A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types

Existing literature in the field of e-learning technology acceptance reflects a significant number of independent studies that primarily investigate the causal relationships proposed by technology acceptance theory, such as the technology acceptance model (TAM). To synthesize the existing knowledge in the field of e-learning technology acceptance, we have conducted a systematic literature review of 42 independent papers, mostly published in major journals. Furthermore, in order to view the research context by combining and analyzing the quantitative results of the reviewed research studies, a meta-analysis of the causal effect sizes between common TAM-related relationships was conducted. The main findings of this study, which is the first of its kind, are: (1) TAM is the most-used acceptance theory in e-learning acceptance research, and (2) the size of the causal effects between individual TAM-related factors depends on the type of user and the type of e-learning technology. The results of the meta-analysis demonstrated a moderating effect for user-related factors and technology-related factors for several evaluated causal paths. We have gathered proof that the perceived ease of use and the perceived usefulness tend to be the factors that can influence the attitudes of users toward using an e-learning technology in equal measure for different user types and types of e-learning technology settings.     

A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery

In the past, oil palm density has been determined by manually counting trees every year in oil palm plantations. The measurement of density provides important data related to palm productivity, fertilizer needed, weed control costs in a circle around each tree, labourers needed, and needs for other activities. Manual counting requires many workers and has potential problems related to accuracy. Remote sensing provides a potential approach for counting oil palm trees. The main objective of this study is to build a robust and user-friendly method that will allow oil palm managers to count oil palm trees using a remote sensing technique. The oil palm trees analysed in this study have different ages and densities. QuickBird imagery was applied with the six pansharpening methods and was compared with panchromatic QuickBird imagery. The black and white imagery from a false colour composite of pansharpening imagery was processed in three ways: (1) oil palm tree detection, (2) delineation of the oil palm area using the red band, and (3) counting oil palm trees and accuracy assessment. For oil palm detection, we used several filters that contained a Sobel edge detector; texture analysis co-occurrence; and dilate, erode, high-pass, and opening filters. The results of this study improved upon the accuracy of several previous research studies that had an accuracy of about 90–95%. The results in this study show (1) modified intensity-hue-saturation (IHS) resolution merge is suitable for 16-year-old oil palm trees and have rather high density with 100% accuracy; (2) colour normalized (Brovey) is suitable for 21-year-old oil palm trees and have low density with 99.5% accuracy; (3) subtractive resolution merge is suitable for 15- and 18-year-old oil palm trees and have a rather high density with 99.8% accuracy; (4) PC spectral sharpening with 99.3% accuracy is suitable for 10-year-old oil palm trees and have low density; and (5) for all study object conditions, colour normalized (Brovey) and wavelet resolution merge are two pansharpening methods that are suitable for oil palm tree extraction and counting with 98.9% and 98.4% accuracy, respectively.     

Operating strategies for acid phase digestion: an industrial case study

Acid phase digestion was investigated for enhanced operation in an industrial wastewater treatment plant. In particular, sludge retention time (SRT), temperature and pH were assessed for determining optimal conditions under operating constraints. Volatile fatty acid (VFA) production and soluble chemical oxygen demand (SCOD) were the key process parameters used to assess system performance. Increase in SRT from 0.8 to 1.4 days (at 22°C) had moderate effect on VFA production (approximately 15% increase), achieving a maximum VFA production of 3600 mg/L. High VFA production rate (0.101 ± 0.035 mg/mgVS.d) was obtained at the highest operating retention time during summer (27°C). The degree of solubilisation of particulate organic matter increased with temperature and retention time. Despite an increase in SCOD (6472 ± 873 mg/L max.), a corresponding increase in VFA was not observed. SCOD showed a linear correlation with decrease in pH, while acidogenesis (SCOD conversion to VFA) was found to be favourable at a pH of 4.5.     

Theoretical foundations and implementation of business process diagrams’ complexity management technique based on highlights

Software and Systems Modeling - The main purpose of business process diagrams is to make the communication between process-related stakeholders more effective. To this end, they need to be simple...     

Efficient high utility itemset mining using buffered utility-lists

Discovering high utility itemsets in transaction databases is a key task for studying the behavior of customers. It consists of finding groups of items bought together that yield a high profit. Several algorithms have been proposed to mine high utility itemsets using various approaches and more or less complex data structures. Among existing algorithms, one-phase algorithms employing the utility-list structure have shown to be the most efficient. In recent years, the simplicity of the utility-list structure has led to the development of numerous utility-list based algorithms for various tasks related to utility mining. However, a major limitation of utility-list based algorithms is that creating and maintaining utility-lists are time consuming and can consume a huge amount of memory. The reasons are that numerous utility lists are built and that the utility-list intersection/join operation to construct a utility-list is costly. This paper addresses this issue by proposing an improved utility-list structure called utility-list buffer to reduce the memory consumption and speed up the join operation. This structure is integrated into a novel algorithm named ULB-Miner (Utility-List Buffer for high utility itemset Miner), which introduces several new ideas to more efficiently discover high utility itemsets. ULB-Miner uses the designed utility-list buffer structure to efficiently store and retrieve utility-lists, and reuse memory during the mining process. Moreover, the paper also introduces a linear time method for constructing utility-list segments in a utility-list buffer. An extensive experimental study on various datasets shows that the proposed algorithm relying on the novel utility-list buffer structure is highly efficient in terms of both execution time and memory consumption. The ULB-Miner algorithm is up to 10 times faster than the FHM and HUI-Miner algorithms and consumes up to 6 times less memory. Moreover, it performs well on both dense and sparse datasets.     

Improved multiple secret sharing using generalized chaotic image scrambling

Multimedia Tools and Applications - This paper presents a new technique on (n,?n)-Multiple Secret Sharing (MSS) of color images. In this task, n shared images are generated from n secret...     

An approach to optimizing software development team size

This paper explores the relationship between software size, development effort and team size. We propose an approach aimed at finding the team size where the project effort has its minimum. The approach was applied to the ISBSG repository containing nearly 4000 software projects. Based on the results we provide our recommendation for the optimal or near-optimal team size in seven project groups defined by four project properties.