigated. The electrochemical studies showed that NE inhibitor retards both cathodic and anodic processes through the inhibitor adsorption on the metal surface and blocking the active corrosion sites. A good fit to Langmuir adsorption isotherm was obtained between surface coverage degree and inhibitor concentration. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to characterize the microstructure and the nature of the metal surface. SEM and AFM images and energy dispersion spectrometer analysis for mild steel specimens in the absence and presence of the NE inhibitor confirmed the protective layer formation on the metal surface and proved the results obtained by the electrochemical experiments. Inhibition efficiency up to 92.24% has been achieved in 1?M acid solution containing 0.3?g/l inhibitor at 40?°C.     

Improvements in tribological properties of polyoxymethylene by aramid short fiber and polytetrafluoroethylene

In order to improve tribological properties of polyoxymethylene (POM), the effects of aramid short fibers (ASF) and polytetrafluoroethylene (PTFE) solid lubricants, as two classes of additives, were studied. The appropriate composites of the polymer and the additives were prepared by melt mixing process. Distribution of additives in the polymer matrix was characterized by scanning electron microscopy (SEM). Mechanical properties in tension such as modulus of elasticity, yield stress, and stress-at-break as well as the fracture energy in impact test were studied to explore friction and wear mechanisms of the composites against a smooth steel surface. Tribological measurements showed that both additives reduce friction and wear of the POM. However, both additives reduced fracture energy of POM in impact test, which dismisses the role of abrasive mechanism of wear under applied conditions. On the other hand, tensile results showed that addition of ASF mechanically reinforces POM, while PTFE degrades mechanical properties of this polymer, especially yield stress. Considering the role of yield stress in the adhesive mechanism of friction and wear, this property was used to define tribological behavior of samples. Since ASF induces mechanical stiffening to POM, increase in yield stress improves tribological properties. However, PTFE introduces transfer films at the interface, thus reduction of yield stress is in favor of tribological properties of this composite. Finally, it is shown that frictional heating and contact temperature rise has a significant degrading effect on wear resistance.     

Hydrothermal carbonization of sawdust for hydrochar production to prepare solid fuels

Hydrothermal carbonization (HTC) of sawdust for producing hydrochar was optimized using response surface methodology (RSM). After optimization, the combustion behaviour and other relevant fuel properties of hydrochar that was obtained at the optimized conditions were studied. Additionally, the aqueous phase obtained at the optimized conditions was recycled as the reaction medium for producing hydrochar and the influence of the aqueous phase recycling on the hydrochar yield and properties was evaluated. The results indicated that the highest hydrochar yield of 84.23 wt.% under the predicted optimum conditions of temperature of 162.23°C, 2.51 h, feedstock loading of 10.71 wt.%, and catalyst loading of 7.99 wt.%. Furthermore, it was found that the use of recycled aqueous phase as the reaction medium led to an increase in the hydrochar yield, higher heating value (HHV), and energy yield, and difference in combustion behaviour was minor. Overall, this study filled the gap in HTC literature regarding the effect of catalyst on hydrochar production and provided a practical solution to treat the aqueous phase.     

Estimation of Transverse Mixing Coefficient in Straight and Meandering Streams

Transverse mixing coefficient (TMC) is one of the key factors in the modelling of lateral dispersion of pollutants. Several researchers have attempted to estimate this coefficient using various models. However, robust equations that can accurately estimate lateral mixing in both straight and meandering streams are still required. In this study, novel formulae were developed using the hydraulic and geometric parameters of rivers. The multiple linear regression (MLR), genetic programming based symbolic regression (GPSR) and dimensionless parameters were used for this purpose. Two extensive data sets including data from straight channels/streams and meandering ones were employed to develop the formulae. The main advantage of the developed formula for meandering streams is proper consideration of the effects of aspect ratio, friction, and sinuosity. The formulae performances were then compared quantitatively with those of existing ones using accuracy metrics such as RMSE (Root Mean Square Error). The results illustrated that the proposed formulae outperform others in terms of accuracy and can be used for estimating TMC in straight and meandering streams. In addition, the comparison of MLR and GPSR models showed that the latter is marginally more accurate than MLR specially in meandering streams. However, the MLR models presented a more justifiable relationship between the TMC and governing dimensionless parameters. The main advantages of the presented formulae are that they are more accurate than previous models, can be used in both meandering and straight streams; and can be easily implemented in numerical models to estimate the pollutant concentration and mixing length.     

An improved feature transformation method using mutual information

The feature transformation is a very important step in pattern recognition systems. A feature transformation matrix can be obtained using different criteria such as discrimination between classes or feature independence or mutual information between features and classes. The obtained matrix can also be used for feature reduction. In this paper, we propose a new method for finding a feature transformation-based on Mutual Information (MI). For this purpose, we suppose that the Probability Density Function (PDF) of features in classes is Gaussian, and then we use the gradient ascent to maximize the mutual information between features and classes. Experimental results show that the proposed MI projection consistently outperforms other methods for a variety of cases. In the UCI Glass database we improve the classification accuracy up to 7.95 %. Besides, the improvement of phoneme recognition rate is 3.55 % on TIMIT.     

Human–robot skills transfer interfaces for a flexible surgical robot

In minimally invasive surgery, tools go through narrow openings and manipulate soft organs to perform surgical tasks. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools. The aim of the STIFF-FLOP European project is to develop a soft robotic arm to perform surgical tasks. The flexibility of the robot allows the surgeon to move within organs to reach remote areas inside the body and perform challenging procedures in laparoscopy. This article addresses the problem of designing learning interfaces enabling the transfer of skills from human demonstration. Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level imitation of the underlying intent extracted from the demonstrations. By focusing on this last form, we study the problem of extracting an objective function explaining the demonstrations from an over-specified set of candidate reward functions, and using this information for self-refinement of the skill. In contrast to inverse reinforcement learning strategies that attempt to explain the observations with reward functions defined for the entire task (or a set of pre-defined reward profiles active for different parts of the task), the proposed approach is based on context-dependent reward-weighted learning, where the robot can learn the relevance of candidate objective functions with respect to the current phase of the task or encountered situation. The robot then exploits this information for skills refinement in the policy parameters space. The proposed approach is tested in simulation with a cutting task performed by the STIFF-FLOP flexible robot, using kinesthetic demonstrations from a Barrett WAM manipulator.     

Formulation of soil angle of shearing resistance using a hybrid GP and OLS method

In the present study, a prediction model was derived for the effective angle of shearing resistance (?′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates ?′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation coefficient, root mean squared error, and mean absolute percent error were used to evaluate the performance of the models. Sensitivity and parametric analyses were conducted and discussed. The GP/OLS-based formula precisely estimates the ?′ values for a number of soil samples. The proposed model provides a better prediction performance than the traditional GP and regression models.     

Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

Meteorological data are key variables for hydrologists to simulate the rainfall-runoff process using hydrological models. The collection of meteorological variables is sophisticated, especially in arid and semi-arid climates where observed time series are often scarce. Climate Forecast System Reanalysis (CFSR) Data have been used to validate and evaluate hydrological modeling throughout the world. This paper presents a comprehensive application of the Soil and Water Assessment Tool (SWAT) hydrologic simulator, incorporating CFSR daily rainfall-runoff data at the Roodan study site in southern Iran. The developed SWAT model including CFSR data (CFSR model) was calibrated using the Sequential Uncertainty Fitting 2 algorithm (SUFI-2). To validate the model, the calibrated SWAT model (CFSR model) was compared with the observed daily rainfall-runoff data. To have a better assessment, terrestrial meteorological gauge stations were incorporated with the SWAT model (Terrestrial model). Visualization of the simulated flows showed that both CFSR and terrestrial models have satisfactory correlations with the observed data. However, the CFSR model generated better estimates regarding the simulation of low flows (near zero). The results of the uncertainty analysis showed that the CFSR model predicted the validation period more efficiently. This might be related with better prediction of low flows and closer distribution to observed flows. The Nash-Sutcliffe (NS) coefficient provided good- and fair-quality modeling for calibration and validation periods for both models. Overall, it can be concluded that CFSR data might be promising for use in the development of hydrological simulations in arid climates, such as southern Iran, where there are shortages of data and a lack of accessibility to the data.     

Tunable band-stop plasmonic filter based on square ring resonators in a metal-insulator-metal structure

A novel high-performance plasmonic filter based on a metal–insulator–metal structure is analysed for band-rejection applications. A square ring is used in proximity to the waveguide in order to resonate with some transmitted wavelengths and drop them to prevent from propagation towards the output. The effect of the structural parameters of square ring resonator is studied deploying the finite difference time domain method and the possibility of tuning the rejected wavelength is investigated in detail. The simulation results demonstrate that the rejected wavelength has a red-shift with increase in the size of the ring’s dimensions. A further study is carried out considering narrowing the bandwidth. To improve the quality factor of the proposed filter, a small ring within the resonator is introduced that considerably decreases the bandwidth of the peak with respect to its central wavelength.     

Experimental and theoretical investigation of sensing parameters in carbon nanotube‐based DNA sensor

Nowadays, sensitive biosensors with high selectivity, lower costs and short response time are required for detection of DNA. The most preferred materials in DNA sensor designing are nanomaterials such as carbon and Au nanoparticles, because of their very high surface area and biocompatibility which lead to performance and sensitivity improvements in DNA sensors. Carbon nanomaterials such as carbon nanotubes (CNTs) can be considered as a suitable DNA sensor platform due to their high surface‐to‐volume ratio, favourable electronic properties and fast electron transfer rate. Therefore, in this study, the CNTs which are synthesised by pulsed AC arc discharge method on a high‐density polyethylene substrate are used as conducting channels in a chemiresistor for the electrochemical detection of double stranded DNA. Moreover, the response of the proposed sensor is investigated experimentally and analytically in different temperatures, which confirm good agreement between the presented model and experimental data.Inspec keywords: electrochemical sensors, polymers, arcs (electric), biological techniques, nanosensors, carbon nanotubes, DNAOther keywords: C, chemiresistor, double stranded DNA detection, CNT, electronic properties, surface‐to‐volume ratio, nanoparticles, biosensors, electrochemical detection, high‐density polyethylene substrate, pulsed AC arc discharge method, electron transfer rate, carbon nanomaterials, carbon nanotube‐based DNA sensor