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This paper aims to unveil the darker side of cryptocurrencies by delving into its role as an obstacle to investments in Middle East and African (MEAs) countries, unravelling the challenges involved. Indeed, despite the rise of blockchain-related technologies, specifically cryptocurrencies, having undeniably unlocked new avenues for business and society, crypto for venture funding purposes may exhibit a “dark side” due to their use for unethical purposes, for example, money laundering or terrorism financing, largely diffused in certain areas of MEA countries.

Through an explorative research design, using a mix of techniques based on both qualitative and interpretive methods, we conducted in-depth interviews among 33 European managers of companies engaged in MEA markets or aspiring to invest in such foreign markets, to analyse their thoughts, perceptions and possible strategies concerning the management of the “dark side” of cryptocurrencies in MEAs.

Our investigation unearthed seven pivotal issues, which manifest as significant barriers related to the ambivalent use of crypto for funding projects, encompassing seven important consequential elements: (1) lack of knowledge about the technology’s potentialities; (2) perceptions of crypto technology’s ambivalence; (3) reputation and image consequences; (4) uncertainty about the destination of the invested funds; (5) decreased attractiveness of MEAs; (6) competition and market; and (7) lack of control and regulation. We grouped these into technology-related, business-related and legal- and policy-related barriers. Such findings underline the probable decrease in attractiveness of MEAs in terms of investments, together with the triggering factors and potential strategic solutions to mitigate such circumstances.

Future studies could explore a broader sample of managers since we only considered the perception of European managers operating in companies that invest (or are intending to invest) in MEAs. Moreover, future research may extend the analysis to MEA-native companies or those engaging in reciprocal exchanges with Western countries.

Practically, our findings suggest several elements in which to intervene to mitigate managers’ negative perception of the unethical use of cryptocurrencies in MEAs and to support CEOs’ and CFOs’ strategies, together with requirements to ensure the unaltered attractiveness of investments in an otherwise thriving region of the world, without overlooking the protection and safeguarding of investments and the health of the market and competition. Furthermore, a call for future research in this domain, along with at least minimal regulatory mechanisms, clearly emerges.

Our findings underline the social challenges associated with the perception and acceptance of cryptocurrencies in these contexts, influencing cultural and social dynamics. Moreover, the identification of these barriers could underscore the significance of awareness of and education on blockchain technology and cryptocurrencies within society, including implications for policymakers.

Despite prior investigations into the negative effects of cryptocurrencies as a form of venture funding, no studies to date have examined managers’ perceptions by focusing on possible barriers to investment in MEA countries due to the unethical usage of crypto. Importantly, this paper unravels the unexplored complexities of crypto’s impact on ethical investments in MEAs, showcasing an original perspective.

High-nitrogen steel is a common material for the manufacture of drilling equipment such as drill collars. However, its poor surface properties often limit its applications. The purpose of this paper is to find a way to enhance the surface performance of high-nitrogen steel, which is expected to improve the wear resistance of high-nitrogen steel.

The CoCrNi and CoCrFeNi medium-entropy alloy (MEA) coatings were prepared on high-nitrogen steel substrate by laser cladding technology. The microstructure, phase composition and element distribution of the fabricated coatings were investigated using scanning electronic microscopy, electron backscatter diffraction and X-ray diffraction. The phase structure, phase stability and structural stability of the CoCrNi and CoCrFeNi MEAs were investigated in combination with phase diagram calculation and molecular dynamics. Then the wear tests were carried out for coatings.

The results show that both prepared MEA coatings have good quality and contain a single face-centered cubic phase. The wear performance of MEA coatings is improved by the refinement of grain size and the increase of dislocation density. Due to the addition of Fe atoms, the lattice distortion of the CoCrFeNi system increased, resulting in a higher dislocation density of the coating. Cr atoms in the CoCrFeNi system are the largest, and the local lattice distortions induced by them are greater. Through this study, MEA coatings with high hardness can be expanded to drilling field applications.

CoCrFeNi and CoCrNi MEA coatings were successfully prepared on the surface of high-nitrogen steel for the first time without obvious defects. The micromorphology and grain orientation of the different kinds of coatings were discussed in detail. The hardness-strengthening mechanism and structure stability of the coatings were illustrated by experiments and molecular dynamics simulations.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0116/

Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.

The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.

The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.

The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

This study investigates if and how the board size, the board independence, the CEO duality and the board-specific skills are associated with higher-quality Sustainable Development Goals (SDGs) disclosure in European State-Owned Enterprises (SOEs).

We measured SDGs disclosure through a content analysis of SOE's reports from 2017 to 2022. The characteristics of the boards analyzed are board size, board independence, CEO duality and board-specific skills. We performed multiple regression models to test the association between the SDGs disclosure and the characteristics of the boards.

The results show that board size, independent directors and board-specific skills are positively associated with higher-quality SDGs disclosure, while CEO duality is negatively associated with higher-quality SDGs disclosure.

This study provides several practical implications. Shareholders could equip their firms with larger boards, more independent and highly skilled directors, while avoiding a CEO duality for improving the SDGs disclosure; capital providers could examine the characteristics of a firm's board before allocating financial resources to verify which firms are accountable in reaching the SDGs. Also, standard-setters and policymakers could use the results of this research to define new standards or regulatory pathways to push firms to put more efforts in preparing a comprehensive and high-quality SDGs disclosure.

While prior studies mostly focused on sustainability reporting overall, this study adds a specific insight about SDGs disclosure employing an investigation which has not been previously analyzed.

This study aims investigate in-service teachers’ perspectives on the integration of ChatGPT, an artificial intelligence (AI)-driven chatbot, into United Arab Emirates (UAE) private schools. As the UAE progresses towards a knowledge-based economy, aligning with the goals of the UAE 2030 vision, this research assesses the capacity of ChatGPT to enhance the educational experience within the framework of technological pedagogical content knowledge.

A mixed-methods approach is used, combining a survey assessing teachers’ attitudes and a thematic analysis of open-ended responses, to explore the effectiveness, challenges and pedagogical implications of ChatGPT’s use in the classroom.

Findings reveal that teachers value ChatGPT for its potential to individualize learning and streamline the creation of educational materials, aligning with the shift towards student-centred approaches and the demands of 21st-century skills. However, significant challenges are noted, including ethical concerns, the need for reliable content and a necessity for extensive professional development to fully realize ChatGPT’s benefits.

While ChatGPT transforms teaching and learning practices, realizing this potential requires addressing critical issues through adaptive policy-making, continuous educator training and thoughtful integration into the curriculum.

The study highlights the importance of a collaborative approach to dealing with the details of AI in education, ensuring that advancements like ChatGPT align with the evolving educational paradigms of the UAE.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

This study aims to evaluate the usability of Google Drive (GD) in addition to measuring the effectiveness of GD and the challenges faced while using this cloud storage application among public library users of Lahore.

The study adopted a sequential explanatory mixed method design comprising the quantitative (QUAN) and qualitative (QUAL) parts. For the QUAN phase, a questionnaire survey was conducted among 384 users of public libraries through purposive sampling. For the QUAL phase, the think aloud technique was performed on several tasks during experiments and verbalized their thoughts and experiences while interacting with the system. The cloud usability model (CUM) was used to measure the “usability” of GD.

Findings indicated that most users were satisfied with GD's obvious features and inherent functions. QUAL results indicated that respondents did most tasks easily, whereas only some could complete the task.

The study is valuable as it is the first time used CUM to measure the perceived usability of GD. Second, the study used a mixed method study to get insights into perceived usability, effectiveness and challenges while using GD. The findings might be helpful for cloud support teams, including GD, as they can enhance certain features of usability, which lead to increased usage among users.

This research work is based on MPhil thesis.

The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.

The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.

The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.

The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.

The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.

As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.

The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

This paper aims to empirically investigate the effect of facility–maintenance service quality on tenants’ satisfaction and their subsequent willingness to pay higher rent in the National Capital Region (NCR), India.

The data for this study was collected from 1,692 tenants in NCR, India. SmartPLS4.0 was used to analyze the data using structured equation modeling.

The study findings indicate that all parameters of facility–maintenance service quality (tangibles, service personnel quality and empathy) positively impact tenants’ satisfaction. Further, satisfied tenants are willing to pay higher rentals. In addition, customer satisfaction partially mediates the relationship between facility–maintenance service quality and willingness to pay higher rent.

The study extends evidence-based research in the service industry to provide empirical evidence that facility–maintenance service quality positively impacts customer satisfaction in real estate settings in emerging markets (India). This research will guide future researchers to explore other dimensions to support evidence-based research in real estate settings.

Based on the data collected online after personal interaction in residents’ meetings, the study findings provide significant insights for stakeholders such as policymakers, practitioners, landlords, associations and builders. With rising housing demand because of rural migrations toward urban or metro locations coupled with the government’s inability to expand the infrastructure simultaneously, the government has enhanced the role of public–private partnership (PPP) in housing development. The findings will help policymakers incorporate the service angle into key performance indicators in PPP contracts. Additionally, with rising competition in the housing sector, understanding these factors will help landlords and resident associations improve service quality standards, thus enhancing the residential societies’ word-of-mouth publicity and attracting high-paying residents.

To the best of author’s knowledge, this is a pioneer study to empirically investigate the impact of facility–maintenance service quality standards on tenants’ satisfaction and willingness to pay higher rent in a residential setting in India.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.