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Most definitions of empathy stress its interpersonal nature: Empathy requires a human sender and a human receiver, where all parties involved have the theoretical capacity to accurately understand each other’s minds. Because empathy is inherently interpersonal, some scholars have questioned whether empathy between humans and artificial intelligence as it currently exists (e.g., large language models) is “real.” However, the requirement for two minds, each with the theoretical capacity to fully understand the thoughts and emotions of the other, is also not clearly met in other domains where empathy is discussed, such as empathy between humans and non-human animals or empathy with nature. This chapter critically examines these assumptions about what is needed for empathy to occur, offering several possible sets of empathy requirements that researchers might engage with, and outlining potential challenges researchers might face if they adopt any particular set of requirements in their work. Ultimately, we pose the question of whether two minds and/or the ability for minds to understand each other are truly necessary for empathy, or whether all that is required for empathy to occur is an entity’s perception of giving or receiving it.
Radiotherapy is a key component of cancer treatment, yet medical students often receive limited exposure to its principles and practice. This preliminary study evaluates the use of the virtual environment for radiotherapy training (VERT™), a virtual reality simulation, in enhancing medical students’ knowledge, confidence and preparedness before clinical oncology placements.
Methods:
This quasi-experimental study evaluated a one-hour VERT™ session for fourth-year medical students. Using pre- and post-intervention questionnaires with Likert scale and open-ended items, the study assessed changes in knowledge, understanding and attitudes. The mixed-methods design provided a comprehensive evaluation of the intervention’s educational effectiveness.
Results:
Statistical analysis showed significant improvements in 10 of 11 questionnaire items following the VERT™ session. Students reported large increases in perceived knowledge and understanding. Only attitudes toward virtual reality use remained unchanged, indicating limited impact on broader perceptions of VR in medical education. Thematic analysis revealed that VERT™ enhanced students’ understanding of radiotherapy concepts, planning and patient perspectives through immersive, hands-on learning. Participants valued its clarity and realism but suggested longer, more interactive sessions and clearer communication. Despite minor technical and engagement challenges, VERT™ was viewed as a valuable, confidence-building complement to traditional teaching.
Conclusions:
Considering the confines of this preliminary study, the findings begin to suggest that VERT™-based simulation enhances medical students’ knowledge, understanding and confidence in radiotherapy. Participants felt better prepared for oncology placements, supporting VERT™’s value as an immersive learning tool. Despite limitations, results advocate its integration into medical education and further research on long-term and comparative effectiveness.
Although many factors influence the incidence of medical error and adverse patient outcome, this chapter focuses on human and system performance and its impact on patient safety in the delivery room. First, we review general concepts and models of patient safety. Second, approaches taken by high-risk industries other than healthcare to improve human and system performance will be explored. Next, how those approaches can be adapted and adopted to address similar safety problems in healthcare will be discussed. Finally, we underscore how human factors research in healthcare can be used to improve patient safety. Healthcare must incorporate objective performance data, briefing, debriefing, simulation, and human factors science to identify and remediate human and system weaknesses inherent in patient care. Only then will we be able to improve performance, reduce error, and minimize risk.
I argue that there are lessons for both philosophers of science and for astrophysicists in the emerging literature on the epistemology of astrophysics. First, contrary to the manner in which it has sometimes been represented by philosophers, astrophysics is not a purely observational science. This complicates arguments that disparage the epistemic status of astrophysics in contrast with experimental sciences. Second, the epistemological structures of arguments in astrophysical research benefit from close philosophical analysis. Such analysis can reveal both important limitations and opportunities. In addition, I argue for a lesson for astronomers from the epistemology of astrophysics. Even when it involves purely observational research, astronomy is not necessarily epistemically inferior to experimental sciences. This is because observation is not generally epistemically inferior to experimentation.
Due to the indeterminacy problem in factor analysis, factors are not uniquely determined by observed data. We investigate the consequences of this problem for the interpretation of simulation studies and for linear structural equation models. For simulation procedures, factor indeterminacy implies that as long as the simulated data have the correct covariance matrix, there exists a possibly large space of factors and error terms that can generate the simulated data via the model equations. For linear structural equation models, the linearity of the structural part of the model is only partly testable by the distribution of the data. Practical recommendations for conducting simulation studies are provided. We give numerical illustrations, including a case where the observed data are normal and fit a linear structural equation model perfectly, yet are generated by a highly non-linear structural equation model.
Small fixed-wing unmanned air vehicles (UAVs) weighting under 20 kg are typically not protected against icing. Yet, commercial and military users find interest in operating this class of platform under cold climates where encounters with icing conditions are likely. This paper introduces a new concept named sense-and-egress that aims at safeguarding small UAVs in case of an inadvertent icing encounter. A short overview of the system’s operating principles and a plausible architecture candidate are given. Then, the benefits and drawbacks of the proposed solution are assessed with respect to both unprotected (no ice detection or deicing devices active) and fully ice-protected vehicles. Enabling technologies and knowledge gaps are briefly explored in order to gauge the maturity of the concept. The non-linear flight dynamics model of an existing UAV is built up using results from numerical aerodynamics software. The model is then improved and validated using system identification from existing flight test data. A bespoke path-following autopilot is designed to control the UAV during the escape manoeuvre. The selected control system structure and the tuning procedures for both the attitude controller and the total energy control system are detailed. The ability of the baseline autopilot to perform a simple egress manoeuvre with both clean and iced airframes is assessed in simulation. Simple yet effective solutions are implemented to improve the autopilot performance in icing while preserving its gust resistance capabilities.
Francis Galton invented the quincunx, or Galton board, to demonstrate that sums of large numbers of binary events could produce quasi-continuous normally distributed outcomes. The authors of this chapter suggest that the quincunx poses a deeper problem: whether the statistical devices we invent to model complex developmental processes represent the causal basis of development, as opposed to simply being statistical methods that model complex outcomes without explaining their etiology. Turkheimer and Kaplan simulate populations of large quincunx in which the pins have biases: deviations from equal probabilities of bouncing a ball left and right. The simulations reproduce many common results from complex quantitative genetics: quincunx with similar biases produce similar outcomes, and aggregate measures of biases in the pins are correlated with the final placement of the balls. A simulated GWAS, however, that attempted to infer the individual biases of the pins from variations in outcomes was completely unsuccessful. The authors conclude that the simulations are a proof of concept, demonstrating that there are causal structures that will not yield to reductive analysis.
Rotational setup errors remain a challenge in radiotherapy centers without six-degree-of-freedom (6DoF) couch correction systems. Small angular deviations may compromise target coverage and organ-at-risk sparing. Although image-guided radiotherapy (IGRT) has improved setup verification, many centers continue to rely on three-degree-of-freedom (3DoF) couches.
Innovation:
This technical report evaluates an approximation in which small rotational setup deviations are represented as equivalent translational displacements using the vector cross-product formulation (Δ = θ × r). Head-and-neck, breast, lung, pelvis, and thorax scenarios were modeled using an Elekta Infinity linear accelerator and Monaco TPS (version 6.1.4.0) with Monte Carlo dose calculation. Simulated rotational perturbations were assessed with and without translation-based correction using dose–volume histogram endpoints Dmean, D95, and V95.
Discussion:
Translation-based correction improved D95 across all treatment sites. The largest improvement was observed in head-and-neck geometry (approximately 2%), while pelvic and thoracic scenarios demonstrated intermediate gains. Breast and lung cases showed measurable improvements. Dmean increased modestly, whereas V95 remained essentially unchanged, suggesting improved target coverage without compromising volumetric conformity.
Recommendations:
Translation-based correction is not a substitute for 6DoF couch technology but may provide a low-cost mitigation strategy for centers limited to 3DoF correction capability. Future studies should include phantom validation and automated correction approaches.
This chapter delves into the narrative aspect of emergency medicine, highlighting the importance of storytelling in patient care and the oral board examination process. It discusses the structured nature of the oral examination, which combines elements of an Observed Structured Clinical Encounter (OSCE) and simulation to evaluate candidates’ clinical skills. The chapter provides insights into the virtual platform used for the exam, the unfolding of cases, and the examiner’s role in guiding the narrative. Strategies for approaching cases, organizing information, and interacting with examiners are detailed, emphasizing the need for efficient data collection and clear communication. Practical tips for exam preparation, including technology setup, time management, and professional conduct, are outlined to help candidates navigate the challenging oral board experience. The structured interview component is also explored, focusing on critical thinking and decision-making processes. Differences between the American Board of Emergency Medicine (ABEM) and the American Osteopathic Board of Emergency Medicine (AOBEM) exams are discussed, with a glimpse into the future transition to an in-person certifying exam by ABEM in 2026.
Patient safety is a critical challenge in modern healthcare, with significant mortality rates due to lapses in safety protocols. This chapter explores the multifaceted nature of patient safety, emphasizing procedural skills competency, critical thinking, and effective team communication. Simulation is recognized as an optimal educational tool, offering a risk-free environment to practice common and rare clinical scenarios.
Various simulation modalities, including high-fidelity simulation (HFS), standardized patients, virtual reality, and screen-based simulations, have distinct roles in identifying and addressing patient safety gaps. In situ simulation, conducted in real clinical environments, and simulation center-based training each have advantages and challenges.
In situ simulation enhances realism but poses logistical challenges, while simulation centers offer controlled environments with advanced equipment. Clear learning objectives and performance metrics are essential for effective simulation-based training, as demonstrated by the successful training outcomes at Louisiana State University Health Sciences Center.
Research findings indicate that while simulation training effectively improves skills in the short term, repeated practice is necessary for long-term retention. The COVID-19 pandemic has significantly impacted the planning and delivery of simulation training, leading to innovations in remote learning through virtual reality and online modules. Future directions for simulation in healthcare include increased accessibility, integration with telemedicine, and artificial intelligence for personalized learning.
Simulation is a flexible and proven methodology to enhance patient safety through ongoing clinician education and health systems improvement.
Providing psychosocial support to pediatric patients and their families at the end of life represents one of the most challenging yet vital aspects of healthcare practice. Despite the presence of grief and loss training in many pediatric healthcare professionals’ educational backgrounds, opportunities for practical training experience in delivering end-of-life care remain limited. This study explored the use of simulation-based training to enhance the self-reported knowledge, skills, and comfort levels of child life specialists in providing psychosocial care during end-of-life situations.
Methods
Forty-three child life specialists participated in the simulation-based training, which was combined with traditional didactic instruction, and the associated research study. Pre- and post-training surveys were used to assess impact of the training on child life specialists’ self-reported knowledge of end-of-life care and comfort in providing this care.
Results
A statistically significant increase was seen in all measured aspects of self-reported knowledge and comfort in providing end-of-life care following the training.
Significance of results
Simulation combined with traditional instruction methods provides an effective way to train healthcare professionals in providing high-stakes psychosocial care while protecting patients and families from the added strain of trainees and excess staff presence during sensitive times.
Archaeological sampling is a critical yet inconsistently applied aspect of field methodology. Poorly designed strategies produce biased or irreproducible results, especially when recovery is labor-intensive and research expectations are high. This article addresses that challenge through the lens of spatial microrefuse analysis, using simulation modeling to evaluate current practices and improve sampling design, training, and planning. A review of 27 published microrefuse studies reveals wide variation in sampling strategies, unit sizes, and volumes, with little evidence of statistical justification. To explore the consequences of this variation, I introduce the Archaeological Sampling Experiment Laboratory (tASEL), an open-source simulation tool developed in NetLogo and archived in the CoMSES model library. tASEL allows archaeologists to construct artifact distributions and test random, systematic, or hybrid sampling frames with immediate visual and statistical feedback. I used tASEL to conduct 22,000 virtual sampling experiments across two artifact distributions: a diffuse random scatter and a highly clustered pattern. Results show that sampling performance varies significantly by distribution, sample size, and frame design. Random strategies produced the highest accuracy and lowest bias. I conclude by demonstrating how tASEL can be used in classroom and field contexts to improve sampling literacy and support more robust archaeological practice.
Fractional Brownian motion, with its long-time correlated increments, has been applied in many fields in recent years. Since volatility was shown to be rough by Gatheral, Jaisson, and Rosenbaum, fractional Brownian motion has gained popularity as a financial model. In this work, we revisit the definitions and properties of the univariate and multivariate fractional Brownian motions, and consider four simulation methods. We demonstrate the issues associated with applying the standard Euler scheme for simulating stochastic processes driven by fractional Brownian motion with $H < \frac{1}{2}$ (which we call the rough models). We then introduce a novel approximate method for simulating such rough models based on the fast algorithm by Ma and Wu, which accounts for a factor of 10 speedup. Finally, we consider applications of these methods to option pricing.
This chapter covers the use of simulation as a method of disaster response preparation. It addresses case creation, high-fidelity techniques, and execution of a large, live action disaster simulation. It discusses how to build out a case from planned objectives, as well as pairing debriefing points for after the case is finished. It also gives advice on how to retain optimal control over the case to help ensure it runs smoothly. It gives advice on logistics and case flow, avoiding common pitfalls in planning such drills, and proper communication between instructors during the drill. It discusses how to implement a twist into the case to further constrain resources available to the learners and how to integrate such twists into the case without disruption.
This chapter is a quick introduction to the history of simulation, why simulation is a powerful tool in medical education, and basic tools needed to run a successful simulation.
Extropy-based divergence measures offer distinct advantages over entropy-based counterparts, owing to their mathematical simplicity and enhanced interpretability. Relative extropy by Lad et al. [5] is a symmetric divergence measure between two probability distributions, and Mohammadi et al. [8] introduced the asymmetric divergence between two distributions based on extropy. We further study these measures, their properties, and interrelationships in this article. To address the divergence between truncated lifetime distributions, we define dynamic relative extropy for residual and past lifetime scenarios. Exploring the interrelationships of dynamic cases of relative extropy, extropy divergence, and extropy inaccuracy, we derive some unique properties and characterizations for the exponential distribution. A nonparametric estimator for relative extropy is developed, and its performance is assessed through numerical simulation studies. The practical applicability of relative extropy is used to analyze the divergence in lifetime patterns of mice under a lifetime feeding experiment and the shopping patterns of customers based on age and income groups. Further, the application of relative extropy is also applied to find the dissimilarity between two images.
Understanding the causes and drivers of extinction is critical for mitigating future anthropogenic extinctions. This study explored the extinction process of the Crested Ibis Nipponia nippon in Japan, focusing on the last wild population on Sado Island. An integrated population model–population viability analysis (IPM-PVA) framework was applied to estimate demographic parameters and population dynamics using four historical data sources, i.e. population counts, records of dead or rescued individuals, reproductive data, and captures for captive breeding. The IPM estimated an average of 0.703 fledglings per breeding pair per season, with adult and juvenile survival rates of 0.870 and 0.730, respectively. Human disturbances were found to substantially reduce fecundity. PVA results indicated an extinction probability of 56.6% under observed historical conditions, which could have been reduced to 11.2% if human access to nesting forests had been restricted. The study identified low fecundity caused by human disturbance at nest-sites as a likely contributor to the species’ extinction. Despite the need for cautious interpretation due to data limitations, this study highlights the practical utility of the IPM-PVA framework in providing detailed insights into the extinction process.
Overseas large-scale combat operations (LSCOs) could require domestic hospitals to treat large numbers of combat casualties. Our goal was to evaluate the financial impact on hospitals of treating combat casualties during an LSCO.
Methods
Using a discrete event simulation model, we explored how 5 civilian hospitals in Omaha, Nebraska, would fare after accepting combat casualties during a National Disaster Medical System (NDMS) activation. We compared changes in financial measures (government payments, hospital revenues) and occupancy measures (civilian patient displacement) under different scenarios for combat casualty reimbursement rates as fractions (75%-125%) of Medicare rates.
Results
Combat casualties replaced 100% of civilian patients at 3 of 5 hospitals, displacing a total of 10,905 civilian patients [95% CI: 10551-11248]. Combat casualty reimbursement at 125% of Medicare rates resulted in government payments of $462 million and net income gains for civilian hospitals of approximately 23 times pre-activation baselines. Combat casualty reimbursement below 125% of Medicare rates led to net income losses.
Conclusions
Large influxes of combat casualties could result in rapid, profound displacement of civilian patients and revenue loss at NDMS-participating facilities, potentially affecting hospitals’ ability and willingness to treat them. Policymakers need to identify appropriate reimbursement rates for combat casualties.
Word frequency databases like SPALEX and SUBTLEX-ESP treat Spanish as a uniform language, but prior studies and an initial survey (Experiment 1) revealed significant lexical differences between Spanish in Spain and Latin American countries, especially Chile. To establish subjective frequencies of Spanish word usage, an extended survey (Experiment 2) was conducted with Chilean participants, categorizing words by usage area: General, Spain, Chile, and Latin America. Consistent with the initial survey, Chilean participants assigned subjective higher ratings to General and Chilean words. In a lexical decision experiment (Experiment 3), participants responded faster and more accurately to words from these categories. Using survey data, simulations with Multilink+ (Experiment 4) revealed that subjective word ratings better predicted Chilean reaction times than frequencies from existing databases. These findings emphasize the need to address Spanish dialectal differences in research, with word ratings offering a more accurate measure of region-specific lexical nuances than current databases.
Exercises are an essential component of preparedness and should be used to enhance capability and contribute to continuous improvement. An exercise can be as simple as a planning group discussing an emergency plan or as complex as a major multi-agency event involving several organizations and participants. This study aims to identify and conceptualize quality indicators (QIs) influencing prehospital disaster exercises across structure, conduct, and outcome.
Methods
This research was conducted through a systematic review and searching of the databases of PubMed, Scopus, Web of Science, and Google Scholar. Thematic content analysis was used for data analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for systematic search, and the Critical Appraisal Skills Program (CASP) was used for quality assessment of the final extracted articles.
Results
From an initial set of 3,083 articles, 10 high-quality studies were included for analysis. The quality indicators influencing prehospital disaster exercises were analyzed into 3 themes, 8 categories, and 21 subcategories. The primary themes and related main categories included: Exercise structure QIs (knowledge promotion and cognitive skills, supply of exercise hardware and software requirements and resources desirable management), Exercise conduct QIs (practical proficiency in essential skills and decision-making capacity), and Exercise outcome QIs (evaluation and reporting of exercise, promotion of managerial capabilities and competencies, and development of psychological capabilities).
Conclusion
The findings of this research present a knowledge framework that can help exercise planners in prehospital settings in designing scientifically sound and standardized exercises aimed at enhancing disaster response processes. Furthermore, the implementation and evaluation of both discussion-based and operation-based disaster exercises informed by these identified quality indicators can foster the development of knowledge and promote behavioral change among prehospital staff, and facilitate a standardized response to emergencies and disasters.