Search Results (307)

Antarctica’s extreme cryospheric conditions impose severe thermodynamic constraints on the natural attenuation of hydrocarbon pollutants. Despite the Antarctic Treaty System’s protections, the footprint of human logistics has left persistent reservoirs of petroleum hydrocarbons that threaten endemic biodiversity. This review critically synthesizes the state-of-the-art in Antarctic bioremediation, moving beyond traditional culture-dependent studies to integrate recent multi-omics breakthroughs (2020–2025). We analyze the molecular mechanisms limiting bioavailability in frozen soils and highlight the adaptive strategies of psychrophilic consortia, including the modification of membrane fluidity and the expression of cold-active enzymes (e.g., RHDs, AlkB). Notably, we discuss emerging findings on novel long-chain alkane degradation genes (almA, ladA) identified in 2025, which challenge previous assumptions about recalcitrance. Furthermore, the review evaluates the engineering bottlenecks of in situ versus ex situ strategies, emphasizing the synergistic potential of bacterial–fungal co-cultures and the ecological necessity of “climate-smart” remediation to mitigate methane emissions from thawing permafrost. By bridging the gap between fundamental microbial genetics and applied field engineering, we propose a roadmap for the next generation of biotechnological solutions in the warming polar environment. Full article

The simultaneous analysis of geometric morphology and thermodynamic states from heterogeneous sensing modalities is essential for high-temperature industrial inspection. While supervoxel segmentation is effective for extracting fine structures, conventional fixed-weighting schemes often struggle with the inherent heterogeneity between spatial sensors and thermal sensors. This paper proposes a segmentation framework for thermo-geometric point clouds based on Pareto-optimal weight learning and gradient anisotropy. A multi-objective evolutionary optimization algorithm is employed for multi-modal Pareto weight learning to adaptively balance geometric and thermal constraints. The developed gradient-anisotropic supervoxel generation algorithm introduces a local saliency factor to achieve fine-grained thermodynamic segmentation. Furthermore, a gradient damping mechanism is implemented to ensure high thermal-boundary adherence even in geometrically planar regions by imposing anisotropic penalty forces. Finally, a region-growing method based on the optimized multi-sensor fusion weights is utilized to merge similar supervoxels. Experimental results demonstrate that our approach outperforms traditional baselines by achieving high-fidelity thermal segmentation and multi-modal boundary preservation, while accepting a modest and necessary compromise in geometric compactness to accommodate spatial–thermal inconsistencies. Full article

Conductive core–shell superabsorbent polymers (SAPs) are emerging as multifunctional additives for cementitious materials, combining moisture management with electrical functionality. In cement-based systems, a swellable polymeric core enables internal curing and crack-sealing through controlled water uptake and release, while a conductive shell introduces ionic and/or electronic charge transport, addressing key limitations of conventional non-conductive SAPs. This dual functionality provides a pathway toward smart cementitious composites with enhanced durability, self-sensing capability, and moisture-responsive behavior. This review focuses on the physical chemistry mechanisms governing conductive core–shell SAPs in cementitious environments, with emphasis on swelling thermodynamics, water transport kinetics, interfacial phenomena, and charge transport mechanisms. The roles of osmotic pressure, elastic network constraints, ionic effects, and pore solution chemistry are critically discussed, together with their impact on conductivity, hydration processes, microstructure development, and long-term performance. The relative contributions of ionic and electronic conduction are examined in relation to hydration state, shell morphology, and percolation of conductive networks. In addition, the relevance of core–shell SAP architectures to sustainable packaging is briefly discussed as a secondary application, illustrating how similar physicochemical principles—such as moisture buffering and functional coatings—apply beyond construction materials. Finally, key knowledge gaps are identified, including long-term stability in highly alkaline environments, trade-offs between swelling capacity and conductivity, environmental impacts of conductive phases, and the need for integrated experimental and modeling approaches. Addressing these challenges is essential for the rational design and practical implementation of conductive core–shell SAPs in next-generation cementitious materials. Full article

Economic entropy, as an emerging concept in econophysics, has gained increasing relevance in the analysis of complex systems characterized by uncertainty, nonlinearity, and out-of-equilibrium dynamics. However, its integration into conventional economic modeling—particularly in production functions such as the Cobb–Douglas function—remains fragmented and lacks systematic empirical validation. This study conducts a scientometric analysis of 345 Scopus-indexed documents (1973–2024) addressing the intersection between entropy, econophysics, and production functions, with the aim of mapping the intellectual structure of the field, characterizing its growth trends, identifying its core contributions, and highlighting its main research gaps. The results reveal that the field has experienced sustained growth since 2004, with a notable acceleration between 2020 and 2023, although it exhibits a fragmented authorship structure that does not conform to Lotka’s Law, suggesting that the field is still in a stage of scientific consolidation. The Cobb–Douglas function emerges as a niche topic within the econophysics literature, with limited integration between entropy-based approaches—informational, thermodynamic, and maximum entropy—and the empirical modeling of production. Furthermore, weak citation linkages between econophysics and conventional economics are observed, confirming the interdisciplinary fragmentation of the field. These findings provide a structured reference for researchers interested in advancing toward analytical frameworks that explicitly incorporate uncertainty, information, and physical constraints into economic analysis, thereby contributing to the development of econophysics as an integrative discipline. Full article

Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics-regularized neural network framework for data-efficient PV power modeling using only 45 real experimental measurements of irradiance and temperature. To address data sparsity while preserving physical realism, a physics-guided synthetic augmentation strategy is introduced to generate additional training samples strictly within experimentally validated operating bounds. The proposed Physics-Informed Neural Network (PINN) incorporates two complementary physical constraints directly into the training objective: (i) enforcement of the Shockley–Queisser thermodynamic efficiency limit to maintain compliance with theoretical conversion bounds and (ii) monotonicity regularization to ensure non-negative power gradients with respect to irradiance. Unlike conventional post-processing correction methods, these physical constraints are embedded during model training, enabling simultaneous improvement in predictive accuracy and physical consistency. When benchmarked against a structurally identical unconstrained Artificial Neural Network (ANN), the proposed framework achieves strong predictive performance (R² = 0.9947, RMSE = 5.21 W) while reducing monotonicity violations by approximately 82%. Robustness evaluations under extrapolated irradiance conditions and elevated temperature scenarios further demonstrate stable and physically admissible behavior beyond the training domain. Overall, the results demonstrate that integrating limited experimental measurements with embedded physical priors enables reliable and physically consistent PV power prediction in sparse-data environments, highlighting the potential of physics-regularized learning for renewable energy modeling applications. Full article

AI data-center (DC) growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI DC cooling, treating cooling as an energy service that can be supplied through grade matching rather than solely through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning objects by explicitly accounting for thermal attenuation, absorption-based conversion, and parasitic electricity associated with delivery and auxiliaries. Three results structure the analysis. First, a reference-case energy-service ledger shows how a representative regulated WtE plant with municipal solid-waste throughput of 1500 t/day and lower heating value of 10 MJ/kg yields ~78.1 MWth of exportable driving heat and, at a 20 km corridor, ~53.0 MWcool of delivered cooling and ~8.0 MWe of net avoided cooling electricity after parasitic debiting. Second, the coupled system is governed by operating regimes, not a single efficiency score. Under the baseline package, full thermal coverage is maintained up to ~20.9 km, the stricter quality-adjusted criterion remains positive to ~22.9 km, and the electricity–relief criterion remains positive to ~44.7 km. Third, deployment-scale translation for a 1 GW IT campus (u = 0.70, L = 5 km) implies a net grid relief of ~116.9–264.4 MW across scenario packages, while the required WtE footprint ranges from roughly three to 148 equivalent representative plants, or about 0.6–40 full-load-equivalent plants at a 25% displacement target. The contribution is a siting-ready planning framework that identifies when WtE-coupled cooling remains corridor-feasible, when it becomes hybrid and marginal, and when infrastructure scale rather than thermodynamic benefit becomes the binding constraint. It is intended as a screening tool for planning and comparison, not as a project-specific hydraulic or plant-cycle design. Full article

The emergence of classicality through quantum decoherence is commonly described from complementary perspectives emphasizing stability (environment-induced superselection), objectivity (Quantum Darwinism), or physical feasibility (information thermodynamics). In realistic open quantum systems, however, these aspects coexist and compete under finite physical resources. In this work we argue that classical structure selection is most naturally understood as a resource-constrained, multi-objective process. We introduce the Informational Economy Functional (IEF), an effective accounting framework that places loss of distinguishability, energetic dissipation, and the generation of redundantly accessible records on equal footing. The associated Principle of Informational Economy characterizes emergent classical structures as those achieving an optimal compromise among stability, objectivity, and energetic feasibility. Classicality is thus neither maximally stable, nor maximally redundant, nor maximally energy-efficient, but instead reflects a Pareto-optimal balance shaped by environmental constraints. The IEF yields falsifiable predictions concerning pointer-structure variability, redundancy deformation, and resource-sensitive trade-offs, and suggests concrete experimental tests in continuously monitored quantum platforms. Classical reality is thereby reinterpreted as the most economical configuration in which information can stably form, propagate, and persist. Full article

The Zeta-Minimizer Theorem establishes that the Riemann zeta function $\zeta \left(s\right)$ and the primes arise variationally as unique minimizers of a phase functional defined on a symmetric measure space $\left(X,\mu ,G\right)$ equipped with helical operators. Three fundamental axioms—strict concave entropy maximization (Axiom 1), spectral Gibbs minima with non-vanishing ground states (Axiom 2), and irreducible bounded oscillations with flux conservation (Axiom 3)—allow for the selection of the non-proper Archimedean conical helix as the sole topology satisfying all constraints. Primes emerge as indivisible minimal cycles in the associated representation graph $\Gamma$ (via Hilbert irreducibility and Maschke’s theorem), while the Euler product is recovered through the spectral Dirichlet mapping of the helical eigenvalues. The partial zeta product, $Z\left(s\right)=\prod _j{\displaystyle \frac{1}{1-p_j^{-s}}},s\in \mathbb{R}\left\{0\right\},$ constitutes the exact grand partition function of any finite subsystem. Numerical inversion of this product directly recovers the mixture frequency $s$ from any experimental compressibility factor $Z_{\mathrm{m}\mathrm{i}\mathrm{x}}$. Mole fractions $x_i\left(s\right)$, interaction parameters $\Delta \left(x_i\right)$, and the Lyapunov spectrum $\lambda \left(x_i\right)$ then follow deductively via the helical transfer matrix and the closed-form linear ODE for $\Delta$. Occupation numbers $N\left(x_i\right)$ attain sharp maxima precisely at Fibonacci ratios $F_r/F_{r+1}$, leading to the molecular prime-ID rule. For twelve representative purely binary (irreducible) systems spanning atomic noble gases, simple diatomics, polar molecules, and an aromatic ring, the residuals satisfy $|Z\left(s\right)-Z_{\mathrm{m}\mathrm{i}\mathrm{x}}|<1.5\times {10}^{-8}$. The resulting $\lambda \left(x_i\right)$ curves accurately reproduce critical points, liquid ranges, and thermodynamic anomalies with zero adjustable parameters. The Riemann Hypothesis follows rigorously as a theorem: the unique fixed point of the duality functor $s\mapsto 1-s$ that preserves the orthogonality condition $\sum {\mathrm{c}\mathrm{o}\mathrm{s}}^2{\theta }^k=1$ is $\mathrm{R}\mathrm{e}\left(s\right)=1/2$, enforced by Axiom 1 concavity and Axiom 3 irreducibility. The framework is fully deductive and parameter-free and extends naturally to arbitrary mixtures and multiplicities through the helical representation graph. It provides a variational unification of analytic number theory, spectral geometry, thermodynamic phase behavior, and the Riemann Hypothesis from first principles. Full article

We show that thermodynamic consistency in systems with finite excluded volume implies compact support of the grand canonical particle-number distribution. Understanding whether fundamental bounds on information and matter content can arise purely from statistical-mechanical principles—independent of gravitational dynamics—is of central interest in thermodynamics, information theory, and cosmology. For any nonzero excluded volume parameter b, the partition function vanishes identically beyond $N_{max}=V/b$, enforcing a strict upper bound on admissible macrostates. We demonstrate that this compactness induces bounded particle-number fluctuations and finite Fisher information with respect to the chemical potential, thereby rendering the associated statistical manifold effectively finite-dimensional. This informational compactness provides a structural mechanism limiting distinguishability of macrostates independently of gravitational considerations. We argue that such thermodynamically enforced bounds are compatible with entropy bounds and holographic scaling principles, suggesting that informational finiteness may arise from statistical-mechanical consistency alone. Cosmological implications are discussed cautiously: infinite matter content at fixed volume is incompatible with compact support induced by finite excluded volume. Accordingly, the Fisher metric and associated thermodynamic lengths remain bounded when particle-number fluctuations are restricted by excluded-volume constraints. These results show that excluded-volume constraints induce a natural information-geometric compactness of the thermodynamic manifold, providing a general mechanism by which statistical distinguishability and curvature remain finite in finite-occupancy systems. Full article

The efficient recovery of low-enthalpy geothermal resources $(T\le 150$ °C) faces significant thermodynamic limitations due to thermal mismatch in evaporators when pure fluids are utilized. This study investigates low-GWP zeotropic mixtures (Pentane/Isobutane), optimized using the NSGA-II algorithm, to enhance both the efficiency and operational resilience of Organic Rankine Cycles (ORCs). The isothermal behavior of conventional fluids limits exergy recovery and increases the Levelized Cost of Energy (LCOE). To address this, an advanced simulation tool, “ORC Master Suite”, was developed and validated against recent literature. Exergetic efficiency and LCOE were simultaneously optimized under strict Pinch Point constraints. Results show that the low-GWP zeotropic mixture of Pentane/Isobutane (70/30% w/w) achieves a 15–25% increase in exergetic efficiency compared to pure fluids, mainly due to the temperature glide, which reduces irreversibilities. Despite the increase in required heat transfer area and the strict capital expenditure penalties associated with ATEX safety protocols for highly flammable hydrocarbons, the LCOE remained competitive against the reference fluid. Overall, low-GWP zeotropic mixtures not only improve thermodynamic performance but also exhibit higher operational resilience to geothermal source fluctuations, making them a promising and sustainable alternative for future geothermal power plants. Full article

The operational safety and endurance of unmanned aerial vehicles (UAVs) are strongly affected by lithium-ion battery degradation under extreme thermal environments. However, conventional physics-based models often rely on simplified assumptions, whereas purely data-driven methods usually lack physical interpretability and robust generalization. To address these limitations, this study proposes a Physics-Informed Deep Neural Network (PIDNN) for predicting UAV battery degradation under complex environmental conditions. The proposed framework integrates thermodynamic and fluid dynamic principles with deep neural networks by incorporating physical constraints derived from heat generation, heat conduction, and convective heat transfer into the loss function. This design enables the model to capture nonlinear degradation patterns while maintaining consistency with fundamental physical laws. Comprehensive simulation-based experiments were conducted under high-temperature (45 °C), low-temperature (−20 °C), and room-temperature (25 °C) conditions, together with varying discharge rates, humidity levels, wind speeds, and multi-factor coupled scenarios. The results show that the proposed PIDNN consistently outperforms conventional physics-based models and several representative data-driven methods, including SVM, LSTM, and GAN-based approaches. It achieves lower prediction errors across all evaluated conditions, as reflected by reduced mean absolute error and root mean square error. By providing physically consistent predictions of capacity fade, internal resistance growth, and remaining useful life, the proposed framework supports degradation-aware monitoring and early warning for intelligent battery management systems. These findings provide a robust methodological basis for improving the reliability, safety, and service life of UAV power systems operating in complex climatic environments. Full article

The subject concerns the determination of activation energy under dynamic conditions using two theoretical isothermal models, and subsequently experimental data, with reference to the α–T relationship matrix. In recent years, the Vyazovkin method, classified as one of the isoconversional variants, has gained the greatest recognition. Comparison was made between two isothermal models of the thermal dissociation of calcite, which in chronological terms are associated with a kinetic–nucleation reaction/process (the H-CL, as a kinetic model) and a kinetic–desorption reaction/process (the V, as a thermodynamic model). A comparison of numerical values, understood as the logarithm of the reaction/process rate with respect to temperature, shows correspondence in the temperature range up to the equilibrium temperature. The H-CL model is characterized by a strong dominance of the nucleation process relative to the chemical reaction, whereas the V model exhibits a certain type of balance resulting from the course of the chemical decomposition reaction combined with the transformation of a metastable oxide into a crystalline form. It was confirmed that both models describe the same phenomenon within the transformation process, which implies that for a constant conversion degree, the proportions of the chemical reaction and the physical process vary. Pointwise with increasing temperature, the H-CL model leads to a minimum activation energy E → 0, whereas the V model reaches a negative activation energy E < 0. In both cases, the apparent activation energy summed over the process is constant, and the assigned conversion degree, treated as isoconversional, remains fixed and corresponds to the assumed activation energy of the completed reaction/process. Several simple methods for its determination under dynamic/isoconversion conditions are used. Full article

Background: Genome-scale metabolic models (GEMs) are foundational tools for systems biology, enabling quantitative interrogation of human metabolism across physiological and pathological states. However, many legacy reconstructions exhibit heterogeneous identifier usage, incomplete pathway integration, and limited thermodynamic refinement, constraining reproducibility, interoperability, and translational applicability. Methods: We present EHMN 2026, an update of the Edinburgh Human Metabolic Network. The reconstruction was refined through systematic identifier reconciliation using MetaNetX and ChEBI mappings, duplicate reaction consolidation, thermodynamic directionality assessment, and structured pathway annotation via Reactome. The final model was encoded in Systems Biology Markup Language (SBML) Level 3 Version 2 with the Flux Balance Constraints (FBC2) package, ensuring explicit gene–protein–reaction (GPR) representation and compatibility with modern constraint-based modelling toolchains. Results: EHMN 2026 comprises 11 compartments, 14,321 metabolites (species), and 22,642 reactions, supported by 3996 gene products. Of all reactions, 9638 (42.6%) contain GPR associations, linking metabolic transformations to 2887 unique Ensembl gene identifiers (ENSG). Pathway integration yielded 2194 unique Reactome identifiers, providing structured pathway-level organisation of metabolic functions. Thermodynamic refinement reduced infeasible energy-generating cycles and improved reaction directionality coherence while preserving global network connectivity. The reconstruction is fully SBML-compliant and portable across major modelling platforms. Compared with Recon3D and Human1, EHMN 2026 uniquely combines native Reactome reaction-level annotation, systematic MetaNetX identifier harmonisation, documented thermodynamic cycle elimination (37 cycles, 0 remaining), and an 11-compartment architecture supporting organelle-specific modelling—features designed for QSP and multi-layer integration applications. Conclusions: EHMN 2026 delivers a rigorously harmonised, thermodynamically refined, and pathway-annotated human metabolic reconstruction with enhanced annotation depth and standards-based interoperability. By combining genome-scale coverage with structured gene and pathway integration, the model establishes a robust computational backbone for reproducible metabolic analysis and provides a scalable foundation for future multi-layer systems pharmacology and integrative modelling frameworks. Full article

In this work, we propose a modified Schwarzschild geometry inspired by the Asymptotic Safety approach to quantum gravity, in which the Newtonian coupling becomes a running quantity depending on the radial coordinate. We employ an infrared cutoff at the proper distance and obtain a new quantum-corrected black hole metric. We provide a thermodynamical analysis, first using standard methods and then proceeding to a geometrothermodynamical study of the phase space and to a topological analysis of phase transitions. We also calculate the grey-body factors of our solution, providing exact lower bounds in the quantum-corrected transmission coefficients. Finally, we present the shadow size and intensity profile of our solution, showing its consistency with current observational constraints. Full article

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