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In a well-known paper, Jeremy England derived a bound on the free energy dissipated by a self-replicating system [J. L. England, "Statistical physics of self-replication," J. Chem. Phys. 139, 121923 (2013)]. This bound is usually interpreted as a universal relationship that connects thermodynamic dissipation to replicator per-capita decay and growth rates. We argue from basic thermodynamic principles against this interpretation. In fact, we suggest that such a relationship cannot exist in principle, because it is impossible for a thermodynamically consistent replicator to undergo both per-capita growth and per-capita decay back into reactants. Instead, replicator may decay into separate waste products, but in that case, replication and decay are two independent physical processes, and there is no universal relationship that connects their thermodynamic and dynamical properties. [ABSTRACT FROM AUTHOR]

The strengths of glasses are intricately linked to their atomic-level heterogeneity. Atomistic simulations are frequently used to investigate the statistical physics of this relationship, compensating for the limited spatiotemporal resolution in experimental studies. However, theoretical insights are limited by the complexity of glass structures and the accuracy of the interatomic potentials used in simulations. Here, we investigate the strengths and fracture mechanisms of 2D silica, with all structural units accessible to direct experimental observation. We develop a neural network force field for fracture based on the deep potential-smooth edition framework. Representative atomic structures across crystals, nanocrystalline, paracrystalline, and continuous random network glasses are studied. We find that the virials or bond lengths control the initialization of bond-breaking events, creating nanoscale voids in the vitreous network. However, the voids do not necessarily lead to crack propagation due to a disorder-trapping effect, which is stronger than the lattice-trapping effect in a crystalline lattice, and occurs over larger length and time scales. Fracture initiation proceeds with void growth and coalescence and advances through a bridging mechanism. The fracture patterns are shaped by subsequent trapping and cleavage steps, often guided by voids forming ahead of the crack tip. These heterogeneous processes result in atomically smooth facets in crystalline regions and rough, amorphous edges in the glassy phase. These insights into 2D crystals and glasses, both sharing SiO 2 chemistry, highlight the pivotal role of atomic-level structures in determining fracture kinetics and crack path selection in materials. [ABSTRACT FROM AUTHOR]

The Ising model, initially proposed about 100 years ago to explain ferromagnetism and phase transitions, has become a central pillar of statistical physics and a powerful tool for diverse applications in other fields including environmental studies. In this paper, we introduce continuous spin values between −1 and +1 to a two-dimensional Ising model and utilize the generalized Ising lattice to simulate the dynamics of sea ice/water transition for a large area of 1500 km by 1500 km in the Arctic region. The simulation process follows the Metropolis-Hastings algorithm and incorporates an innovative factor to account for the inertia of spin value changes. Using the sea ice concentration data collected by the National Snow and Ice Data Center, our results exhibit striking similarity between the simulated and the observed ice melting and freezing dynamics, and two numerical measures from the simulation—the ice coverage percentage and the ice extent—match closely with the data statistics. Moreover, the model's best-fit parameters demonstrate the substantial impact of the external forces, which can be further enriched and linked to the environmental factors in other climate change research. [ABSTRACT FROM AUTHOR]

In nonequilibrium statistical physics, quantifying the nearest (and higher-order) neighbors and free volumes of particles in many-body systems is crucial to elucidating the origin of macroscopic collective phenomena, such as glass/granular jamming transitions and various aspects of the behavior of active matter. However, conventional techniques (based on a fixed-distance cutoff or the Voronoi construction) have mainly been applied to equilibrated, homogeneous, and monodisperse particle systems. In this paper, we implement simple and efficient methods for local structure analysis in nonequilibrium, inhomogeneous, and polydisperse hard disk systems. We show how these novel methods can overcome the difficulties encountered by conventional techniques as well as demonstrate some applications. [ABSTRACT FROM AUTHOR]

This Perspective focuses on the several overlaps between quantum algorithms and Monte Carlo methods in the domains of physics and chemistry. We will analyze the challenges and possibilities of integrating established quantum Monte Carlo solutions into quantum algorithms. These include refined energy estimators, parameter optimization, real and imaginary-time dynamics, and variational circuits. Conversely, we will review new ideas for utilizing quantum hardware to accelerate the sampling in statistical classical models, with applications in physics, chemistry, optimization, and machine learning. This review aims to be accessible to both communities and intends to foster further algorithmic developments at the intersection of quantum computing and Monte Carlo methods. Most of the works discussed in this Perspective have emerged within the last two years, indicating a rapidly growing interest in this promising area of research. [ABSTRACT FROM AUTHOR]

The ab initio path integral Monte Carlo (PIMC) method is one of the most successful methods in statistical physics, quantum chemistry and related fields, but its application to quantum degenerate Fermi systems is severely hampered by an exponential computational bottleneck: the notorious fermion sign problem. Very recently, Xiong and Xiong [J. Chem. Phys. 157, 094112 (2022)] have suggested to partially circumvent the sign problem by carrying out simulations of fictitious systems guided by an interpolating continuous variable ξ ∈ [−1, 1], with the physical Fermi- and Bose-statistics corresponding to ξ = −1 and ξ = 1. It has been proposed that information about the fermionic limit might be obtained by calculations within the bosonic sector ξ > 0 combined with an extrapolation throughout the fermionic sector ξ < 0, essentially bypassing the sign problem. Here, we show how the inclusion of the artificial parameter ξ can be interpreted as an effective penalty on the formation of permutation cycles in the PIMC simulation. We demonstrate that the proposed extrapolation method breaks down for moderate to high quantum degeneracy. Instead, the method constitutes a valuable tool for the description of large Fermi-systems of weak quantum degeneracy. This is demonstrated for electrons in a 2D harmonic trap and for the uniform electron gas (UEG), where we find excellent agreement (∼ 0.5 %) with exact configuration PIMC results in the high-density regime while attaining a speed-up exceeding 11 orders of magnitude. Finally, we extend the idea beyond the energy and analyze the radial density distribution (2D trap), as well as the static structure factor and imaginary-time density–density correlation function (UEG). [ABSTRACT FROM AUTHOR]

Freezing of water under nanoconfinement exhibits physical peculiarities with respect to the bulk water. However, experimental observations are extremely challenging at this scale, which limits our understanding of the effect of confinement on water properties upon freezing. In this study, we use molecular dynamic simulations to investigate how confinement affects the kinetics of growth of ice and the thermodynamic equilibrium of ice-liquid coexistence. TIP4P/Ice water model and CSH-FF model were applied to simulate ice crystallization in a confined cement system at temperatures down to 220 K. We adapted an interface detection algorithm and reparameterized the CHILL/CHILL+ algorithm to capture ice growth. The confinement leads to a shift of the maximum growth rate of ice to a higher temperature than for bulk water. Both the confinement and surface impurities contribute to slowing down the ice growth. For the ice-liquid coexistence at equilibrium, we derive a formulation of Thomson's equation adapted to statistical physics quantities accessible by molecular simulation, and we show that this adapted equation predicts accurately the melting line of bulk and confined ice Ih as a function of pressure. The confinement decreases systematically the melting temperature of ice of about 5 K compared with bulk ice Ih. A premelted water film about 1 nm thick is observed between the solid wall and ice, and its thickness is found to decrease continuously as temperature is lowered. We note that the surface impurities are key to the formation of the premelted water nanofilm when the temperature is lower than 250 K. [ABSTRACT FROM AUTHOR]

AbstractThis study was performed to evaluate the adsorptive properties of bentonite. Natural bentonite was found to adsorb considerable amounts of Salicylic acid (SA) after modification by hexadecyltrimethylammonium bromide (HDTMA). Batch studies investigated the adsorptive properties of the modified natural bentonite (organobentonite). The characterization results show the increase in the d001 spacing from 1.13 to 1.53 nm for raw bentonite and organobentonite, respectively. The FTIR analysis indicates the asymmetric vs. (CH2) and symmetric vs. (CH2) stretching modes for clay loaded with surfactant molecules are 2919 and 2845 cm−1, respectively. We investigate parameters such as contact time, pH, temperature, and initial concentration. Desorption and regeneration were also studied. The kinetic analysis shows that the adsorption equilibrium of salicylic acid onto organoclay was reached after 120 min and fits well to a pseudo-second-order kinetic model with the contribution of intraparticular diffusion. In addition, the adjustment of the isothermal models indicated that the Langmuir-Freundlich model best fit the isotherms data for unmodified bentonite and the Langmuir model for modified bentonite. The maximum adsorption capacity of organobentonite is 128 mg g−1 at 25 °C. According to the statistical physics model, the SA could be adsorbed on the surface of organoclay in a non-parallel orientation. The desorption-regeneration investigation revealed that the material could be successfully reused for three cycles of SA adsorption. Mechanistic studies of SA sorption on organobentonite have highlighted several mechanisms: hydrogen bonding, electrostatic attraction, and hydrophobic interaction. [ABSTRACT FROM AUTHOR]

Recently, Succar and Porfiri (Nature Cities 1(3):216–224, 2024) reported sublinear scaling for firearm ownership in the United States. Their analysis hinted at a causal role of prevalence of homicides and firearm accessibility on firearm ownership, supporting self-protection as a driver of firearm ownership. In this study, we propose a microscopic, individual-level model to explain these macroscopic, city-level findings. In the model, individuals dwell in a city and buy a gun if they experience a violent interaction and know a dealer. We examine the model from a network science perspective and show the emergence of sublinear scaling with an exponent matching empirical observations. Beyond scaling, the model provides accurate predictions of city rankings in terms of firearm ownership, underscoring the explanatory power of the self-protection theory. [ABSTRACT FROM AUTHOR]

One of the most striking examples of the close connections between global optimization processes and statistical physics is the simulated annealing method, inspired by the famous Monte Carlo algorithm devised by Metropolis et al. in the middle of last century. In this paper, we show how the tools of linear kinetic theory allow the description of this gradient-free algorithm from the perspective of statistical physics and how convergence to the global minimum can be related to classical entropy inequalities. This analysis highlights the strong link between linear Boltzmann equations and stochastic optimization methods governed by Markov processes. Thanks to this formalism, we can establish the connections between the simulated annealing process and the corresponding mean-field Langevin dynamics characterized by a stochastic gradient descent approach. Generalizations to other selection strategies in simulated annealing that avoid the acceptance–rejection dynamic are also provided. [ABSTRACT FROM AUTHOR]

We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes. This process has similarities to prototype learning in human cognition. We provide a substantial literature review of prototype learning in associative memories, covering contributions from psychology, statistical physics, and computer science. We analyze prototype formation from a theoretical perspective and derive a stability condition for these states based on the number of examples of the prototype presented for learning, the noise in those examples, and the number of nonexample states presented. The stability condition is used to construct a probability of stability for a prototype state as the factors of stability change. We also note similarities to traditional network analysis, allowing us to find a prototype capacity. We corroborate these expectations of prototype formation with experiments using a simple Hopfield network with standard Hebbian learning. We extend our experiments to a Hopfield network trained on data with multiple prototypes and find the network is capable of stabilizing multiple prototypes concurrently. We measure the basins of attraction of the multiple prototype states, finding attractor strength grows with the number of examples and the agreement of examples. We link the stability and dominance of prototype states to the energy profile of these states, particularly when comparing the profile shape to target states or other spurious states. [ABSTRACT FROM AUTHOR]

Protists, an umbrella term first coined by Ernst Haeckel in 1866, are a vast collection of (primarily unicellular) eukaryotes that are "neither animals nor plants". This basic definition by exclusion has been exercised for centuries, even though recent advances have led to more rigorous taxonomic assignment of various protist groups. Pioneering comparative phylogenetic approaches have been applied to these organisms to reconstruct the deep branches of the eukaryotic tree, revealing essential clues about early eukaryotic evolution. Protists, including amoebae, flagellates, ciliates, and algae, are also vital constituents of global ecosystems, where they appear at the base of food chains, control the relative abundance of other microbes, and participate in global biogeochemical recycling. Due to their typically small size and lack of nervous systems, protists are often associated with the unfortunate label 'primitive'. Yet they exhibit remarkable behavioural sophistication and are able to feed, predate, navigate and interact with their surroundings. Unlike macroscopic animals, many protists reside in a non-intuitive physical regime where viscous forces dominate over inertia, and where they use diverse propulsion and navigation strategies. Interdisciplinary research into these cell-scale phenomena, characterised by a complex interplay of physical forces and mechanical constraints, has significantly advanced the emerging fields of active matter, microhydrodynamics, and non-equilibrium statistical physics. This primer discusses the biophysics of protist behaviour, with a focus on locomotion and feeding. I will highlight the most extensively studied principles and describe some more esoteric behaviours that have not yet been systematically explored. Kirsty Wan discusses the biophysics of protist behaviour, with a focus on locomotion and feeding, highlighting the most extensively studied principles and describing some more esoteric behaviours that have not yet been systematically explored. [ABSTRACT FROM AUTHOR]

This work presents a new method called Dimensionless Fluctuation Balance (DFB), which makes it possible to obtain distributions as solutions of Partial Differential Equations (PDEs). In the first case study, DFB was applied to obtain the Boltzmann PDE, whose solution is a distribution for Boltzmann gas. Following, the Planck photon gas in the Radiation Law, Fermi–Dirac, and Bose–Einstein distributions were also verified as solutions to the Boltzmann PDE. The first case study demonstrates the importance of the Boltzmann PDE and the DFB method, both introduced in this paper. In the second case study, DFB is applied to thermal and entropy energies, naturally resulting in a PDE of Boltzmann's entropy law. Finally, in the third case study, quantum effects were considered. So, when applying DFB with Heisenberg uncertainty relations, a Schrödinger case PDE for free particles and its solution were obtained. This allows for the determination of operators linked to Hamiltonian formalism, which is one way to obtain the Schrödinger equation. These results suggest a wide range of applications for this methodology, including Statistical Physics, Schrödinger's Quantum Mechanics, Thin Films, New Materials Modeling, and Theoretical Physics. [ABSTRACT FROM AUTHOR]

The article delves into the behavior of liquid nano-threads under external hydrodynamic perturbations and thermal fluctuations, utilizing a stochastic lubrication equation to analyze instability and rupture dynamics. It identifies two distinct regimes - hydrodynamic and thermal-fluctuation - with the transition influenced by external perturbation characteristics. Molecular dynamics simulations validate theoretical predictions, emphasizing the interplay between external perturbations and thermal fluctuations in perturbation growth evolution and thread breakup patterns. The document also includes a compilation of scientific research articles on fluid dynamics topics like jet breakup length, droplet manipulation, and thermal fluctuations' effects on nanoscale flows, published in reputable journals. [Extracted from the article]

In the realm of statistical physics, the number of states in which a system can be realized with a given energy is a key concept that bridges the microscopic and macroscopic descriptions of physical systems. For quantum systems, many approaches rely on the solution of the Schrödinger equation. In this work, we demonstrate how the recently developed rodeo algorithm can be utilized to determine the number of states associated with all energy levels without any prior knowledge of the eigenstates. Quantum computers, with their innate ability to address the intricacies of quantum systems, make this approach particularly promising for the study of the thermodynamics of those systems. To illustrate the procedure's effectiveness, we apply it to compute the number of states of the 1D transverse field Ising model and, consequently, its specific heat, proving the reliability of the method presented here. [ABSTRACT FROM AUTHOR]

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain. [ABSTRACT FROM AUTHOR]

Context: In this comparative study of the adsorption of l-phenylalanine (l-Phe) on two modified low-activated carbons (ACK and ACZ) at four temperatures (293–313 K), steric and energetic characteristics of adsorption were investigated. An advanced statistical physics multilayer model involving single-layer and double-layer adsorption scenarios was developed to interpret the l-Phe adsorption phenomenon. Modeling results indicate that two and three l-Phe layers were arranged depending on the tested adsorption systems. The estimated number of l-Phe molecules per leading adsorption site varied from 1.71 to 2.09 and from 1.76 to 1.86 for systems l-Phe-ACK and l-Phe-ACZ, respectively. The results show that a multimolecular adsorption mechanism might connect this amino acid molecule on ACZ and ACK surfaces in a non-parallel location. These parameters changed as follows, according to the adsorbed quantity at saturation analysis: Qasat (l-Phe-ACK) ˃ Qasat (l-Phe-ACZ). This indicates that ACK material was more efficient and valuable for l-Phe adsorption than ACZ. It was also shown that the adsorption capacity decreases as the temperature increases, proving the exothermicity of the adsorption process. This analytical substantiation is confirmed by calculating the binding energies, suggesting the occurrence of physical bonds between l-Phe amino acid molecules and ACK/ACZ binding sites and among l-Phe–l-Phe molecules. Pore size distribution was interpreted and calculated by applying the Kelvin theory to data from single adsorption isotherms. All used temperatures depicted a distribution of pores below 2 nm. The docking analysis involving l-Phe and the ACZ and ACK adsorbents reveal a significant resemblance in how receptors detect ligands. Consequently, the findings from the docking process confirm that the calculated binding affinities fall within the spectrum of adsorption energy. Methods: This study analyzed the adsorption capacity of the l-Phe through a model proposed by statistical physics formalism. Molecular docking was used to determine the various types of interactions between the two activated carbons. Two aspects, including orientation of l-Phe on the site, number of molecules per site n, interaction energy, density of receptor site, and adsorption capacity, were discussed to elucidate the influence of activation on the two adsorbents. [ABSTRACT FROM AUTHOR]

Understanding how cell identity is encoded by the genome and acquired during differentiation is a central challenge in cell biology. I have developed a theoretical framework called EnhancerNet, which models the regulation of cell identity through the lens of transcription factor-enhancer interactions. I demonstrate that autoregulation in these interactions imposes a constraint on the model, resulting in simplified dynamics that can be parameterized from observed cell identities. Despite its simplicity, EnhancerNet recapitulates a broad range of experimental observations on cell identity dynamics, including enhancer selection, cell fate induction, hierarchical differentiation through multipotent progenitor states and direct reprogramming by transcription factor overexpression. The model makes specific quantitative predictions, reproducing known reprogramming recipes and the complex haematopoietic differentiation hierarchy without fitting unobserved parameters. EnhancerNet provides insights into how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution. [ABSTRACT FROM AUTHOR]

This article examines the treatment of statistics and error analysis in introductory physics lab manuals. It compares lab manuals from community colleges and universities and identifies inconsistencies in how these topics are addressed. The article suggests that a probabilistic approach to data analysis could enhance the teaching of measurement and uncertainty in the lab. It also highlights the International Organization for Standardization's guide to uncertainty in measurement as a valuable resource for standardizing lab instruction. The article concludes that there is a need for more consistent and rigorous treatment of statistics and error in introductory physics lab manuals. [Extracted from the article]

An upgrade is presented of a recently published model for the calculation of statistical averages of turbulent flow variables. Instead of empirical constructions, important parts of the model are based on general principles of statistical turbulence and physics. The upgrade concerns transparent and simplified descriptions of turbulent diffusion and Reynolds stresses which express their dependency of mean flow gradients in a direct manner. As before, prediction comparisons are satisfactory in relation to the results of DNS of channel flow. Implementation in a CFD code is straightforward and its application provides a significant improvement to the results of the widely used empirical basic k- ϵ model. [ABSTRACT FROM AUTHOR]

Baroreflex is critical to maintain blood pressure homeostasis, and the quantification of baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment, and healthcare. Current quantification of BRF such as baroreflex sensitivity cannot represent BRF systematically. From the perspective of complex systems, we regard that BRF is the emergence result of fluctuate states and interactions in physiological mechanisms. Therefore, the three‐layer emergence is studied in this work, which is from physiological mechanisms to physiological indexes and then to BRF. On this basis, since the entropy in statistical physics macroscopically measures the fluctuations of system's states, in this work, the principle of maximum entropy is adopted, and a new index called PhysioEnt is proposed to quantify the fluctuations of four physiological indexes, i.e., baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure, which aims to represent BRF in the resting condition. Further, two datasets with different subjects are analyzed, and some new findings can be obtained, such as the contributions of the physiological interactions among organs/tissues. With measurable indexes, the proposed method is expected to support individualized medicine. [ABSTRACT FROM AUTHOR]

The remarkable technical contributions of Michael E. Fisher to statistical physics and the development of the renormalization group are widely known and deeply influential. But less well known is his early and profound appreciation of the way in which renormalization group created a revolution in our understanding of how physics — in fact, all science — is practiced and the concomitant adjustment that needs to be made to our conception of the purpose and philosophy of science. In this chapter, I attempt to redress this imbalance, with examples from Fisher’s writings and my own work. It is my hope that this tribute will help remove some of the confusion that surrounds the scientific usage of minimal models and renormalization group concepts, as well as their limitations, in the ongoing effort to understand emergence in complex systems. [ABSTRACT FROM AUTHOR]

The brain can be seen as a self-organized dynamical system that optimizes information processing and storage capabilities. This is supported by studies across scales, from small neuronal assemblies to the whole brain, where neuronal activity exhibits features typically associated with phase transitions in statistical physics. Such a critical state is characterized by the emergence of scale-free statistics as captured, for example, by the sizes and durations of activity avalanches corresponding to a cascading process of information flow. Another phenomenon observed during sleep, under anesthesia, and in in vitro cultures, is that cortical and hippocampal neuronal networks alternate between "up" and "down" states characterized by very distinct firing rates. Previous theoretical work has been able to relate these two concepts and proposed that only up states are critical whereas down states are subcritical, also indicating that the brain spontaneously transitions between the two. Using high-speed high-resolution calcium imaging recordings of neuronal cultures, we test this hypothesis here by analyzing the neuronal avalanche statistics in populations of thousands of neurons during "up" and "down" states separately. We find that both "up" and "down" states can exhibit scale-free behavior when taking into account their intrinsic time scales. In particular, the statistical signature of "down" states is indistinguishable from those observed previously in cultures without "up" states. We show that such behavior can not be explained by network models of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression, even when realistic noise levels, spatial network embeddings, and heterogeneous populations are taken into account, which instead exhibits behavior consistent with previous theoretical models. Similar differences were also observed when taking into consideration finite-size scaling effects, suggesting that the intrinsic dynamics and self-organizationmechanisms of these cultures might be more complex than previously thought. In particular, our findings point to the existence of different mechanisms of neuronal communication, with different time scales, acting during either high-activity or low-activity states, potentially requiring different plasticity mechanisms. [ABSTRACT FROM AUTHOR]

An alternative synthesis route to obtain ultramicroporous zeolitic imidazolate framework‐8 (ZIF‐8) is reported that is rapid and does not require organic solvent or heating. The polyethylene glycol‐templated ZIF‐8 nanoparticles (<50 nm size) exhibited a high BET (Brunauer, Emmett and Teller) surface area of 1853 m2/g, a total pore volume of 0.73 cm3/g, and 0.54 nm ultramicropores. A new approach is introduced here to better understand gas adsorption in porous metal organic frameworks (MOFs) that combines theoretical and experimental isotherm data obtained for five gases with statistical physics modelling. The multiscale analytical model reveals that the gas molecules, irrespective of their structure, adsorb in a mixed orientation at low temperature, and a parallel multi‐molecular orientation at higher temperature. The number of gas molecules adsorbed per site ranged from 0.83–2.2 while the number of adsorbent layers ranged between 1.4–5.6, depending on the gas and the temperature. The multilayer adsorption processes involved adsorption energies from 2.85 kJ/mol–9.77 kJ/mol for all gases, consistent with physisorption via van der Waals and London dispersion forces. The initial adsorption energies were higher and therefore stronger. A particularly high capacity for CO2 of 1088 mg(CO2)/g at 298 K was observed while H2 adsorption was only 9 mg(H2)/g. The other gases adsorbed at 145 mg/g–245 mg/g at 298 K. Thermodynamic functions that governed the adsorption process such as the internal energy, the enthalpy, and Gibbs free energy, are also reported, as well as the adsorption entropies, for the 5 gases. [ABSTRACT FROM AUTHOR]

AbstractBetween the two world wars, a group of Italian mathematicians, statisticians and economists set out to use the tools of statistical mechanics to interpret the Pareto income distribution curve as a probability distribution. Models based on Boltzmann's thermal equilibrium were independently developed to find an economic variable, comparable to the kinetic energy of gas theory, that could explain the heavy tail of the Pareto curve. The various avenues explored eventually led them back to Pareto's much debated explanation, namely that the hyperbolic distribution of income was due to the unequal distribution of talent. [ABSTRACT FROM AUTHOR]

Three different partition functions are well-known and described in statistical physics. Here, a novel partition function for the description of intra-particular interactions and with this for the mass of particles is presented below. In statistical physics, three different partition functions are already well-established. These are the microcanonical, the canonical, and the macro-canonical partition functions. Here a fourth, novel partition function is added to these already well-established three. Thereby due to the properties of quantum mechanics and superposition, this novel partition function is of fundamental different nature. According to this concept, the new partition function for particles is first defined. The masses and energies of the elementary particles are then calculated using this novel partition function. The energy of an elementary particle is proportional to the mass on the one hand and to the novel partition function on the other. The constant of proportionality with respect to the new partition function is found to be identical with Rydberg constant and Rydberg energy. The relationships found for the proton, the electron, and the sigma particle are then generalized to all elementary particles. [ABSTRACT FROM AUTHOR]

This paper bridges contemporary neuroscience theories and Hegelian philosophy, centering on Karl Friston's Free Energy Principle (FEP). Neuroscience models like the Bayesian brain hypothesis and predictive coding depict the brain as a predictive machine, echoing Hermann von Helmholtz's concept of unconscious inference, where perception is shaped by prior knowledge. The FEP, rooted in information theory and statistical physics, suggests organisms minimize sensory surprise through unconscious and active inference, providing a model for behavior and explaining the purposiveness of biological systems. Some scholars assert that Georg W. F. Hegel's view of living beings in his Philosophy of Nature aligns with the FEP, portraying them as purposive and enactive systems. This paper extends this idea, proposing that Hegel's 'System of Science' in the Phenomenology of Spirit functions as a free energy-minimizing system. It discusses predictive coding and the FEP, establishing criteria for a system that minimizes free energy, and applies these criteria to Hegel's work. The paper argues that the dialectical narrative in the Phenomenology operates as a reflective system driven to minimize logical or conceptual free energy, ultimately advancing the spirit towards absolute spirit. This Hegelian predictive model generates expectations essential for dialectical progression. [ABSTRACT FROM AUTHOR]

In a recent article [Kawamoto, J. Phys. Complexity 4, 035005 (2023)], Kawamoto evoked statistical physics methods for the problem of counting graphs with a prescribed degree sequence. This treatment involved truncating a particular Taylor expansion at the first two terms, which resulted in the Bender-Canfield estimate for the graph counts. This is surprisingly successful since the Bender-Canfield formula is asymptotically accurate for large graphs, while the series truncation does not a priori suggest a similar level of accuracy. We upgrade this treatment in three directions. First, we derive an exact formula for counting d-regular graphs in terms of a d-dimensional Gaussian integral. Second, we show how to convert this formula into an integral representation for the generating function of d-regular graph counts. Third, we perform explicit saddle point analysis for large graph sizes and identify the saddle point configurations responsible for graph count estimates. In these saddle point configurations, only two of the integration variables condense to significant values, while the remaining ones approach zero for large graphs. This provides an underlying picture that justifies Kawamoto's earlier findings. [ABSTRACT FROM AUTHOR]

The original Choi–Davis–Jensen's inequality, known for its extensive applications in various scientific and engineering fields, has inspired researchers to pursue its generalizations. In this study, we extend the Choi–Davis–Jensen's inequality by introducing a nonlinear map instead of a normalized linear map and generalize the concept of operator convex functions to include any continuous function defined within a compact region. Notably, operators can be matrices with structural symmetry, enhancing the scope and applicability of our results. The Stone–Weierstrass theorem and the Kantorovich function play crucial roles in the formulation and proof of these generalized Choi–Davis–Jensen's inequalities. Furthermore, we demonstrate an application of this generalized inequality in the context of statistical physics. [ABSTRACT FROM AUTHOR]

A highly versatile evaluation method is proposed for transient plasmas based on statistical physics. It would be beneficial in various industrial sectors, including semiconductors and automobiles. Our research focused on low-energy plasmas in laboratory settings, and they were assessed via our proposed method, which incorporates relative entropy and fractional Brownian motion, based on a revised collisional–radiative model. By introducing an indicator to evaluate how far a system is from its steady state, both the trend of entropy and the radiative process' contribution to the lifetime of excited states were considered. The high statistical weight of some excited states may act as a bottleneck in the plasma's energy relaxation throughout the system to a steady state. By deepening our understanding of how energy flows through plasmas, we anticipate potential contributions to resolving global environmental issues and fostering technological innovation in plasma-related industrial fields. [ABSTRACT FROM AUTHOR]

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing. [ABSTRACT FROM AUTHOR]

Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence (AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide range of matter, energy, and space-time scales through data, knowledge, priors, and laws. At the same time, the AI paradigm also draws on and introduces the knowledge and laws of physics to promote its own development. Then this new paradigm of using physical science to inspire AI is the physical science of artificial intelligence (PhysicsScience4AI, PS4AI). Although AI has become the driving force for development in various fields, there is still a “black box” phenomenon that is difficult to explain in the field of AI deep learning. This article will briefly review the connection between relevant physics disciplines (classical mechanics, electromagnetism, statistical physics, quantum mechanics) and AI. It will focus on discussing the mechanisms of physics disciplines and how they inspire the AI deep learning paradigm, and briefly introduce some related work on how AI solves physics problems. PS4AI is a new research field. At the end of the article, we summarize the challenges facing the new physics-inspired AI paradigm and look forward to the next generation of artificial intelligence technology. This article aims to provide a brief review of research related to physics-inspired AI deep algorithms and to stimulate future research and exploration by elucidating recent advances in physics. [ABSTRACT FROM AUTHOR]

In the present study, the hydrogen-absorption properties of the LaNi5 and the La0.7Ce0.1Ga0.3Ni5 compounds were determined and compared. This work is therefore divided into two parts: an experimental part that presents and discusses the kinetics and isotherms of hydrogen absorption in the two compounds at two different temperatures (298 K and 318 K). In addition, the temperature variations inside the hydride bed were determined. In the second section, the experimental isotherms were compared to a numerical model processed using statistical physics. Following that, thanks to the perfect agreement between the experimental data and the proposed model, the stereographic and energetic parameters associated with the hydrogen absorption reaction, such as the number of hydrogen atoms per receptor site (n1, n2), the densities of the sites (Nm1, Nm2), the half-saturation pressures (P1, P2) and the absorption energies (ΔE1, ΔE2) for each receptor site, were calculated. All of these parameters are acquired by making numerical adjustments to the experimental data. Thermodynamic functions, such as internal energy and Gibbs energy, which regulate the absorption process, were then identified using these parameters. For both compounds, all of the aforementioned were compared and discussed in relation to initial temperature and pressure. The results demonstrated that the hydrogen-storage properties in LaNi5 are enhanced by more than 30% of stored mass and kinetics when Ce and Ga are substituted at the La sites. [ABSTRACT FROM AUTHOR]

This document provides information about the data availability for a study conducted by a group of authors. The authors' names are listed, and it is stated that the data supporting the findings of the study are available within the article. The document does not provide any further details about the study or its findings. [Extracted from the article]

In recent years, tensor problems have been studied in multiple fields of science and engineering including applied mathematics, the theory of completely integrable quantum, data mining, statistics, physics, chemistry, machine learning, medical engineering, and others. In machine learning, the word tensor informally refers to two different concepts that organize and represent data. In this work, at first, the concept of the sign function of a tensor is developed using the sign function of a matrix. Then, we propose an iterative method to find the sign function of a tensor. We prove that the order of convergence of the proposed method is three. Finally, we extend the iterative method for solving the Young–Baxter equation, which has many applications in fully integrable quantum theory, classical systems, and exactly solvable models of statistical physics. The accuracy and effectiveness of the proposed method in comparison to well-known methods are demonstrated by various numerical examples. [ABSTRACT FROM AUTHOR]

Abstract Recently, Succar and Porfiri (Nature Cities 1(3):216–224, 2024) reported sublinear scaling for firearm ownership in the United States. Their analysis hinted at a causal role of prevalence of homicides and firearm accessibility on firearm ownership, supporting self-protection as a driver of firearm ownership. In this study, we propose a microscopic, individual-level model to explain these macroscopic, city-level findings. In the model, individuals dwell in a city and buy a gun if they experience a violent interaction and know a dealer. We examine the model from a network science perspective and show the emergence of sublinear scaling with an exponent matching empirical observations. Beyond scaling, the model provides accurate predictions of city rankings in terms of firearm ownership, underscoring the explanatory power of the self-protection theory.

The computation of entropy in liquids and liquid crystal (LC) phases is a big challenge in statistical physics. In this work, we extend the two-phase thermodynamic model (2PT) to shape anisotropic soft repulsive spherocylinders (SRSs) and report the absolute values of entropy for different LC phases for a range of aspect ratios L/D = 2 − 5. We calculate the density of states for different LC phases and decompose it into contributions arising from translational and rotational degrees of freedom. The translational and rotational modes are further partitioned into diffusive, gas-like, and non-diffusive, solid-like components using a fluidicity factor. In the dilute limit, the entropy values obtained from the 2PT method match exactly those of an ideal rigid rotor. We find that, for a given packing fraction, the magnitude of the total entropy is roughly equal regardless of the different LC phases associated with different aspect ratios. We also compute the excess entropy (for L/D = 5) and compare those with the values obtained using the standard integration approach of MD or Monte Carlo equation of state of SRS. The values obtained using both approaches match very well. The rotational and translational fluidicity factors are further used to determine the phase boundaries of different LC phases. [ABSTRACT FROM AUTHOR]

Natural bentonite was functionalised by methanol to obtain a novel methoxy bentonite structure (MXBE) of enhanced adsorption capacity for malachite green dye (MG.D). The adsorption properties of MXBE adsorbent were illustrated based on the classic equilibrium studies and the advanced equilibrium modelling according to the statistical physics theory. The uptake of MG.D by MXBE follows the kinetic properties of the Pseudo-First order model (R2 > 0.9) and exhibits the equilibrium behaviour of a classic Langmuir isotherm (R2 > 0.9). This demonstrates the physical adsorption of MG.D in homogeneous and monolayer form considering the Gaussian energy value (4.24–6.53 kJ/mol). The description of the adsorption system according to the statistical physics theory and the assumption of the Monolayer model with one energy site was accomplished considering the steric and energetic parameters. The saturation adsorption capacity of MXBE during the uptake of MG.D is 435.12 mg/g (313.13 K) and this value depends on the saturation of the structure with numerous active adsorption sites (Nm = 121 to 240.6 mg/g) and the efficiency of each active site to adsorb up to three MG.D molecules (n = 1.92 to 2.68). Considering the adsorption energy (ΔE = 16.02 to 17.94 kJ/mol), the uptake of MG.D occurred by physical mechanisms involving mainly hydrogen bonding and dipole bonding forces. The recognised thermodynamic functions (entropy, internal energy, free enthalpy) validate the spontaneous and endothermic behaviours of the MG.D adsorption reactions by MXBE. [ABSTRACT FROM AUTHOR]

Quantum thermodynamics aims to extend standard thermodynamics and nonequilibrium statistical physics to systems with sizes well below the thermodynamic limit. It is a rapidly evolving research field that promises to change our understanding of the foundations of physics, while enabling the discovery of novel thermodynamic techniques and applications at the nanoscale. Thermal management has turned into a major obstacle in pushing the limits of conventional digital computers and could also represent a crucial issue for quantum computers. The practical realization of quantum computers with superconducting loops requires working at cryogenic temperatures to eliminate thermal noise, and ion-trap qubits also need low temperatures to minimize collisional noise. In both cases, the sub-nanometric sizes also bring about the thermal broadening of the quantum states; and even roomtemperature photonic computers eventually require cryogenic detectors. A number of thermal and thermodynamic questions, therefore, take center stage, such as quantum re-definitions of work and heat, thermalization and randomization of quantum states, the overlap of quantum and thermal fluctuations, and many others, even including a proper definition of temperature for the small open systems constantly out of equilibrium that are the qubits. This overview provides an introductory perspective on a selection of current trends in quantum thermodynamics and their impact on quantum computers and quantum computing, with language that is accessible to postgraduate students and researchers from different fields. [ABSTRACT FROM AUTHOR]

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needlein-a-haystack problem has remarkably close resemblance to the old Protein Folding Problem, for which the solution is now known from statistical physics. Based on the logic that Origins must have come only after there was an operative evolution mechanism--which selects on phenotype, not genotype--we give a perspective that proteins and their folding processes are likely to have been the primary driver of the early stages of the origin of life. [ABSTRACT FROM AUTHOR]

We prove that the lowest free energy of a classical interacting system at temperature T with a prescribed density profile ρ(x) can be approximated by the local free energy ∫fT(ρ(x))dx, provided that ρ varies slowly over sufficiently large length scales. A quantitative error on the difference is provided in terms of the gradient of the density. Here fT is the free energy per unit volume of an infinite homogeneous gas of the corresponding uniform density. The proof uses quantitative Ruelle bounds (estimates on the local number of particles in a large system), which are derived in an appendix. [ABSTRACT FROM AUTHOR]

Underground hydrogen storage (UHS) has been considered as an integral part of energy transition from fossil fuels to renewable sources. Porous aquifers can serve as typical sites for this purpose due to their worldwide distribution and huge storage capacity. However, the diverse microbial species that inhabit the aquifers are known to be able to catalyze in-situ biochemical reactions within the hosting rock. These reactions can normally lead to microbial consumption of hydrogen, microbial clogging of pore space and thus affect hydrogen injection and withdrawal rates as reported in the literature. So far, these phenomena have been widely reported but rarely quantified. In this study, we build a coupled hydrological-mechanical-chemical-biological (HMCB) multiphysics model to simulate these microbe-related processes during UHS in aquifers. The model consists of a complete set of partial differential equations to describe: (1) rock deformation; (2) water-hydrogen two-phase flow; (3) microbes and dissolved hydrogen transport; (4) mineral dissolution/precipitation; and (5) microbial activities involving adsorption/desorption and growth/decay. All these processes are linked together through the porosity/permeability models which consider the joint impacts of microbial clogging, mineral dissolution/precipitation and effective stress. This multiphysics model is verified against laboratory biochemical reaction data and microbial transport data. Then, the verified model is used to investigate the impacts of iron-reduction bacteria (IRB) activities on UHS in aquifers. Based on the simulation results, it can be concluded that (1) hydrogen saturation at the top surface of the aquifer is the greatest while microbial activities surrounding the injection well is stimulated the most; (2) microbial activities influence the initial few cycles of hydrogen injection and withdrawal but the impacts gradually diminish with the dissolution of Fe 2 O 3 ; (3) hydrogen recovery efficiency is degraded due to the combined effects of hydrogen consumption, water production and microbial clogging with the microbial clogging impact being the most significant; and (4) effective stress impacts aquifer permeability throughout UHS operations while microbial clogging influences it in the initial few cycles. For mineral dissolution/precipitation, the impact can be neglected. • Coupled multiphysics model has been developed to quantify the impact of IRB activities on UHS in aquifers. • Interactions among hydraulic-mechanical-chemical-biological processes are captured. • The model has been verified against laboratory microbial transport data and chemical reaction data. • Dependence of hydrogen recovery performance on various microbe-related parameters are evaluated. [ABSTRACT FROM AUTHOR]

How does social complexity depend on population size and cultural transmission? Kinship structures in traditional societies provide a fundamental illustration, where cultural rules between clans determine people's marriage possibilities. Here, we propose a simple model of kinship interactions that considers kin and in-law cooperation and sexual rivalry. In this model, multiple societies compete. Societies consist of multiple families with different cultural traits and mating preferences. These values determine interactions and hence the growth rate of families and are transmitted to offspring with mutations. Through a multilevel evolutionary simulation, family traits and preferences are grouped into multiple clans with interclan mating preferences. It illustrates the emergence of kinship structures as the spontaneous formation of interdependent cultural associations. Emergent kinship structures are characterized by the cycle length of marriage exchange and the number of cycles in society. We numerically and analytically clarify their parameter dependence. The relative importance of cooperation versus rivalry determines whether attraction or repulsion exists between families. Different structures evolve as locally stable attractors. The probabilities of formation and collapse of complex structures depend on the number of families and the mutation rate, showing characteristic scaling relationships. It is now possible to explore macroscopic kinship structures based on microscopic interactions, together with their environmental dependence and the historical causality of their evolution. We propose the basic causal mechanism of the formation of typical human social structures by referring to ethnographic observations and concepts from statistical physics and multilevel evolution. Such interdisciplinary collaboration will unveil universal features in human societies. [ABSTRACT FROM AUTHOR]

Amorphous materials undergo a transition from liquid-like to solid-like states through processes like rapid quenching or densification. Under external loads, they exhibit yielding, with minimal structural changes compared to crystals. However, these universal characteristics are rarely explored comprehensively in a single granular experiment due to the added complexity of inherent friction. The discernible differences between static configurations before and after yielding are largely unaddressed, and a comprehensive examination from both statistical physics and mechanical perspectives is lacking. To address these gaps, we conducted experiments using photoelastic disks, simultaneously tracking particles and measuring forces. Our findings reveal that the yielding transition demonstrates critical behavior from a statistical physics standpoint and marginal stability from a mechanical perspective, akin to the isotropic jamming transition. This criticality differs significantly from spinodal criticality in frictionless amorphous solids, highlighting unique characteristics of granular yielding. Furthermore, our analysis confirms the marginal stability of granular yielding by assessing the contact number and evaluating the balance between weak forces and small gaps. These factors serve as structural indicators for configurations before and after yielding. Our results not only contribute to advancing our understanding of the fundamental physics of granular materials but also bear significant implications for practical applications in various fields. [ABSTRACT FROM AUTHOR]

The adsorption of potentially toxic element ions from contaminated water sources has garnered significant attention due to its critical role in environmental remediation and ensuring safe drinking water. Potentially toxic element ions can be removed from water using conventional adsorbents such as activated zeolites; however, these materials have low absorption and slow kinetics. To solve these issues, carbon-based adsorbents that exhibit easy synthesis, high porosity, designability, and stability have been proposed. In this study, a carbon-based adsorbent, named Magnetic Nitrogen-Doped Carbon (M-NC), and graphene oxide were developed for the selective removal of potentially toxic element ions. To increase the potential for HM immobilization, sulfide-modified biochar was created via a process called simultaneous carbon layer encapsulation. A theoretical physicochemical and thermodynamic investigation of the adsorption of potentially toxic elements s Zn2+, Cd2+, Ni2+, Ag2+, Pb2+ and Cu2+ on carbon-based adsorbents was performed with statistical physics fundaments. The biochar with large surface areas is used to remove potentially toxic element ions, one of the most important potentially toxic element pollutants, from aqueous solutions. The capacity of the adsorbent for removing potentially toxic element ions was studied using Langmuir adsorption isotherm under ultrasound-assisted conditions. The MNCs can be applied to the Langmuir model and pseudo-second-order kinetics. It is possible to use the Langmuir and second-order kinetic equations to accurately explain the adsorption method. Thermodynamic limitations were also envisioned because sorption is exothermic when it happens spontaneously. A homogeneous statistical physics adsorption model was used to describe and analyze the experimental potentially toxic element removal isotherms at 30 °C and pH5 utilizing adsorbents produced by pyrolysis of biomasses (broccoli stalks). The findings show the proposed adsorbent, with an efficiency of 98.7 % and even reaching 99.3 % in certain cases, making it a standout choice for potentially toxic element removal applications. This research holds significance in advancing the understanding of environmentally sustainable potentially toxic element removal processes, particularly in the context of biomass-derived adsorbents, offering potential solutions for water purification and environmental remediation. [ABSTRACT FROM AUTHOR]

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies. [ABSTRACT FROM AUTHOR]

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less sti dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience. [ABSTRACT FROM AUTHOR]