This contribution aims to shed light on the character of the observation-modeling link, and the role of the matching of its faces, in the management of different events. These include intelligent theories and digital tools, as well as the complexity of dynamic processes of natural and artificial phenomena. Such matching in the link could be practiced in offline or real-time mode. Offline mode mainly concerns the governance of intelligent theories and digital tools mimicking physical paradigms. Real-time mode concerns dynamic processes involving a significant degree of complexity. This exists in natural events like wildlife and human biology. It is also present in autonomous supervised artificial procedures, which involve complex real phenomena mathematically replicated by coupled multiphysics in the framework of matched real-virtual pairs. This communication involves analyses and discussions of these different pairings and their affected events, supported by examples allied to the literature. This corresponds to cases of intelligent theories, computational tools mimicking physics, real-time matching in natural wildlife and human biology, as well as twins supervising complex artificial procedures.

As industrialisation progresses, electric motors are increasingly utilised in manufacturing sectors, and their regular operation plays a crucial role in enhancing production efficiency, safety, and ease. Consequently, there's a growing emphasis on developing technology for monitoring the condition of electric motors. This study focuses on the analysis of common issues like rotor bar failure and eccentricity in induction motors, examining their causes, creating motor models in both normal and malfunctioning conditions through computer simulations, identifying the stator current signals, and comparing their spectra to validate the stator current data. Additionally, this research offers a dependable and efficient dataset for further analysis. The complex and fluctuating nature of the current signals in induction motors necessitates the use of advanced techniques like the tunable-Q wavelet transform (TQWT) and box dimension method for feature extraction, which is more effective in signal characterisation than other approaches. The study then explores the application of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) in fault diagnosis, achieving accuracies of 91.67% and 100%, respectively. The findings indicate that ANN is superior to SVM and suggest this strategy for the automatic detection of motor faults. Implementing such intelligent systems can prevent unexpected and unplanned production interruptions caused by electric motor failures.

This paper examines the recent evolution of Enterprise Resource Planning (ERP) systems and explores the critical success factors (CSFs) for project implementation in the digital age. Adopting a qualitative inductive approach, the article first reports on CSFs evident in relevant literature drawn from the past two decades. In the second research phase, interview feedback from nine industry project managers is analysed to identify the CSFs now considered of particular relevance in the digital era. The article concludes that many of the established CSFs remain relevant, but recent research suggests the deployment of digital technologies and the availability of the cloud for ERP operation will mean that CSFs will be re-formulated in new technology and business environments. CSFs related to cloud-based vs on-premise software operation, system configuration and functionality trade-offs, and the integration of digital technologies into ERP products, are likely to emerge in the digital era. Future studies could profitably focus on these largely unresearched aspects of ERP projects, to which this article makes a small contribution that may provide a useful point of reference for subsequent studies.

Brain-computer interfaces (BCIs) hold immense promise for human benefits, enabling communication between the brain and computer-controlled devices. Despite their potential, BCIs face significant cybersecurity risks, particularly from Bluetooth vulnerabilities. This study investigates Bluetooth vulnerabilities in BCIs, analysing potential risks and proposing mitigation measures. Various Bluetooth attacks such as Bluebugging, Bluejacking, Bluesnarfing, BlueBorne, Location Tracking, Man-in-the-Middle Attack, KNOB, BLESA and Reflection Attack are explored, along with their potential consequences on commercial BCI systems. Each attack is examined in terms of its modus operandi and effective mitigation strategies.

This research delves into growth hacking models embraced by Born Digitals, Born Globals, and other firms, emphasizing the dynamic interplay between innovation and internationalization. Despite their unique origins and focuses, these concepts frequently intersect in business strategies, models, and entrepreneurial pursuits. Growth hacking models can be conceptually categorized based on the degree of digital innovation and the scope of business, whether local or global. This study underscores the pivotal role of growth hacking across diverse company profiles, transcending the domain of Born Digitals reliant on digital platforms. Various strategic pathways within each company's business model showcase diverse innovation routes to achieving growth and global presence. The analysis accentuates the importance of hybridity or synergistic approaches in growth hacking business model innovation within today's market landscape, offering valuable managerial insights for growth-oriented companies from their inception. It advocates for companies to embrace innovative and international strategies for enduring success, while judiciously balancing considerations of scalability and profitability against novelty and efficiency in business model design.

The Internet of Things and spatial computing are increasingly popular in today's technological environment. These devices can sometimes produce counterproductive effects, complicating the interaction between non-expert end users and the device itself. In this paper, we propose a simple, user-friendly, and cost-effective configurable smart mirror, which can display usefully relevant real-time information. This system is designed based on a low-cost Raspberry Pi paired with an LCD screen. The system can connect with a personal computer (PC) through the IEEE 802.15 wireless communication protocol. The preliminary results in this paper show the intuitive usability of the device in daily life.

This paper introduces an innovative approach for designing, optimizing, and simulating a low voltage MEMS switch specialized for micro-pump applications. The primary goal is to improve the efficiency of micro-pumps used in drug delivery. The design process focuses on tailoring the switch’s geometry for micro-pump purposes and employs objective functions encompassing actuation voltage, insertion loss in the up-state, and isolation in the down-state. To solve the intricate optimization task, mathematical programming is combined with the Multi-Objective Particle Swarm Optimization (MOPSO) meta-heuristic algorithm, enabling simultaneous consideration of actuation voltage, insertion loss, and isolation. By analyzing the Pareto front derived from these parameters, the study identifies design requirements and optimal levels for the switch. The proposed MEMS switch demonstrates remarkable performance metrics, including  and  values of –11.74 dB and –34.62 dB at 40 GHz, a pull-in voltage of 2.8 V, and an axial residual stress of 25 MPa. This research presents an innovative strategy for optimizing capacitive switch MEMS models, using a multi-objective approach and the MOPSO algorithm to enhance efficiency in micro-pump applications.

Intersections are among the places where the highest number of accidents occur, thus, studying their safety and considering countermeasures to increase their safety should improve the overall safety of a traffic system. Prediction models, such as Artificial Neural Networks, have not been used for planning purposes in terms of providing countermeasures for accidents. This shortcoming forces the practitioners to employ traditional statistical methods which may be less accurate and have restricted applications. Hence, the Artificial Neural Networks models of this study were developed with the application of suggested countermeasures. Their performance was also compared with the traditional method given in the Highway Safety Manual after calibrating the procedure for local conditions. In this study, the intersections with the highest reported accidents isn the Kingdom of Bahrain were analyzed. The data was taken for the years 2013–2016, courtesy of the data provided by the Bahrain General Directorate of Traffic. Using this data, two predictive Artificial Neural Networks models were developed and used to forecast the accident number and severity in these selected intersections. Four intersections were selected to showcase the findings and to study the potential countermeasures that can be applied to reduce the occurrence of accidents. The comparison between Artificial Neural Networks and Highway Safety Manual procedures showed that Artificial Neural Networks models were more convenient to use with generic applications to different types of intersections. Moreover, they also provided higher accuracy while the Highway Safety Manual model was found to be heavily dependent upon traffic demand, which greatly affected its accuracy. The countermeasures suggested in this study were shown to reduce the accidents at the selected locations.

The well-being and sociability of individuals have always been part of modernity. The development of new technologies that meet these aspirations is receiving increasing attention. Thus, strengthening the desired objectives of these technologies and minimizing their undesirable side effects is the subject of growing commitment. The present contribution aims, in this context, to evaluate and analyze the desired and undesirable effects of the interaction of electromagnetic fields with living tissues in general. These are routines based on mathematical modeling reinforcing the expected functions as well as those of control and protection against undesirable effects. These adverse effects correspond to the “One Health” concept, which encompasses the health of animals, plants and humans, as well as ecological disorders created by human activity. First, in this article, the interactions of electromagnetic fields with tissues are analyzed, involving their thermal biological effects of desired and undesired exposures. The roles of blood and sap fluids in bio-affected tissues are then analyzed. Secondly, the equations governing electromagnetics and bio-heat, as well as their coupled solution are studied. Third, the thermal behavior of tissues and the adverse effects of exposure are examined. Next, monitoring and defending the effects of exposures are discussed. This contribution, supported by a review of the literature, illustrates routines for mathematical modeling of the generalized interaction of electromagnetic fields with living tissues.

According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.