Search Results (1,613)

Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning models often fail to capture these complex characteristics, limiting their ability to support accurate and physically consistent long-term SST prediction. To address these issues, we propose PAMSTnet, a unified deep learning framework for physics-informed adaptive multiscale spatiotemporal prediction. PAMSTnet leverages three-dimensional empirical wavelet transform (3DEWT) to learn interpretable multiscale spatiotemporal dynamics from raw observations, and applies multivariate spatiotemporal empirical orthogonal function (MSTEOF) to identify dominant cross-variable coupled modes. These physically meaningful representations are integrated into a deep ConvLSTM predictive network (DCPN) to support coordinated multiscale dynamical learning. Furthermore, PAMSTnet introduces physics-informed consistency learning (PICL) to enforce thermodynamic and dynamic constraints, enhancing physical consistency and interpretability. Extensive experiments demonstrate that PAMSTnet achieves superior performance against state-of-the-art baselines in long-term global SST prediction, reducing RMSE by 8.1% and improving ACC by 2.8% compared with the best-performing baseline, particularly under extreme climate events. Interpretation insights further highlight PAMSTnet’s adaptive representation of variable contributions and regional physical drivers. These findings position PAMSTnet as a promising paradigm for developing intelligent ocean prediction systems with enhanced physical consistency and interpretability. Full article

In structural health monitoring, purely data-driven methods for deformation prediction are often susceptible to time-varying boundary conditions under complex operating scenarios, leading to insufficient physical interpretability and limited generalization across different conditions. To address these challenges, this study proposes a Physics-Informed Dual-branch Long Short-Term Memory framework (PINN-DualSHM). The framework employs dual-branch LSTMs to separately extract temporal features of structural mechanical responses and environmental thermal effects. Dynamic decoupling and fusion of these heterogeneous features are achieved through an adaptive cross-attention mechanism. Furthermore, physical priors, including the thermodynamic superposition principle and structural settlement monotonicity, are embedded into the loss function as regularization terms, complemented by a dual uncertainty quantification system based on heteroscedastic regression and MC Dropout. Experimental results based on long-term measured data from an industrial base project in Shenzhen demonstrate that PINN-DualSHM significantly outperforms baseline models such as LSTM, CNN-LSTM, and GAT-LSTM. Specifically, the Root Mean Square Error (RMSE) is reduced by 65.25%, and the coefficient of determination (R²) reaches 0.925. Physical consistency analysis confirms that the introduction of physical constraints effectively suppresses anomalous predictive fluctuations that violate mechanical laws. Uncertainty decomposition reveals that aleatoric uncertainty is dominant (93.7%), objectively indicating that the current system’s accuracy bottleneck lies in sensor noise rather than model capability. By enhancing prediction accuracy while providing credible quantitative assessments and physical interpretability, the proposed method provides a scientific basis for the operation, maintenance optimization, and upgrading decisions of SHM systems. Full article

Thermodynamics is commonly presented as a theory of macroscopic systems in stable equilibrium, built upon assumptions of extensivity and scaling with system size. In this paper, we present a universal formulation of the elementary foundations of thermodynamics, in which entropy and energy are defined and employed beyond equilibrium and without assuming extensivity. The formulation applies to all systems—large and small, with many or few particles—and to all states, whether equilibrium or nonequilibrium, by relying on carefully stated operational definitions and existence principles rather than macroscopic idealizations. Key thermodynamic concepts, including adiabatic availability and available energy, are developed and illustrated using the energy–entropy diagram representation of nonequilibrium states, which provides geometric insight into irreversibility and the limits of work extraction for systems of any size. A substantial part of the paper is devoted to the analysis of entropy transfer in non-work interactions, leading to precise definitions of heat interactions and heat-and-diffusion interactions of central importance in mesoscopic continuum theories of nonequilibrium behavior in simple and complex solids and fluids. As a direct consequence of this analysis, Clausius inequalities and the Clausius statement of the second law are derived in forms explicitly extended to nonequilibrium processes. The resulting framework presents thermodynamics as a universal theory whose concepts apply uniformly to all systems, large and small, and provides a coherent foundation for both teaching and modern applications. Full article

The Earth’s climate is an open nonlinear system, sustained far from thermodynamic equilibrium by solar radiation and energy and matter exchange among its four major subsystems: atmosphere, ocean, land, and cryosphere. Among these four subsystems, the ocean significantly influences and sustains Earth’s climate over decadal to millennial timescales. Although modern numerical models increasingly capture intricate dynamical details, the fundamental concepts of large-scale ocean variability are less frequently explored. This study revisits ocean circulation through the lens of self-organization theory and synergetics. The key synergetic concepts of mode competition, order parameters, and the slaving principle are interpreted within the framework of general ocean circulation and the Atlantic Meridional Overturning Circulation (AMOC). The Brusselator, a simplified model of a nonlinear dynamical system initially developed in chemical kinetics, serves as a conceptual analog for ocean circulation energy conversion. Despite its high abstraction, this proxy model effectively captures essential bifurcation behaviors, such as Hopf bifurcation transitions and limit-cycle behaviors. This clarifies feedback regulation, instability, and potential regime transitions in the AMOC. The synthesis in this study is intended for an interdisciplinary readership and highlights the broader applicability of synergetic principles to the complex Earth climate system maintained far from equilibrium. Full article

In this work, iron–phosphorus-based composite biochar (FPBC) was prepared by modification with the leachate of spent LiFePO₄ batteries. The effects of solution pH, dosage, adsorption time, initial concentration, and temperature on the adsorption performance of FPBC were investigated by batch adsorption experiments with Pb(II) and Sb(III) as the target pollutants, and the adsorption mechanism was explored using SEM, BET, XPS, FTIR and XRD characterization. The results indicated that as the initial pH of the solution increased, the removal efficiency of FPBC for Pb(II) gradually increased, while the removal efficiency for Sb(III) remained largely unchanged. The removal of Pb(II) and Sb(III) by FPBC fitted the pseudo-second-order kinetic model and the three-step intraparticle diffusion model, indicating that their removal was primarily controlled by chemical adsorption. Isothermal adsorption studies revealed that FPBC adsorption of Pb(II) better fitted the Langmuir and D-R models, suggesting a monolayer-dominated adsorption process. In contrast, adsorption of Sb(III) fitted the Langmuir, Freundlich, and Temkin models, suggesting a combination of monolayer and multilayer adsorption characteristics. The maximum adsorption capacities of FPBC for Pb(II) and Sb(III) were 312.54 mg·g⁻¹ and 219.20 mg·g⁻¹ at 30 °C, which were approximately 12.85 and 3.37 times those of commercial corn stalk biochar (BC). Thermodynamic analysis confirmed that the removal of Pb(II) and Sb(III) by FPBC was a spontaneous and endothermic process. In addition, FPBC demonstrated strong selective adsorption of Pb(II) in the binary co-adsorption system of Pb(II) and Sb(III). Mechanism studies indicated that Pb(II) removal primarily occurred through co-precipitation, complexation, ion exchange, and electrostatic adsorption, while Sb(III) was mainly adsorbed by FPBC via redox reactions and complexation. Therefore, this work not only provides a low-cost, high-performance adsorbent for the remediation of water contaminated with Pb(II) and Sb(III), but also opens up new avenues for the resource recovery of the leachate of spent LiFePO₄ batteries. Full article

The adsorption and activation of CO₂ on Cu_xSc_y nanoclusters with x + y equal to 4 were analyzed using DFT and PDOS and TDOS signatures. The geometries of Cu₃Sc, Cu₂Sc₂, and CuSc₃ were optimized in the gas phase, and the minima were verified by frequencies in ORCA using M06-2X/def2-TZVP. Multiplicities 1, 3, and 5, temperatures between 298 and 400 K, and four CO₂ coordination modes R1 to R4 were evaluated. Naked and complex cluster comparison panels were constructed, and two energy windows, −18 to −10 eV and −8 to 6 eV around the Fermi level, were analyzed, complemented by frontier orbitals and charge maps. Thermodynamics indicated that mode and multiplicity control the adsorption energy, with ANOVA p-values of 0.002 and 0.008, while temperature was not significant (p = 0.682). In Cu₃Sc–C₂v(1), the R1 singlet at 298 K showed E_ads −33.43 kcal·mol⁻¹ with spin contamination, while alternative modes in the singlet were unfavorable. In PDOS and TDOS, the bare cluster exhibits a Cu d band at −11 to −10 eV and a valley around −5 eV. The exergonic complexes show CO₂ signals near the Fermi level, superimposed on Cu and Sc states, with state filling and broadening. Transferable indicators based on CO₂ intensity in the −8 to 6 eV range and metal–adsorbate overlap are proposed as predictors of exergonic adsorption. Full article

The present work set out to examine the antidiabetic capacity of Abelmoschus esculentus (okra) fruit extract through a combined experimental and computational framework. Enzyme inhibition assays were carried out against four metabolic targets, and IC₅₀ values stood at 7.66 ± 0.31 mg/mL for alpha-glucosidase, 5.21 ± 0.18 mg/mL for alpha-amylase, 2.11 ± 0.15 microg/mL for DPP-4, and 9.17 ± 0.54 mg/mL for pancreatic lipase. The extract showed moderate-to-weak activity relative to standard inhibitors acarbose, sitagliptin, and orlistat. Sixteen drug-like phytochemicals obtained from the IMPPAT 2.0 database were docked against the crystal structures of all four tested enzymes (PDB: 8CB1, 5E0F, 2ONC, 1LPB). Alpha-Carotene, Vitamin E, and Spiraeoside emerged as the top-ranked compounds across all targets, with alpha-Carotene recording the strongest binding affinity of −11.1 kcal/mol against pancreatic lipase, which was 4.2 kcal/mol more negative than the positive control orlistat (−6.9 kcal/mol). PLIP-based interaction profiling mapped out hydrogen bonds, hydrophobic contacts, pi-stacking, and salt bridges at the atomic level. Absorption, distribution, metabolism, and excretion (ADME) and toxicity screening of alpha-Carotene returned a favourable pharmacokinetic profile with predicted LD₅₀ of 1510 mg/kg (Class 4) and inactivity across most toxicity endpoints. A 100 ns molecular dynamics simulation of the pancreatic lipase-alpha–Carotene complex, alongside the orlistat control, showed stable root mean square deviation (RMSD) (0.15–0.22 nm), a consistent Rg (~1.97 nm), and sustained hydrogen bonding throughout the trajectory. Free-energy landscape analysis revealed a well-defined single energy basin for alpha-Carotene, suggesting a thermodynamically stable binding conformation. These findings lay the molecular basis for using okra phytochemicals as adjunctive agents in diabetes management, though in vivo validation remains necessary. Full article

How does the complex, adaptive, and autonomous organization of life emerge from the laws of physics and information? This review argues that the answer lies in a convergent set of universal organizational principles that constitute a physical and informational grammar of the living. Living systems are dissipative structures that achieve organizational closure—materially and energetically open, yet causally closed—thereby attaining genuine autonomy and agency. Their architecture exhibits fractal and modular scaling laws that maximize energy flow, robustness, and evolvability under universal physical constraints. Critically, organisms operate at critical transitions—zones of controlled instability where fluctuations amplify information processing, transforming noise into adaptive signal. This self-organized criticality enables functional degeneracy, relational redundancy, and evolutionary antifragility. Cognition emerges as a distributed process of active inference, operating through a predictive–corrective cycle that integrates perception, action, and learning under the Free Energy Principle. From molecular networks to ecosystems, the same physico-informational grammars unfold recursively, revealing a deep organizational holography: the principles of organization are replicated across scales. Evolution under the Law of Increasing Functional Information is not random drift, but a directional expansion of functional complexity—a thermodynamic gradient towards greater agency. This synthesis challenges biological exceptionalism: the trajectory from thermodynamics to cognition is continuous, physically constrained, and potentially inevitable. Life does not violate physical laws—it fulfills them in regimes of high informational complexity, instantiating fundamental principles in self-organized architectures capable of prediction, memory, and purpose. The objective of this work is to articulate how the synthesis of these principles not only unifies physics and biology, but also illuminates the profound continuity between thermodynamics, chemistry, informational constraints, organization, and the mind. Full article

To achieve high-precision and real-time quantitative evaluation of gas production in underground gas storage (UGS), this study focused on 11 typical injection-production wells in the Sudong UGS group. To address the common challenges posed by deviated well structures and complex wellbore temperature field distributions, a gas flow-rate calculation method based on Distributed Temperature-Sensing (DTS) data was developed. By standardizing the processing of multi-well temperature data, deviated wellbore trajectories were straightened to convert measured depth (MD) to true vertical depth (TVD). By incorporating a geothermal correction mechanism, temperature anomalies closely related to fluid flow were extracted, and a spatially unified temperature field model was constructed. On this basis, a “Dual-Point Temperature Difference Method” is proposed as a novel approach for single-well production evaluation. Based on thermodynamic phenomena such as the Joule–Thomson effect and expansion cooling, two critical sensing points, upstream and downstream of the production layer, were selected, with their temperature anomaly difference (∆T) serving as a sensitive indicator of flow rate variations. Combined with downhole pressure parameters and synchronized wellhead metering data, a nonlinear quantitative relationship model between ∆T and gas production rate Q was established, enabling accurate conversion of wellbore thermal response to macroscopic flow parameters. The results indicated that the gas production rates calculated by this method align well with traditional wellhead metering data, with errors maintained within engineering tolerances. Notably, the method demonstrates higher reliability and corrective capabilities in wells with drifting or faulty meters. This achievement breaks the reliance of traditional methods on specific layers or mechanical meters. It enables the effective application of multi-well, full-section, and non-contact temperature data in gas volume assessment. This research provides new technical support for dynamic monitoring, efficient operation, and remaining gas evaluation of UGS, offering significant prospects for engineering applications. Full article

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article

Reverse osmosis concentrate (ROC) contains relatively high levels of refractory organic pollutants, posing significant challenges due to its difficult treatment and high environmental risks. Therefore, efficient and convenient removal strategies are essential. In this study, a self-developed iron-modified activated carbon fiber (Fe-ACF) was employed as an adsorbent to remove organic pollutants from ROC. Additionally, response surface methodology (RSM) was applied to model the adsorption process, identify and evaluate key influencing parameters, and optimize operational conditions. The adsorption mechanisms and regeneration stability of Fe-ACF were also investigated. Kinetic analysis revealed that the adsorption process is predominantly governed by chemisorption, with intraparticle diffusion identified as the primary rate-limiting step. Isothermal adsorption studies demonstrated that the Langmuir–Freundlich model best describes the adsorption behavior, yielding a theoretical maximum adsorption capacity of 12.21 ± 0.80 mg/g. Thermodynamic analysis confirmed that the adsorption process is spontaneous, endothermic, and driven by an increase in entropy. The RSM optimization identified pH as the dominant factor. The optimal adsorption conditions were a pH of 4.18, a temperature of 34.63 °C, a stirring speed of 547.91 rpm, and an adsorbent dosage of 1.55 g/L. The adsorption mechanism involves hydrogen bonding, π–π interactions, surface complexation, and electrostatic forces. Fe-ACF exhibits competitive regeneration stability and structural integrity. In summary, Fe-ACF demonstrates significant potential as a treatment material for ROC. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

Engineering education often emphasizes technical competencies while underemphasizing and devaluing the social, ethical, and political contexts of engineering systems. This gap is particularly pronounced in middle-year courses, where students develop technical fluency but rarely confront the sociotechnical complexity of real-world problems. We propose sociotechnical judgment as a framework to help students see the intimately intertwining nature of technical knowledge and social, ethical, and contextual reasoning, using energy systems—particularly offshore wind—as an illustrative domain. We designed three course-integrated case studies in thermodynamics, circuits, and statics/dynamics to embed sociotechnical judgment in middle-year engineering courses. These cases include pedagogical strategies, such as project-based learning, problem-based learning, and role-play exercises connecting technical analysis with social, environmental, and policy considerations. The design of these case studies is rooted in real-world problems surrounding U.S. offshore wind, engineering science learning outcomes, and ABET student outcomes. In these pedagogies, we have created opportunities for students to analyze technical systems while engaging with social, ecological, and political factors. Offshore wind projects, including turbine siting, transmission system design, and efficiency trade-offs, provide opportunities to operationalize sociotechnical reasoning in authentic, regionally relevant contexts. Sociotechnical judgment provides a practical framework for bridging technical competency and contextual reasoning in engineering education. Integrating sociotechnical cases into core courses will prepare students to navigate complex, real-world systems through engagement with ethical, social, and environmental considerations inherent in engineering practice. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

Enrofloxacin (ENR), as a widely used antimicrobial agent in aquaculture, poses potential risks to ecosystems and human health due to its environmental persistence. Therefore, it is of great significance to explore efficient methods for removing ENR from aquaculture wastewater. In this study, a series of shrimp shell-derived aerogel (MBC300–MBC700) were fabricated from Litopenaeus vannamei shells through chemical modification followed by pyrolysis at 300–700 °C, and their adsorption performance and mechanisms toward ENR were systematically investigated. The modified porous materials exhibited a well-developed micro–mesoporous structure, high specific surface area, and abundant surface functional groups. Meanwhile, MBC400 demonstrated the highest adsorption capacity for ENR, reaching 14.56 mg/g, with a corresponding specific surface area of 77.71 m²/g. The adsorption kinetics followed the pseudo-second-order model, and the isothermal data were better fitted by the Freundlich model, indicating a chemisorption-dominated, heterogeneous multilayer adsorption process. Thermodynamic analysis revealed that the adsorption was spontaneous (ΔG < 0) and endothermic (ΔH > 0). In regeneration experiments, 30% ethanol solution achieved the best desorption efficiency for MBC400, with adsorption efficiency remaining above 75% after three cycles. Based on the characterization and adsorption results, adsorption mechanism of ENR on MBC400 was elucidated as a synergistic effect of hydrogen bonding, π–π stacking, electrostatic interaction, and surface complexation. This study provides a novel strategy and theoretical basis for the high-value utilization of shrimp shell waste and for the efficient removal of fluoroquinolone antibiotics from aquaculture effluents. Full article