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This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

The purpose of this paper is to examine the social sustainability strategies of multinational mining companies in addressing risks in areas of exploratory intensity and contributing to social capital in local communities.

This study is situated within an interpretivist paradigm and uses a qualitative research methodology, drawing on data from semi-structured interviews with multinational mining companies operating in Ghana and key stakeholder groups.

The results of this study show that multinational mining companies use strategies broadly categorised as social responsibility, social compliance, local content and relationship proximity to address challenges embedded in the extractive process.

By examining the strategies in addressing risks to sustainable social development in mining communities, this study contributes to fill the social sustainability gaps in mining research and adds to relevant literature.

This study aims to clarify the evolution law of stress field and fracture field during the mining process of inclined coal seam, to prevent the occurrence of roof burst water and impact ground pressure accident during the advancing process of working face.

The evolution law of stress-fracture field under different mining conditions of inclined coal seam was studied by using discrete element method and similar material simulation method.

The overburden stress at the lower end of the coal seam was mainly transmitted to the deep rock mass on the left side, and the overburden stress at the upper end was mainly transmitted to the floor direction. With the increase of the inclined length of the mining coal seam, the development of the fracture zone gradually evolves from the “irregular arch” form to the “transversely developed trapezoid” form. The development range of the fracture zone was always in the internal area of the stress concentration shell.

An original element of this paper is based on the condition that the dip angle of coal seam is 35°, and the evolution law of overburden stress-fracture field during the excavation of coal seam with different lengths was analyzed by UDEC numerical simulation software. The coupling relationship between stress shell and fracture field was proposed, and the development range of fracture zone was determined by stress. The value of this paper is to provide technical support and practical basis for the safety production of a mine working face.

This study aims to explore how closed factories could be transformed and provide a path for sustainable development for a territory. The authors focus on the case of the Great Mine Serbariu, located in Carbonia (Sardinia), which used to be the largest coal mine in Italy between 1939 and 1964.

The authors adopt a qualitative research design based on an exploratory single-case study, drawing on interviews with the main stakeholders, on a survey conducted among 5,158 visitors, and on administrative documentation of the City Council.

The analysis of the Great Mine Serbariu case showed that the regeneration of an exhausted mine serves a model of sustainable development, especially for the redevelopment of other urban and industrial degraded areas. The Great mine Serbariu was restored and turned into a place of culture, tourism, research and higher education, with the Italian Cultural Centre of Coal Mining (ICCCM) establishing its headquarters in the heart of the former mine. It attracted almost 220,000 visitors, generating both domestic and international tourist flows and making an industrial heritage a real resource for the area.

This article advances the authors’ understanding of how closed industries could become an instrument for sustainable development on the social, economic, touristic and cultural levels. This study would help local governments with examples to enhance the historical resources to create a new identity that led to a sustainable development of an urban landscape, and to create networks with other comparable museums all over Europe to better exploit the touristic and cultural potential.

We develop a conceptual framework as a basis for thinking about the impact of extractive industries and emancipatory potential of alternative accounts. We then review selected alternative accounts literature on some contemporary issues surrounding the extractive industries and identify opportunities for accounting, auditing, and accountability research. We also provide an overview of the other contributions in this special issue.

Drawing on alternative accounts from the popular and social media as well as the alternative accounting literature, this primarily discursive paper provides a contemporary literature review of identified issues within the extractive industries highlighting potential areas for future research. The eight papers that make up the special issue are located within a conceptual framework is employed to illustrate each paper’s contribution to the field.

While accounting has a rich literature covering some of the issues detailed in this paper, this has not necessarily translated to the extractive industries. Few studies in accounting have got “down and dirty” so to speak and engaged directly with those impacted by companies operating in the extractive industries. Those that have, have focused on specific areas such as the Niger Delta. Although prior studies in the social governance literature have tended to focus on disclosure issues, it is questionable whether this work, while informative, has resulted in any meaningful environmental, social or governance (ESG) changes on the part of the extractive industries.

The extensive extractive industries literature both from within and outside the accounting discipline makes a comprehensive review impractical. Drawing on both the accounting literature and other disciplines, this paper identifies areas that warrant further investigation through alternative accounts.

This paper and other contributions to this special issue provide a basis and an agenda for accounting scholars seeking to undertake interdisciplinary research into the extractive industries.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

This study seeks to collaborate with the discussions on the usefulness of the narrative approach in accounting. In this context, this study aims to elaborate small collective stories, developed from interviews, to expose the population’s perception of the social and environmental impact (positive and negative externalities) resulting from iron ore mining in the city of Congonhas-Minas Gerais (MG).

This research, using counternarratives, aims to elaborate small collective stories, developed from 52 interviews, to expose the population’s perception of externalities resulting from the exploitation of iron ore in the city of Congonhas-MG, Brazil, to give more insight for social and environmental accounting reporting. A qualitative investigation is used with a narrative approach that focuses on a specific event in the participants’ lives.

The authors sought to create a sense of collective experiences of the interviewees through narratives representative of the residents’ perception of externalities in the form of small collective stories. However, it can be observed that the local population recognizes the impact of numerous externalities. Likewise, the use of narratives allows the reader to experience another reality – a reflection on the impact of business activities in a given context. Unlike conventional corporate social reporting, models based on qualitative information can be inclusive, produced by/for the community toward action that transforms the local reality.

This study intends to contribute to the debate on reporting models that are developed by and for external stakeholders. This approach has the potential to improve participants’ both awareness and engagement, supporting transformative social action. This study makes several contributions. It contributes to the literature with a narrative approach, which is not often used in the accounting literature; it brings insights from the Latin American context, which is especially valuable given how the Anglo-American accounting literature includes few papers addressing this context; it presents the view of marginalized communities that are too often overlooked (this narrative approach offers important insights into the lived experience of people at a very granular level).

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.