Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.     

Neural network based controller for Cr–Fe batch reduction process

An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr⁶⁺–Fe²⁺ reduction process. Simulated data from the Cr⁶⁺–Fe²⁺ model derived are validated with online data and laboratory analysis using ICP-AES analysis method. The distinctive trend or patterns exhibited in the ORP profiles for the non-equilibrium model derived have been utilized to train neural network-based controllers for the process. The implementation of this process control is to ensure sufficient Fe²⁺ solution is dosed into the wastewater sample in order to reduce all Cr⁶⁺–Cr³⁺. The neural network controller has been utilized to compare the capability of set-point tracking with a PID controller in this process. For this process neural network-based controller dosed in less Fe²⁺ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr⁶⁺ wastewater samples obtained from an electro-plating factory has also been tested on the pilot plant using the neural network-based controller to determine its effectiveness to control the reduction process for a real plant. The results indicate the proposed controller is capable of fully reducing the Cr⁶⁺–Cr³⁺ in the batch treatment process with minimal dosage of Fe²⁺.     

Plasmonic enhanced fluorescence spectroscopy using side-polished microstructured optical fiber

We report excitation of surface plasmon in a gold-coated side-polished D-shape microstructure optical fiber (MOF). As the leaky evanescent field from the fiber core becomes highly localized by the plasmon wave, its intensity also gets amplified significantly. Here we demonstrate an efficient use of this intensified field as excitation in fluorescence spectroscopy. The so-called plasmonic enhanced fluorescence emission from Rhodamine B has been investigated experimentally. First, plasmonic effect alone was found to provide an immediate fluorescence enhancement factor of two. Second, experimental results show a good agreement with theoretical modeling. Strong evanescent field generation and surface enhancement with simple metallic coating makes this fiber based device a good candidate for compact fluorescence spectroscopy.     

Selective determination of urea using urease immobilized on ZnO nanowires

Well-aligned zinc oxide (ZnO) nanowire arrays were fabricated on gold-coated plastic substrates using a low-temperature aqueous chemical growth (ACG) method. The ZnO nanowire arrays with 50–130 nm diameters and ∼1 μm in lengths were used in an enzyme-based urea sensor through immobilization of the enzyme urease that was found to be sensitive to urea concentrations from 0.1 mM to 100 mM. Two linear sensitivity regions were observed when the electrochemical responses (EMF) of the sensors were plotted vs. the logarithmic concentration range of urea from 0.1 mM to 100 mM. The proposed sensor showed a sensitivity of 52.8 mV/decade for 0.1–40 mM urea and a fast response time less than 4 s was achieved with good selectivity, reproducibility and negligible response to common interferents such as ascorbic acid and uric acid, glucose, K⁺ and Na⁺ ions.     

High sensitive and selective formaldehyde sensors based on nanoparticle-assembled ZnO micro-octahedrons synthesized by homogeneous precipitation method

Nanoparticle-assembled ZnO micro-octahedrons were synthesized by a facile homogeneous precipitation method. The ZnO micro-octahedrons are hexagonal wurtzite with high crystallinity. Abundant structure defects were confirmed on ZnO surface by photoluminescence. Gas sensors based on the ZnO micro-octahedrons exhibited high response, selectivity and stability to 1–1000 ppm formaldehyde at 400 °C. Especially, even 1 ppm formaldehyde could be detected with high response (S = 22.7). It is of interest to point out that formaldehyde could be easily distinguished from ethanol or acetaldehyde with a selectivity of about 3. The high formaldehyde response is mainly attributed to the synergistic effect of high contents of electron donor defects (Zn_i and V_O) and highly active oxygen species (O²⁻) on the ZnO surface.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

The effect of DNA probe distribution on the reliability of label-free biosensors

As the design of label-free DNA biosensors matures, and their sizes reduced to enhance their sensitivity, not much has been researched about the variations in the received signal with the positioning of the probes on the sensitive surface. We approach this issue computationally in this paper. By adopting the finite-element model on a three-dimensional biological field-effect transistor (BioFET) slice, and running Monte-Carlo simulations on the positions of the DNA molecules, we extract the expected variations in the signal. Then, we show that signal-to-noise (SNR) ratio can be low enough to hinder the functionality of the device, placing a limitation on how low the sensitivity of a sensor of a certain size can be.     

Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

This paper addresses the analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays. The considered transition probabilities are assumed to be partially unknown. The parameter uncertainties are considered to be norm-bounded. A sufficient condition for the stability of the addressed neural networks is derived, which is expressed in terms of a set of linear matrix inequalities. A numerical example is given to verify the effectiveness of the developed results.     

Ordered mesoporous Pd/SnO2 synthesized by a nanocasting route for high hydrogen sensing performance

Ordered mesoporous SnO₂ and mesoporous Pd/SnO₂ have been successfully synthesized via nanocasting method using the hexagonal mesoporous SBA-15 as template. Two different procedures, impregnation technique and direct synthesis, were utilized for the doping of Pd in the mesoporous SnO₂. The results of small angle X-ray diffraction (SAXD), nitrogen adsorption–desorption and transmission electron microscopy (TEM) demonstrate that the SnO₂ and Pd/SnO₂ display ordered mesoporous structures and high surface areas. Wide angle X-ray diffraction (WAXD) and X-ray photoelectron spectroscopy (XPS) reveal tetragonal structure of SnO₂ and the existence of Pd element. The sensing properties of mesoporous SnO₂ and mesoporous Pd/SnO₂ for H₂ were detected. The sensor utilizing mesoporous Pd/SnO₂ via direct synthesis method exhibits excellent response and recovery behavior and much higher sensitivity to H₂, compared to those using mesoporous SnO₂ and mesoporous Pd/SnO₂ via impregnation technique. It is believed that its high gas sensing performance is derived from the large surface area, high activity and well dispersion of Pd additive, as well as high porosity, which lead to highly effective surface interaction between the target gas molecules and the surface active sites.     

A Chinese liquor classification method based on liquid evaporation with one unmodulated metal oxide gas sensor

Electronic nose has been widely used in the classification of liquid samples, such as vinegars, wines and liquors, which have complex components. The difficulty of these classifications is how to get the information of the trace components in these samples. In this paper a method for liquor recognition based on liquid evaporation was presented. This method makes use of the distinct evaporation characteristics of different components in liquor samples. And during the evaporation process, one metal oxide gas sensor was used to detect the headspace of liquor samples for classification. Due to the distinct evaporation characteristics of different components, volatile compounds in the headspace evaporating from samples would change with the testing time. Meanwhile, the gas sensor would respond to these volatile compounds. Accordingly, more information of liquor samples during evaporation may be acquired with the proposed method. To verify the performance of this method, 8 different Chinese liquors with 50% alcohol for comparison were tested under the method. The results showed that the evaporation characteristics of these liquor samples were quite distinct. The correct classification accuracy of discriminant function analysis was 100%, which indicated this method may be a simple and effective way for complex-component liquid sample analysis.