The tidal Schuylkill River in Philadelphia, USA, presents a waterscape at once singular and uniquely local while also typical of a novel ecosystem. Heavy with centuries of legacy pollutants from coal and oil refining, the river water presents a liquid archive with few finding aids, catalogs, or guides. Propelled by a fundamental paradox (if "wisdom sits in places," how do we know "forgotten places"), this article considers a series of collaborative learning experiments animated by insights from academic and community researchers about Black ecologies. It traces a series of public environmental humanities experiments undertaken over a five-year period in and with the river's historic wetlands and their various communities. Sharing insights from insurgent learning practices, the article aims to further the environmental humanities' transformational premise and promise to envision and foster more just futures. As we, academic researchers, have learned with and from community partners, we have sought to make mutually beneficial work, including much that hardly resembles conventional academic outcomes. A "living" digital river archive offers a compendium to the article.
Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposes CryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.
Achieving carbon neutrality requires governments to reorganize their procurement and supply systems so that efficiency, cost, and environmental performance are optimized jointly. Yet public supply chains face frequent disturbances including policy revisions, climate disasters, geopolitical shocks, and demand surges, that traditional optimization frameworks struggle to absorb. This paper proposes GRACE (Green Resilient Attention-driven Coordination Engine), a multi-agent reinforcement learning (MARL) framework that evaluates and improves the resilience of digital-government green supply chains under the dual-carbon goal. GRACE represents the government as a demand-side coordinator and each supplier as an independent agent in a decentralized partially observable Markov decision process. A gated recurrent demand-forecasting module supplies forward-looking state features, and a hybrid PPO/MADDPG policy optimizer learns a carbon-aware quota allocation policy under a triple-objective reward that balances service level, cost, and embedded carbon. We further introduce a Resilience Triangle Score (RTS) that captures the absorptive, adaptive, and restorative capacities of the network during disturbance. Experiments built on the U.S. General Services Administration (GSA) supply chain open datasets, augmented with stochastic disruption scenarios, show that GRACE outperforms classical mixed-integer optimization, single-agent DDPG/PPO, and recent MARL baselines: relative to the strongest baseline it improves RTS by 14.3%, reduces embedded carbon by 9.9%, and shortens recovery time by 34.2%, while relative to a static mixed-integer optimizer these gains reach 38.4%, 27.2%, and 55.9% respectively. Sensitivity analyses confirm robustness across disruption magnitudes, supplier counts, and randomness seeds. The framework is generic and can be transferred to humanitarian logistics, energy dispatch, and pandemic resource allocation.
In this work we study the relativistic kinetic theory of a boost-invariant conformal gas on a static, maximally symmetric background dS3 & times; , considering all constant-curvature slicings of dS3-flat, spherical, or hyperbolic-and their associated symmetry groups. Using a symmetry-driven cotangent bundle approach, we show that the isometry group of each slicing acts on phase space in such a way that only its Casimir invariants and the timelike coordinate are unconstrained, so the distribution function depends solely on these quantities. This yields a unified boost-invariant exact solution of the Boltzmann equation valid for each constant-curvature foliation of dS3 & times; . Specializing this general solution to the flat and spherical foliations reproduces the Bjorken and Gubser flows, respectively, while its restriction to the hyperbolic foliation produces a genuinely new analytic solution ("Grozdanov flow"). Hydrodynamics and free streaming emerge naturally as limiting regimes of this novel exact solution. We further comment on several relevant aspects of the new boost-invariant solution on the hyperbolic slicing and on their interpretation once mapped back to Minkowski space.
The research delves into the impact of contradictory thinking on new employees’ psychosocial adaptation, emphasizing the crucial mediating role of social distance. As workplace environments evolve and become increasingly complex, effective psychosocial adaptation is essential for individual mental well-being and achieving positive organizational outcomes. Systematically explore how contradictory thinking shapes adaptation processes through a series of three studies. Study 1 identifies a positive correlation between contradictory thinking and psychosocial adaptation, observed consistently across both Eastern and Western cultural contexts, suggesting a potentially universal effect. Study 2 establishes that contradictory thinking is critical in reducing perceived social distance between new employees and their colleagues, enhancing their adaptation process. Finally, Study 3 confirms the mediating effect of social distance in the relationship between contradictory thinking and psychosocial adaptation, indicating that reduced social distance is a pathway through which contradictory thinking promotes better integration. The research findings suggest that encouraging contradictory thinking in new employees could significantly improve their psychosocial adaptation by fostering a sense of closeness and belonging, ultimately contributing to improved mental health and smoother workplace integration.
