A boronic acid-based fluorescent hydrogel for monosaccharide detection

A boronic acid-based anthracene fluorescent probe was functionalised with an acrylamide unit to incorporate into a hydrogel system for monosaccharide detection. In solution, the fluorescent probe displayed a strong fluorescence turn-on response upon exposure to fructose, and an expected trend in apparent binding constants, as judged by a fluorescence response where D-fructose>D-galactose>D-mannose>D-glucose. The hydrogel incorporating the boronic acid monomer demonstrated the ability to detect monosaccharides by fluorescence with the same overall trend as the monomer in solution with the addition of D-fructose resulting in a 10-fold enhancement (≤0.25 mol/L).     

Combustion of jojoba methyl ester in an indirect injection diesel engine

An experimental investigation has been carried out to examine for the first time the performance and combustion noise of an indirect injection diesel engine running with new fuel derived from pure jojoba oil, jojoba methyl ester, and its blends with gas oil. A Ricardo E6 compression swirl diesel engine was fully instrumented for the measurement of combustion pressure and its rise rate and other operating parameters. Test parameters included the percentage of jojoba methyl ester in the blend, engine speed, load, injection timing and engine compression ratio. Results showed that the new fuel derived from jojoba is generally comparable and good replacement to gas oil in diesel engine at most engine operating conditions, in terms of performance parameters and combustion noise produced.     

Performance analysis of an air rock thermocline TES tank for concentrated solar power plants using the coupled DEM–CFD approach

Clean Technologies and Environmental Policy - One of the most common solutions currently available to meet future energy needs in the world is concentrated solar power (CSP) plants combined with...     

Production of Well-Dispersed Aqueous Cross-Linked Chitosan-Based Nanomaterials as Alternative Antimicrobial Approach

In the current study, chitosan was extracted by deacetylation of chitin, which is extracted from shrimp shell. chitosan nanoparticles (CSNPs) were prepared by ionotropic gelation technique. chitosan/tripolyphosphate ratio (CS:TPP) was kept at 3:1 to prepare CSNPs. chitosan/silver nanocomposite (CS/AgNCs) were prepared by incorporating silver nanoparticles into CSNPs. The quality of the prepared nanocomposite was evaluated by infrared spectroscopy, UV–Vis spectroscopy, transmission electron microscopy, and antibacterial activity. Results showed that chitosan/silver nanocomposite in which, both chitosan and silver are in nanoscale was successfully prepared for the first time in a well-dispersed aqueous form. Whereas CSNPs act as a host material to form the nanocomposite unlike the previously prepared forms of chitosan–silver nanocomposites, that used chitosan bulk as host materials and the dispersion medium was slightly acidic. Moreover, results revealed that the antibacterial activity of CSNPs was significantly enhanced after incorporating trace amount of silver nanoparticles (0.535% w/w AgNPs/CSNPs).     

Periodicity detection in time series databases

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.     

A Bayesian decision model for cost optimal record matching

In an error-free system with perfectly clean data, the construction of a global view of the data consists of linking - in relational terms, joining - two or more tables on their key fields. Unfortunately, most of the time, these data are neither carefully controlled for quality nor necessarily defined commonly across different data sources. As a result, the creation of such a global data view resorts to approximate joins. In this paper, an optimal solution is proposed for the matching or the linking of database record pairs in the presence of inconsistencies, errors or missing values in the data. Existing models for record matching rely on decision rules that minimize the probability of error, that is the probability that a sample (a measurement vector) is assigned to the wrong class. In practice though, minimizing the probability of error is not the best criterion to design a decision rule because the misclassifications of different samples may have different consequences. In this paper we present a decision model that minimizes the cost of making a decision. In particular: (a) we present a decision rule: (b) we prove that this rule is optimal with respect to the cost of a decision: and (c) we compute the probabilities of the two types of errors (Type I and Type II) that incur when this rule is applied. We also present a closed form decision model for a certain class of record comparison pairs along with an example, and results from comparing the proposed cost-based model to the error-based model, for large record comparison spaces.     

Artificial aging and deterioration of oil‐painted Fabriano paper and cardboard paper supports

Fabriano paper and cardboard are commonly used for oil paintings as paper supports. There are different practices for oil painting. Sometimes, painters paint directly on paper supports. They may also impregnate paper supports with linseed oil before painting to prevent the absorption of the binder from the oil‐based paint by the paper, or they may apply a background (e.g., animal glue and calcium carbonate) on the paper supports and then paint their drawings. The effects of aging by heat or ultraviolet light on the mechanical properties (tensile strength, tensile elongation, and tearing resistance), optical properties (changes in color indices), and pH of Fabriano paper and cardboard painted with the aforementioned practices were studied. In addition, the thermal stability of paper painted with these practices was studied with thermogravimetric analysis. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization

Many optimization problems that involve practical applications have functional constraints,and some of these constraints are active,meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits.Therefore,the optimal solution usually lies on the boundary of the feasible region.In order to converge faster when solving such problems,a new ranking and selection scheme is introduced which exploits this feature ...     

Incremental, online, and merge mining of partial periodic patterns in time-series databases

Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Incremental mining refers to the issue of maintaining the discovered patterns over time in the presence of more items being added into the database. Because of the mostly append only nature of updating time-series data, incremental mining would be very effective and efficient. Several algorithms for incremental mining of partial periodic patterns in time-series databases are proposed and are analyzed empirically. The new algorithms allow for online adaptation of the thresholds in order to produce interactive mining of partial periodic patterns. The storage overhead of the incremental online mining algorithms is analyzed. Results show that the storage overhead for storing the intermediate data structures pays off as the incremental online mining of partial periodic patterns proves to be significantly more efficient than the nonincremental nononline versions. Moreover, a new problem, termed merge mining, is introduced as a generalization of incremental mining. Merge mining can be defined as merging the discovered patterns of two or more databases that are mined independently of each other. An algorithm for merge mining of partial periodic patterns in time-series databases is proposed and analyzed.     

Modeling and simulation of quadrotor UAV with tilting rotors

Quadrotors have recently been drawing greater research and commercial attention to the point that they have become one of the most popular types of unmanned aerial vehicles. Their applications vary from entertainment to transportation, commercial and even military applications. In this paper, a novel quadrotor design is proposed. The design decouples all motions by allowing each rotor to tilt in two directions about the quadrotor fixed frame. This modification improves the stability and safety of the quadrotor and gives it more manoeuvrability and robustness. The mathematical model of the proposed system is carried out using Newton-Euler technique. Several flight scenarios are also simulated under a simple PID controller to illustrate the superiority over conventional quadrotor designs.