Development of non-enzymatic ZnO nanocomposite-based optical sensor for l-lactate detection in tomato samples

l -lactate is a conjugate base of α-hydroxy acid, a key organic chemical in food processing, fermentation, biology and chemical industries. We are reporting for the first time a facile and rapid non-enzymatic nanosensor for l -lactate detection in tomato-based products using 3-aminophenylboronic acid (3-APBA)-added zinc oxide nanoparticles (ZnO NPs). 3-APBA-functionalised ZnO NPs have a selective and stable SPR intensity change at pH 5.5 to 7.5 and forms a reversible ester with bound l -lactate revealing the wavelength shift from λ = 300 nm to 288 nm in UV-VIS absorption spectrometry. In the spiked (2.5–20 mm ) samples of tomato puree, absorption peak increased as per l -lactate addition with the lower detection limit of 3.98 mm . l -lactate binds with a trigonal form of 3-boronate, thereby generating the tetrahedral complex. ZnO NPs stabilise the tetragonal complex. Nanosensor validated with standard HPLC. This method is useful for l -lactate detection and alternates to conventional techniques.     

Investigation on effects of system parameters on transmission depth in underwater wireless optical communication

Photonic Network Communications - Performance of underwater wireless optical communication (UWOC) with different vertical water channel conditions is experimentally analyzed. Experiment has been...     

A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.     

Single-electron transfer living radical copolymerization of SWCNT-g-PMMA via graft from approach

The sidewall functionalization of single-walled carbon nanotubes (SWCNTs) was established by poly (methyl methacrylate) (PMMA) using controlled radical polymerization (CRP) method. A bromide functionalized SWCNT (SWCNT-Br) has been used as an initiator for the synthesis of SWCNT-graft-PMMA (SWCNT-g-PMMA). The efficiency of the sacrificial initiator (S) was monitored during the polymerization process. The obtained polymers possess a uniform distribution of molecular weight with a lower polydispersity index of 1.36. The SWCNTs-based initiator acts as an efficient medium for the controlled growth of polymer on the SWCNTs surfaces. The presence of bimodal gel permeation chromatographic (GPC) curve for the SWCNT-g-PMMA(S) obtained through sacrificial initiator (S) confirms uncontrolled behavior. However, the clear sharp peak for SWCNT-g-PMMA obtained without sacrificial initiator shows its well-controlled process of polymerization, which acts as a mimic to bone cement. The efficiency of the functionalization of SWCNT and the controlled formation of SWCNT-g-PMMA composites were characterized by TGA, Raman, TEM, NMR, XPS and dispersion measurements.     

Photoelectrochemical properties of CdSxSe1−x films

CdS_xSe_1−x films of different composition (0 < x < 1) were deposited by pulse plating technique at different duty cycles in the range of 10-50%. The films were polycrystalline and exhibited hexagonal structure. The band gap of the films varies from 1.68 to 2.39 eV as the concentration of CdS increases. Energy Dispersive analysis of X-rays (EDAX) measurements indicate that the composition of the films are nearly the same as that of the precursors considered for the deposition. Atomic force microscopy studies indicated that the grain size increased from 20 to 200 nm as the concentration of CdSe increased. Photoelectrochemical (PEC) cell studies indicated that the films of composition CdS_0.9Se_0.1 exhibited maximum photoactivity. Mott-Schottky studies indicated that the films exhibit n-type behaviour. Spectral response measurements indicated that the photocurrent maxima occurred at the wavelength value corresponding to the band gap of the films.     

Electrostatic assembly of nanoparticles and biomacromolecules

The controlled assembly of nanoparticles in thin film form on solid supports, both as monolayers and as superlattice structures, is a problem of considerable topical interest. Among the many interactions used to program the assembly of nanoparticles, electrostatic forces are particularly interesting for many reasons. This Account deals with assembling surface-modified nanoparticles in thin film form using electrostatic interactions at the air-water interface and in thermally evaporated lipid films. The generality of the electrostatic assembly protocol is demonstrated in the immobilization of DNA and proteins in lipid films.     

Thermomechanical Framework for the Constitutive Modeling of Asphalt Concrete

This study is concerned with the constitutive modeling of asphalt concrete. Unlike most constitutive models for asphalt concrete that do not take into account the evolution of the microstructure of the material, this study incorporates the evolution of the microstructure by using a framework that recognizes that a body’s natural configurations can evolve as the microstructure changes. The general framework, on which this study is based, is cast within a full thermomechanical setting. In this paper, we develop models within the context of a mechanical framework that stems from the general framework for models based on the full thermodynamic framework and the resulting equations represent a nonlinear rate type viscoelastic model. The creep and stress relaxation experiments of Monismith and Secor are used for validating the efficacy of the model, and it is found that the predictions of the theory agree very well with the available experimental results. The advantages of using such a framework are many, especially when one wants to model the diverse mechanical and thermodynamic response characteristics of asphalt and asphalt concrete.     

Car insurance and the risk of car crash injury

Despite speculation about the role of vehicle insurance in road traffic accidents, there is little research estimating the direction or extent of the risk relationship. Data from the Auckland Car Crash Injury Study (1998–1999) were used to examine the association between driving an uninsured motor vehicle and car crash injury. Cases were all cars involved in crashes in which at least one occupant was hospitalized or killed anywhere in the Auckland region. Controls were 588 drivers of randomly selected cars on Auckland roads. Participants completed a structured interview. Uninsured drivers had significantly greater odds of car crash injury compared to insured drivers after adjustment for age, sex, level of education, and driving exposure (odds ratio 4.77, 95% confidence interval 2.94–7.75). The causal mechanism for insurance and car crash injury is not easily determined. Although we examined the effects of multiple potential confounders in our analysis including socioeconomic status and risk-taking behaviours, both of which have been previously observed to be associated with both insurance status and car crash injury, residual confounding may partly explain this association. The estimated proportion of drivers who are uninsured is between 5 and 15% in developed countries, representing a significant public health problem worthy of further investigation.     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.     

Accurate Characterization of Integrated Continuous-Time Filters

We present techniques for accurately characterizing the frequency response and noise spectral density of integrated continuous-time filters. A 75-MHz fifth-order Chebyshev Gm-C ladder filter designed in a 0.35-mum CMOS process and packaged in a 40-pin DIP is used as a test vehicle to validate the ideas proposed in this work. When compared to conventional frequency response measurement methods, the proposed techniques show a significantly enhanced measurement accuracy in the stopband, while being less sensitive to package characteristics.