Mining Experts Research Articles

Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.

Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.

Unveiling the causes of waiting time in business processes from event logs

Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times in activity transitions can help analysts identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose observed waiting times in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the process cycle time efficiency. The approach is implemented as a software tool called Kronos that process analysts can use to upload event logs and obtain analysis results of waiting time causes. The proposed approach was empirically evaluated using synthetic event logs to verify its ability to discover different direct causes of waiting times. The applicability of the approach is demonstrated in a real-life process. Interviews with process mining experts confirm that Kronos is useful and easy to use for identifying improvement opportunities related to waiting times.

Data Mining Techniques Against Cyber Threats: A Review

Abstract Data mining is a technique used to extract useful data from existing databases. Those datasets are now being shared globally. Secure communication and confidentiality are required because data from multiple sources must be collected and stored in one central location. Data mining technology involves methods that rapidly and efficiently convert vast quantities of data into relevant insights adapted to the user's requirements. Unfortunately, the utilization of data mining expertise to acquire sensitive personal information poses a threat to individuals' privacy rights. This paper provides a review of the current techniques for preventing cyber risks and safeguarding privacy through the application of data mining. Data mining is used to examine, analyze, and figure out the structure and behavior of data mining organizations. Implementing data mining with optimal outcomes is a challenging task. In the past decade, academics have extensively studied many elements of data extraction. Therefore, it is crucial to provide published research evidence pertaining to this field. For this study, a thorough evaluation was carried out of more than thirty research papers sourced from reputable literature databases. The objective was to extract significant information regarding the field of data mining. The collected data was then used to address various study inquiries about cutting-edge extraction methodologies, data mining mechanisms in cyber dangers, data extraction procedures, algorithms, and evaluation techniques. This paper discusses various research areas and issues in data mining, serving as a valuable reference for researchers in this domain.

Model of the Formation of Work Safety Culture in Polish Mining Enterprises

The article presents the results of research on the level of work safety culture, which is an important element influencing the organisational culture in mining enterprises. The article aims to measure the safety culture among managers in Polish hard coal mines. The assumed goal was achieved in the area of literature and empirical research. Research shows that the precise identification of hazards defined the thematic areas shaping safe working conditions in underground mines. The main part of the work was to survey 135 employees in 3 hard coal companies in Poland. The questionnaires were developed in cooperation with mining experts, and the 5-point Likert scale was used to evaluate individual questions. This methodological approach identified the subjective feelings and experiences of employees for building appropriate attitudes and behaviours that shape the organisational culture. The use of a culture grid enabled the creation of seven thematic groups of safety areas related to the so-called referents of organisational culture. The result of the empirical research is to propose an approach for the transformation of safety culture in the selected area for mining companies. The approach uses the organisational culture grid to shape the attitudes and behaviours of employees, which identifies and designates weak areas, defining them as unsatisfactory and unacceptable, and being the key conclusion of the research.

Fuzzy analytical hierarchy process based environmental, social and governance risks assessment for the future phosphorite mining in Estonia

Although Estonia has significant phosphate rock reserves within the EU, their extraction and processing in Estonia has been a sensitive issue in the past. The current study focuses on identifying and understanding the most critical Environmental Social Governance (ESG) risks for any potential phosphorite mining and processing by compiling and analysing the opinions of Estonian mining experts and identifying similarities and differences between the perceptions of mining experts and wider society groups. The results indicate that governance risks are more critical and social ones are less significant, leaving environmental risks in the middle. A comparison with other stakeholders’ opinions from the earlier studies reveals that governance risks are most important for mining experts, while environmental risks are more critical for other stakeholders.A deeper insight also shows that mining experts do not consider the risk of negative impacts on the living environment of the local community and that their engagement is not particularly important for the company`s activities. However, other stakeholders have valued it the most. Mining experts have considered profitability as the most critical risk, while this is less the case for other stakeholders. As a result, the findings of the ESG risk analysis conducted can be outlined as follows: the importance of risks is multi-layered and differs among stakeholders; if phosphorite is planned to be extracted in Estonia, significant ESG risks need to be mitigated for different stakeholders at different stages of development; a framework should be developed for policymakers to use for sustainable phosphorite extraction.

T-shaped expert mining: a novel approach based on skill translation and focal loss

T-shaped expert mining: a novel approach based on skill translation and focal loss

Maintaining Community During Disruption: Lessons From the Accelerating Change in Medical Education Consortium.

When the COVID-19 pandemic emerged early in 2020, the American Medical Association's (AMA) Accelerating Change in Medical Education Consortium focused on maintaining its community of 37 medical schools and 11 graduate medical education projects along with the core substance of its work. The initial response was to cancel events and reduce the workload of consortium members, but it quickly became clear that the consortium needed additional strategies. The constituents needed resources, support, and community. The authors, along with the rest of the AMA team, learned to maximize the benefits and minimize the drawbacks of operating virtually. These insights supported the continuation, and even expansion, of the consortium's innovations despite extremely stressful circumstances. In this Scholarly Perspective, the authors outline the actions that they and the AMA team took in 2020 and 2021 at the beginning of the pandemic and as that crisis progressed and was further intensified by the significant distress reflected in the protests after the murder of George Floyd. The goal of these actions was to maintain the consortium's core substance, innovation, momentum, and sense of community. The authors describe lessons learned in 2020 and 2021 via a novel model developed to facilitate ongoing collaboration and respond rapidly to the needs of overwhelmed medical educators. This model is composed of 4 phases: assessing needs, mining experts, convening people, and generating products. This model for leveraging a community of practice can help educators optimize collaboration, whether educators are pursuing innovation in the training of physicians or other health care professionals and whether in times of extreme stress or stability, the model provides a pathway for maintaining community. The prepandemic way of working will not return. Virtual participation and collaboration will remain a part of work and daily life for the foreseeable future and beyond.

APPLICATION OF DATA MINING IN HEALTHCARE OF BANGLADESH

The paper intends to discuss the status of data mining applications in the normal and critical healthcare system of Bangladesh. The paper also focuses on the data mining strategies and processes in the current healthcare system in Bangladesh. This is a secondary source-based review paper. The methodology chosen for the study is a literature review and document analysis. The literature review was done through the analysis of book chapters, research-based articles, review-based journal articles, organizational reports, and conference papers. The key findings of the study indicate that as Bangladesh is not a developed country and is a developing one, the country’s healthcare system is not up to the required standard, and the application of data mining techniques is very limited and in most cases, it is at the policy level. The implementation and practice of data-mining techniques is available in internationally recognized organizations and institutions. In these circumstances, the country’s healthcare policymakers and administrators should employ experts in data mining in healthcare services and facilities and standard technologies should be included in their administering the data mining in healthcare facilities. Besides, data mining should be incorporated with other related concepts and strategies to make the highest outcomes in health care systems.

Investigate the factors affecting safety culture in the Malaysian mining industry

Nowadays, safety culture is critical in preventing near-misses, accidents, and disasters. It has three dimensions: psychological, situational, and behavioural. To the best of the author's knowledge, lack of research has been done on psychological and behaviour safety culture in the Malaysian mining industry. The objective of the study is to investigate the factors affecting psychological and behaviour safety culture in the Malaysian mining industry. A two-round online Delphi method was performed among mining experts. The Delphi Experts (n = 21) with extensive experience in mining operations volunteered in this study. The Delphi I (open ended interview) and Delphi II (questionnaire survey) were completed by 100% (n = 21/21) and 87.5% (n = 18/21) of the experts, respectively. Thematic analysis was used to analyse the Delphi I and generate five and nine themes for psychological, situational and behaviour factors. For psychological safety culture, the Delphi II resulted median, IQD and percentage distribution analysis with 83.3%, 83.3% with the IQD cut-off (IQD ≤1) and 64.81% respectively.For situational, safety culture, the consensus was achieved with median, IQD and total percentage distribution analysis with 94.4%, 61.1% and 73.15%, respectively For behavioural safety culture, the consensus was achieved with median, IQD and total percentage distribution analysis with 94.4%, 66.7% and 73.6%, respectively. To conclude, the Delphi analysis revealed that experts agreed and consensuses were achieved on five, ten and nine factors that have major impacts on psychological, situational and behavioural safety culture in the Malaysian mining industry.

DASS Good: Explainable Data Mining of Spatial Cohort Data.

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.

Modelling the enablers of work-family balance among working single mothers integrating expert-mining and TISM-MICMAC

PurposeThis study intends to recognize and prioritize the work-family balance (WFB) enablers for working sole mothers by employing total interpretive structural modelling (TISM) and “Matrice-d’impacts-croisés-multiplication-appliqués-à-un-classment” method (MICMAC).Design/methodology/approachThis paper utilizes the integrated approach in two stages. In initial stage, strategic literature review and expert mining technique have been conducted to recognize and validate WFB enablers. In subsequent stage, TISM has been applied to observe the contextual relationships among WFB enablers in the direction to construct a TISM-based structural model. Furthermore, MICMAC technique has been employed to categorize the WFB enablers based on their driver and dependence power.FindingsThis paper has identified novel 13 key enablers of WFB among working sole mothers and constructed a unique TISM-based hierarchical model. Moreover, WFB enablers have been categorized into four clusters using MICMAC analysis. In the developed TISM model, working sole mother-related WFB personal enablers are primarily at the upper level, family-related WFB enablers are in the center and work-related WFB enablers are in the lowest level.Practical implicationsThe developed framework on WFB enablers among working sole mothers can provide a resolution to difficulties faced by sole mothers in managing WFB by providing a pathway to enhance their performance by improving the organizational effectiveness through improving WFB policies.Originality/valueBased on the best of authors' awareness, this study first incorporates the TISM-MICMAC technique to recognize and prioritize the WFB enablers for working sole mothers.

An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline. Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights. However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations. Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns. In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in inter-organizational settings. We implement our approach and demonstrate its effectiveness using real-life event logs.

Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

Performance Metric Development to Measure Overall Vehicle Effectiveness in Mining Transportation

Several performance metrics to measure the effectiveness of manufacturing equipment have been studied, and one of the most used is overall equipment effectiveness (OEE). However, its application and development in the road transportation sector remains little studied. The purpose of this paper is to establish a new metric derived from OEE to measure the overall effectiveness of coal mining transportation and apply this metric to actual operation. This study employed in-depth interviews and Delphi techniques to explore the specific metrics. Three rounds of Delphi involving mining experts, academia, and consultants identified six potential metrics to measure coal mining transportation operations that contributed to a new metric to measure mining transportation overall vehicle effectiveness. This paper also discusses a case study of how this newly developed metric was tested and applied to the actual operation of the truck fleet at a coal-hauling company in Indonesia. According to the case study, this metric accommodated the important factors in coal mining transportation operations and reflected operational performance. This study contributes to measuring the effectiveness of coal-hauling transportation by providing an effective metric that will help the managerial team make better decisions for process improvement.

EIA/Amdal in risk society: A study in developing country Indonesia

Although coal mining activities have an Environmental impact assessment EIA/Amdal, these activities still pose serious risks to communities in rural Indonesia. This study links EIA/Amdal with the social context of a risk society. This study aims to evaluate the legitimacy of Amdal for coal mining in Indonesia. The legitimacy of the Amdal for coal mining is based on the beliefs of experts, science, and technology (Rational model). The survey and interviews involved stakeholders in 6 coal mining villages and the Indonesian Mining Experts Association (PERHAPI). The results show that rural stakeholders are increasingly distrustful of Amdal (rational model) in avoiding risk. Meanwhile, mining expert groups still rely on rational models in making environmental decisions.

Operationalising a process model of innovation for the mining industry

Technological innovations in mining help improve the efficiency, sustainability and productivity of the mining sector. However, most research on the development and implementation of technological innovations is at the level of the organisation and often not context-specific, providing limited insights into specific practices of individual employees and teams, underpinning the innovation process. This study operationalises a process model of innovation for the mining industry. Using data from twenty-five interviews with mining experts, our study identified and mapped actionable practices that mining organisations can adopt to effectively identify, develop and implement innovation. Three main contributions emerged from the study. The study offers an actionable process model of innovation for mining that considers the mining context at the level of individual and team behaviours. The study reveals the need to involve multiple types of stakeholders and that the mix of stakeholder types varies between innovation phases. Finally, the study finds that the innovation practices identified map to six innovation culture enablers, all of which are required but vary in importance across each phase of the innovation process.

Weaving words for textile museums: the development of the linked SILKNOW thesaurus

The cultural heritage domain in general and silk textiles, in particular, are characterized by large, rich and heterogeneous data sets. Silk heritage vocabulary comes from multiple sources that have been mixed up across time and space. This has led to the use of different terminology in specialized organizations in order to describe their artefacts. This makes data interoperability between independent catalogues very difficult. To address these issues, SILKNOW created a multilingual thesaurus related to silk textiles. It was carried out by experts in textile terminology and art historians and computationally implemented by experts in text mining, multi-/cross-linguality and semantic extraction from text. This paper presents the rationale behind the realization of this thesaurus.

Personalized Mobile App Recommendation by Learning User’s Interest from Social Media

Abstract: In social media, user interests and knowledge are vital but often overlooked resources. There are a few ways to get a sense of what people are known for, such as Twitter lists and LinkedIn Skill Tags, but most people are untagged, so their interests and expertise are effectively hidden from applications like personalised recommendation and community detection and expert mining. We obtain personalised app recommendations by learning the interest's association between applications and tweets by introducing an unique generative model called IMCF+ to convert user interest from rich tweet information to sparse app usage. We analyse the performance of this technique predicts the top ten apps with an 82.5 percent success rate using only 10% training data. Furthermore, in the high sparsity situation and user cold-start scenario, this purpose technique outperforms the other six state-of-the-art algorithms by 4.7 percent and 10%, demonstrating the effectiveness of our technology. All of these findings show that our method can reliably extract user interests from tweets in order to aid in the solution of the personalised app recommendation problem. Keywords: Social Media, User Profile, deep learning, Privacy, matrix factorization,App recommendation.

Climate change risks and vulnerabilities during mining exploration, operations, and reclamation: A regional approach for the mining sector in Québec, Canada

ABSTRACT Climate change (CC) has already and will continue to have a significant impact on the mining sector. A comprehensive and thorough analysis was performed to evaluate the risks and vulnerabilities resulting from CC during the exploration, operation, and reclamation phases of the mining life cycle. This analysis focused on six mining regions in Québec. Climate scenarios were produced for each region and, along with a literature review, used to assess the effects of CC on the mining sector. The results were presented to a panel of experts who highlighted the risks for each activity at each phase of the mining life cycle. A second group of experts evaluated the level of risk and the mining industry’s vulnerability for each risk identified by the first group. Six main risks due to CC were identified: activity schedule disruptions, loss or limitation of site accessibility, water management issues, instabilities and failures of storage facilities, operations infrastructure instabilities, and reduction of reclamation cover performance. The mining experts performed a Delphi-type survey for each mining activity to determine the risk level for each region, leading to the production of a risk matrix. Results indicate that CC is expected to particularly affect reclamation cover performance, and extreme climate events are projected to have significant impacts on operations. Vulnerability levels were assigned by a third group of experts based on the industry’s ability to adapt to these risks. Results showed a low vulnerability for exploration, low to high vulnerability for operations, and low to very high vulnerability for reclamation.

Medical Performance and the Alchemy of Plants in the Ventures of Leonhard Thurneisser zum Thurn

Among the cross currents of social and intellectual life in the early modern era, wonder, utility, and playfulness combined to inspire curiosity and to give value to novel alchemical procedures and chemical remedies. One of the most skilful alchemical and medical performers, who brought theatrical techniques to bear upon an economy of alchemical secrets and magic, was the self-trained Paracelsian physician, mining expert, and alchemical adept, Leonhard Thurneisser zum Thurn (1531–1596). In creatively designed and illustrated books produced for a luxury market, he constructed, in words and images, theatres of procedure, instrumentation, and chemical curiosity based in traditions of Renaissance magic and Paracelsian natural philosophy. Thurneisser's books combined strategies of spectacle and performance within the context of chemical analysis, and in one text especially brought the dramatic technique of “making strange” to bear upon promoting alchemical procedures for purposes of exposing the hidden powers within plants. In staging analytical spectacles involving measurement, instrumentation, and distillation as part of the analysis of minerals, waters, and plants, Thurneisser brought together laboratory-based procedures and theoretically grounded performances within the alchemical marketplace and engaged the agency of readers in establishing the credibility of the philosophy of nature that underscored the products he produced and sold.