Non-hierarchical Model Research Articles

Burden of Parkinson’s disease in Central Asia from 1990 to 2021: findings from the Global Burden of Disease study

BackgroundCentral Asia is known to face various ecological challenges that constitutes major risk factors for Parkinson’s disease (PD). This study examines the burden of PD in Central Asia, a region where data on neurological disorders is notably sparse.MethodsBuilding on the latest Global Burden of Disease Study (GBD 2021), this study investigates the Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs) associated with PD in Central Asia and its countries from 1990 to 2021. The authors calculated average annual percent change (AAPC) to analyze trends, and compared individual country estimates to global figures. Additionally, incorporating data from the World Bank, both Bayesian hierarchical and non-hierarchical frequentist regression models were employed to assess their impact on DALYs.ResultsThe DALYs varied across the study period, primarily driven by YLLs. While YLLs showed a uniform trend, YLDs were mostly incremental. Kazakhstan had the highest estimates across all metrics and was the only country aligned with global patterns. Age- and sex-specific estimates revealed substantial variations, with notably high figures found in male subjects from Tajikistan. The YLLs, YLDs, and DALYs for Kazakhstan, Uzbekistan, and Turkmenistan saw a significant increase in AAPCs. In contrast, Kyrgyzstan and Tajikistan saw declines, likely attributable to civic conflict and inter-country differences in population structure. Further comparison of DALY trends revealed significant deviations for all countries from the global pattern.ConclusionThis study showed an overall increase in PD burden from 1990 to 2021. These findings underscore the need for targeted strategies to reduce PD burden, with a particular focus on Kazakhstan. Integrating historical information is crucial for discussing the plausible mechanisms in studies sourced from the GBD.

Towards Holistic Functional Task Analysis

ABSTRACTTask analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

Bayesian parameter inference for epithelial mechanics

Cell-based mechanical models, such as the Cell Vertex Model (CVM), have proven useful for studying the mechanical control of epithelial tissue dynamics. We recently developed a statistical method called image-based parameter inference for formulating CVM model functions and estimating their parameters from image data of epithelial tissues. In this study, we employed Bayesian statistics to improve the utility and flexibility of image-based parameter inference. Tests on synthetic data confirmed that both our non-hierarchical and hierarchical Bayesian models provide accurate estimates of model parameters. By applying this method to Drosophila wings, we demonstrated that the reliability of parameter estimation is closely linked to the mechanical anisotropies present in the tissue. Moreover, we revealed that the cortical elasticity term is dispensable for explaining force-shape correlations in vivo. We anticipate that the flexibility of the Bayesian statistical framework will facilitate the integration of various types of information, thereby contributing to the quantitative dissection of the mechanical control of tissue dynamics.

Fast maximum likelihood estimation for general hierarchical models

Hierarchical statistical models are important in applied sciences because they capture complex relationships in data, especially when variables are related by space, time, sampling unit, or other shared features. Existing methods for maximum likelihood estimation that rely on Monte Carlo integration over latent variables, such as Monte Carlo Expectation Maximization (MCEM), suffer from drawbacks in efficiency and/or generality. We harness a connection between sampling-stepping iterations for such methods and stochastic gradient descent methods for non-hierarchical models: many noisier steps can do better than few cleaner steps. We call the resulting methods Hierarchical Model Stochastic Gradient Descent (HMSGD) and show that combining efficient, adaptive step-size algorithms with HMSGD yields efficiency gains. We introduce a one-dimensional sampling-based greedy line search for step-size determination. We implement these methods and conduct numerical experiments for a Gamma-Poisson mixture model, a generalized linear mixed models (GLMMs) with single and crossed random effects, and a multi-species ecological occupancy model with over 3000 latent variables. Our experiments show that the accelerated HMSGD methods provide faster convergence than commonly used methods and are robust to reasonable choices of MCMC sample size.

Cybercriminal Networks and Operational Dynamics of Business Email Compromise (BEC) Scammers: Insights from the “Black Axe” Confraternity

ABSTRACT I explored the relationship between the “Black Axe” Confraternity and cybercrime, with a particular emphasis on the structural dynamics of the Business Email Compromise (BEC) schemes. I investigated whether a conventional hierarchical system governs the membership and remuneration for BEC roles as perpetrators by interviewing an accused “leader” of the “Black Axe” affiliated cybercriminal incarcerated in a prominent Western nation. I supplemented the analysis of interview data with insights from tapped phone records monitored by a law enforcement entity. I merged Actor-network theory and Social Network theory as analytical frameworks and thematically analyzed data to produce six overarching themes: (1) The fluidity of Structure and Adaptive Roles in BEC, (2) Challenges in Visualizing Criminal Networks, (3) Globalization and Transnational Dimensions, (4) Social and Cultural Influences, (5) Internal Cybersecurity Threats and Money Laundering, and (6) Remuneration and Influences of Cryptocurrency, casting a brighter light on the topic. Unlike traditional organized crime, BEC scammers have adopted a nonhierarchical model that is flexible and fluid. I found no evidence of a rigid hierarchy dictating positions and remuneration for BEC roles. Instead, I observed that cybercriminals involved in BEC activities functioned horizontally, promoting fluidity, maneuverability, collaboration, and specialization across various facets of their illicit pursuits. I also highlighted the cross-border links BEC offenders have with other criminal actors facilitated by Black Axe organizational machinery. My research adds value to the existing body of knowledge, utilizing a unique dataset comprising direct testimonies of a high-profile BEC offender affiliated with the ‘hard–to–access,’ Black Axe gang.

Hierarchical Classification of Botnet Using Lightweight CNN

This paper addresses the persistent threat of botnet attacks on IoT devices, emphasizing their continued existence despite various conventional and deep learning methodologies developed for intrusion detection. Utilizing the Bot-IoT dataset, we propose a hierarchical CNN (HCNN) approach featuring three levels of classification. The HCNN approach, presented in this paper, consists of two networks: the non-hierarchical and the hierarchical network. The hierarchical network works by combining features obtained at a higher level with those of its descender. This combined information is subsequently fed into the following level to extract features for the descendant nodes. The overall network consists of 1790 parameters, with the hierarchical network introducing an additional 942 parameters to the existing backbone. The classification levels comprise a binary classification of normal vs attack in the first level, followed by 5 classes in the second level, and 11 classes in the third level. To assess the effectiveness of our proposed approach, we evaluate performance metrics such as Precision (P), Recall (R), F1 Score (F1), and Accuracy (Acc). Rigorous experiments are conducted to compare the performance of both the hierarchical and non-hierarchical models and existing state-of-the-art approaches, providing valuable insights into the efficiency of our proposed hierarchical CNN approach for addressing botnet attacks on IoT devices.

Cluster methods in function better selection

Cluster analysis methods, also known as taxonomic methods, are intended for grouping objects and subjects according to certain characteristics, attributes and properties. Cluster analysis looks at relevant objects and attributes, classifying them into two or more independent groups. Cluster analysis supplemented with discriminant analysis is used in confirmatory and fundamental research. In numerous statistical-methodological procedures, these methods are applied when setting up and testing various hypotheses. Grouping methods are particularly useful in the process of different selections with the aim of forming coherent groups, which may or may not necessarily be statistically different. There are several models of clustering (grouping), always with one goal, which is greater proximity (similarity) of an entity belonging to a group compared to an entity belonging to another group. Two basic grouping models are recognizable, Hierarchical and Non-Hierarchical. Both models have the same goal, which is the formation of several independent homogeneous groups from one common group of entities. The hierarchical approach does not define the number of clusters in advance (a priori), in contrast to the Non-Hierarchical Model which defines in advance number of clusters. The grouping model is chosen depending on the specific problem and the set goal of grouping. In the process, several different models are often applied, and then one is chosen as in this research. It is important to point out that the theoretical number of clusters (groups) is often not realistically applicable in practice. Using the example of this research, it was proven that the first grouping was not a good solution. Through the subsequent, second and third iteration, as well as the application of additional discriminative methods, three optimal clusters were determined in the population of girls and boys. Satisfactory optimal grouping was obtained on the basis of gender criteria and achieved results on psycho-motor tests.

The bifactor model of the Hungarian self-report version of the Strengths and Weaknesses of ADHD and Normal Behaviors scale

IntroductionAttention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neuropsychiatric conditions, maintaining its presence well into adolescence and adulthood, resulting in impaired functioning. Evaluating ADHD symptoms through self-reporting plays a crucial role in assessing individuals within these age groups. The novel self-report version of the Strengths and Weaknesses of ADHD and Normal Behaviors (SWAN) scale offers a comprehensive assessment of behaviour, extending beyond just focusing on the typical signs and symptoms of ADHD, thus providing a more holistic perspective.ObjectivesOur goal was to assess the factorial validity of the Hungarian version of the SWAN self-report by comparing a two-factor model with bifactor models with a general and 1) two specific factors (inattention, hyperactivity/impulsivity), 2) three specific factors (inattention, motor hyperactivity/impulsivity, verbal hyperactivity/impulsivity) in a community sample.MethodsData from 717 adolescents and young adults (mean age = 20.0 years, SD = 3.10, range: 14 - 25 years, female: N = 664, 92.6%) were analysed. Participants completed an online questionnaire including the SWAN scale after giving informed consent. Confirmatory factor analyses were conducted based on the maximum likelihood estimator (ML).ResultsThe bifactor model with a general and three specific factors demonstrated the best fit to our data (CFI = .933, RMSEA = .064 [90% CI: .058 – .071], SRMR = .038). While the overall composite reliability was excellent (ω = .91), the reliability of the specific verbal hyperactivity/impulsivity factor fell below acceptable (ωh = .40).ConclusionsIn line with previous studies, the fit indices of the bifactor models were superior to the non-hierarchical two-factor model. Our results support the existence of a strong general factor but suggest uncertainty in the capacity of the specific factors to consistently explain the distinct variance in observed variables, particularly when compared to the overarching influence of the general factor.This work was supported by the ÚNKP-22-2 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund (grant number ÚNKP-22-2-I-ELTE-854).Disclosure of InterestNone Declared

Internal Factor Structure and Convergent Validity of a Perceived Partner Violence Scale in Peruvian Adults: Bifactor Model of Emotional, Physical, and Sexual Abuse and Sexual Manipulation

Intimate partner violence is a social problem that requires appropriate instruments for its correct and accurate assessment. The aim of this study was to construct and report evidence of the psychometric properties of the Perceived Partner Violence Scale. Four samples were selected using nonprobability sampling. An exploratory factor analysis (EFA) was performed with the first sample. With the second sample, a confirmatory factor analysis (CFA) and validity testing based on the relationship with loneliness and self-esteem were carried out. With the third sample, validity was assessed based on the relationship with emotional dependence. With the fourth sample, validity was analyzed in relation to love compatibility. Robust methods such as the robust maximum likelihood method were used for the hierarchical models (bifactor model and second-order model), and diagonally weighted least squareswere used for the nonhierarchical models (correlated factor model, uncorrelated factor model, and unidimensional model). Validity tests report adequate fit indices in the EFA and CFA for a four-factor model (emotional violence, physical violence, sexual abuse, and sexual manipulation) as well as validity based on the relationship with other variables (self-esteem, loneliness, emotional dependence, and love compatibility). Robust alpha and omega estimators indicate acceptable internal consistency.

Benefits of the Curious Behavior of Bayesian Hierarchical Item Response Theory Models-An in-Depth Investigation and Bias Correction.

When using Bayesian hierarchical modeling, a popular approach for Item Response Theory (IRT) models, researchers typically face a tradeoff between the precision and accuracy of the item parameter estimates. Given the pooling principle and variance-dependent shrinkage, the expected behavior of Bayesian hierarchical IRT models is to deliver more precise but biased item parameter estimates, compared to those obtained in nonhierarchical models. Previous research, however, points out the possibility that, in the context of the two-parameter logistic IRT model, the aforementioned tradeoff has not to be made. With a comprehensive simulation study, we provide an in-depth investigation into this possibility. The results show a superior performance, in terms of bias, RMSE and precision, of the hierarchical specifications compared to the nonhierarchical counterpart. Under certain conditions, the bias in the item parameter estimates is independent of the bias in the variance components. Moreover, we provide a bias correction procedure for item discrimination parameter estimates. In sum, we show that IRT models create a unique situation where the Bayesian hierarchical approach indeed yields parameter estimates that are not only more precise, but also more accurate, compared to nonhierarchical approaches. We discuss this beneficial behavior from both theoretical and applied point of views.

Оценивание эффектов возрастных когорт в потреблении аддиктивных благ

The research focuses on the investigation of birth cohort effects in post-month consumption of alcohol, cigarettes and marijuana. The authors utilize hierarchical and non-hierarchical multivariate probit models to examine the relationship between the consumption of addictive goods taking into account the endogeneity of subjective health evaluation. Based on the US National Survey on Drug Use and Health from 2002 to 2020 data the study provides evidence of significant age and gender differences in alcohol, cigarettes and marijuana consumption According to the findings, the consumption of each of the substances in question increases the propensity to use other addictive goods, and it differs significantly across birth cohorts and men and women. The correlation between alcohol, cigarettes and marijuana consumption increases from older to younger cohorts. There is also evidence of a closing gender gap in addictive goods consumption, particularly concerning the younger cohorts.

Nonadjacent dependencies and sequential structure of chimpanzee action during a natural tool-use task.

Many of the complex behaviours of humans involve the production of nonadjacent dependencies between sequence elements, which in part can be generated through the hierarchical organization of sequences. To understand how these structural properties of human behaviours evolved, we can gain valuable insight from studying the sequential behaviours of nonhuman animals. Among the behaviours of nonhuman apes, tool use has been hypothesised to be a domain of behaviour which likely involves hierarchical organization, and may therefore possess nonadjacent dependencies between sequential actions. However thus far, evidence supporting hierarchical organization of great-ape tool use comes from methodologies which have been criticised in their objectivity. Additionally, the extent to which nonadjacent dependencies appear in primate action sequences during tool use has not been formally investigated. We used estimations of mutual information (MI)-a measure of dependency strength between sequence elements-to evaluate both the extent to which wild chimpanzees produce nonadjacent dependencies during a naturalistic tool-use task (nut cracking), as well as how sequences of actions are likely organized during tool use. Half of adult chimpanzees produced nonadjacent dependencies at significantly greater sequential distances than comparable, nonhierarchical Markov models once repeated actions had been accounted for. Additionally, for the majority of chimpanzees, MI decay with increasing sequential distance included a power-law relationship, which is a key indicator that the action sequences produced by chimpanzees likely entail some degree of hierarchical organization. Our analysis offered the greatest support for a system of organization where short subroutines of actions (2-8 actions long) are hierarchically arranged into longer sequences-a finding which is consistent with previous qualitative descriptions of ape tool-use behaviours. Interindividual variability was detected within our analysis in both the maximum distance dependencies were detected, and the most likely structuring mechanism for sequential action organization. We discuss these results in light of possible interindividual variation in the systems of action organization used by chimpanzees during tool use, in addition to methodological considerations for applications of MI estimations to sequential behaviours. Moreover, we discuss our main findings alongside hypotheses for the coevolution of complex syntax in language and tool-action across hominin evolutionary history.

A flexible Bayesian hierarchical quantile spatial model for areal data

This article introduces a new class of nested models that extends the literature standard combination of spatial autoregressive model for areal data with parametric quantile regression by considering an asymmetric Laplace distribution for the random errors. In addition to being more flexible, the new proposed model can incorporate a hierarchical structure, allowing it to deal with clustered data. Such an approach produces a robust statistical method for modeling the quantiles of areal data distributed in a geographically hierarchical setting. The proposed non-hierarchical model is evaluated using a wellknown house pricing dataset and a simulation study. In addition, its hierarchical version is applied to a real dataset of math scores related to public high schools within the metropolitan area of Rio de Janeiro, Brazil.

Relational responsibility, social discipline and behaviour in school: re-orienting discipline and authority through a distributed network of relational accountability

ABSTRACT This paper provides an exploration of a non-hierarchical model of discipline observed in one Secondary School in the North-East of England, that employed the whole-school use of Restorative Practice enhanced by vertically structured Coaching Groups. This model supported a school community characterised by working restoratively with others to achieve an environment of high challenge-high support (Wachtel, 2013). Drawing on an original evaluation of Embedding Restorative Practice in Schools (Warin & Hibbin, 2020), the ‘Distributed Network of Relational Accountability’ (Hibbin & Warin, 2021) that this school created was based on relationship over authority and collective accounts of responsibility over individualised notions of blame. This model is described and unpacked, to understand how such a relationship-centred approach to behaviour that disrupts traditional ways of engaging with discipline in school, can be implemented and sustained over time. Key themes in relation to the modelling and practice of pro-social skills within the context of Coaching, and the democratization of the disciplinary system through discernment and knowing the child, are explored. It is suggested that such practice that is based on notions of Relational Responsibility (McNamee & Gergen, 1999) alongside a whole school ethos of care (Warin, 2017) fosters commitment, pro-social ability and ultimately leads to more democratic versions of Restorative Practice. In addition, it supplies both students and teachers with important opportunities for community building and disclosure, through exposure to diversity within the secure family base of the Coaching Group.

Research on optimization design of physics teaching objectives based on information fusion technology

Abstract Physics experiment is one of the key points and difficulties of physics teaching, and it has become a recognized bottleneck that restricts the quality of physics teaching. This paper firstly explores the integration of high school physics experimental teaching with information technology based on physics experimental teaching practice and physics curriculum standards through various ways and means. Secondly, it proposes constructing a collaborative physics experimental teaching design scheme based on information fusion technology, using three non-hierarchical multi-model fusion methods with Gaussian stochastic processes for modeling to achieve effective fusion of multiple non-hierarchical models of physics response information and physics teaching. Finally, a practical study in the form of a physics experimental teaching case is analyzed, and the teaching effect is measured to verify the effectiveness and feasibility of the design scheme. The results show that the physics teaching design based on information fusion technology effectively improves students’ experimental performance by 21.2%, learning interest by 36.4%, and learning participation by 19.8%. This study takes physics experimental teaching and information fusion technology as the entry point and improves the overall teaching quality of physics as the goal, which provides reference or reference teaching reform and practice in the context of information technology.

EPIPHANIC SYNERGISM IN JAMES JOYCE’S “THE DEAD” and ROBERT MUSIL’S “TONKA”

The article uses the linguistic-synergetic approach to examine the narrative structure and verbalization patterns in modernist short fiction based on “The Dead” by James Joyce and “Tonka” by Robert Musil. From this perspective, the study relates the epiphany-oriented non-hierarchical LIKE-THAT – SOMETHING-LIKE-THAT – NEVER-SOMETHING-LIKE-THAT narrative structure to verbalization of self-organized simultaneity in modernist short fiction. From this study’s findings, both writers similarly replace a conventional narrative hierarchy with a verbal event whose verbalization, namely, repetitions at all linguistic levels, allusive associations, and aposiopesis, supports an epiphany-oriented attractor of change. The results of this study extend insights of current research on verbalization of self-organized simultaneity in the process of constructing a non-hierarchical epiphanic model. They contribute to the current linguistic-synergetic research on the emergence of modernist epiphany and have practical applications with regard to modernist writers across languages and cultures.

Evaluating the complexity and falsifiability of psychological models.

Understanding model complexity is important for developing useful psychological models. One way to think about model complexity is in terms of the predictions a model makes and the ability of empirical evidence to falsify those predictions. We argue that existing measures of falsifiability have important limitations and develop a new measure. KL-delta uses Kullback-Leibler divergence to compare the prior predictive distributions of models to the data prior that formalizes knowledge about the plausibility of different experimental outcomes. Using introductory conceptual examples and applications with existing models and experiments, we show that KL-delta challenges widely held scientific intuitions about model complexity and falsifiability. In a psychophysics application, we show that hierarchical models with more parameters are often more falsifiable than the original nonhierarchical model. This counters the intuition that adding parameters always makes a model more complex. In a decision-making application, we show that a choice model incorporating response determinism can be harder to falsify than its special case of probability matching. This counters the intuition that if one model is a special case of another, the special case must be less complex. In a memory recall application, we show that using informative data priors based on the serial position curve allows KL-delta to distinguish models that otherwise would be indistinguishable. This shows the value in model evaluation of extending the notion of possible falsifiability, in which all data are considered equally likely, to the more general notion of plausible falsifiability, in which some data are more likely than others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

‘Don’t just do something, sit there’: contemplative activism and ‘being political’ as prefigurative politics

ABSTRACT In recent years, scholars have considered how activists are ‘being the change they want to see in the world’ and have developed the concept of prefigurative politics to describe how movements for social change align the means of their changemaking practices with the end goals of their organisations. Whereas being and embodiment seem to be central to this slogan, relatively little systematic scholarly reflection has occurred on how ‘being political’ emerges as a form of prefigurative politics. This article introduces one example of how being can be a form of prefigurative politics by thinking through contemplative activism: the use of contemplation as a way of responding to global challenges. It shows that the political imagination of transformation that contemplative activists offer is a form of being (a state of existence) rather than doing (working towards a particular goal). This article considers how a study of contemplative activism as a prefigurative form of being political can contribute to three main debates within the literature on prefigurative politics: the specific relationship between means and ends, temporal distinctions between the present and the future and the actualization of goals. Ultimately, the article argues that considering social change to occur on an ontological level contributes to advancing a broad understanding of prefigurative politics that not just includes non-hierarchical models of organization but also comprises affective, embodied, and spiritual experiences as forms of prefigurative changemaking.

Multilevel joint model of longitudinal continuous andbinary outcomes for hierarchically structured data.

Joint modeling has been a useful strategy for incorporating latent associations between different types of outcomes simultaneously, often focusing on a longitudinal continuous outcome characterized by an LME submodel and a terminal event subject to a Cox proportional hazard or parametric survival submodel. Applications to hierarchical longitudinal studies have been less frequent, particularly with respect to a binary process, which is commonly specified by a GLMM. Furthermore, many of the joint model developments have not allowed for investigations of nested effects, such as those arising from multicenter studies. To fill this gap, we propose a multilevel joint model that encompasses the LME submodel and GLMM through a Bayesian approach. Motivated by the need for timely detection of pulmonary exacerbation and characterization of irregularly observed lung function measurements in people living with cystic fibrosis (CF) receiving care across multiple centers, we apply the model to the data arising from US CF Foundation Patient Registry. In parallel, we examine the extent of bias induced by a non-hierarchical model. Our simulation study and application results show that incorporating the center effect along with individual stochastic variation over time within the LME submodel improves model estimation and prediction. Given that the center effect is evident in lung function observed in the CF population, accounting for center-specific power parameters by incorporating the symmetric power exponential power (spep) link function in the GLMM can facilitate more accurate conclusions in clinical studies.

A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals nonhierarchical model of development

The specialized cell types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing Xenopus MCE from pluripotent to mature stages by single-cell transcriptomics, identifying multipotent early epithelial progenitors that execute multilineage cues before specializing into late-stage ionocytes and goblet and basal cells. Combining in silico lineage inference, in situ hybridization, and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors and chart cell type emergence and fate progression into specialized cell types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programs across vertebrates. We uncover a continuous nonhierarchical model of MCE development alongside a data resource for understanding respiratory biology.