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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.