This study explores the application of wireless local area network (WLAN) in mobile learning. Although still in its infancy, Taiwan¡¦s government has devoted much resource on mobile learning as the extention of e-Taiwan project. National museums are among the key projects where the effectiveness of mobile learning can be soundly demonstrated. Taking national museums as observing objects, this study examines whether museum visitors appreciate WLAN environment within which personal digital assistants (PDA) operate as mobile educational tools. It further explores the key success factors that leads to successful deployment of mobile learning system. The researcher conducted a two-wave survey, firstly on museum visiters then on museum and e-learning experts for data gathering. Using analytical hierarchy process (AHP) as the research method, this study filters out the priority setting of those six categories as follows according to their significance: learning content, network connection, interface design, mareking and promotion, and business model. It also shows that the priority setting might change as a matter of time. As far as implication for education is concerned, the study suggests that, in the learning space of museum, PDA equipped with WLAN proves to be an appropriate mobile learning tool, notably useful in conducting blending learning and self-directed learning. Such finding should contribute to the understanding and further deployment of mobile learning activities in the context of museums. Keywords: wireless local area network (WLAN), mobile learning, blending learning, self-directed learning, personal digital assistant (PDA), fuzzy analytical hierarchy process (FAHP), key success factors, museum

Assuring production forms a crucial part of mining business profitability. Factors related to various mine operations, activities and business processes can threaten required/planned mine production. To address problems and ensure production level in mining, it is necessary to implement a mine production assurance program (MPA). Since such a guideline does not exist for mining as a process industry, this study started by reviewing four such techniques used in similar industries. These methods include: total productive maintenance, six sigma, a method prescribed by European foundation of quality management, and production assurance program (PAP) used in the oil and gas industry.These methods and techniques were reviewed according to their objectives and applications. Their implementation and achieved success was determined through a literature review and field participation/study. Comparing the tools, techniques and focus with mining productivity and production factors, it was observed that applicability of these methods for mining is limited due to a lack of tools for specific analysis or a lack of consideration of the requirements of mining. However, given certain similarities in objective and methods, PAP from the oil and gas industry may provide some guidance for MPA.As a basis of MPA, an index is required to create a clear relationship between different situations which can occur in mining operation and production loss. A literature review on mining productivity improvement methods shows availability, utilisation and production performance of equipment are the key factors in determining overall production. A single index applicable for chain operation in mining is needed. Overall equipment effectiveness (OEE) which includes these three elements has some limitations for application in mining. A Mine Production index (MPi) is thus proposed. This index involves all three parameters for equipment productivity mentioned above. It also consists of weights for each parameter. The weights in this study are determined through expert opinions/judgements using fuzzy analytical hierarchy process (FAHP). Equipment with low MPi can be labelled as bottlenecks. Weights associated with MPi calculation for bottleneck equipment can point out critical factors in equipment operation. Once bottleneck equipment and relevant critical factors are known, further analysis can be carried out to determine the exact cause of production loss.By using MPi for machine operations, it is possible to rank machines in terms of production effectiveness. When the study applied MPi to chain operations in a mining case study, a crusher was determined as bottleneck equipment. Further root cause analysis and uncertainty detection for bottleneck equipment is also possible, and this forms the basis for MPA. / CAMM - Lean mining

Ventilation is a critical task in underground mining operation. Lack of a good ventilation system causes accumulation of harmful gases, explosions, and even fatalities. A proper ventilation system provides adequate fresh air to miners for a safe and comfortable working environment. Fans, which provide air flow to different faces of a mine, have great impact in ventilation systems. Thus, selection of appropriate fans for a mine is the acute task. Unsuitable selection of a fan decreases safety and production rate, which increases capital and operational costs. Moreover, pitch angle of fans&rsquo / blades plays an important role in fan&rsquo / s efficiency. Therefore, selection of a fan and its pitch angle, which yields the maximum efficiency, is an emerging issue for an efficient mine ventilation. The main objective of this research study is to provide a decision making methodology for the selection of a main fan and its appropriate pitch angle for efficient mine ventilation. Nowadays, analytical hierarchy process as multi criteria decision making is used, and it yields outputs based on pairwise comparison. On the other hand, Fuzzy Logic as a soft computing method was combined with analytical hierarchy process and combined model did not yield appropriate results / because Fuzzy AHP increased uncertainty ratio in this study. However, fuzzy analytical hierarchy process might be inapplicable when it faces with vague and complex data set. Soft computing methods can be utilized for complicated situations. One of the soft computing methods is a Neuro-Fuzzy algorithm which is used in classification and DM issues. This study has two phases: i) selection of an appropriate fan using Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and ii) selection of an appropriate pitch angle using Neuro-Fuzzy algorithm and Fuzzy AHP method. This study showed that AHP can be effectively utilized for main fan selection. It performs better than Fuzzy AHP because FAHP contains more expertise and makes problems more complex for evaluating. When FAHP and Neuro-Fuzzy is compared for pitch angle selection, both methodologies yielded the same results. Therefore, utilization of Neuro-Fuzzy in situation with complicated and vague data will be applicable.