Recent publications
The auto-induced uplink (a-UL) radio-frequency electromagnetic field (RF-EMF) exposure, often the dominant part of the total RF-EMF exposure, has not been included in previous microenvironmental studies. As 5G exposure depends more on mobile phone usage, monitoring typical transmit power levels is crucial towards more accurate personal exposure assessment. This study describes spatial differences in average mobile phone transmit power and investigates the influence of uplink duty cycles and frequency band usage. A novel methodology using the network monitoring application QualiPoc in fourth-generation (4G) and non-standalone fifth-generation (5G)networks was presented. For the first time, the assessment of 4G and 5G a-UL RF-EMF exposure was conducted simultaneously in a large-scale microenvironmental study in Europe. Measurements were performed along predefined routes in 282 different microenvironments (e.g., parks, residential areas) across seven European countries, during a maximum uplink usage scenario. The Netherlands had the highest average transmit powers per microenvironment (median 20.6 dBm). Transmit powers in villages were 0.6–2.1 dB higher than in big cities. The study suggested that base station density is a key predictor of a-UL exposure. Comparing technologies and frequency bands, average transmit powers for 5G were about 3.3 dB lower than for 4G and lowest for frequency bands with a time division duplexing (TDD) scheme due to the low uplink duty cycle (below 20%). This study provides crucial measurement data for epidemiologists and governments to enhance the understanding of the a-UL component of personal RF-EMF exposure.
This chapter discusses the network-oriented computational analysis that was used to obtain insight on how an AI coach in cyberspace can provide support to medical teamwork and contribute to safety and security. Several options were analyzed. Many of these options were illustrated by a several realistic examples that were formalized computationally by adaptive network models. Other options are offered as interesting options for future research. As a first step to this future, we are discussing setting up a Cybersecurity Simulation Lab and a Living Lab. In these labs network-oriented modeling and AI coaching in cyberspace will be a basis to handle cyber risk management problems. This chapter describes the conclusions and further envisioned impact of this work. In this chapter the integrated results of separate scientific contributions are evaluated that focus on the development of an AI coach for health safety and cybersecurity. The integrated contribution is considered, and the future potential is examined. In health systems, risk management of cybersecurity directly also implies health safety management. The AI coach solution is a crucial future venue for supporting such risk management processes. Several related knowledge gaps are assessed, and a Cybersecurity Simulation Lab and Living Lab approach are suggested as a main method of future research for safety and cybersecurity in health systems. The expected social impact of this approach is described, and our main conclusion is presented.
This chapter introduces the reader to current technological trends that are shaping the healthcare sector and the pressing cyber security risks associated with these trends. The importance of phishing as a common attack vector is highlighted and the execution of phishing training simulations as a behavioral intervention is described. In the discussion section some challenges regarding the use of the COM-B framework as a basis for network-oriented models are discussed and potential future improvement opportunities are highlighted. Following the description of a phishing training simulation scenario, a multi-level adaptive network model based on key elements of the COM-B framework is described.
This chapter focusses on using a self-modelling network modeling approach to analyse and predict the behavior of people within organizations, specifically on the implementation of EHS standards. The study focusses on a real-world scenario in which EHS standards are implemented in a multi-level organizational learning scenario. To ensure the health and safety of employees worldwide, the company provides standards for every EHS aspect of activities. The standards are fed by state-of-the-art technologies and science. They require local implementation at every site, also incorporating legislation for the country where it is based. The technique used in this study is dynamic computational modelling wielding the self-modelling network modeling approach. In the base scenario, the organization started with the implementation in a pilot phase. After a while the organization acknowledges it needs to take the lessons learned in the pilot and apply them to all other standards, enabling feed-forward and feedback learning within the organisation. Subsequently five alternative scenarios are described in which the organization is challenged with issues complicating the implementation process. The study shows the possibility of using a self-modelling social network in the execution of EHS activities. It demonstrates the importance of cooperation and open communication between the system owner, EHS expert and department. This is needed to be able to learn about processes and efficiently safeguard the organization and its members.
Although making mistakes is a crucial part of learning, it is still often being avoided in companies as it is considered as a shameful incident. This goes hand in hand with a mindset of a boss who dominantly believes that mistakes usually have negative consequences and therefore avoids them by only accepting simple tasks. Thus, there is no mechanism to learn from mistakes. Employees working for and being influenced by such a boss also strongly believe that mistakes usually have negative consequences but in addition they believe that the boss never makes mistakes, it is often believed that only those who never make mistakes can be bosses and hold power. That's the problem, such kinds of bosses do not learn. So, on the one hand, we have bosses who select simple tasks to be always seen as perfect. Therefore, also they believe they should avoid mistakes. On the other hand, there exists a mindset of a boss who is not limited to simple tasks, he/she accepts more complex tasks and therefore in the end has better general performance by learning from mistakes. This then also affects the mindset and actions of employees in the same direction. This chapter investigates the consequences of both attitudes for the organizations. It does so by computational analysis based on an adaptive dynamical systems modeling approach represented in a network format using the self-modeling network modeling principle.
Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient care by reducing medical error risk. Speaking up by health care professionals (e.g., nurses, residents) remains difficult to execute in practice despite increasing awareness of its importance. Therefore, this chapter introduces a computational model concerning the mechanisms known from psychological, observational, and medical literature which underlie the speaking up behaviour of a health care professional. It also addresses how a doctor may respond to the communicated message. Through several scenarios, we illustrate what pattern of factors causes a healthcare professional to speak up when witnessing a (near) medical error. We moreover demonstrate how introducing an observant agent can facilitate effective communication and helps to ensure patient safety through speaking up when a nurse can not. In conclusion, the current chapter introduces a computational model which predicts speaking up behaviour from the perspective of the speaker and receiver, with the addition of a virtual coach to further optimise patient safety when a patient could be in harm's way.
This chapter describes an extension of a safety culture within hospital organizations providing more transparency and acknowledgement of all actors, and in particular the parents. It contributes a model architecture to support a hospital to develop such an extended safety culture. It is illustrated for prevention of postpartum depression. Postpartum depression is a commonly known consequence of childbirth for both mothers and fathers. In this research, we computationally analyze the risk factors and lack of support received by fathers. Therefore, we use shared mental models to model the effects of poor and additional communication by healthcare practitioners to mitigate the development of postpartum depression in both the mother and the father. Both individual mental models and shared mental models are considered in the design of the computational model. The chapter illustrates the benefits of simple support for communication during childbirth, which has lasting effects, even outside the hospital. For the impact of additional communication, a Virtual Safety Coach is designed that intervenes when necessary to provide support, i.e., when a health care practitioner doesn’t. Moreover, organizational learning is also modelled to improve the mental models of both the Safety Coach and the Health Care Practitioner.
High‐performance soft–hard interfaces are inherently difficult to fabricate due to the dissimilar mechanical properties of both materials, especially when connecting extremely soft biomaterials, such as hydrogels, to much harder biomaterials, such as rigid polymers. Nevertheless, there is significant clinical demand for synthetic soft–hard interfaces. Here, soft–hard interface geometries are proposed, designed with the aid of computational analyses and fabricated as 3D‐printed hydrogel‐to‐polylactide (PLA) structures. Two primary interlocking geometries (i.e., anti‐trapezoidal (AT) and double‐hook (DH)) are used to study the envelope of 2.5D geometric interlocking designs, fabricated through hybrid 3D printing, combining pneumatic extrusion with fused deposition modeling. Finite‐element analysis, uniaxial tensile tests, and digital image correlation (DIC) are used to characterize the geometries and identify parameters that significantly influence their mechanical performance. These findings reveal significant differences between geometric designs, where DH geometries performed significantly better than AT geometries, exhibiting a 190% increase in the maximum force, Fmax, and a 340% increase in the fracture toughness, W. Compared to the control groups (i.e., flat, inset, and 90° interfaces), Fmax and W values increased by 500%–990% and 350%–1200%, respectively. The findings of this study can serve as a guideline for the design and fabrication of efficient soft–hard interfaces with performances close to predicted values.
This chapter presents an approach to enhancing neonatal care through the application of artificial intelligence (AI). Utilizing network-oriented modeling methodologies, the study aims to develop a network model to improve outcomes in neonatal respiratory support. The introduction sets the stage by outlining the significance of neonatal respiratory support and the challenges faced in this domain. The literature review delves into the existing body of work, highlighting the gaps and the need for a network modeling approach. The network-oriented modeling approach provides a robust framework that captures various states, such as world states, doctors’ mental states, and AI coach states, facilitating a comprehensive understanding of the complex interactions in neonatal respiratory support. Through Matlab simulations, the study investigates multiple scenarios, from optimal conditions to deviations from standard protocol. The main contribution focuses on the introduction of an AI coach, which serves as a real-time intervention mechanism to fill in the doctor's knowledge gaps. The research serves as a seminal work in the intersection of artificial intelligence and healthcare, demonstrating the potential of network-oriented modeling in improving patient outcomes and streamlining healthcare protocols.
In this chapter, it is shown how second-order adaptive agent-based network models can be used to support a medical team in healthcare institutions to adhere to specific Neonatal Hypoglycemia and Neonatal Hyperbilirubinemia treatment guidelines through the integration of an Artificial Intelligence (AI) Virtual Coach. The proposed AI Coach is designed to provide timely interventions and correct deviations when lapses in the health care practitioner’s internal mental model occur. Through simulating three different scenarios, the internal dynamics of these mental models, adaptive changes of these mental models (learning and forgetting), and the interaction between health care practitioners and the world is shown when: (1) There is perfect adherence to guidelines, (2) There is imperfect adherence to guidelines and (3) There is both perfect and imperfect adherence to guidelines alongside interventions of the AI Coach in the latter case.
This chapter presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.
In this chapter, it is shown how second-order adaptive agent-based network models can be used to model a medical team supported by a virtual AI Coach. It is illustrated for the case of a newborn baby in danger. The design of these computational agent models is based on an adaptive self-modeling network modeling approach. It also addresses how the AI Coach can play a central role in organizational learning. The agent models enable representations and processing of all actors’ internal mental models and internal simulation of these mental models, adaptive changes of these mental models (learning and forgetting), and the interaction between the actors and the world.
Multilevel organisational learning concerns an interplay of different types of learning at individual, team, and organisational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.
This chapter describes a second-order adaptive network model for mental processes making use of shared mental models (SMM) for team performance. The chapter illustrates on the one hand the value of adequate SMM’s for safe and efficient team performance and on the other hand in cases of imperfections of such shared team models how this complicates the team performance. To this end, the adaptive network model covers use, adaptation and control of the shared mental model. It is illustrated for an application context of a medical team performing a tracheal intubation, executed by a nurse and a medical specialist. Simulations illustrate how the adaptive network model is able to address the type of complications that can occur in realistic scenarios.
This chapter presents an introduction to this book on a computational analysis approach for safety and security through cyberspace. It briefly introduces main concepts such as mental models, shared mental models, organizational learning, and how dynamics and adaptivity can be modeled by adaptive networks. Finally, it will provide an overview of the parts and chapters of the book.
The aim of the current study is to better understand how financial cybercriminal networks recruit co-offenders. We address important knowledge gaps, as not much is known about pathways into cybercrime due to a lack of research. More knowledge can be used in practice in the context of cybercrime offender prevention. For these purposes, we analyzed 15 closed police investigations into cybercriminal networks from the Netherlands, in accordance with the approach of the Dutch Organized Crime Monitor. This is a valuable and well-established research methodology to study criminal activities of which little is known. Analysis revealed that core members need a variety of co-offenders to successfully commit financial cybercrimes such as phishing and online consumer fraud. These co-offenders can be categorized as professional facilitators, recruited facilitators, and money mules. They were approached on social media, especially on Telegram and Snapchat, as well as in the physical world. Some were promised a financial reward and were actually paid out the money, but others were manipulated and possibly forced to cooperate under threats of violence. It was not always necessary for cybercriminal networks to actively reach out, as co-offenders also found their way to the network. Taken together, our study indicates there are various offline and online pathways into cybercrime, and that cybercrime offenders apply different strategies to recruit co-offenders.
We hypothesise that warm bounded learning communities (WBLC) contribute to social and academic integration of students. Eleven students facing study delay participating in a WBLC to write their bachelor thesis were interviewed. They described important episodes in their graduation process, prior to and during their participation. Results indicate that a WBLC that supports interaction, stimulates the development of a community identity, focuses on student collaboration, and mutual appropriation, guides students believing in student agency and supporting a positive self-belief system, can break down barriers students experience. Characteristics of the implemented WBLC and appropriate teacher roles can enlarge motivation, sense of belonging, academic knowledge and self-efficacy. Social interdependence is an important engine to increase social connections and academic self-efficacy, enhancing the growth of academic skills. Our research indicates that well implemented WBLCs can contribute to social and academic integration of students with a study delay.
The advantages and drawbacks of components of flexible assessment have been studied mostly from the standpoint of students and, to a lesser extent, teachers. A gap persists in understanding the collective perspectives of teachers and students concerning flexible assessment. This study aimed to explore experiences and perspectives of students and teachers regarding flexible assessment within the specific context of nursing education. Seven focus groups comprised four sessions with teachers and three with students, each involving 5-8 participants. Results showed that students and teachers have a predominantly positive perspective towards flexible assessment. They acknowledge the opportunities that flexible assessment provides for diverse forms to present evidence. However, concerns were raised regarding the design of flexible assessments, issues of fairness in rating evidence, and the understanding among teachers and students regarding the assessment processes. Additionally, discussions focused on the perceived benefit of flexible assessments, particularly concerning the time investment required for their implementation and evaluation. In conclusion, the success of flexible assessments is contingent on the careful consideration of its design, ensuring equitable evaluation of evidence, and fostering comprehensive understanding among both teachers and students. Recognizing potential disparities in views of students and teachers offers valuable insights into the effectiveness of flexible assessment. Achieving a balance between the flexibility of assessment formats, aligned forms of evidence, and an appropriate rating methodology is crucial for effective implementation.
In this study, we regard co-creation as a collaborative process where students, lecturers and working field professionals from outside the university jointly develop innovative products, processes or knowledge. In co-creation all stakeholders equally contribute to the collaborative process and aim to create beneficial outcomes for each participant. Co-creation can be used as a valuable pedagogical method to support continuous interaction between learning and working in higher education to foster innovation. However, this process is not necessarily mastered by co-creation groups. In order to identify which components of this collaboration process can be further improved, we developed a questionnaire to assess co-creation processes in higher education. Students, lecturers and working field professionals participating in co-creation projects completed the questionnaire. We validated the questionnaire using a principal component analysis. The seven extracted scales proved to be sufficiently reliable. The final questionnaire consists of seven components: positive interdependence, individual accountability, collaboration, shared mental models, safe and supporting conditions, creative community, and group evaluation. We described how the tool can be used in practice.
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