Minnesota State Colleges and Universities System

Mangrove ecosystems function as important carbon sinks; however, accurately quantifying their carbon dynamics remains challenging because of the complex interplay of tidal fluctuations, sediment-water interface interactions, and biogeochemical cycling. Many existing models lack the temporal resolution and mechanistic precision necessary to adequately capture these dynamic processes, particularly in intertidal environments. To address this issue, we developed a mangrove ecosystem model [EMAGIN-B.C_MG] by incorporating mangrove physiology and tidal hydrodynamics into the EMAGIN-B.C. model, which represents benthic-pelagic coupled ecosystems, including phytoplankton, benthic algae, zooplankton, benthic fauna, detritus, and nutrients, through biological, chemical, and physical processes. Model validation was conducted by comparing the results with time series observations of dissolved inorganic carbon (DIC), total alkalinity (TA), and dissolved oxygen, as well as annual carbon flux measurements. Simulation results showed that carbon absorption and fixation increased during spring and summer because of enhanced photosynthetic activity, whereas carbon storage remained relatively stable throughout the year. Residual ponding during ebb tide significantly influenced DIC and TA concentrations via benthic mineralization and calcium carbonate (CaCO3) dissolution. Comparative analysis revealed that the presence of mangroves increased carbon absorption, fixation, and storage by factors of 9, 3, and 6, respectively. These findings demonstrate that high-temporal resolution ecosystem models can effectively reproduce key material cycling functions of mangrove ecosystems and contribute to more accurate assessments of Blue Carbon dynamics in intertidal wetlands. EMAGIN-B.C_MG provides a novel framework for evaluating the carbon mitigation potential of mangroves in the context of climate change.

In this work, we demonstrate that the hyperboloidal foliation technique, applied to the study of black hole quasinormal modes, where the spatial boundary is shifted from spacelike infinity to the future event horizon and null infinity, is effectively equivalent to the continued fraction approach, in which the asymptotic wave function typically diverges at both ends of spatial infinity. Specifically, a given hyperboloidal slicing, corresponding to a particular choice of coordinates, always uniquely determines a scheme for extracting the asymptotic form of the wave function at the spatial boundary. Owing to the mathematical equivalence, it follows that the efficiency and precision observed using the hyperboloidal approach should be attributed, not to avoiding the pathological behavior at the spatial boundaries, but primarily to other factors, such as the use of Chebyshev grids.

Smart Manufacturing (SM) is an important factor for driving innovation, enhancing operational efficiency, and increasing sustainable industrial growth in an increasingly competitive and resource-constrained world. However, it faces several challenges related to increasing energy consumption and climate change. The high energy demands of connected devices and robotic manipulators increase the carbon footprint. To resolve this issue, most enterprises are now transitioning to use Renewable Energy Sources (RES), and optimizing their power and energy usage, while holding the process efficient. To fully achieve this transition, a detailed power modeling of the robotic manufacturing system is crucial and, therefore, it is important to investigate this power modeling of the robotic manipulators' consumption in a Smart Sustainable Manufacturing (SSM) to achieve the best power modeling results and better integrability analytics in optimal power planning of the robotic systems power supply. To this end, this paper presents a deep Generative Artificial Intelligence (GAI)-based modeling of robotic manipulators' power supply interaction with the power grid, and RES. In the proposed system, which is powered by solar energy and the power grid, a SSM equipped with ten 6-Degrees of Freedom (DoF) robotic manipulators is considered in the presence of Battery Energy Storage Systems (BESSs). Subsequently, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to generate synthetic data for the system alongside the real data, thereby expanding the analytical horizons across varying operational characteristics of the system. Following this, a Residual Networks (ResNet) is developed to comprehensively analyze and predictively model the power consumption of the manipulators and their interactions with the power supply resources. Finally, the proposed hybrid GAI modeling strategy is numerically evaluated across a broad spectrum of Key Performance Indicators (KPIs) (MSE= 10-4, MAE= 3.6 x 10-3, R2= 99.98%, MARE= 1.97 x 10-2, RMSPE= 8.83 x 10-2%, MSRE= 7.8 x 10-3, RMSRE= 8.84 x 10-2, MAPE= 1.97 x 10-2%, and Max Error= 2.04 x 10-2), where these metrics demonstrate superior performance in power modeling. As a result, the concept of Grid-to-Robot (G2R) is introduced for the first time as a foundation for further advancements in SSM, enhancing sustainability and mitigating negative impacts on climate change while contributing to the development of an advanced manufacturing system.

In this critical discourse analysis, we examine how conservative media framed the value of a college education in the United States. Drawing on a sample of 70 articles from right-leaning news websites, we analyzed conservative media’s discourse surrounding higher education in the U.S. and how harmful ideologies shaped the text itself. Our analysis addresses modern ways conservative media may attempt to influence beliefs by weaponizing the media with the intent of impacting policymaking. Guided by the theory of moral panics, we discuss how right-wing actors weaponize media discourse to position higher education as a modern-day folk devil. Our primary findings highlight how the right-wing media frames higher education institutions as poorly governed entities with ballooning budgets, places where dangerous leftist political ideology is reproduced, and as promoting costly degrees offering little to no value to the individual, while providing few recommendations for addressing these perceived issues. We provide implications and recommendations for understanding and counteracting the tools and tactics used to delegitimize higher education by right-wing media.

Firearm violence in the United States is highly concentrated within specific demographic, economic, geographic, and social population groups. Prior research indicates elevated violence exposure among gang-involved individuals, but the extent and mental health implications of firearm victimization at the national level remain poorly understood. We analyzed data from a national online survey of 10,000 U.S. adults fielded in 2024. Respondents self-reported lifetime gang membership and lifetime exposure to five forms of firearm victimization: presence at a mass shooting, gun threats, being shot at without injury, accidental gunshot injury, and intentional gunshot injury. Weighted descriptive statistics compared prevalence by lifetime gang status. Multivariable logistic regression estimated adjusted odds ratios controlling for several covariates. Among victims of firearm violence, self-reported psychological impacts, including anxiety, fear, depression, panic attacks, and post-traumatic stress symptoms, were assessed. People with a history of gang involvement reported substantially higher lifetime exposure to all forms of firearm victimization. After adjustment, gang involvement was associated with 2–4 times greater odds of firearm exposure across outcomes. Psychological impacts following firearm victimization were prevalent in both gang and non-gang groups. Differences in reported mental health impacts by gang status were generally modest, with relatively few statistically significant differences in adjusted models. Firearm victimization is highly concentrated among people with a history of gang involvement, and such exposure is associated with substantial psychological distress. Mental health impacts were broadly similar across gang-involved and non-gang victims. These findings highlight the importance of recognizing gang-involved adults as a population with disproportionate exposure to firearm violence and significant trauma-related needs within a public health framework.