Groundwater availability has been a growing problem in the state of Kansas, where the High Plains aquifer (HPA) has been declining. Simultaneously, the Sunflower State is moving toward wind energy, investing in red meat production, and eyeing a proposal for the Kansas Aqueduct (a tremendous water transfer from eastern to semiarid western Kansas, a region with a distinct vulnerability to drought that overlies the HPA). What do Kansans think about these changes in their environment and infrastructure? Using a survey of the state’s residents (n = 864), we find that owning a private water well is a significant predictor of opposition to the colossal aqueduct, while living above the HPA predicts support for the water transfer. Well owners and women oppose the construction of coal-fired power plants, oil pipelines, hydraulic fracturing, and large corporate feedlots, while politically conservative ideologies predict support. Furthermore, well owners and women are nearly twice as likely to disapprove of fracking; conservatives have lower odds of fracking opposition. The Just Transition in Kansas is not only a question of how water, agribusiness, and wind and nuclear energy are developed, but also residents’ perceptions of these projects.
The operational utility of Unmanned Aerial Vehicles (UAVs) has evolved from passive surveillance to active engagement in disputed environments, where autonomous control must operate under highly dynamic and adversarial conditions. Hand-crafted heuristics often exhibit limited robustness when facing stochastic opponent behavior and non-stationary interactions. To address these challenges, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework implemented in a Unity 6–based, physics-driven simulation that models flight dynamics and weapon kinematics. Agents are trained using Proximal Policy Optimization (PPO) with a composite reward function designed to encourage cooperative behaviors (e.g., coordinated target engagement) while enforcing safety constraints such as collision avoidance. In empirical evaluations, the learned policies achieve an 85% win rate against a heuristic baseline under the tested scenarios, exhibiting coordinated maneuvers and adaptive engagement strategies. These results indicate that multi-agent learning with decentralized execution can reduce operator workload and improve swarm effectiveness and survivability in conflict zone.
Congestive heart failure (CHF) encompasses both reduced and preserved ejection fraction phenotypes. Modern management increasingly demands actionable insights into cardiac function beyond standard vitals. Cardiac time intervals (CTIs), including mitral valve closure (MVC), aortic valve opening (AVO), aortic valve closure (AVC), and mitral valve opening (MVO), as well as isovolumetric contraction time (IVCT) and isovolumetric relaxation time (IVRT), offer a window into the electromechanical timing of systole and diastole. These intervals provide clinically relevant markers of systolic function, diastolic filling dynamics, and chamber compliance. In HFrEF (reduced ejection fraction), CTI monitoring captures deterioration in contractile efficiency; in HFpEF (preserved ejection fraction), diastolic stiffness and shortened filling times can be tracked. Remote CTI monitoring facilitates timely therapy adjustments, prevents hospitalizations, empowers patients in their disease management, and provides clinicians with early warning signals of worsening physiology. CTIs enable a comprehensive, non-invasive assessment of cardiac chamber performance. This is especially relevant across the full spectrum of heart failure, including both HFrEF and HFpEF. The ability to deliver precise cardiac timing data outside of traditional clinical settings makes it a transformative tool for proactive, physiology-based heart failure management.
Unidentified Aerial Phenomena (UAP) refer to aerial anomalies that cannot be identified as known objects or natural occurrences. Despite historical reports, research into the medical impacts of UAP encounters remains in its early stages, lacking a systematic framework and substantial clinical data. This review provides an overview of the medical evidence regarding UAP-related injuries, including clinical case reports, injury mechanisms, epidemiological data, and the application of neuroimaging and forensic medicine. By analyzing declassified U.S. Defense Intelligence Agency documents, medical case reports, and scientific studies, we highlight the multisystem health issues associated with UAP contact, particularly neurological damage and non-ionizing electromagnetic radiation effects. We also explore the significant rise in UAP incident reports near sensitive military and nuclear facilities, suggesting a growing concern for human health. Future research must focus on prospective studies, interdisciplinary collaboration, and advanced forensic technologies to better understand the long-term pathophysiological mechanisms underlying UAP-induced injuries.
Waldenström macroglobulinemia (WM) is a lymphoplasmacytic lymphoma characterized by monoclonal immunoglobulin M (IgM) overproduction, leading to hyperviscosity syndrome and microvascular complications. While increased plasma viscosity is a well-recognized feature of WM, the impact of extreme IgM elevation on intrinsic red blood cell (RBC) mechanical properties remain incompletely characterized. Here, we report a case of WM with markedly elevated IgM associated with profound impairment of RBC deformability. Therapeutic plasma exchange rapidly reduced serum IgM levels, accompanied by parallel and sustained improvement in RBC deformability. Given the importance of RBC deformability in microvascular blood flow, these findings highlight a reversible, IgM-mediated alteration in RBC mechanics and provide novel insights into microcirculatory dysfunction in WM.
To address the environmental challenges posed by massive phosphogypsum (PG) stockpiles and groundwater fluoride contamination, this study developed an eco-friendly strategy for synthesizing lanthanum-doped hydroxyapatite (La-PGHAP) from PG waste via an acid precipitation-hydrothermal method. The synthesized La-PGHAP exhibited a spherical morphology, high crystallinity, and a significantly enhanced specific surface area of 53.11 m2/g. Batch adsorption experiments revealed that pH critically influenced fluoride (F−) removal, with maximum adsorption capacities of 8.20 mg/g (PGHAP) and 31.98 mg/g (La-PGHAP) at pH 4. The adsorption process followed pseudo-second-order kinetics and the Langmuir isotherm model, indicating chemisorption-dominated monolayer adsorption. La doping introduced Lewis acid-base interactions through La3+–F− coordination, improving both adsorption capacity and stability across a wide pH range (2–10). Reusability tests demonstrated that La-PGHAP retained 85.4% of its initial capacity after 4 cycles. This “waste-to-waste” approach not only repurposes PG into a high-efficiency adsorbent but also provides a sustainable solution for mitigating fluoride pollution, showcasing significant potential for industrial-scale water treatment applications.
This paper investigates anomaly diagnosis for grid-tied three-phase inverters in cyber–physical smart grids, with an emphasis on distinguishing physical IGBT open-circuit physical faults from anomalies induced by denial-of-service (DoS) cyber-attacks. A super-twisting-based second-order interval sliding-mode observer is developed to estimate three-phase currents with bounded errors in the presence of uncertainties and disturbances. Based on analytical residual relationships, fault localization is achieved using the residual sign pattern and magnitude ratios for single-switch and same-leg double-switch open-circuit faults. In contrast, DoS-induced anomalies primarily manifest as effective current attenuation without deterministic residual sign or ratio patterns, enabling fault-type discrimination. Simulation results demonstrate that the proposed method achieves reliable anomaly diagnosis within one fundamental cycle, without requiring additional sensors or training data.
Rights of Nature (RoN) represent an innovative form of environmental governance. However, the diverse application of RoN across varying socio-ecological contexts remains under-researched. This paper employs the “Roots of Rights” (RoR) approach for a comparative analysis. We examine RoN’s institutionalisation, implementation, and contestation in Germany and Aotearoa New Zealand, focusing on underlying relational values. Our analytical framework investigates two core dimensions: political dynamics of marginalisation and the role of relational approaches in the codification process. The findings reveal a fundamental divergence in RoN’s function. In Germany, RoN operates primarily as a radical theoretical tool. It is used by civil society to challenge the prevailing anthropocentric legal tradition. Conversely, legal personhood in New Zealand (e.g., Whanganui River) is a direct political product of Treaty Settlements. These frameworks serve the political self-determination and emancipation of Māori Iwi. Crucially, they codify a deeply-rooted, pre-existing relational worldview (tikanga). We conclude that RoN functions as a “thin” conceptual instrument in Germany, but as a ‘”hick”, politically instrumental means of securing non-hegemonic norms in New Zealand.
Climate change has become a critical global concern due to its adverse impacts on both humans and the environment. In alignment with Sustainable Development Goal 13, which calls for urgent action to combat climate change and its effects, this study examines community perceptions of climate change in Ghana, using evidence from Dakodwom in the Ashanti Region. The study specifically aims to: (1) examine the association between perceived climate change and the perceptions of its causes within the Dakodwom community, (2) assess the association between perceived climate change, its indicators, and trends, (3) examine the determinants of perceived climate change, and (4) identify practices that could mitigate climate change–related challenges. A structured questionnaire comprising closed-ended questions was used to collect data. Pearson’s chi-square test was employed to determine the relationship between perceived climate change and its perceived causes, as well as to assess the significance of respondents’ perceptions of various climate indicators and trends. Binary logistic regression was further applied to identify the factors influencing perceived climate change. The findings reveal that respondents attribute perceived climate change primarily to burning, deforestation, vehicle emissions, industrial emissions, agricultural activities, and urbanization. Participants demonstrated statistically significant awareness of changes in rainfall patterns, temperature increases, wind activity, and extreme weather events, indicating noticeable environmental changes. The regression results show that employment status and awareness of activities such as burning, agricultural activities, and industrial emissions are the significant determinants of perceived climate change. Additionally, the study identifies recycling, composting, community education, and the adoption of innovative waste-management technologies as practical strategies with potential to mitigate climate change–related challenges. Based on these findings, local authorities and environmental agencies should prioritize investments in improved waste-management systems, community composting facilities, and green infrastructure initiatives, including tree planting and environmentally sustainable agricultural practices, to address the observed increases in temperature, wind activity, and extreme weather events.
Unmanned aerial vehicles (UAVs is also known as drones) have significant applications in smart cities, and the information exchange between UAVs and the control server (CS) is conducted through wireless communication channels, which are susceptible to various security risks, such as network attacks and drone capture. To ensure the security and integrity of information in the Internet of Drones (IoD), identity authentication and key agreement protocols can be designed for protection. However, due to the unique characteristics of IoD, such as the extremely high mobility of drones in real scenarios and the resource constraints of drones, there is a need to meet the requirements for lightweight protocols. This paper proposes a strategy that uses cancelable biometric features to protect the biometric features of users during the authentication process. The method combines Fast Fourier Transform, Gaussian random projections, Position-Sensitive Hashing, fuzzy extractors, and Physical Unclonable Functions (PUF), meeting the security and lightweight needs of IoD authentication protocols. We use the Real-or-Random (ROR) model and the Avispa simulation tool to prove that our protocol is secure. Through comparative research, the proposed cancelable method has higher matching efficiency and better unlinkability, and our protocol offers higher security and faster computational efficiency.