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The rapid, human-induced changes in the Earth system during the Anthropocene present humanity with critical sustainability challenges. Social-ecological systems (SES) research provides multiple approaches for understanding the complex interactions between humans, social systems, and environments and how we might direct them towards healthier and more resilient futures. However, general theories of SES change have yet to be fully developed. Formal evolutionary theory has been applied as a dynamic theory of change of complex phenomena in biology and the social sciences, but rarely in SES research. In this paper, we explore the connections between both fields, hoping to foster collaboration. After sketching out the distinct intellectual traditions of SES research and evolutionary theory, we map some of their terminological and theoretical connections. We then provide examples of how evolutionary theory might be incorporated into SES research through the use of systems mapping to identify evolutionary processes in SES, the application of concepts from evolutionary developmental biology to understand the connections between systems changes and evolutionary changes, and how evolutionary thinking may help design interventions for beneficial change. Integrating evolutionary theory and SES research can lead to a better understanding of SES changes and positive interventions for a more sustainable Anthropocene. This article is part of the theme issue Evolution and sustainability: gathering the strands for an Anthropocene synthesis.

On November 3, 2015, the majority (61%) of Houston voters voted to strike down Proposition 1: Houston’s Equal Rights Ordinance. Houston’s Equal Rights Ordinance (HERO) sought to expand anti-discrimination protections based on 15 demographic classifications including both sexual orientation and gender identity (Morris, 2014). The original passage of HERO in May 2014 by Houston’s City Council sparked not only a legal battle but also an 18-month-long media battle in which both supporters and opponents worked to sway voters to their side of the issue. This paper examines the political discourse surrounding the failed passage of HERO in three key arenas: media coverage of the event, campaign materials from both sides of the issue and politicians’ comments on the issue. In order to examine the discourse around the bill, I analyzed newspaper articles about the issues (N=358), campaign ads (N=29) and tweets from prominent local and state politicians (N= 776). Using the hybrid model of moral panics framework (Klocke and Muschert, 2010), this paper argues that the political discourse around HERO served as the catalyst for America’s newest moral panic: transgender bathroom access.

Spatio-temporal data analysis is crucial in many research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing 4DModeller (fdmr), a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The software package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution, highdimensional spatio-temporal data. Furthermore, "fdmr" provides intuitive and interactive visual analytics tools that facilitate the exploration of data patterns across both space and time. This paper aims to introduce the "fdmr" package, and outline its core modelling framework through an example study on the spread of COVID-19 infection rates in England from 19 December, 2020 to 20 March, 2021.

This paper proposes an efficient and compact hybrid switched-capacitor (SC) converter for direct 48-to-1-V power conversion in data centers. The proposed topology can be viewed as a 2-to-1 SC converter merged with two 8-to-1 series-capacitor-buck (SCB) modules through two switching buses. Different from the conventional DC bus architecture, the switching bus architecture removes the bulky DC bus capacitors and reduces the number of switches. Compared with existing 48-to-1-V hybrid SC demonstrations, the proposed converter achieves the highest SC stage conversion ratio with the lowest normalized switch volt-ampere (VA) rating, which promises lightened conversion burden on the following buck stage and reduced switch stress. A hardware prototype is designed, constructed and tested up to 500-A output current, achieving 94.7% peak power stage efficiency, 86.4% full-load efficiency and 464-W/in3 power density.

One-dimensional site response analysis (1D SRA) remains the state of practice to estimate site-specific seismic response, despite the ample evidence of discrepancies between observations and 1D SRA-based predictions. These discrepancies are due to errors in the input parameters, intrinsic limitations in the predicting capabilities of 1D SRAs even for sites relatively compliant with the 1D SRA assumptions, and the inability of 1D SRAs to model three-dimensional (3D) wave propagation phenomena. This article aims at reducing 1D SRA mispredictions using small-strain damping profiles factored by a damping multiplier (Dmul) and randomized shear-wave velocity (VS) profiles. An approach for conducting 1D SRAs for site-specific site response assessment is developed to reduce the 1D SRA errors in magnitude and variability. First, sites from a database of 534 downhole sites are classified as 1D- or 3D-like, depending on the substructure conditions inferred from observed transfer functions. Second, data from the 1D-like sites are compared against predictions from 1D SRAs conducted using various trials of Dmul and VS standard deviations (Formula presented.) for VS randomization. Third, Dmul and (Formula presented.) are selected based on their combined ability to reduce the root mean square error (RMSE) in SRA predictions. Results indicate that 1D SRAs conducted with Dmul = 3 and (Formula presented.) lead to an overall minimum RMSE and thus provide more accurate site response estimates. The use of these parameters in forward SRA predictions is discussed in a companion paper.

Site response estimates from one-dimensional (1D) site response analyses (SRAs) carry inaccuracies due to modeling and parametric errors. Modeling errors are due to the condensation of the three-dimensional (3D) wave propagation phenomenon to the vertical propagation of a horizontally polarized wave through a soil column, and parametric errors are due to the incomplete knowledge of the distributions of soil parameters, leading to the selection of nonoptimal input parameters for a site of interest. While parametric errors are traditionally handled using different soil parameters (e.g. alternative shear-wave velocity profiles), modeling errors are generally neglected. This paper proposes an approach for conducting linear elastic 1D SRAs to improve site response predictions and account for modeling errors. First, ground-motion data from borehole array sites are collected, processed, and screened for appropriateness (e.g. expected shear strains lower than 0.01%, signal-to-noise ratio higher than 3). Second, 1D SRA predictions in terms of transfer functions and amplification factors are compared against observations, and the discrepancies are quantified as residuals. Finally, the residuals are partitioned into a model bias term (Formula presented.), a site term (Formula presented.) with standard deviation (Formula presented.), and a event- and site-specific remaining residual (Formula presented.) with standard deviation (Formula presented.). Values for (Formula presented.) and (Formula presented.) for forward predictions are recommended. The sensitivity of the site response residuals to region, site type (1D- or 3D-like), and the applicability of findings to outcropping applications are discussed, and an example application for a hypothetical project site is presented.

Breast cancer is the second most common cancer for women in the United States and, accurate identification of cancerous masses is critical to reduce death rates. This thesis explores several classification models that can accurately identify malignant and benign breast masses using data from the from the UCI Machine Learning Repository. In this paper, logistic regression with PCA, random forests, and Naive Bayes are compared to identify the best model for classification. Identifying a powerful classification model can help health care providers streamline screening and diagnosis processes.

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when

Many types of bio-inspired appendages have been designed for locomotion on various terrains. In this paper, a novel Jagged Anisotropic Mechanically Jamming (JAMJam) Appendage is presented which is passively compliant and super-underactuated, needing only one actuator to power multiple appendages. The JAMJam's characteristics are studied, with 'straight' appendages generating less force than 'curved' appendages, but requiring smaller sweep angle. A robot utilizing the appendages is designed for shallow-water environments, which can both walk while semi-submerged at up to 9 cm/s and swim at 7.2 cm/s. The robot is capable of locomotion in both turf and sandy laboratory conditions, and can traverse over obstacles in its path.

The importance of studying suicidal behavior cannot be overstated given the concerning prevalence. Despite a wide range of studies on suicidal behaviors, three major limitations remain. First, most studies are cross-sectional. Second, most studies examined risk factors of suicidal ideation in isolation. A meta-analysis covering the past 50 years of suicidal research found that prediction was only slightly better than chance because researchers rarely tested the combined effect of multiple risk factors. Researchers recommended utilizing machine learning (ML) approaches to study suicidal ideation instead because ML can address complex classification problems. Third, many studies have ignored the possibility that suicidal ideation can shift throughout different developmental stages. Disregarding the fact that there are different trajectories of suicidal ideation can lead to biased results. In this paper, I addressed the limitations of past research by first investigating different trajectories of suicidal ideation using Latent Class Growth Analysis on a large, nationally representative longitudinal dataset (n = 7,295). I then used two ML approaches, classification trees and random forests, to examine which risk factors are predictive of the identified trajectories. Two hundred and eighty-one predictors were included in the ML models, spanning demographic, psychological, behavioral, economic, social, and environmental variables linked to suicidal behaviors, such as mental health, violence exposure, family, peer, and school functioning, neighborhood characteristics, community engagement, negative life events, expectations for the future, risky behaviors, self-esteem, and substance use. The Latent Class Growth Analysis identified three distinct trajectories: (a) Resilient Class, (b) Declining Class, and (c) Escalating Class. When comparing the predictive performance, machine learning models, specifically classification tree (AUC = 0.73) and random forest (AUC = 0.79), out

Maternal mortality remains a significant global health challenge, with roughly 300,000 maternal deaths occurring annually, most of which are preventable. The relationship between socioeconomic status (SES) and maternal mortality rates (MMR) has been widely studied, with a growing body of evidence suggesting that lower SES is associated with higher MMR. This meta-analysis aimed to investigate the relationship between SES and MMR across different countries. We conducted a comprehensive search of electronic databases and included studies that reported the association between SES and MMR. We used a random-effects model to estimate the overall effect size and explored potential sources of heterogeneity using subgroup analyses. Our findings suggest that there is a significant inverse association between SES and MMR, with higher SES being associated with lower MMR. However, the magnitude of the effect varied across different regions, with the strongest association observed in low and middle-income countries. While previous studies have examined the relationship between various variables, they often do so within a narrow context, focusing on specific regions or communities. As a result, the findings of such studies may not be generalizable or applicable to other settings. Our research, therefore, takes a more comprehensive approach, examining the interplay between SES and MMR across different regions and countries, and considering a range of social, economic, and health-related factors that could potentially influence this relationship. Our research employs a meta-analysis of research papers and scientific data, this approach allowed for a more comprehensive and rigorous examination of the research question and can help identify patterns and trends across studies. A systematic literature search/screening coupled with data extraction from online databases informed our results

A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.

The simultaneous optimization of multiple objective functions is needed in many particle accelerator applications. In this paper, we present a parallel evolution based multi-objective optimizer that uses a variable population from generation to generation and an external storage to save good solutions. Two heuristic optimization methods, one uses the unified differential evolution and the other uses the real-coded genetic algorithm, are included in the optimizer to generate next generation candidate solutions, and are compared in the test examples. As an application, we applied this optimizer to the beam dynamics design optimization of a photoinjector and attained the optimal front solutions after 200 generations with the unified differential evolution offspring production scheme.

Video Games are a media platform that cannot be ignored - yet when we ask what methods can we use to make a successful video game, the answers are blurry. Players seem to care about the emergent properties of games that science traditionally struggles with: fun, balance, feel, and so on. These terms tend to be stigmatized in a research community - yet they also seem to be some of the strongest indicators of a game’s success. This thesis presents two papers that offer a framework to understand how we might start getting good design recommendations for emergent properties through a deep analysis of the conscious experience of players. In the first, we present an automated game design system (AGD) that aims to generate “balanced” games. In the second, we provide a theoretical discussion on why we might need to take unconventional routes of analysis to produce impactful games. Together, these papers present a picture not only of how to think about design strategies of emergent properties in games, but also a case-study of developing such a system and deploying it amongst users.

This review aims to summarize the putative role of histone deacetylases (HDACs) in rhabdomyosarcoma (RMS) and the effects of HDAC inhibitors (HDACi) on RMS by elucidating and highlighting known oncogenic pathways, mechanisms of resistance, and the synergistic potential of histone deacetylase inhibitors. We searched two databases (PubMed and Google Scholar) for the keywords Rhabdomyosarcoma, histone deacetylase, histone deacetylase inhibitors. We excluded three publications that did not permit access to the full text to review and those that focus exclusively on pleiomorphic RMS in adults. Forty-seven papers met the inclusion criteria. This review highlights that HDACi induce cytotoxicity, cell-cycle arrest, and oxidative stress in RMS cells. Ultimately, HDACi have been shown to increase apoptosis and the cessation of embryonal and alveolar RMS proliferation in vivo and in vitro, both synergistically and on its own. HDACi contain potent therapeutic potential against RMS. This review discusses the significant findings and the biological mechanisms behind the anti-cancer effects of HDACi. Additionally, this review highlights important clinical trials assessing the efficacy of HDACi in sarcomas.

Youth in the United States are experiencing mental health concerns at alarming rates. Considering the nation's legacy of racism and growing recognition of the impact of social determinants of health on educational and mental health inequities, it is imperative to re-envision how we approach mental health screening in schools to center equity. A focus on mental health screening for the sole purpose of identifying individual at-risk students ignores key contextual considerations, is ineffective in addressing health and educational inequities, and has the potential to perpetuate oppressive practices in schools. Equity-focused mental health screening requires a shift from individual- and deficit-focused approaches to systems- and holistic-focused approaches that (a) identify strengths and stressors among individuals, groups, and communities; (b) dismantle structural forms of oppression; and (c) promote positive mental health outcomes for minoritized youth. Integrating recommendations from the educational equity literature and critical school mental health frameworks, this paper identifies core considerations for equitable school mental health screening and provides guiding principles for each phase of the screening process, from screening readiness to execution to follow up. To implement these recommendations and transform school-based mental health care, schools should (a) incorporate multiple perspectives; (b) prioritize student, family, and community voices; and (c) build collaborative partnerships to co-construct a vision for equitable school mental health.

This paper offers a model of the semantic content of spatial nouns as generic terms in place names (e.g. Square in Trafalgar Square) and as descriptors for places ("place nouns", e.g. street in the second street). The model is based on a variant of Frame Semantics in which different context- and community-based uses (e.g. general, daily uses; specialised uses; legal, normative uses) are modelled as as sets/matrices of attribute-value pairs, or frames. The attributes forming these frames are based on data extraction from corpora (general uses), Wikipedia articles (specialised uses), and professional geographical dictionary (legal uses) as contexts. It is shown that uses associated to each context define frames varying considerably in content; however, a semantic overlap relation connects these frames. Consequences for a general theory of the semantics of place and geonames are discussed.

We consider a spatial model of cancer in which cells are points on the d-dimensional torus $\mathcal{T}=[0,L]^d$ , and each cell with $k-1$ mutations acquires a kth mutation at rate $\mu_k$ . We assume that the mutation rates $\mu_k$ are increasing, and we find the asymptotic waiting time for the first cell to acquire k mutations as the torus volume tends to infinity. This paper generalizes results on waiting for $k\geq 3$ mutations in Foo et al. (2020), which considered the case in which all of the mutation rates $\mu_k$ are the same. In addition, we find the limiting distribution of the spatial distances between mutations for certain values of the mutation rates.

In 1879, James McNeill Whistler created The Gold Scab, a painting depicting Frederick R. Leyland, his former friend and patron, as an anthropomorphized peacock sitting atop a white house in front of a piano covered with bags of gold. The Gold Scab has been described as a self-portrait, a satirical portrait, a “Japanese grotesquerie,” and a monumental caricature. Regardless of what one categorizes it as, the work is an unsatisfactory misfit that deviates from the conventions of these genres. By analyzing how The Gold Scab borrows from the genres of portraiture and caricature yet deviates from the conventions and intentions of these genres, this paper situates The Gold Scab within a larger movement, away from the rigidity of the genre of nineteenth-century portraiture. In my examination of The Gold Scab through the lens of anti-portraiture, a framework that deprioritizes likeness as the defining convention of portraiture and instead prioritizes consideration of the artist’s intentions, it becomes clear that The Gold Scab is not a misfit; rather, it serves as a more than satisfactory example of the shift in the ideological discourse regarding likeness and portraiture taking place in the nineteenth century.