Academic departments are the primary unit of scholarship and education at universities, and they vary vastly in their sizes. However, the consequences and natural dynamics of department size are poorly understood. Small departments face disproportionate teaching and administrative overhead per faculty member, while large ones face coordination costs and thematic incoherence. Here, we characterize and model the dynamics of academic department sizes using $14,000$ U.S.-based departments in eight academic domains. Across all domains, similar broad-tailed distributions reveal a common size range from 4 to 23 faculty members, widening across domains at its upper border. Annual size-dependent closure risks and growth rates indicate that stability is greatest in this size range: below it, small departments either close or grow quickly; within it, closure risk is low and sizes stabilize; above it, large departments can persist, with marginal attrition and minimal closure risk. An analytically tractable model of size-dependent coagulation and fragmentation, informed only by the aggregated size distribution, reproduces department dynamics across the full size range. Rescaling each domain by
AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstraction: a Company World Model, defined as a persistent asset-to-value state representation with transition models, explicit value functions, planning, and updating across scientific, regulatory, BD, commercial, financial, and execution constraints. We introduce a dry-lab benchmark for testing whether AI-agent organizations should mimic departments or operate around such a world model. The benchmark contains 45 retrospective public-information decision cases with strict time cutoffs, hidden outcomes, common schemas, automatic scoring, and blinded pairwise judging. We compare human-org-mimic, stronger human-org-mimic-plus, AI-native asset-centric, and AI-native value-conversion architectures. The value-conversion architecture is a prompt-level approximation of a Company World Model: a Live Asset Value Record updated by Deal, Approval, Revenue, and Investment Arbiter loops. Under a success function defined by external BD, regulatory approval and launch, and revenue discipline, it achieved the highest automatic value-conversion score and was strongly
Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and flexible model and develop a hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) simulating highly configurable ED environments. We specifically focus on the validation of the modeling approach. We derive configurations for ED sizes, patient load, and staffing from real-world studies. We then validate the model expressivity by matching its key performance indicators and metrics with their values known from literature. We proceed by implementing scientifically established and practice-proven resource optimization strategies. Comparing the documented real-world outcomes with our model's results demonstrates that the DES-ABM based simulation can effectively replicate real-world ER dynamics under interventions. We lastly integrate a Proof-of-Concept multi-agent system (MAS) that can autonomously explore resource allocation strategies within the simulated ER environment based on a temporal ledger of ED event records. This modular DES-ABM-MAS framework offers a powerful tool to explore resource optimization strategies in emerg
Medical emergency departments are complex systems in which patients must be treated according to priority rules based on the severity of their condition. We develop a model of emergency departments using Petri nets with priorities, described by nonmonotone piecewise linear dynamical systems. The collection of stationary solutions of such systems forms a "phase diagram", in which each phase corresponds to a subset of bottleneck resources (like senior doctors, interns, nurses, consultation rooms, etc.). Since the number of phases is generally exponential in the number of resources, developing automated methods is essential to tackle realistic models. We develop a general method to compute congestion diagrams. A key ingredient is a polynomial time algorithm to test whether a given "policy" (configuration of bottleneck tasks) is achievable by a choice of resources. This is done by reduction to a feasibility problem for an unusual class of lexicographic polyhedra. Furthermore, we show that each policy uniquely determines the system's throughput. We apply our approach to a case study, analyzing a simplified model of an emergency department from Assistance Publique - Hôpitaux de Paris.
Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and high-stakes environment of emergency departments (EDs), small language models (SLMs), characterized by a reduction in parameter count compared to LLMs, offer significant potential due to their inherent reasoning capability and efficient performance. This enables SLMs to support physicians by providing timely and accurate information synthesis, thereby improving clinical decision-making and workflow efficiency. In this paper, we present a comprehensive benchmark designed to identify SLMs suited for ED decision support, taking into account both specialized medical expertise and broad general problem-solving capabilities. In our evaluations, we focus on SLMs that have been trained on a mixture of general-domain and medical corpora. A key motivation for emphasizing SLMs is the practical hardware limitations, operational cost constraints, and privacy concerns in the typical real-world deployments. Our benchmark datasets include MedMCQA, MedQA-4Options, and PubMedQA, with the medical abstracts dataset emulating ta
Emergency departments struggle with persistent triage errors, especially undertriage and overtriage, which are aggravated by growing patient volumes and staff shortages. This study evaluated three AI models [TRIAGEMASTER (NLP), URGENTIAPARSE (LLM), and EMERGINET (JEPA)] against the FRENCH triage scale and nurse practice, using seven months of adult triage data from Roger Salengro Hospital in Lille, France. Among the models, the LLM-based URGENTIAPARSE consistently outperformed both AI alternatives and nurse triage, achieving the highest accuracy (F1-score 0.900, AUC-ROC 0.879) and superior performance in predicting hospitalization needs (GEMSA). Its robustness across structured data and raw transcripts highlighted the advantage of LLM architectures in abstracting patient information. Overall, the findings suggest that integrating LLM-based AI into emergency department workflows could significantly enhance patient safety and operational efficiency, though successful adoption will depend on addressing limitations and ensuring ethical transparency.
We provide an example of the application of quantitative techniques, tools, and topics from mathematics and data science to analyze the mathematics community itself in order to quantify and document inequity in our discipline. This work is a contribution to the new and growing interdisciplinary field recently termed "mathematics of Mathematics," or "MetaMath." Using data about PhD-granting institutions in the United States and publicly available funding data from the National Science Foundation, we highlight inequalities in departments at U.S. institutions of higher education that produce PhDs in the mathematical sciences. Specifically, we determine that a small fraction of mathematical sciences departments receive a large majority of federal funding awarded to support mathematics in the United States. Additionally, we identify the extent to which women faculty members are underrepresented in mathematical sciences PhD-granting institutions in the United States. We also show that this underrepresentation of women faculty is even more pronounced in departments that received more federal grant funding.
We present comparative case study of three physics department culture from different institutions using the experiences undergraduate women. The three studies conducted in the United States include Johnson's 2020 study in a small physics department at a small predominantly White liberal arts college, Santana and Singh's 2023 study at a large predominantly White research institution, and Santana and Singh's 2024 study in a medium-sized physics department at a small predominantly White private liberal arts college. Using synergistic frameworks such as Standpoint Theory, Domains of Power, and the Holistic Ecosystem for Learning Physics in an Inclusive and Equitable Environment and reflections from undergraduate women, we aim to understand how those in the position of power, e.g., instructors, have important roles in establishing and maintaining safe, equitable, and inclusive environments for undergraduate students. Their accounts help us contrast the experiences of undergraduate women in physics departments with very different cultures. This comparative analysis is especially important for reflecting upon what can be done to improve the physics culture so that historically marginalize
This article presents an anatomy of PhD programmes in Hellenic universities' departments of computer science/engineering from the perspective of research productivity and impact. The study aims at showing the dynamics of research conducted in computer science/engineering departments, and after recognizing weaknesses, to motivate the stakeholders to take actions that will improve competition and excellence. Beneficiaries of this investigation are the following entities: a) the departments themselves can assess their performance relative to that of other departments and then set strategic goals and design procedures to achieve them, b) supervisors can assess the part of their research conducted with PhDs and set their own goals, c) former PhDs who can identify their relative success, and finally d) prospective PhD students who can consider the efficacy of departments and supervisors in conducting high-impact research as one more significant factor in designing the doctoral studies they will follow.
Scholar Ranking 2023 is the second edition of U.S. Computer Science (CS) departments ranking based on faculty citation measures. Using Google Scholar, we gathered data about publication citations for 5,574 tenure-track faculty from 185 U.S. universities. For each faculty, we extracted their t10 index, defined as the number of citations received by their 10th highest cited paper. For each department, we calculated four quality metrics: median t10 (m10), the geometric mean of t10 (g10), and the number of well-cited faculty with t10 above 40% (c40) and 60% (c60) of the national average. We fitted a linear regression model using those four measures to match the 2022 U.S. News ranking scores of CS doctoral programs. The resulting model provides Scholar Ranking 2023, which can be found at https://chi.temple.edu/csranking.
Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare manager. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilized, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.
Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years' (2010-2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of a network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analy
In this paper, the scientometric evaluation of faculty members of 50 Greek Science and Engineering University Departments is presented. 1978 academics were examined in total. The number of papers, citations, h-index and i10-index have been collected for each academic, department, school and university using Google Scholar and the citations analysis program Publish or Perish. Analysis of the collected data showed that departments of the same academic discipline are characterized by significant differences on the scientific outcome. In addition, in the majority of the evaluated departments a significant difference in h-index between academics who report scientific activity on the departments website and those who do not, was observed. Moreover, academics who earned their PhD title in the USA demonstrate higher indices in comparison to scholars who obtained their PhD title in Europe or in Greece. Finally, the correlation between the academic rank and the scholars h-index (or the number of their citations) is quite low in some departments, which, under specific circumstances, could be an indication of the lack of meritocracy.
Self-harm presentations to emergency departments (EDs) are strongly associated with higher suicide risk. NLP models have shown robust performance in detecting self-harm from triage notes within single hospitals, yet performance often declines across institutions. To examine potential causes, we compare ED triage notes from two hospitals by analyzing lexical characteristics, highly associated predictive features, and salient topics. Our results reveal variation in lexical expression and feature importance related to self-harm across hospitals, despite consistent core themes such as self-poisoning and self-injury. These documentation differences are associated with reduced cross-site performance. Our findings provide insight into how institutional variation affects the identification of self-harm in clinical text and highlight potential methods to improve model generalisability.
Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits. Effective ED planning requires forecasts at multiple decision-making levels: hospitals need local demand estimates for staffing and bed management, regions require forecasts to coordinate healthcare units, and national authorities need system-wide projections for capacity planning. However, most existing approaches forecast ED demand independently at a single level, ignoring the hierarchy linking hospitals, regions, and national systems. This can produce incoherent predictions, where hospital-level forecasts do not aggregate consistently to regional or national demand. We propose HierSTT, a hierarchical Transformer-based framework for coherent multi-level ED forecasting. HierSTT jointly predicts hospital, regional, and national level demand in a single end-to-end model. A Temporal Fusion Transformer captures national dynamics, while spatio-temporal Transformer encoder-decoder modules model regional and hospital demand conditioned on higher-level forecasts. A coherence-aware loss penalizes cross-lev
Emergency departments (EDs) often use a shared-queue setup in which physicians self-assign cases from a pool of triaged patients. We conduct a multi-method study to examine this self-assignment behavior and its effects on system performance. Using data from five EDs spanning 1.4 million patient visits, we show that batching, i.e., self-assigning multiple patients at once, is common and associated with longer stays for batched patients, even after controlling for clinical acuity, physician fixed effects, and ED congestion. We then develop a continuous-time queueing model that characterizes the optimal self-assignment policy under individual and group throughput incentives. We use the model predictions to test experimentally with 203 healthcare workers and 73 ED physicians whether batching is a rational response to incentives or a deeper behavioral tendency that persists independent of incentives. Indeed, batching is pervasive across both samples, with 94% of healthcare workers and 73% of physicians choosing to batch even when it reduces their own payoffs -- a behavior that we term the personal productivity bias. Together, these results suggest that compensation redesign alone is unl
Many cyberattacks succeed because they exploit flaws at the human level. To address this problem, organizations rely on security awareness programs, which aim to make employees more resilient against social engineering. While some works have suggested that such programs should account for contextual relevance, the common praxis in research is to adopt a "general" viewpoint. For instance, instead of focusing on department-specific issues, prior user studies sought to provide organization-wide conclusions. Such a protocol may lead to overlooking vulnerabilities that affect only specific subsets of an organization. In this paper, we tackle such an oversight. First, through a systematic literature review, we provide evidence that prior literature poorly accounted for department-specific needs. Then, we carry out a multi-company and mixed-methods study focusing on two pivotal departments: human resources (HR) and accounting. We explore three dimensions: threats faced by these departments; topics covered in the security-awareness campaigns delivered to these departments; and delivery methods that maximize the effectiveness of such campaigns. We begin by interviewing 16 employees of a mul
Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while awaiting inpatient bed placement, is a key indicator of this congestion. Predicting ED boarding time in advance enables proactive operational decision making before congestion escalates. We developed and evaluated a multi-horizon time series forecasting framework to predict ED boarding time at 6, 8, 10, 12, and 24-hour horizons. Real-world data from a university-affiliated urban hospital in the United States were utilized and integrated with external contextual data sources, including weather, holidays, and major local events. Decomposition-based Linear (DLinear) and Normalization-based Linear (NLinear) time series forecasting deep learning models showed superior performance across multiple horizons. Models were also evaluated under extreme congestion scenarios characterized by elevated boarding times. In addition, a Machine Learning Operations (MLOps) web application prototype was developed to support translation of the forecasting framework into
Serious illness conversations (SICs) align care with patients' values, goals, and preferences, yet they rarely occur in emergency departments (EDs), where time constraints and emotional burden often leave clinicians making high-stakes decisions without documented insight into what matters most to patients. We present a case study of ED GOAL-AI, a voice-based conversational agent for brief, structured values discussions with older adults in the ED, evaluated with 55 patients for feasibility and acceptability. Most participants completed the conversation and reported the interaction as acceptable and feasible, with ratings of feeling heard and understood comparable to clinicians. However, we also observed critical failure modes, including boundary violations such as hallucinated diagnostic statements, highlighting ethical and emotional risks. This work points to early promise for AI-mediated SICs while underscoring the need for careful boundary setting and participatory design before broader deployment.
Serious Illness Conversations (SICs), discussions about values and care preferences for patients with life-threatening illness, rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting real-time conversations, and automating documentation, but emphasized concerns about preserving human connection and clinical autonomy. This tension highlights the need for technologies that enhance efficiency without undermining the interpersonal nature of SICs. We propose design guidelines for ambient and peripheral AI systems to support providers while preserving the essential humanity of these conversations.