Simulation-based decision-making system for optimal mine production plan selection

Price uncertainty is one of the major uncertainties in the life of mine (LOM) planning process which can have a decisive effect on the overall profitability. Today’s mine planning software tools provide block-sequencing optimisation for a given static price assumption that is then used as a basis of managerial decision-making process. This paper proposes a complementary approach to this by introducing a simulation-based decision-making tool that, with the help of simulation, seeks for the optimal mine plan when a managerially estimated price development with minimum and maximum boundaries is used as a data input for the given period. To demonstrate the approach, a realistic gold mine case study is presented with five alternative and technically feasible mine plans calculated in a static optimiser from a commercial mine planning software package. These mine planning scenarios are then subjected to price uncertainty in simulation with and without a price trend assumption to highlight the effect of price on the mine’s expected performance. Based on the results, we derive and demonstrate a simulation-based system that automates the matching of optimal mine plan with the managerial insight of long-term price development.

AN ENHANCEMENT EDAS METHOD BASED ON PROSPECT THEORY

Decision-making is the process of carefully considering multiple options and choosing the best one. The EDAS (evaluation based on distance from average solution) method has been studied in many multi-attributes decision-making (MADM) problem which assumes decisionmaking under absolute rationality. However, people usually show the characteristics of bounded rationality in the real decision-making process. Prospect theory (PT) utilizes gains and losses relative to the reference point to explain this phenomenon better. In this paper, an enhancement EDAS method based on PT will be proposed, which shows better properties in practice. We apply the traditional EDAS method and enhancement EDAS method to the same case and we utilize the sensitivity analysis and comparative analysis to analyze their performances. The result shows that our approach has a superiority compared with the traditional EDAS method. The methods we present are of great significance for investment decision-making problems, new product development, design plan selection and supplier selection.

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

Bridge expansion and contraction installation (BECI) has proved to be an essential component of the bridge structure due to its stability, comfort, and durability benefits. At present, traditional replacement technologies for modular type, comb plate type, and seamless type BECIs are widely applied worldwide. Nevertheless, it is unfortunate that the research conducted on decision-making (DM) approaches for the technical condition assessment and the optimal replacement plan selection of existing BECIs remain scarce, which results in the waste of resources and the increase in cost. Therefore, a BECI technical condition assessment approach, which contains specific on-site inspection regulations with both qualitative and quantitative descriptions, is proposed in this research, and a corresponding calculation program has been developed based on the MATLAB platform, which provides the basis for the necessity of replacement. Simultaneously, the hybrid chaotic whale optimization algorithm is designed and performed to improve and automate the process of optimal replacement plan selection under the assistance of the analytic hierarchy process (AHP), where both the achievement in consistency modification and the reservation of initial information are perused, and its superiority and effectiveness are verified via the comparative experimental analysis. The improved BECI replacement decision system is established, and the corresponding case study demonstrates that the proposed system in this research proves reasonable and feasible. The improved system can effectively assist bridge managers in making more informed operation and maintenance (O and M) decisions in actual engineering projects.

Using of Peer Assisted Learning to Conduct Pre-Training Situational Analysis for Nurse Intern

<strong>Background:</strong> Clinical placement is the main site for interns’ training, however clinical placement is an uncomfortable and inconvenient environment for learning. Trainees need intelligent conversation with self and others to advance in their careers. In this regard, Peers informally teach each other through discussion. Nowadays, there is an international need and interest in formal peer-assisted learning. Hence, Situational model for curriculum development is a suitable model for training as it is culturally driven and emphasizes context. The research objectives are to apply a situational curricular model, using a peer-assisted learning. In addition, to explore the students’ perception towards peer-assisted learning. <strong>Methods:</strong> A mixed method quantitative and qualitative design single group post-test design was applied in this study, where all the nursing interns in the current year were selected. Peers conducted orientation sessions to make situational analysis. After that, each intern, with his educational supervisor, develops personal objectives and a development plan. Selection of contents and learning opportunities is the responsibility of interns guided by the general outcomes and list of required competencies during the training year. The last step is assessment and evaluation of performance. Furthermore, interns’ perceptions towards using peers-assisted learning were measured through distribution of self-administered questionnaire, and conduction of focus group with interns. <strong>Results:</strong> Overall students’ perceptions toward this reform were positive, all means were above four, especially their perception towards Peer Assisted Learning (PAL) as a relaxed and comfortable environment and as a new learning opportunity. <strong>Conclusion:</strong> Interns found that reform of their training through conducting situational model for training is an effective method, especially when using Peer Assisted Learning (PAL) in its conduction, which, they recommended its implementation to all units’ orientation.

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.