An Empirical Study of Factors Affecting e-Commerce Adoption among Small- and Medium-Sized Enterprises in a Developing Country: Evidence from Malaysia

The purpose of this paper is to empirically examine determinants of e-commerce adoption among Malaysian small- and medium-sized enterprises (SMEs). This study tested eight hypotheses on determinants affecting e-commerce adoption with empirical data from several survey instruments: an online questionnaire-based survey, mailed survey and questionnaire collected in-person from a sample of 307 SMEs in Malaysia. The findings show that e-commerce adoption within Malaysian SMEs is affected by perceived relative advantage, perceived compatibility, managers/owner's knowledge and expertise, management characteristics and external change agents. This study contributes to enhancing the understanding of the determinants of adopting e-commerce in SMEs and provides some interesting perspective from Malaysia. Those parties interested in promoting their business online may find these results helpful in guiding their efforts.     

Folksonomy link prediction based on a tripartite graph for tag recommendation

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.     

Silver (Ag) as a novel masking material in glass etching for microfluidics applications

In this paper, we report the use of a single masking film for deep glass etching in hydrofluoric acid (HF). Thin film silver (Ag) is the key masking material in this work enabling a simple and low cost fabrication of microfluidic structures. The Ag film was deposited by evaporation and etched in a diluted nitric acid and de-ionized water solution at a ratio of 1:3. Surface morphology for different thicknesses of Ag film and its correlation to the maximum achievable etch depth is analyzed. AFM results shows low roughness values (<5 nm), indicating the Ag films are of smooth surface. With a 100 nm Ag film, a 220 μm etch depth in borosilicate glass substrates were produced and by further thickening the Ag to 300 nm, etch depths exceeding 300 μm were successfully achieved. SEM images show that thinner Ag films are of finer grains, potentially a source for pinholes formation where rapid penetration of HF along the grain boundaries peels off the Ag film from the glass surface. However, the Ag film was found not to react with HF. The process was demonstrated in the fabrication of cavities for integration with other microfluidic devices.     

A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm

Nowadays, time and cost are familiar criteria for every logistic provider, and both have been long treated to be minimized simultaneously. However, the criteria are naturally conflicted even with flexibilities and/or constraints appeared in the logistic networks. This paper is concerned with three-level logistic networks with potential suppliers, distributed centers (DCs), and deterministic demands from available consumers. The networks also benefit from potential direct shipments from suppliers to consumers as long as suppliers and DCs facilities might face limited capacity in their own seasonal supplying and warehousing processes. The goal is (re)configure the networks in order to minimize response time to consumers, transportation cost and facility cost. Therefore, the networks are formulated as multiple criteria decision making problems, which have more than one configuration through the time and cost optimizing at the same time. Due to the flexibility and the constraints, the decision maker(s) needs a set of compromise solutions for the networks that represent optimal configurations based on the objectives without considering prior knowledge. To this end, the problems are formulated into four individual logistic network models varying with the flexibility option and/or the capacitated facilities. To find the compromise solutions, Pareto-based multi-objective evolutionary algorithm, NSGA-II is customized and then utilized to deal with an illustrative case study. The results are analyzed through the two performance measures, hypervolume and the number of optimal solutions obtained so far.     

Detection of stressed oil palms from an airborne sensor using optimized spectral indices

Existing vegetation indices and red-edge techniques have been widely used for the assessment of vegetation status and vegetation health from remote-sensing instruments. This study proposed and applied optimized Airborne Imaging Spectrometer for Applications (AISA) airborne hyperspectral indices in assessing and mapping stressed oil palm trees. Six vegetation indices, four red-edge techniques, a standard supervised classifier and three optimized AISA spectral indices were compared in mapping diseased oil palms using AISA airborne hyperspectral imagery. The optimized AISA spectral indices algorithms used newly defined reflectance values at wavelength locations of 734 nm (near-infrared (NIR)) and 616 nm (red). The selection of these two bands was based on laboratory statistical analysis using field spectroradiometer reflectance data. These two bands were then applied to the AISA airborne hyperspectral imagery using the three optimized algorithms for AISA data. The newly formulated AISA hyperspectral indices were D2 = R ₆₁₆/R ₇₃₄, normalized difference vegetation index a (NDVIa)?=?(R ₇₃₄ – R ₆₁₆)/(R ₇₃₄?+?R ₆₁₆) and transformed vegetation index a (TVIa)?=?((NDVIa?+?0.5)/(abs (NDVIa?+?0.5))?×?[abs (NDVIa?+?0.5)]^1/2. The classification results from the optimized AISA hyperspectral indices were compared with the other techniques and the optimized AISA spectral indices obtained the highest overall accuracy. D2 and NDVIa obtained 86% of overall accuracy followed by TVIa with 84% of overall accuracy.     

Efficiency of hydrogen internal combustion engine combined with open steam Rankine cycle recovering water and waste heat

A hydrogen internal combustion engine (HICE) wastes more heat, and producing nearly three times more water than a conventional engine. This paper describes the principle behind a novel waste heat recovery sub-system that exploits the water produced by an HICE as the working fluid for an open-cycle power generation system based on the Rankine cycle. Water from the HICE exhaust is superheated by the waste heat from the HICE and used to produce power in a steam expander. A fundamental thermodynamic model shows the contribution of the sub-system to the overall thermal efficiency of the HICE at various engine speeds, with and without a condenser. The results show that the condenser is not cost-effective and that the overall thermal efficiency with the proposed sub-system is 27.2% to 33.6%, representing improvements of 2.9% to 3.7%, at engine speeds of 1500 to 4500 rpm.     

Gene subset selection using an iterative approach based on genetic algorithms

Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.