Search Results (143)

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

In device-independent (DI) quantum protocols, security statements are agnostic to the internal workings of the quantum devices—they rely solely on classical interactions with the devices and specific assumptions. Traditionally, such protocols are set in a non-local scenario, where two non-communicating devices exhibit Bell inequality violations. Recently, a new class of DI protocols has emerged that requires only a single device. In this setting, the assumption of no communication is replaced by a computational one: the device cannot solve certain post-quantum cryptographic problems. Protocols developed in this single-device computational setting—such as for randomness certification—have relied on ad hoc techniques, making their guarantees difficult to compare and generalize. In this work, we introduce a modular proof framework inspired by techniques from the non-local DI literature. Our approach combines tools from quantum information theory, including entropic uncertainty relations and the entropy accumulation theorem, to yield both conceptual clarity and quantitative security guarantees. This framework provides a foundation for systematically analyzing DI protocols in the single-device setting under computational assumptions. It enables the design and security proof of future protocols for DI randomness generation, expansion, amplification, and key distribution, grounded in post-quantum cryptographic hardness. Full article

The statistical mechanics of structured particles with arbitrary size and shape adsorbed onto discrete lattices presents a longstanding theoretical challenge, mainly due to complex spatial correlations and entropic effects that emerge at finite densities. Even for simplified systems such as hard-core linear k-mers, exact solutions remain limited to low-dimensional or highly constrained cases. In this review, we summarize the main theoretical approaches developed by our research group over the past three decades to describe adsorption phenomena involving linear k-mers—also known as multisite occupancy adsorption—on regular lattices. We examine modern approximations such as an extension to two dimensions of the exact thermodynamic functions obtained in one dimension, the Fractional Statistical Theory of Adsorption based on Haldane’s fractional statistics, and the so-called Occupation Balance based on expansion of the reciprocal of the fugacity, and hybrid approaches such as the semi-empirical model obtained by combining exact one-dimensional calculations and the Guggenheim–DiMarzio approach. For interacting systems, statistical thermodynamics is explored within generalized Bragg–Williams and quasi-chemical frameworks. Particular focus is given to the recently proposed Multiple Exclusion statistics, which capture the correlated exclusion effects inherent to non-monomeric particles. Applications to monolayer and multilayer adsorption are analyzed, with relevance to hydrocarbon separation technologies. Finally, computational strategies, including advanced Monte Carlo techniques, are reviewed in the context of high-density regimes. This work provides a unified framework for understanding entropic and cooperative effects in lattice-adsorbed polyatomic systems and highlights promising directions for future theoretical and computational research. Full article

By introducing the concept of nanoreaction-based fluctuating mixing entropy, the challenge posed by the smallness of a closed molecular system is addressed through equilibrium statistical–mechanical averaging over fluctuating reaction extent. Based on the canonical partition function, the interplay between the mixing entropy and fluctuations in the reaction extent in nanoscale environments is unraveled while maintaining consistency with macroscopic behavior. The nanosystem size dependence of the mixing entropy, the reaction extent, and a concept termed the “reaction extent entropy” are modeled for the combination reactions $A+B\leftrightarrow 2C$ and the specific case of $H_2+I_2\leftrightarrow 2HI$. A distinct inverse correlation is found between the first two properties, revealing consistency with the nanoconfinement entropic effect on chemical equilibrium (NCECE). To obtain the time dependence of the instantaneous mixing entropy following equilibration, the Stochastic Simulation (Gillespie) Algorithm is employed. In particular, the smallest nanosystems exhibit a step-like behavior that deviates significantly from the smooth mean values and is associated with the discrete probability distribution of the reaction extent. As illustrated further for molecular adsorption and spin polarization, the current approach can be extended beyond nanoreactions to other confined systems with a limited number of species. Full article

The Second Law of Thermodynamics states that entropy S increases in a spontaneous process in an ideal isothermal and isolated system. Real systems are influenced by external forces and fields, including the temperature field. In this case, only entropy is not enough, and we suggest using a new function, $L_s$, which is analogous to the Lagrangian in classical mechanics. It includes total potential energy but instead of mechanical kinetic energy, $L_s$ includes the product ST, and the system always evolves towards increasing this modified Lagrangian. It reaches an equilibrium when total potential force is balanced by both entropic and thermal forces. All forces have the same units, Newton/mol, and may be added or subtracted. For condensed systems with friction forces, it is a molecular transport velocity, and not acceleration, which is proportional to the acting force. Our approach has several advantages compared to Onsager’s non-equilibrium thermodynamics with its thermodynamic forces, which may have different units, including 1/T for energy transport. For isolated systems, the description is reduced to Second Law and Clausius inequality. It easily explains diffusion, Dufour effect, and Soret thermodiffusion. The combination of electric, thermal, and entropic forces explains thermoelectric phenomena, including Peltier–Seebeck and Thomson (Lord Kelvin) effects. Gravitational and entropic forces together inside a black hole may lead to a steady state or the black hole evaporation. They are also involved in and influenced by solar atmospheric processes. Full article

The review presents arguments emphasizing the importance of using the entropic measure of time (EMT) in the study of irreversible evolving systems. The possibilities of this measure for obtaining the laws of system evolution are shown. It is demonstrated that EMT provides a novel and unified perspective on the principle of maximum entropy production (MEPP), which is established in the physics of irreversible processes, as well as on the laws of growth and evolution proposed in biology. Essentially, for irreversible processes, the proposed approach allows, in a certain sense, to identify concepts such as the duration of existence, MEPP, and natural selection. EMT has been used to generalize prior results, indicating that the intrinsic time of a system is logarithmically dependent on extrinsic (Newtonian) time. Full article

In the past, measures of the “Earth-likeness” of exoplanets have been qualitative, considering an abiotic Earth, or requiring discretionary choices of what parameters make a planet Earth-like. With the advent of high-resolution exoplanet spectroscopy, there is a growing need for a method of quantifying the Earth-likeness of a planet that addresses these issues while making use of the data available from modern telescope missions. In this work, we introduce an informational–entropic metric that makes use of the spectrum of an exoplanet to directly quantify how Earth-like the planet is. To illustrate our method, we generate simulated transmission spectra of a series of Earth-like and super-Earth exoplanets, as well as an exoJupiter and several gas giant exoplanets. As a proof of concept, we demonstrate the ability of the information metric to evaluate how similar a planet is to Earth, making it a powerful tool in the search for a candidate Earth 2.0. Full article

The transformation of healthcare organizations is essential to address their inherent complexity and dynamic nature. This study emphasizes the role of Data Science, with the incorporation of Artificial Intelligence tools, in enabling data-driven and interconnected management strategies. To achieve this, a thermodynamic approach to Knowledge Representation and Reasoning was employed, capturing healthcare workers’ perceptions of their work environment through structured questionnaires. Over several months, the entropic efficiency in healthcare workers’ responses was analyzed, offering insights into the intricate relationships between leadership, teamwork, work engagement, and their influence on organizational performance and worker satisfaction. This approach demonstrates Data Science’s potential to enhance organizational effectiveness and adaptability while empowering healthcare workers. By bridging technological innovation with human-centric management, it provides actionable insights for sustainable improvements in healthcare systems. The study underscores that involving healthcare workers in decision-making processes not only could enhance satisfaction but also facilitate meaningful organizational transformation, creating more responsive and resilient healthcare organizations capable of navigating the complexities of modern healthcare. Full article

In this paper, we define and characterize the embedding of edges and higher-order entities in directed graphs (digraphs) and relate these embeddings to those of nodes. Our edge-centric approach consists of the following: (a) Embedding line digraphs (or their iterated versions); (b) Exploiting the rank properties of these embeddings to show that edge/path similarity can be posed as a linear combination of node similarities; (c) Solving scalability issues through digraph sparsification; (d) Evaluating the performance of these embeddings for classification and clustering. We commence by identifying the motive behind the need for edge-centric approaches. Then we proceed to introduce all the elements of the approach, and finally, we validate it. Our edge-centric embedding entails a top-down mining of links, instead of inferring them from the similarities of node embeddings. This analysis is key to discovering inter-subgraph links that hold the whole graph connected, i.e., central edges. Using directed graphs (digraphs) allows us to cluster edge-like hubs and authorities. In addition, since directed edges inherit their labels from destination (origin) nodes, their embedding provides a proxy representation for node classification and clustering as well. This representation is obtained by embedding the line digraph of the original one. The line digraph provides nice formal properties with respect to the original graph; in particular, it produces more entropic latent spaces. With these properties at hand, we can relate edge embeddings to node embeddings. The main contribution of this paper is to set and prove the linearity theorem, which poses each element of the transition matrix for an edge embedding as a linear combination of the elements of the transition matrix for the node embedding. As a result, the rank preservation property explains why embedding the line digraph and using the labels of the destination nodes provides better classification and clustering performances than embedding the nodes of the original graph. In other words, we do not only facilitate edge mining but enforce node classification and clustering. However, computing the line digraph is challenging, and a sparsification strategy is implemented for the sake of scalability. Our experimental results show that the line digraph representation of the sparsified input graph is quite stable as we increase the sparsification level, and also that it outperforms the original (node-centric) representation. For the sake of simplicity, our theorem relies on node2vec-like (factorization) embeddings. However, we also include several experiments showing how line digraphs may improve the performance of Graph Neural Networks (GNNs), also following the principle of maximum entropy. Full article

The observed scaling properties in the three aftershock sequences of the recent strong earthquakes of magnitudes M_w 6.1, M_w 6.4 and M_w 6.7, which occurred in the Ionian island region on the 26 January 2014 (onshore Cephalonia Island), 17 November 2015 (Lefkada Island) and 25 October 2018 (offshore Zakynthos Island), respectively, are presented. In the analysis, the frequency–magnitude distributions in terms of the Gutenberg–Richter scaling relationship are studied, along with the temporal evolution of the aftershock sequences, as described by the Omori–Utsu formula. The processing of interevent times distribution, based on non-extensive statistical physics, indicates a system in an anomalous equilibrium with long-range interactions and a cross over behavior from anomalous to normal statistical mechanics for greater interevent times. A discussion of this cross over behavior is given for all aftershock sequences in terms of superstatistics. Moreover, the common value of the Tsallis entropic parameter that was obtained suggests that aftershock sequences are systems with very low degrees of freedom. Finally, a scaling of the migration of the aftershock zones as a function of the logarithm of time is discussed regarding the rate strengthening rheology that governs the evolution of the afterslip process. Our results contribute to the understanding of the spatiotemporal evolution of aftershocks using a first principles approach based on non extensive statistical physics suggesting that this view could describe the process within a universal view. Full article

Aflatoxin B1 (AFB1), a highly toxic secondary metabolite produced by Aspergillus species, represents a significant health hazard due to its widespread contamination of agricultural products. The urgent need for sensitive and sustainable detection methods has driven the development of diverse analytical approaches, most of which heavily rely on organic solvents, posing environmental challenges for routine food safety analysis. Here, we introduce a supramolecular platform leveraging acyclic cucurbit[n]uril (acCB) as a host molecule for environmentally sustainable AFB1 detection. Screening various acCB derivatives identified acCB6 as a superior host capable of forming a stable 1:1 complex with AFB1 in an aqueous solution, exhibiting a high binding affinity. Proton nuclear magnetic resonance (1H NMR) spectroscopy confirmed that AFB1 was deeply encapsulated within the host cavity, with isothermal titration calorimetry (ITC) experiments and molecular dynamics simulations further substantiating the stability of the interaction, driven by enthalpic and entropic contributions. This supramolecular host was incorporated into a scaffold-assembly-based bioluminescent enzyme immunoassay (SA-BLEIA), providing a green detection platform that rivals the performance of traditional organic solvent-based assays. Our findings highlight the potential of supramolecular chemistry as a foundation for eco-friendly mycotoxin detection and offer valuable insights into designing environmentally sustainable analytical methods. Full article

We introduce a novel entropy minimization approach for the solution of constrained linear regression problems. Rather than minimizing the quadratic error, our method minimizes the Fermi–Dirac entropy, with the problem data incorporated as constraints. In addition to providing a solution to the linear regression problem, this approach also estimates the measurement error. The only prior assumption made about the errors is analogous to the assumption made about the unknown regression coefficients: specifically, the size of the interval within which they are expected to lie. We compare the results of our approach with those obtained using the disciplined convex optimization methodology. Furthermore, we address consistency issues and present examples to illustrate the effectiveness of our method. Full article

Cosmic rays exhibit anomalous diffusion behaviors in the heliospheric environment that cannot be adequately described by classical diffusion models. In this paper, we develop a theoretical framework employing a fractional Fokker–Planck equation to model the anomalous transport of cosmic rays. This approach accounts for the observed non-Gaussian distributions, long-range correlations and memory effects in cosmic ray fluxes. We derive analytical solutions using the Adomian Decomposition Method and express them in terms of Mittag-Leffler functions and Lévy stable distributions. The model parameters, including the fractional orders $\alpha$ and $\mu$ and the entropic index q, are estimated by a short comparison between theoretical predictions and observational data from cosmic ray experiments. Our findings suggest that the integration of fractional calculus and non-extensive statistics can be employed for describing the cosmic ray propagation and the anomalous diffusion observed in the heliosphere. Full article

In this work, we have studied the thermodynamic properties of the Van der Waals black hole in the framework of the relativistic Kaniadakis entropy. We have shown that the black hole properties, such as the mass and temperature, differ from those obtained by using the the Boltzmann–Gibbs approach. Moreover, the deformation $\kappa$-parameter changes the behavior of the Gibbs free energy via introduced thermodynamic instabilities, whereas the emission rate is influenced by $\kappa$ only at low frequencies. Nonetheless, the pressure–volume ($P\left(V\right)$) characteristics are found independent of $\kappa$ and the entropy form, unlike in other anti-de Sitter (AdS) black hole models. In summary, the presented findings partially support the previous arguments of Gohar and Salzano that, under certain circumstances, all entropic models are equivalent and indistinguishable. Full article

By performing differential scanning calorimetry(DSC) measurements on RNase A, we studied the stabilization provided by the addition of potassium aspartate(KAsp) or potassium glutamate (KGlu) and found that it leads to a significant increase in the denaturation temperature of the protein. The stabilization proves to be mainly entropic in origin. A counteraction of the stabilization provided by KAsp or KGlu is obtained by adding common denaturants such as urea, guanidinium chloride, or guanidinium thiocyanate. A rationalization of the experimental data is devised on the basis of a theoretical approach developed by one of the authors. The main contribution to the conformational stability of globular proteins comes from the gain in translational entropy of water and co-solute ions and/or molecules for the decrease in solvent-excluded volume associated with polypeptide folding (i.e., there is a large decrease in solvent-accessible surface area). The magnitude of this entropic contribution increases with the number density and volume packing density of the solution. The two destabilizing contributions come from the conformational entropy of the chain, which should not depend significantly on the presence of co-solutes, and from the direct energetic interactions between co-solutes and the protein surface in both the native and denatured states. It is the magnitude of the latter that discriminates between stabilizing and destabilizing agents. Full article