Electronic properties and gas adsorption behaviour of pristine,silicon-, and boron-doped (8, 0) single-walled carbon nanotube: A first principles study

Carbon nanotubes (CNTs) have received enormous attention due to their fascinating properties to be used in various applications including electronics, sensing, energy storage and conversion. The first principles calculations within density functional theory (DFT) have been carried out in order to investigate the structural, electronic and optical properties of un-doped and doped CNT nanostructures. O₂, CO₂, and CH₃OH have been chosen as gas molecules to study the adsorption properties based on zigzag (8,0) SWCNTs. The results demonstrate that the adsorption of O₂, CO_2, and CH₃OH gas molecules on pristine, Si-doped and B-doped SWCNTs are either physisorption or chemisorption. Moreover, the electronic properties indicating SWCNT shows significant improvement toward gas adsorption which provides the impact of selecting the best gas sensor materials towards detecting gas molecule. Therefore, these pristine, Si-, and B-doped SWCNTs can be considered to be very good potential candidates for sensing application.     

Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin

After complete or partial paralysis due to stroke or spinal cord injury, electrical nerve stimulation can be used to artificially generate functional muscle contractions. This technique is known as Functional Electrical Stimulation (FES). In combination with appropriate sensor technology and feedback control, FES can be empowered to elicit also complex functional movements of everyday relevance. Depending on the degree and phase of impairment, the goal may be temporary support in a rehabilitation phase, e.g. during re-learning of gait after a stroke, or permanent replacement/support of lost motor functions in form of assistive devices often referred to as neuro-prostheses.In this contribution a number of real-time capable and portable approaches for sensing muscle contractions and motions are reviewed that enable the realization of feedback control schemes. These include inertial measurement units (IMUs), electromyography (EMG), and bioimpedance (BI). This contribution further outlines recent concepts for movement control, which include e.g. cascaded control schemes. A fast inner control loop based on the FES-evoked EMG directly controls the amount of recruited motor units. The design and validation of various novel FES systems are then described that support cycling, walking, reaching, and swallowing. All methods and systems have been developed at the Technische Universität Berlin by the Control Systems Group within the last 10 years in close cooperation with clinical and industrial partners.     

On decomposition methods in interactive user-preference based optimization

Evolutionary multi-objective optimization (EMO) methodologies have been widely applied to find a well-distributed trade-off solutions approximating to the Pareto-optimal front in the past decades. However, integrating the user-preference into the optimization to find the region of interest (ROI) [1] or preferred Pareto-optimal solutions could be more efficient and effective for the decision maker (DM) straightforwardly. In this paper, we propose several methods by combining preference-based strategy (like the reference points) with the decomposition-based multi-objective evolutionary algorithm (MOEA/D) [2], and demonstrate how preferred sets or ROIs near the different reference points specified by the DM can be found simultaneously and interactively. The study is based on the experiments conducted on a set of test problems with objectives ranging from two to fifteen objectives. Experiments have proved that the proposed approaches are more efficient and effective especially on many-objective problems to provide a set of solutions to the DM's preference, so that a better and a more reliable decision can be made.     

Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

复杂网络在传统经济系统上的模型研究

在当今社会,经济系统的模型研究一直是科学家研究的热点对象之一.许多自然界存在的经济系统都可以运用复杂网络建立出不同规模和不同拓扑结构的相关研究模型.在这些复杂的经济数学模型中,当研究复杂经济系统的相对特性时,必须针对性地考虑其复杂网络模型中的特性与模型结构.主要研究了复杂网络上的传统经济与部分社会系统,对于复杂网络上的社会经济系统的研究给出了一种新的思路.     

Development of an image-based model for capillary vasculature of retina

The paper presents a method of development of a detailed network model to represent retinal capillary vasculature. The capillary model is a circular mesh consisting of concentric rings with an increasing diameter. Each of the rings has uniformly distributed bifurcation nodes to represent capillary vessels. The model is customized using the data that has been measured from confocal microscopic images of a mouse retina. The capillary model developed can be connected to networks of larger vessels of the vasculature such as arterial and venous networks to form a complete model of the retinal network. A method to automate such interface connections between capillary and other vascular networks using connecting vessels (i.e., pre-capillary and post-capillary) is also presented in the paper. Such a detailed image-based capillary model together with the arterial and venular networks can be used for various circulation simulations to obtain accurate information on hemodynamic quantities such as the spatial distribution of pressure and flow in the vasculature for both physiological and pathological conditions. The method presented for the development of the capillary model can also be adopted for vasculatures of other organs.     

Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network

Conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications. But conventional regular moments are only invariant to translation, rotation and equal scaling. It is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x- and y-axis directions. We address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling. However, the technique produces a set of features that are only invariant to translation, unequal/equal scaling and reflection. They are not invariant to rotation. To make them invariant to rotation, moments are calculated with respect to the principal axis of the image. To perform this, the exact angle of rotation must be known. But the method of using the second-order moments to determine this angle will also be inclusive of an undesired tilt angle. Therefore, in order to correctly determine the amount of rotation, the tilt angle which differs for different scaling factors in the x- and y-axis directions for the particular image must be obtained. In order to solve this problem, a neural network using the back-propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined. Next, the new moments are derived and a Fuzzy ARTMAP network is used to classify these images into their respective classes. Sets of experiments involving images rotated and scaled unequally in the x- and y-axis directions are carried out to demonstrate the validity of the proposed technique.     

Automation of palm oil mills

Automation is the function of monitoring and regulating process operations non-manually or remotely to check or maintain the required processing conditions and product quality, the use of instrumental techniques of analysis in the laboratory and the use of computers for data processing and other miscellaneous applications. Applications where automation is justifiable and the impact of changing trends in automation technology, especially the recent influx of microprocessor-based systems, are discussed briefly. Applications in the palm oil mill where automation is fairly well-developed and is being developed, as well as ideas for future applications are reviewed. An example of a practical approach to automation in palm oil mills is given using automatic control of crude oil dilution.     

Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized.     

Content-based image quality metric using similarity measure of moment vectors

In this paper, the similarity of moment vectors between the test and the reference image blocks together with the result from the block classification are used in the formulation of an image quality metric (IQM). First, the reference and the test images are divided into non-overlapping 8×8 blocks and transformed into moment domain using Discrete Tchebichef Transform. The moment features are then used in two operations: the local quality index calculation and the image content (block) classification. The local quality index is obtained from the similarity measure of moment vectors between the reference and the test image blocks. Next, the content of each reference image block is classified into three types: “plain”, “edge” and “texture”, based on its moment energy level and moment energy distribution. The local quality indices obtained from all the image blocks are then averaged based on the block types to obtain three mean quality scores for each test image. The performance of these three mean quality scores and their combinations are studied using the LIVE database. The results show that the performance of the metric is significantly improved by combining the mean quality scores from the edge and texture image region. The best combination (the proposed metric) is then compared with five other IQMs using the LIVE database and four other independent databases. The results show that the proposed metric performs comparatively well for all the databases.