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The purpose of this study is to propose a methodological approach for modeling catastrophic consequences caused by black swan events, based on complexity science, and framed on Feyerabend’s anarchistic theory of knowledge. An empirical application is presented to illustrate the proposed approach.

Thom’s nonlinear differential equations of morphogenesis are used to develop a theoretical model of the impact of catastrophes on international business (IB). The model is then estimated using real-world data on the performance of multinational airlines during the SARS-CoV-2 (COVID-19) pandemic.

The catastrophe model exhibits a remarkable capability to simultaneously capture complex linear and nonlinear relationships. Through empirical estimations and simulations, this approach enables the analysis of IB phenomena under normal conditions, as well as during black swan events.

To the best of the author’s knowledge, this study is the first attempt to estimate the impact of black swan events in IB using a catastrophe model grounded in complexity theory. The proposed model successfully integrates the abrupt and profound effects of catastrophes on multinational corporations, offering a critical perspective on the theoretical and practical use of complexity science in IB.

High resolution simulations of body-internal electric field strengths induced by magneto-quasistatic fields from wireless power transfer systems are computationally expensive. The exposure simulation can be split into two separate simulation steps allowing the calculation of the magnetic flux density distribution, which serves as input into the second simulation step to calculate the body-internal electric fields. In this work, the magnetic flux density is interpolated from in situ measurements in combination with the scalar-potential finite difference scheme to calculate the resulting body-internal field. These calculations are supposed to take less than 5 s to achieve a near real-time visualization of these fields on mobile devices. The purpose of this work is to present an implementation of the simulation on graphics processing units (GPUs), allowing for the calculation of the body-internal field strength in about 3 s.

This work uses the co-simulation scalar-potential finite difference scheme to determine the body-internal electric field strength of human models with a voxel resolution of 2 × 2 × 2 mm³. The scheme is implemented on GPUs. This simulation scheme requires the magnetic flux density distribution as input, determined from radial basis functions.

Using NVIDIA A100 GPUs, the body-internal electric field strength with high-resolution models and 8.9 million degrees of freedom can be determined in about 2.3 s.

This paper describes in detail the used scheme and its implementation to make use of the computational performance of modern GPUs.

This paper aims to address the issue of model discontinuity typically encountered in traditional Denavit-Hartenberg (DH) models. To achieve this, we propose the use of a local Product of Exponentials (POE) approach. Additionally, a modified calibration model is presented which takes into account both kinematic errors and high-order joint-dependent kinematic errors. Both kinematic errors and high-order joint-dependent kinematic errors are analyzed to modify the model.

Robot positioning accuracy is critically important in high-speed and heavy-load manufacturing applications. One essential problem encountered in calibration of series robot is that the traditional methods only consider fitting kinematic errors, while ignoring joint-dependent kinematic errors.

Laguerre polynomials are chosen to fitting kinematic errors and high-order joint-dependent kinematic errors which can avoid the Runge phenomenon of curve fitting to a great extent. Levenberg–Marquard algorithm, which is insensitive to overparameterization and can effectively deal with redundant parameters, is used to quickly calibrate the modified model. Experiments on an EFFORT ER50 robot are implemented to validate the efficiency of the proposed method; compared with the Chebyshev polynomial calibration methods, the positioning accuracy is improved from 0.2301 to 0.2224 mm.

The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed calibration methods on an industrial serial robot.

This study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.

Three multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.

STC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.

This is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

Purpose: Students’ transition from education to employment is influenced by factors like the length and calibre of their education, demography, labour market conditions, and the general state of the economy. Regardless of the economy, education systems should seek to ensure that students have the skills required for the labour market. This will help them better transition from school to work. This study examines the work skills that companies require for entry-level positions in Romania.

Need for Study: Previously, text analysis studies treated the job market only for the IT industry in Romania. To understand the demand-side opportunities and restrictions, assessing the employment opportunities for young people in the Romanian labour market is necessary.

Methodology: A text mining approach from 842 unstructured data of the existing job positions in October 2022 for fresh graduates or students is used in this chapter. The study uses data from LinkedIn job descriptions in the Romanian job market. The methodology involved is focused on text retrieval, text-pre-processing, word cloud analysis, network analysis, and topic modelling.

Findings: The empirical findings revealed that the most common words in job descriptions are experience, team, work, skills, development, knowledge, support, data, business, and software. The correlation network revealed that the most correlated pairs of words are gender–sexual–race–religion–origin–diversity–age–identity–orientation–colour–equal–marital.

Practical Implications: This study looked at the job market and used text analytics to extract a space of skill and qualification dimensions from job announcements relevant to the Romanian employment market instead of depending on subjective knowledge.

This paper aims to quantify the loss (or leakage) of organic cattle to conventional value chains in Ireland and assess its economic and environmental impacts.

The paper adopts a Bio-economy Input-Output (BIO) model, a quantitative economic model representing the interdependencies between different sectors of the economy, to assess the economic and environmental impacts of organic leakage in the Irish beef sector.

The study reveals that 17% of organic cattle aged under 1 year old leave the organic value chain, leaking to the conventional market as a result of imbalances in the development of the beef value chain. The economic cost of this organic leakage is 5.66 million euros. Leakage also has environmental effects because of changes in lifecycle methane and nitrogen emissions based on longer finishing times on organic farms and chemical fertilisers applied on conventional farms. The organic leakage results in a reduction of 82 tons of methane emission and 52 additional tons of nitrogen emission, which leads to 11,484 tons of net global warming potential (GWP) for a 100-year time horizon.

Because of data availability, the research focussed on the baseline year 2015, which had national data available for disaggregation in Ireland. Therefore, researchers are encouraged to assess the economic and environmental impacts when more recent data are available and to analyse the change in the impacts over the years.

This study contributes to the discussion on organic conversion and provides valuable insights for stakeholders, especially policymakers, for the design of future organic schemes.

This is the first paper to assess organic leakage in the beef sector.

Purpose: This chapter aims to perform text analysis to investigate the academic area delimitated by economic and financial performance and money laundering.

Need for the study: The findings contribute to the body of literature by providing important insights in terms of money laundering and financial performance.

Methodology: In order to achieve the research objective, further than 640 papers were retrieved from the Web of Science from 1994 to 2022, concentrating on the most referenced documents found in the superior quartile.

Findings: The empirical findings emphasise that the article with the unique words Fraud Detection System: A Survey by Abdallah A., Maarof M. A., and Zainal A., examines a complete and systematic assessment of the concerns and obstacles that impede the performance of fraud detection systems. Furthermore, topic modelling findings highlighted the presence of four main topics: topic 1 – identified by ‘performance’, ‘firms’, ‘financial’, ‘fraud’, and ‘board’; topic 2 – described in terms of ‘fraud’, ‘accounting’, ‘evidence’, ‘audit’, and ‘research’; topic 3 – identified by ‘firms’, ‘fraud’, ‘financial’, ‘CEO’, and ‘results’ while topic 4 – identified through ‘fraud’, ‘detection’, ‘data’, ‘cost’, and ‘card’.

Practical implications: This study will act as a guide for researchers of the financial performance field to explore the scientific publications in the field of money laudering.

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.

Digital transformation becomes the future path for all organizations. Organizations are in need to progress the technology in the event of rapid environmental changes in all aspects. This implies the essential need to adapt to these changes, not only to benefit from the vast opportunities it offers yet even to stay relevant in this instability, complexity, uncertainty and vagueness environment. This paper aims to examine the impact of different variables such as disruptive change, technological process innovation and industry 4.0 (I4.0) on digital transformation. It helps identify the different capabilities needed for digitalization and digital maturity, identify the supporting methods for adopting different technologies and offer answers to overwhelmed those challenges and obstacles resulting in this environment.

A quantitative approach was used in conducting this research, whereas a questionnaire survey strategy was used for this investigation. In total, 450 participants have been surveyed from three major private mining organizations in the Jordanian context. Structural equation modeling was used for the analysis stage and hypotheses testing.

The results of the analysis revealed that support the direct impact of the event of disruptive change, technological process innovation on digital transformation. In addition, the results showed that there is a positive direct impact of the event of disruptive change on technological process innovation. While I4.0 was found to moderate the relationship between the event of disruptive change and digital transformation.

Decision-makers are responsible for directing their organization toward digitalization. This transformation needs capabilities that help organizations in competing and survive in this challenging environment. That is, it is essential to increase process innovation and moving toward more adoption of I4.0. However, the event of disruptive change should be considered as a motivation for the organizations rather than an obstacle. Moreover, different populations, methods and other variables that may affect digitalization may generate novel insights in further research.

Theoretically, novel insights into the event of the disruptive change and its implications have been added to the literature. The models used in the current examination provide new directions for understanding and studying digital transformation and organizational capabilities that are needed for transformation. From the managerial perspective, these findings enhance understanding of practices in which the event of disruptive change supports innovation and highlight the values added through recommending more adopting of I4.0 applications to yield more innovative harvests.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.