Physical and chemical properties and adsorption type of activated carbon prepared from plum kernels by NaOH activation

Activated carbon was prepared from plum kernels by NaOH activation at six different NaOH/char ratios. The physical properties including the BET surface area, the total pore volume, the micropore ratio, the pore diameter, the burn-off, and the scanning electron microscope (SEM) observation as well as the chemical properties, namely elemental analysis and temperature programmed desorption (TPD), were measured. The results revealed a two-stage activation process: stage 1 activated carbons were obtained at NaOH/char ratios of 0-1, surface pyrolysis being the main reaction; stage 2 activated carbons were obtained at NaOH/char ratios of 2-4, etching and swelling being the main reactions. The physical properties of stage 2 activated carbons were similar, and specific area was from 1478 to 1887m(2)g(-1). The results of reaction mechanism of NaOH activation revealed that it was apparently because of the loss ratio of elements C, H, and O in the activated carbon, and the variations in the surface functional groups and the physical properties. The adsorption of the above activated carbons on phenol and three kinds of dyes (MB, BB1, and AB74) were used for an isotherm equilibrium adsorption study. The data fitted the Langmuir isotherm equation. Various kinds of adsorbents showed different adsorption types; separation factor (R(L)) was used to determine the level of favorability of the adsorption type. In this work, activated carbons prepared by NaOH activation were evaluated in terms of their physical properties, chemical properties, and adsorption type; and activated carbon PKN2 was found to have most application potential.     

Isolated-utterance speech recognition using hidden Markov modelswith bounded state durations

Hidden Markov models (HMMs) with bounded state durations (HMM/BSD) are proposed to explicitly model the state durations of HMMs and more accurately consider the temporal structures existing in speech signals in a simple, direct, but effective way. A series of experiments have been conducted for speaker dependent applications using 408 highly confusing first-tone Mandarin syllables as the example vocabulary. It was found that in the discrete case the recognition rate of HMM/BSD (78.5%) is 9.0%, 6.3%, and 1.9% higher than the conventional HMMs and HMMs with Poisson and gamma distribution state durations, respectively. In the continuous case (partitioned Gaussian mixture modeling), the recognition rates of HMM/BSD (88.3% with 1 mixture, 88.8% with 3 mixtures, and 89.4% with 5 mixtures) are 6.3%, 5.0%, and 5.5% higher than those of the conventional HMMs, and 5.9% (with 1 mixture), 3.9% (with 3 mixtures) and 3.1% (with 1 mixture), 1.8% (with 3 mixtures) higher than HMMs with Poisson and gamma distributed state durations, respectively     

Design of linear phase FIR filters using fractional derivative constraints

In this paper, the designs of linear phase FIR filters using fractional derivative constraints are investigated. First, the definition of fractional derivative is reviewed briefly. Then, the linear phase FIR filters are designed by minimizing integral squares error under the constraint that the ideal response and actual response have several same fractional derivatives at the prescribed frequency point. Next, the fractional maximally flat FIR filters are designed by letting the number of fractional derivative constraints be equal to the number of filter coefficients. Finally, numerical examples are demonstrated to show that the proposed method has larger design flexibility than the conventional integer derivative constrained methods.     

Initial and boundary conditions in multidimensional wave digital filter algorithms for plate vibration

Recently, multidimensional wave digital filter (MDWDF) structures have been proposed for the modeling of plate vibration problems. In this paper, we discuss how initial and boundary conditions may be properly embedded into such an algorithm in terms of the state quantities that are an integral part of the algorithm. Due to the essential feature of fully-local interconnectivity in the MDWDF model, different types of boundary conditions can be easily satisfied in a very simple and efficient manner. Instead of remodifying the whole algorithm, usually required by finite elements based methods, boundary conditions in terms of state outputs are simply attached to the model. This feature is especially useful when dealing with the mixed-edges boundary conditions frequently encountered in practice. Graphical results obtained from implementing the MDWDF algorithm are given to further demonstrate the capacities of the method in efficiently handling a fourth-order Mindlin plate vibration system with various types of boundary conditions.     

Resistance projection welding for TO-Can style package

The effects of the process parameters on the joint quality of TO-Can using resistance projection welding were investigated.A capacitor discharge stored energy welding machine was used for joining caps and headers both coated with nickel SPCC steel.The electrode materials used in this study were brass and W-Cu alloy.Experimen-tal results indicated that the peak current of the W-Cu electrode was higher than that of the brass electrode,irrespective of the operating pressure variation.Increased operating pressu...     

A comparison of methods for detecting hot topics

In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.     

Localised heating of tumours utilising injectable magnetic nanoparticles for hyperthermia cancer therapy

This study reports an investigation of hyperthermia cancer therapy utilising an alternating magnetic field to induce a localised temperature increase on tumours by using injectable magnetic nanoparticles. In-vitro and in-vivo experiments represent the feasibility of hyperthermia cancer therapy. A feedback temperature control system was first developed to keep the nanoparticles at a constant temperature to prevent overheating in the tumours such that a safer and more precise cancer therapy becomes feasible. By using the feedback temperature control system, magnetic nanoparticles can be heated up to the specific constant temperatures, 37, 40, 42, 45, 46 and 47degC, respectively, with a variation less than 0.2degC. With this approach, the in-vitro survival rate of tumour cells at different temperatures can be systematically explored. It was experimentally found that the survival rate of cancer cells can be greatly reduced while CT-26 cancer cells were heated above 45degC. Besides, localised temperatures increase as high as 59.5degC can be successfully generated in rat livers by using the proposed method. Finally, complete regression of tumour was achieved. The developed method used injectable magnetic nanoparticles and may provide a promising approach for hyperthermia cancer therapy.     

Numerical simulation of protein stamping process driven by capillary force

Numerical simulations based on first-principle conservation laws of mass and momentum are performed to observe flow characteristics during the stamping process. The protein solution is transferred by a new design of microstamps with microchannels and printed on a bottom substrate. Furthermore, key physics of the stamping process and the control factors achieving uniform spot size can be identified and optimized after these simulations.     

A Genetic Algorithm for Assessing Flatness in Automated Manufacturing Systems

The measurement of flatness for manufactured parts is one of the most frequently used procedures in automated manufacturing systems. Measuring instruments are commonly utilized in taking measurement data from manufactured surfaces for inspection purposes. The measurement data is then used to evaluate the geometric information, from errors associated with its surface. The study proposes the computational approaches for flatness with respect to ASME Y14.5M-1994 standard. The proposed methods consider the trade-off between the accuracy of flatness and the efficiency of inspection. Two approaches of computational metrology based on genetic algorithms are proposed to explore the optimality of flatness measurement and the flatness feasibility analysis. The results show that the optimization algorithms provide exact flatness errors and adequate tolerance size. Received: February 2004 / Accepted: September 2005     

A novel manufacturing defect detection method using association rule mining techniques

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.