Environmentally friendly disposable sensors with microfabricated on-chip planar bismuth electrode for in situ heavy metal ions measurement

This paper presents an environmentally friendly disposable heavy metal ion sensor for in situ and online monitoring in the nature and physiological systems. The miniaturized sensor chip consists of a non-toxic microfabricated bismuth (Bi) working electrode that replaces the conventional mercury electrodes, an integrated Ag/AgCl reference electrode, a gold counter electrode, and microfluidic channels. In this work, the electrochemical behavior of the Bi working electrode was characterized in several non-deaerated buffer solutions using cyclic voltammetry. The detection and quantification of Pb (II) and Cd (II) were statically performed using anodic stripping voltammetry inside the microchannels, in the Pb (II) concentration range of 25–400 ppb (R² = 0.991) with limit of detection of 8 ppb for 60 s deposition, and in the Cd (II) concentration range of 28–280 ppb (R² = 0.986) with limit of detection of 9.3 ppb for 90 s deposition. Particularly, the applications of this sensor chip have been reported with the examples of in situ measurement of Cd (II) concentration in soil pore and ground water and online direct measurement of Cd (II) concentration in cell culture media in its native environment.     

Compact optical diagnostic device for isothermal nucleic acids amplification

We recently reported the successful use of the loop-mediated isothermal amplification (LAMP) reaction for hepatitis B virus (HBV) DNA amplification and its optimal primer design method. In this study, we report the development of an integrated isothermal device for both amplification and detection of targeted HBV DNA. It has two major components, a disposable polymethyl methacrylate (PMMA) micro-reactor and a temperature-regulated optical detection unit (base apparatus) for real-time monitoring of the turbidity changes due to the precipitation of DNA amplification by-product, magnesium pyrophosphate. We have established a correlation curve (R² = 0.99) between the concentration of pyrophosphate ions and the level of turbidity by using a simulated chemical reaction to evaluate the characteristics of our device. For the applications of rapid pathogens detection, we also have established a standard curve (R² = 0.96) by using LAMP reaction with a standard template in our device. Moreover, we also have successfully used the device on seven clinical serum specimens where HBV DNA levels have been confirmed by real-time PCR. The result indicates that different amounts of HBV DNA can be successfully detected by using this device within 1 h.     

Applications of soft computing techniques for prediction of energy dissipation on stepped spillways

In this study, numbers type of soft computing including artificial neural network (ANN), support vector machine (SVM), multivariate adaptive regression splines (MARS), and group method of data handling (GMDH) were applied to model and predict energy dissipation of flow over stepped spillways. Results of ANN indicated that this model including hyperbolic tangent sigmoid as transfer function obtained coefficient of determination (R ² = 0.917) and root-mean-square error (RMSE = 6.927) in testing stage. Results of development of SVM showed that developed model consists of radial basis function as kernel function achieved R ² = 0.98 and RMSE = 2.61 in validation stage. Developed MARS model with R ² = 0.99 and RMSE = 0.65 has suitable performance for predicating the energy dissipation. Results of developed GMDH model show with R ² = 0.95 and RMSE = 5.4 has suitable performance for modeling energy dispersion. Reviewing of results of prepared models showed that all of them have suitable performance to predict the energy dissipation. However, MARS and SVM are more accurate than the others. Attention to structures of GMDH and MARS models declared that Froude number, drop number, and ratio of critical depth to height of step are the most important parameters for modeling energy dissipation. The best radial basis function was found that as best kernel function in developing the SVM.

     

Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting

Desired rock fragmentation is the main goal of the blasting operation in surface mines, civil and tunneling works. Therefore, precise prediction of rock fragmentation is very important to achieve an economically successful outcome. The primary objective of this article is to propose a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO). The proposed PSO–ANFIS model has been compared with support vector machines (SVM), ANFIS and nonlinear multiple regression (MR) models. To construct the predictive models, 72 blasting events were investigated, and the values of rock fragmentation as well as five effective parameters on rock fragmentation, i.e., specific charge, stemming, spacing, burden and maximum charge used per delay were measured. Based on several statistical functions [e.g., coefficient of correlation (R ²) and root-mean-square error (RMSE)], it was found that the PSO–ANFIS (with R ² = 0.89 and RMSE = 1.31) performs better than the SVM (with R ² = 0.83 and RMSE = 1.66), ANFIS (with R ² = 0.81 and RMSE = 1.78) and nonlinear MR (with R ² = 0.57 and RMSE = 3.93) models. Finally, the sensitivity analysis shows that the burden and maximum charge used per delay have the least and the most effects on the rock fragmentation, respectively.

     

Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network

In this paper, responses of a gas sensor array were employed to establish a quality indices model evaluating the peach quality indices. The relationship between sensor signals and the firmness, the content of sugar (CS) and acidity of “Dabai” peach were developed using multiple linear regressions with stepwise procedure, quadratic polynomial step regression (QPST) and back-propagation network. The results showed that the multiple linear regression model represented good ability in predicting of quality indices, with high correlation coefficients (R² = 0.87 for penetrating force CF; R² = 0.79 for content of sugar CS; R² = 0.81 for pH) and relatively low average percent errors (ERR) (9.66%, 7.68% and 3.6% for CF, CS and pH, respectively). The quadratic polynomial step regression provides an accurate quality indices model, with high correlation (R² = 0.92, 0.87, 0.83 for CF, CS and pH, respectively) between predicted and measured values and a relatively low error (5.47%, 3.45%, 2.57% for CF, CS and pH, respectively) for prediction. The feed-forward neural network also provides an accurate quality indices model with a high correlation (R² = 0.90, 0.81, 0.87 for CF, CS and pH, respectively) between predicted and measured values and a relatively low average percent error (6.39%, 6.21%, 3.13% for CF, CS and pH, respectively) for prediction. These results prove that the electronic nose has the potential of becoming a reliable instrument to assess the peach quality indices.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

A rapid and selective method for monitoring the growth of coliforms in milk using the combination of amperometric sensor and reducing of methylene blue

A novel method for monitoring the growth of coliforms in milk was developed based on measuring the current change in an amperometric sensor. The sensor consists of a circuit with a homemade potentiostat and a pair of electrodes. The electrode was immersed in milk samples containing methylene blue with various concentrations of bacterial inoculums. The microbial metabolism led to the reduction of methylene blue resulting in a change of current. The time required to identify readily detectable change (detection time, DT) provided an approximate measurement of the amount of microorganisms in the initial inoculums. The sensor system used in this study has the selectivity towards coliform bacteria such as Escherichia coli and Enterobacter aerogenes. The calibration curve of DT against concentration of coliform showed a linear correlation coefficient (R² = 0.9192) over the range of 10²–10⁸ CFU/mL. The sensor was able to detect the coliform bacteria at initial concentrations of 10⁵ CFU/mL within 6 h, making it suitable for use in real-time monitoring of bacterial growth. This system has potential application in the detection of coliform concentration in milk at dairy farms when a proper selective media is designed.     

Gold nanowire array electrode for non-enzymatic voltammetric and amperometric glucose detection

The non-enzymatic voltammetric and amperometric detection of glucose using a gold nanowire array electrode is described. The voltammetric detection of glucose was performed by cyclic and differential-pulse voltammetry. The detection of glucose by partial and direct oxidation of glucose during the anodic and cathodic potential sweeps was shown in cyclic voltammetry. An unusual decrease in overpotential for partial oxidation of glucose on a Au NW array electrode was observed. A linear differential-pulse voltammetric response for partial oxidation of glucose was observed up to a glucose concentration of at least 20 mM with a sensitivity of 41.9 μA mM⁻¹ cm⁻² and detection limit below 30 μM (signal-to-noise ratio of 3) for glucose oxidation at low potentials, where the influence of possible intermediates can be avoided. The amperometric response was also linear up to a glucose concentration of 10 mM with a sensitivity of 309.0 μA mM⁻¹ cm⁻². The wide dynamic range and high sensitivity, selectivity and stability, as well as good biocompatibility of the Au NW electrode make it promising for the fabrication of non-enzymatic glucose sensors.     

Ultra-sensitive polysilicon wire glucose sensor using a 3-aminopropyltriethoxysilane and polydimethylsiloxane-treated hydrophobic fumed silica nanoparticle mixture as the sensing membrane

In this paper a highly sensitive glucose biosensor is proposed based on a polysilicon (poly-Si) wire structure coated with 3-aminopropyltriethoxysilane (γ-APTES) mixed with polydimethylsiloxane-treated hydrophobic fumed silica nanoparticles (NPs) as the sensing membrane. The γ-APTES and fumed silica NPs mixture was directly transferred to and coated onto the poly-Si wire region with the help of a focus-ion-beam (FIB) processed capillary atomic-force-microscope (C-AFM) tip. After the necessary curing and UV illumination processes, the resultant sensor showed an extremely wide linear detection range from 0.1 μM to 10 mM with a channel current sensitivity as high as 5.33 A mM⁻¹ cm⁻² (or a channel conductance sensitivity of 70 μS mM⁻¹), and a detection limit as low as 10 nM can be achieved. Our experimental results showed that the poly-Si wire sensor has good selective analysis and operational stability on glucose detection under a 10:1 concentration ratio of glucose and uric acid. Its linear range and lowest detection limit remain virtually unimpaired in the presence of uric acid.     

Modelling heat,water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints

A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R² = 0.61, ME = 0.48 MJ m⁻² day⁻¹), soil water contents (R² = 0.68, ME = 0.34%) and carbon-dioxide flux (R² = 0.60, ME = −0.18 g C m⁻² day⁻¹). Multi-objective and multi-criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement.     

Non-tuned machine learning approach for hydrological time series forecasting

Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature, traditional and artificial intelligence models have been applied to this task. An attempt to explore and develop better expert models is an ongoing endeavor for this hydrological application. In addition, the accuracy of modeling, confidence and practicality of the model are the other significant problems that need to be considered. Accordingly, this study investigates modern non-tuned machine learning data-driven approach, namely extreme learning machine (ELM). This data-driven approach is containing single layer feedforward neural network that selects the input variables randomly and determine the output weights systematically. To demonstrate the reliability and the effectiveness, one-step-ahead stream-flow forecasting based on three time-scale pattern (daily, mean weekly and mean monthly) for Johor river, Malaysia, were implemented. Artificial neural network (ANN) model is used for comparison and evaluation. The results indicated ELM approach superior the ANN model level accuracies and time consuming in addition to precision forecasting in tropical zone. In measureable terms, the dominance of ELM model over ANN model was indicated in accordance with coefficient determination (R ²) root-mean-square error (RMSE) and mean absolute error (MAE). The results were obtained for example the daily time scale R ² = 0.94 and 0.90, RMSE = 2.78 and 11.63, and MAE = 0.10 and 0.43, for ELM and ANN models respectively.

     

An electrokinetic mixer driven by oscillatory cross flow

An electrokinetic mixer driven by oscillatory cross flow has been studied numerically as a means for generating chaotic mixing in microfluidic devices for both confined and throughput mixing configurations. The flow is analyzed using numerical simulation of the unsteady Navier–Stokes equations combined with the tracking of single and multi-species passive tracer particles. First, the case of confined flow mixing is studied in which flow in the perpendicular channels of the oscillatory mixing element is driven sinusoidally, and 90° out of phase. The flow is shown to be chaotic by means of positive effective (finite time) Lyapunov exponents, and the stretching and folding of material lines leading to Lagrangian tracer particle dispersion. The transition to chaotic flow in this case depends strongly on the Strouhal number (St), and weakly on the ratio of the cross flow channel length to width (L/W). For L/W = 2, the flow becomes appreciably chaotic as evidenced by visual particle dispersion at approximately St = 0.32, and the transitional value of St increases slightly with increasing aspect ratio. A peak degree of mixing on the order of 85% is obtained for the range of parameter values explored here. In the second phase of the analysis, the effect of combining a fixed throughput flow with the oscillatory cross channel motion for use in a continuous mixing operation is examined in a star cell geometry. Chaotic mixing is again observed, and the characteristics of the downstream dispersion patterns depend mainly on the Strouhal number and the (dimensionless) throughput rate. In the star cell, the flow becomes appreciably chaotic as evidenced by visual particle dispersion at approximately St = 1, slightly higher than for the case of cross cell. The star cell mixing behavior is marked by the convergence of the degree of mixing to a plateau level as the Strouhal number is increased at fixed flow rate. Degree of mixing values from 70 to 80% are obtained indicating that the continuous flow is bounded by the maximum degree of mixing obtained from the confined flow configuration.

A Better Constant-Factor Approximation for Selected-Internal Steiner Minimum Tree

The selected-internal Steiner minimum tree problem is a generalization of original Steiner minimum tree problem. Given a weighted complete graph G=(V,E) with weight function c, and two subsets R ^′ ⊊ R⊆V with |R−R ^′|≥2, selected-internal Steiner minimum tree problem is to find a minimum subtree T of G interconnecting R such that any leaf of T does not belong to R ^′. In this paper, suppose c is metric, we obtain a (1+ρ)-approximation algorithm for this problem, where ρ is the best-known approximation ratio for the Steiner minimum tree problem.     

Optical humidity sensor using porphyrin immobilized Nafion composite films

A Nafion–TMPyP composite film was used in an optical humidity sensor. The composites were prepared with various molar ratios between the sulfonic groups and TMPyP molecules, R = [–SO₃H]/[TMPyP]. The UV–vis measurement of the composite films suggested that the TMPyP molecules were well dispersed at in the range of R = 60–100, but formed aggregates at R ≤ 20. Furthermore, the FTIR indicated that sulfonic group interacted with TMPyP. The reflectance change with relative humidity (%RH) occurred at 426.4 and 465.4 nm with the isosbestic point at 437.5 nm, and also in the Q-band region. The sensors of R = 30 and 40 sensitively responded to humidity in the lower humidity region (10–20%RH), but saturated or even decreased with further increase in humidity. At R = 60, the sensitivity slightly decreased but a measurable range was extended to around 70%RH. Remarkable deterioration in sensitivity occurred at R = 100. The TMPyP molecules were stably immobilized on a Nafion matrix, even in liquid water, without solving out.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

A numerical study of an electrothermal vortex enhanced micromixer

Temperature gradients aroused from the Joule heating in a non-uniform electrical field can induce inhomogeneities of electric conductivity and permittivity of the electrolyte, thus causing an electrothermal force that generates flow motion. A 2D numerical investigation of a micromixer, utilizing electrothermal effect to enhance its mixing efficiency, is proposed in this paper. Results for temperature and velocity distributions, as well as sample concentration distribution are obtained for an electrolyte solution in a microchannel with different pairs of electrodes under AC potentials with various frequencies. Numerical solutions were first carried out for one pair of electrodes, with a length of 10 μm separated by a gap of 10 μm, on one side wall of a microchannel having a length of 200 μm and a height of 50 μm. It is found that the electrothermal flow effect, in the frequency range for which Coulomb force is predominant, induces vortex motion near the electrodes, thus stirring the flow streams and enhancing its mixing efficiency. If more than one pair of electrodes is located on the opposite walls of the microchannel, the mixing efficiency depends on the AC potential applied pattern and the electrodes arrangement pattern. The distance between two pairs of electrodes on two opposite walls is then optimized numerically. Sample mixing efficiencies, using KCl solutions as the working fluid in microchannels with different number of electrodes pairs at optimal electrodes arrangement pattern, are also investigated. If root mean squared voltages of 10 V in an AC frequency range of 0.1–10 MHz are imposed on 16 pairs of electrodes separated at an optimal distance, the numerical results show that a mixing efficiency of 98% can be achieved at the end of the microchannel having a length of 700 μm and a height of 50 μm at Re = 0.01 Pe _C = 100, and Pe _T = 0.07. However, the mixing efficiency decreases sharply at a frequency higher than 10 MHz owing to the drastically decrease in the Coulomb force.     

Analysis of muscle fatigue in helicopter pilots

Helicopter pilots espouse ergonomically unfavourable postures and endure vibration which result in low back pain. The objective of this study was to investigate the effects of a helicopter flight on pilots back and shoulder muscles using surface Electromyography (sEMG) analysis. This study also correlates low back pain symptoms from Rehabilitation Bioengineering Group Pain Scale (RBGPS) questionnaire with muscle fatigue rates obtained. RBGPS was administered on 20 Coast Guard helicopter pilots. sEMG was acquired before and after flight from erector spinae and trapezius muscles in 8 of these 20 pilots. Statistical analysis of time and frequency domain parameters indicated significant fatigue in right trapezius muscle due to flying. Muscle fatigue correlated with average duration of flight (r² = 0.913), total service as pilot (r² = 0.825), pain (r² = 0.463) and total flying hours (r² = 0.507). However, muscle fatigue weakly correlated with Body Mass Index (BMI) (r² = 0.000144) and age (r² = 0.033).     

Electrochemical sensor for hydroperoxides determination based on Prussian blue film modified electrode

An electrochemical sensor for hydroperoxides determination was investigated. The sensor was based on the electrocatalytic reduction of hydroperoxides on Prussian blue (PB)-modified glassy carbon electrode. The modified electrode possesses a high electrocatalytic effect towards all studied peroxides with the highest effect obtained with H₂O₂ followed by tert-butyl hydroperoxide (TBH), cumene hydroperoxide (CH) and linoleic acid hydroperoxide (LAH). In addition, the modified electrode showed a good stability and a fast response time (<20 s). The lower detection limits of H₂O₂, TBH, CH and LAH were found to be 10⁻⁷ mol L⁻¹, 2 × 10⁻⁷ mol L⁻¹, 3.5 × 10⁻⁷ mol L⁻¹ and 4 × 10⁻⁷ mol L⁻¹, respectively. The electrochemical sensor was then applied for amperometric determination of peroxide value (PV) in edible oil at an applied potential of 50 mV (vs. Ag/AgCl (1 M KCl)). A good linearity has been found in the range 0.02–1.0 mequiv. O₂/kg, with a detection limit (S/N = 3) of 0.001 mequiv. O₂/kg. The precision of the method (R.S.D., n = 9) for within and between-days is better than 1.9% and 2.7%, respectively at 0.1 mequiv. O₂/kg. The method was successfully applied to the determination of PV in real edible oil samples with an excellent agreement with results obtained with the official standard procedure. The proposed method is accurate, simple, cheap and could be used to control edible oil rancidity with a high sample throughputs (more than 120 samples/h).