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.     

Influences of haptic communication on a shared manual task

With the advent of new haptic feedback devices, researchers are giving serious consideration to the incorporation of haptic communication in collaborative virtual environments. For instance, haptic interactions based tools can be used for medical and related education whereby students can train in minimal invasive surgery using virtual reality before approaching human subjects. To design virtual environments that support haptic communication, a deeper understanding of humans′ haptic interactions is required. In this paper, human′s haptic collaboration is investigated. A collaborative virtual environment was designed to support performing a shared manual task. To evaluate this system, 60 medical students participated to an experimental study. Participants were asked to perform in dyads a needle insertion task after a training period. Results show that compared to conventional training methods, a visual-haptic training improves user′s collaborative performance. In addition, we found that haptic interaction influences the partners′ verbal communication when sharing haptic information. This indicates that the haptic communication training changes the nature of the users′ mental representations. Finally, we found that haptic interactions increased the sense of copresence in the virtual environment: haptic communication facilitates users′ collaboration in a shared manual task within a shared virtual environment. Design implications for including haptic communication in virtual environments are outlined.     

Global and robust formation-shape stabilization of relative sensing networks

This paper proposes a simple, distributed algorithm that achieves global stabilization of formations for relative sensing networks in arbitrary dimensions with fixed topology. Assuming the network runs an initialization procedure to equally orient all agent reference frames, convergence to the desired formation shape is guaranteed even in partially asynchronous settings. We characterize the algorithm robustness against several sources of errors: link failures, measurement errors, and frame initialization errors. The technical approach combines algebraic graph theory, multidimensional scaling, and distributed linear iterations.     

Synthetic handwritten CAPTCHAs

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.     

Embedding a fault-free hamiltonian cycle in a class of faulty generalized honeycomb tori

Generalized honeycomb torus (GHT) is recognized as an attractive alternative to existing torus interconnection networks in parallel computing systems. Assume that m and d are integers with m ? 2 and d ? 8. This paper addresses the fault-tolerant hamiltonicity of GHT(m, 2d, d) with fault set F = {(w, y), (x, y)}, where w < x, w + y is even and x + y is odd. We show that such a faulty GHT is hamiltonian by presenting a systematic method for constructing a fault-free hamiltonian cycle. This result reveals another appealing feature of GHTs.