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This article explores the role of thought experiments in science, specifically in the fields of black hole physics and thermodynamics. It discusses how thought experiments, which often involve fictional or fantastical scenarios, have been used throughout history to challenge existing theories and generate new ideas. The article highlights famous examples, such as Maxwell's demon and Wheeler's cup of tea, to demonstrate how these thought experiments have led to important breakthroughs in understanding the laws of physics. The author argues that while real experiments are necessary for conclusive evidence, thought experiments are valuable for revealing inconsistencies and conflicts between theories. The article also suggests that there are parallels between science and fiction, and that fiction can be used as a tool to better understand scientific theories and the real world. [Extracted from the article]

Optical trapping microscopy unravels open questions in statistical physics by making ultraprecise measurements of Brownian motion in diverse fluid environments. Moreover, the advent of pulsed lasers promises to push the boundaries even further, offering insights on how the compressibility of fluid flow affects Brownian particles at previously unexplored time scales. [ABSTRACT FROM AUTHOR]

Physicists have been interested in traffic for a long time for various reasons. On the one hand, we all participate in traffic as pedestrians or drivers. On the other hand, traffic systems exhibit interesting, and sometimes counter-intuitive, collective phenomena. They are examples for nonequilibrium systems which allows to apply and test methods developed in statistical physics. [ABSTRACT FROM AUTHOR]

A recent study using game theory suggests that humans may never become optimal decision-makers, despite learning from past experiences. The study, which simulated a large economy, challenges the assumption in economic theories that people can reach a stable state where they know how to maximize their earnings. The researchers found that players in the game were only able to reach "satisficing" states, where they met minimum expectations but not much more. While the study's findings may not be immediately applicable to real-world predictions, they do challenge economists to reconsider certain assumptions in their theories. [Extracted from the article]

The mathematical equivalence between finite state stochastic machine and non-dissipative and dissipative quantum tight-binding and Schrödinger model is derived. Stochastic Finite state machine is also expressed by classical epidemic model and can reproduce the quantum entanglement emerging in the case of electrostatically coupled qubits described by von-Neumann entropy both in non-dissipative and dissipative case. The obtained results show that quantum mechanical phenomena might be simulated by classical statistical model as represented by finite state stochastic machine. It includes the quantum like entanglement and superposition of states. Therefore coupled epidemic models expressed by classical systems in terms of classical physics can be the base for possible incorporation of quantum technologies and in particular for quantum like computation and quantum like communication. The classical density matrix is derived and described by the equation of motion in terms of anticommutator. Existence of Rabi like oscillations is pointed in classical epidemic model. Furthermore, the existence of Aharonov–Bohm effect in quantum systems (Becker et al., 2019) can also be reproduced by the classical epidemic model or in broader sense by finite state stochastic machine. Every quantum system made from quantum dots and described by simplistic tight-binding model by use of position-based qubits can be effectively described by classical statistical model encoded in finite stochastic state machine with very specific structure of S matrix that has twice bigger size as it is the case of quantum matrix Hamiltonian. Furthermore, the description of linear and non-linear stochastic finite state machine is mapped to tight-binding and Schrödinger model. The concept of N dimensional complex time is incorporated into tight-binding model, so the description of dissipation in most general case is possible. Obtained results help in approximation of non-dissipative or dissipative quantum mechanical phenomena by classical statistical physics expressed by finite state stochastic machine. Furthermore, the equivalence of Wannier tight-binding dissipative or non-dissipative formalism to dissipative or non-dissipative Schrödinger formalism and to finite state stochastic machine was shown. [ABSTRACT FROM AUTHOR]

A recent study conducted at the Jan and Dan Duncan Neurological Research Institute in Houston, Texas, utilized a statistical physics approach to identify sex-specific Alzheimer's Disease (AD) risk genes. The research discovered 122 genes under greater selection pressure in AD cases compared to healthy controls, with more than 50% of these genes showing dysregulation in AD brains. By analyzing smaller, sex-separated cohorts, the study identified 82 genes in males and 69 genes in females, indicating shared as well as sex-specific AD mechanisms. The findings suggest that these gene sets can predict AD risk with high accuracy, even when blinded to APOE genotype, highlighting the importance of genetic differences in AD susceptibility between males and females. [Extracted from the article]

Researchers from Beni Suef University in Egypt have discovered a new method for producing an alkali-activated binder (AABLs) using carbonized limestone. This binder was found to effectively remove pharmaceutical contaminants, specifically ibuprofen, from wastewater. The study characterized the binder and determined its adsorption capacities, molecular mechanisms, and stability. The findings suggest that the AABLs binder is highly stable and can be reused in wastewater treatment. This research provides a green and low-cost solution for the removal of pharmaceuticals from water. [Extracted from the article]

Following other fields of science, Deep Learning models are gaining attention within the statistical physics community as a powerful and efficient way for analysing experimental and synthetic time series, and for quantifying properties thereof. Applying such models is nevertheless a path full of pitfalls, not only due to their inherent complexity, but also to a lack of understanding of some of their idiosyncrasies. We here discuss some of these pitfalls in the context of time series classification, covering from the selection of the best model hyperparameters, how the models have to be trained, to the way data have to be pre-processed. While not providing one-fits-all answers, the statistical physics practitioner will here find what questions ought to be posed, and a first guide about how to tackle them. • Assessment of pitfalls and challenges in the use of Deep Learning in statistical physics. • Analysis of hyperparameters' selection, training, and data pre-processing. • Guidelines for the statistical physics practitioner in the use of such models. [ABSTRACT FROM AUTHOR]

Siemens Healthineers AG has been granted a patent for a computer-based method and system that corrects noise effects in x-ray image data. The method involves using an artificial intelligence algorithm to determine a noise effect dataset that describes the noise effects present in the x-ray image data. The algorithm applies a statistical physics model to describe the noise effects and uses model parameters established through training. This correction process can be applied to various noise effects, including scattered radiation, and can improve the accuracy and robustness of the correction compared to existing methods. Siemens Healthineers AG is a prominent medical technology company that has developed a new imaging technology called computed tomography (CT). This technology enables detailed and precise imaging of the human body, which can assist in the diagnosis and treatment of various medical conditions. The company's patent application provides a comprehensive description of the CT technology and its potential applications in the medical field. [Extracted from the article]

This article discusses a study that used single-cell sequencing data of early ascidian embryos to investigate the development of multicellular organisms. The researchers developed a method to identify distinct transcriptomic states or cell types and found correlations within embryos and across cell types, indicating the presence of collective variation. They also inferred minimal networks of cell-cell interactions, revealing the collective modes of gene expression. This research demonstrates how spatial interactions and collective gene expression can be inferred from single-cell gene expression measurements, opening up new possibilities for biological research. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

We investigate the evolution of network effects, specifically focusing on the emerging paradigm of third-generation networks known as group-forming networks (GFNs). In contrast to traditional views where network value adheres to Metcalfe's quadratic growth or Sarnoff's linear growth, GFNs, such as social networks and community-centric platforms like Apple, Microsoft, and Google, exhibit a distinct exponential growth pattern in network value as postulated by Reed's hypothesis. Unlike conventional network science, economic perspectives on network effects do not necessitate precise knowledge of the network topology. Instead, they often align with mean-field models akin to those found in statistical physics. Recent empirical studies challenge earlier assumptions, revealing that many GFNs have undergone exponential or notably superquadratic growth, deviating significantly from established models based solely on user and link counts. This paper introduces a simplified model aimed at capturing the growth dynamics of marketable groups within a network comprising n users. The model explores various hypothetical mechanisms governing the expansion of these groups, and through rigorous analysis and simulation, we uncover compelling evidence that the observed growth in GFNs surpasses traditional quadratic models. Our findings challenge existing notions, suggesting a new framework for understanding the intricacies of network growth dynamics, and providing solid analytical and conceptual foundations for the recent explosive processes of economic and financial value growth of networks that exploit the formation of groups. [Display omitted] • Network Effects: Focus on sub-networks and groups, beyond user size and P2P. • Network Value: Considers revenues from groups of dynamic sizes. • Ad Revenue Growth: Finds explosive growth in groups ad revenues beyond standard economic models. • Superlinear Dynamics: explosive growth in group ads drives platform success and de-facto monopolies. [ABSTRACT FROM AUTHOR]

The total number of fatalities of an epidemic outbreak is a dramatic but extremely informative quantity. Knowledge of the statistics of this quantity allows the calculation of the mean total number of fatalities conditioned to the fact that the outbreak has surpassed a given number of fatalities, which is very relevant for risk assessment. However, the fact that the total number of fatalities seems to be characterized by a power-law tailed distribution with exponent (for the complementary cumulative distribution function) smaller than one poses an important theoretical difficulty, due to the non-existence of a mean value for such distributions. Cirillo and Taleb (2020) propose a transformation from a so-called dual variable, which displays a power-law tail, to the total number of fatalities, which becomes bounded by the total world population. Here, we (i) show that such a transformation is ad hoc and unphysical; (ii) propose alternative transformations and distributions (also ad hoc); (iii) argue that the right framework for this problem is statistical physics, through finite-size scaling; and (iv) demonstrate that the real problem is not the non-existence of the mean value for power-law tailed distributions but the fact that the tail of the different theoretical distributions (which is what distinguishes one model from the other) is far from being well sampled with the available number of empirical data. Our results are also valid for many other hazards displaying (apparent) power-law tails in their size. • A previous transformation to estimate the expected number of fatalities in an epidemic is shown ad hoc and unphysical. • We propose alternative transformations and distributions, also ad hoc. • We argue that the right framework for this problem is statistical physics, through finite-size scaling. • We demonstrate that the real problem is not the non-existence of the mean value for power-law tailed distributions. • The problem is that the tail of the different theoretical distributions is far from being well sampled with the available empirical data. [ABSTRACT FROM AUTHOR]

New research from Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia, focuses on the selective extraction and determination of hydrocortisone and dexamethasone in skincare cosmetics. The study utilizes magnetic molecularly imprinted polymers (MMIPs) and magnetic non-molecularly imprinted polymers (MNIPs) as adsorbents to understand the adsorption of the drugs. Results show that the adsorption amount of MMIPs for the template molecule was greater than that of MNIPs, and the adsorption process is physisorption. The research concludes that the adsorption of these drugs on the tested adsorbents is exothermic and spontaneous. [Extracted from the article]

Measures of economic mobility represent aggregate values for how individual wealth changes over time. As such, these measures may not describe the feasibility of a typical individual to change their wealth. To address this limitation, we introduce mixing, a concept from statistical physics, as a relevant phenomenon for quantifying how individuals move across the wealth distribution. We display the relationship between mixing and mobility both theoretically and using data. By studying the properties of an established model of wealth dynamics, we show that some individuals can move across the distribution when wealth is a non-mixing observable. Only in the mixing case every individual is able to move across the whole wealth distribution. There is also a direct equivalence between measures of mixing and the magnitude of the standard measures of economic mobility, but the opposite is not true. We then describe an empirical method for estimating the mixing properties of wealth dynamics in practice. We use this method to present a pedagogical application using the USA longitudinal data. This, approach, even though limited in data availability, leads to results suggesting that wealth in the USA is either non-mixing or that it takes a very long time for the individuals to mix within the distribution. These results showcase how mixing can be used in tandem with measures of mobility for drawing conclusions about the extent of mobility across the whole distribution. • We introduce mixing as a concept to quantify the mobility of the typical individual. • Mixing equals standard mobility measures when everyone moves across the distribution. • We describe a statistical methodology for estimating mixing in real-world economies. [ABSTRACT FROM AUTHOR]

Static and dynamic equilibria in noisy binary choice (Ising) games on complete and random graphs in the annealed approximation are analysed. Two versions, an Ising game with interaction term defined in accordance with the Ising model in statistical physics and a reduced Ising game with a customary definition of interaction term in game theory on graphs, are considered. A detailed analysis of hysteresis phenomenon shaping the pattern of static equilibria based on consideration of elasticity with respect to external influence is conducted. Fokker–Planck equations describing dynamic versions of the games under consideration are written and their asymptotic stationary solutions derived. It is shown that domains of parameters corresponding to the maxima of these probability distributions are identical with the corresponding hysteresis ranges for static equilibria. Same result holds for domains defining local stability of solutions of the evolution equations for the moments. In all the cases considered it is shown that the results for the reduced Ising game coincide with those obtained for the Ising game on complete graphs. It is shown that for s special case of logistic noise the results obtained for static equilibria for the Ising game reproduce those in the Ising model on graphs in statistical physics. • Static equilibria and dynamic evolution in noisy binary choice (Ising and reduced Ising) games on graphs are considered. • Explicit analysis of static equilibria for Ising and reduced Ising games on complete and random graphs is performed. • Static asymptotic solutions of the Fokker–Planck equations for Ising and reduced Ising games on complete and random graphs are analysed. [ABSTRACT FROM AUTHOR]

Spin glasses are useless. Even the most imaginative physicists, submitted to grant pressure, could not find applications for these materials. Yet their study, triggered by pure intellectual interest, has created a formidable new branch of statistical physics distinguished this year by the Nobel prize attributed to Giorgio Parisi. [ABSTRACT FROM AUTHOR]

Neuroscientist and free-will skeptic Sam Harris has dueled philosopher and free-will defender Daniel Dennett for years and once invited him onto his podcast with the express purpose of finally having a meeting of minds. Christian List, a philosopher at the London School of Economics who specializes in how humans make decisions, has a new book, Why Free Will Is Real, that tries to bridge the gap. A free-will skeptic argues, first, that free will requires one or more properties: intentional, goal-directed agency; alternative possibilities - that it must be possible for me to do otherwise; or causal control over our actions. [Extracted from the article]

Abstract We derive a thermodynamically consistent model of heterogeneous catalysis for chemically reacting fluid mixtures formulated in the framework of continuum thermodynamics. The model takes into account standard transport phenomena in the bulk domain and involves mass transfer of the species between the bulk and the catalytic (active) part of the boundary where the surface counterparts to the bulk transport processes take place. Concerning the balance laws on the catalytic surface, the model benefits from description of the active part of the boundary as an interface between the bulk domain and its exterior which allows to employ the framework of continuum mechanics with interfacial transport phenomena. The constitutive relations involving vectorial and tensorial quantities such as the Cauchy stress, energy and entropy fluxes and diffusive fluxes relevant to individual constituents are constructed in a systematic manner ensuring compatibility with the second law of thermodynamics. The constitutive procedure follows from the specification of constitutive equations for suitable thermodynamic potentials (free energies) of the mixture in the bulk and on the active part of the boundary and from the identification of the bulk and surface entropy productions. The active part of the boundary is described by means of statistical physics and this description then serves as a building block for the derivation of surface continuum thermodynamic potentials. The derived model is suitable for further mathematical, numerical and computational analysis of relevant initial and boundary value problems. While the model employs a relatively simple description of the active part of the boundary as a monolayer lattice with single-site Langmuir-type adsorption, its detailed derivation presented here provides clear guidelines for the incorporation of other sorption models. [ABSTRACT FROM AUTHOR]

This article introduce readers to some of the concepts, terminology, and notation of Bayesian network. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states. A variable may be discrete, having a finite or countable number of states, or it may be continuous. Often the choice of states itself presents an interesting modeling question. In a system for troubleshooting a problem with printing, one may choose to model the variable "print output" with two states "present" and "absent" or one may want to model the variable with finer distinctions such as "absent," "blurred," "cut off," and "ok." In describing a Bayesian network, one uses lower-case letters to represent single variables and upper-case letters to represent sets of variables. The general problem of computing probabilities of interest from a joint probability distribution is called probabilistic inference. All exact algorithms for probabilistic inference in Bayesian networks exploit conditional independence roughly.

In Thermodynamics and Statistical Physics, a system's property is extensive when it grows with the system size. When it happens, the system can be decomposed into separate components, which has been done in many systems with weakly interacting components, such as for various gas models. Similarly, Ruelle conjectured 40 years ago that the Lyapunov exponents (LEs) of some sufficiently large chaotic systems are extensive, which led to study the extensivity properties of chaotic systems with strong interactions. Because of the complexities in these systems, most results achieved so far are restricted to numerical simulations. Here, we derive closed-form expressions for the LEs and entropy rate of coupled maps in finite- and infinite-sized regular graphs, according to the coupling strength, map's chaoticity, and graph's spectral properties. We show that this type of system has either 4 or 5 cases for the LEs, depending on the graph's extreme Laplacian eigenvalues. These cases represent qualitatively different collective behaviours emerging in parameter space, including chaotic synchronisation (N − 1 negative LEs) and incoherent chaos (N positive LEs). From the entropy rate, we show that the ring and complete graphs (nearest-neighbour and all-to-all couplings, respectively) are extensive in all parameter regions outside the chaotic synchronisation region. Although our derivations are restricted to one-dimensional maps with constant positive derivative (i.e., chaotic), our approach can be used to find LE and entropy rates for other regular graphs (such as for cyclic graphs) or be the basis for tackling small world graphs via perturbative methods. • Classification of Lyapunov Exponents into 5 possible cases with different dynamics. • Explicit formulas for the boundaries of the 5 cases in parameter space. • Expressions for the Lyapunov exponents and entropy rates of ring and complete graphs. • Coupled maps in ring graphs show extensive entropy rates for any coupling strength. • Complete graphs show extensive entropy rates outside the synchronisation region. [ABSTRACT FROM AUTHOR]

Being critical, i.e. able to process and distill relevant information, is crucial for living systems. Learning distinguishes living from inanimate matter. Quantifying this distinction may provide a "life meter" [1] that, for example, can allow us to detect alien life forms in astrobiology. Living systems also respond in an anomalous manner to perturbations, as compared to inanimate matter, unless the latter is poised at a critical state (in the statistical physics sense). I argue below that these two notions of criticality are only apparently different, because a system that learns is inherently critical, also in the statistical physics sense. [ABSTRACT FROM AUTHOR]

Statistical physics was originally developed to understand the behaviour of materials like gases, liquids or crystalline solids and the phase transitions between them. But in recent decades, concepts from statistical physics have been applied much more widely, in particular to biological systems. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, the multi-scale entropy and the conditional sampling are used to study the velocity increment in the boundary layer transition process. • Similarities in the distribution of velocity increment in each part (laminar/turbulent) of the intermittent flow are observed. • It is also found that there is a process for both the laminar and turbulent part of the fluid motions to evolve into the mature state before the fully developed turbulence is reached. Abstract Boundary layer transition induced by surface imperfections has a great impact on the performance of the long endurance vehicles. A better understanding of the physical mechanism of the transition process would be much helpful in developing new models for transition prediction. The velocity increment is important in the theory of turbulence, but it is rarely discussed in a transitional boundary layer. In this paper, the multi-scale entropy and the conditional sampling are used to study the velocity increment in the boundary layer transition process. Similarities in the distribution of velocity increment in each part (laminar/turbulent) of the intermittent flow are observed. It is also found that there is a process for both the laminar and turbulent part of the fluid motions to evolve into the mature state before the fully developed turbulence is reached. [ABSTRACT FROM AUTHOR]

Abstract In this short note, we revisit the partial monotonicity of the differential entropy, and point out some errors in Shangari and Chen (2012) and Gupta and Bajaj (2013). The partial monotonicity of the Shannon entropy is also established. [ABSTRACT FROM AUTHOR]

In their modern implementation, computational models based on first principles from Physics can dramatically benefit from the recent explosion of Data Science. In fact, these two branches of applied mathematics can virtuously interplay, and at a large extent they already do. [ABSTRACT FROM AUTHOR]

Rayleigh–Taylor (RT) instability is a classical interface instability of great importance in nature and engineering fields. In this paper, the influences of acceleration on the compressible RT instability is investigated by using the discrete Boltzmann method based on non-equilibrium statistical physics. The differences in RT systems with various accelerations have been analyzed through the physical gradients and non-equilibrium measures. It is interesting to find that the global temperature gradient, the maximum Mach number, and the non-equilibrium strength increase initially, reduce afterwards, and have peaks in the dynamic process. Specifically, in the early stage, the global temperature gradient of the fluid system is higher for a case with larger acceleration, and there is an exponential relationship between them. Moreover, the maximum Mach number is located at the heavy fluid that drops fast in the system's midsection, rises more sharply, and reaches the peak earlier for a larger acceleration. In addition, for a larger acceleration, the non-equilibrium strength rises (reduces) faster in the early (later) stage, and presents an exponential relationship between them in the early stage as well. From the kinetic perspective, these results further enrich our understanding of the physical mechanism of the compressible RT instability with both hydrodynamic and thermodynamic non-equilibrium effects. • The compressible RT instability with various accelerations is simulated via the discrete Boltzmann method based on non-equilibrium statistical physics. • The tendencies of maximum March number, temperature gradients and typical non-equilibrium quantities are discussed in the RT system. • The influences of acceleration on the compressible RT instability are analyzed from the perspectives of hydrodynamic and thermodynamic non-equilibrium manifestations. [ABSTRACT FROM AUTHOR]

Keywords: Qingdao; People's Republic of China; Asia; Drugs and Therapies; Emerging Technologies; Health and Medicine; Nanoparticles; Nanotechnology; Naphthaleneacetic Acids; Naproxen; Naproxen Therapy; Pharmaceuticals; Physics; Statistical Physics EN Qingdao People's Republic of China Asia Drugs and Therapies Emerging Technologies Health and Medicine Nanoparticles Nanotechnology Naphthaleneacetic Acids Naproxen Naproxen Therapy Pharmaceuticals Physics Statistical Physics 1033 1033 1 10/09/23 20231013 NES 231013 2023 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Drug Week -- A new study on Nanotechnology - Nanoparticles is now available. Qingdao, People's Republic of China, Asia, Drugs and Therapies, Emerging Technologies, Health and Medicine, Nanoparticles, Nanotechnology, Naphthaleneacetic Acids, Naproxen, Naproxen Therapy, Pharmaceuticals, Physics, Statistical Physics. [Extracted from the article]

In this work, we study Static and Dynamic Games on Large Networks of interacting agents, assuming that the players have some statistical description of the interaction graph, as well as some local information. Inspired by Statistical Physics, we consider statistical ensembles of games and define a Probabilistic Approximate equilibrium notion for such ensembles. A Necessary Information Complexity notion is introduced to quantify the minimum amount of information needed for the existence of a Probabilistic Approximate equilibrium. We then focus on some special classes of games for which it is possible to derive upper and/or lower bounds for the complexity. At first, static and dynamic games on random graphs are studied and their complexity is determined as a function of the graph connectivity. In the low complexity case, we compute Probabilistic Approximate equilibrium strategies. We then consider static games on lattices and derive upper and lower bounds for the complexity, using contraction mapping ideas. A LQ game on a large ring is also studied numerically. Using a reduction technique, approximate equilibrium strategies are computed and it turns out that the complexity is relatively low. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new extension of cumulative entropy based on k th lower record values. We also consider a dynamic version of this new cumulative entropy using the past lifetime. For these concepts, we study some properties and characterization results under some assumptions. Finally, we provide some examples of application of the results. [ABSTRACT FROM AUTHOR]

The Lie symmetry analysis is performed on the Heisenberg equation from the statistical physics. Its Lie point symmetries and optimal system of one-dimensional subalgebras are determined. The similarity reductions and invariant solutions are obtained. Using the multipliers, some conservation laws are obtained. We prove that this equation is nonlinearly self-adjoint. The conservation laws associated with symmetries of this equation are constructed by means of Ibragimov’s method. [ABSTRACT FROM AUTHOR]

The open source ALPS (Algorithms and Libraries for Physics Simulations) project provides a collection of physics libraries and applications, with a focus on simulations of lattice models and strongly correlated systems. The libraries provide a convenient set of well-documented and reusable components for developing condensed matter physics simulation code, and the applications strive to make commonly used and proven computational algorithms available to a non-expert community. In this paper we present an updated and refactored version of the core ALPS libraries geared at the computational physics software development community, rewritten with focus on documentation, ease of installation, and software maintainability. Program summary Program Title: ALPS Core libraries Program Files doi: http://dx.doi.org/10.17632/fckj5d7wtr.1 Programming language: C++ Licensing provisions: GNU GPLv3 Nature of problem: Need for modern, lightweight, tested and documented libraries covering the basic requirements of rapid development of efficient physics simulation codes, especially for modeling strongly correlated electron systems. Solution method: We present a C++ open source computational library that provides a convenient set of components for developing parallel physics simulation code. The library features a short development cycle and up-to-date user documentation. External routines/libraries: CMake , MPI , Boost , HDF5 . [ABSTRACT FROM AUTHOR]

While many opinion models often concentrate on the stable state and phase transition of the dynamics, the temporal evolution pattern of public opinion rarely is investigated. If the variation of opinions with time can be formulated, it is probable to effectively characterize the intrinsic process or even predict the future trend of the global state. In this paper, we study a trust-aware voter model in which individual trust co-evolves with their opinions and the trust on a target agent propagates from common neighbors. When agents often have similar opinions with their neighbors, they may develop trust on the neighbors, and vice versa. Individual trust changes opinion interactions, and agents tend to adopt opinions of the neighbors they trust. Mean-field analysis and simulations are conducted to explore the transient opinion profiles of the model. Results prove that public opinion changes as an exponential mixture form in both homogeneous and heterogeneous networks. Most agents quickly stick to the majority opinion, and the conservation of magnetization is broken. Indirect trust promotes opinion interactions and drives the system towards consensus. In addition, we conduct empirical experiments on topic discussions of a real-world network, and the results show that the model well fits and predicts opinion formation over online social networks. [ABSTRACT FROM AUTHOR]

The governing equation of self-preserving size distribution can be obtained by similarity transformation from the Smoluchowski coagulation equation. To deal with the variable upper limit of convolution, the one parameter group transformation is introduced to get the invariant form of the governing equation. However, whether the equation has an invariant solution depending on asymptotic property of the coagulation kernel at the lower-end boundary. For homogeneous kernel, it can be discussed with three classes, type I has no similarity solution, type II has similarity solution which should be analyzed, and type III has similarity solution. Under the existence of similarity solution, an improved iDNS algorithm is developed with fourth order Runge–Kutta method to get the invariant solution of Smoluchowski coagulation equation more efficiently. Based on the invariant solution, a new definition of entropy in statistical physics is proposed to distinguish the convergence of Smoluchowski coagulation equation mathematically. The new entropy is a simple function of algebraic mean volume and has a maximum. At long time, the entropy production approaches to zero, which means the convergence of solution for Smoluchowski coagulation equation mathematically, and the Cercignani conjecture is almost true for Smoluchowksi coagulatin equation. • A new entropy is proposed to analysis the convergence of SCE. • The invariant form of governing equation of SPSD is obtained by OPG transformation. • The invariant solution of SCE is solved by the iDNS algorithm. • Fourth order Runge Kutta method is used to improve efficiency of the iDNS. • A simple criterion is proposed to judge the existence of invariant solution of SCE. [ABSTRACT FROM AUTHOR]

The asymptotic (non)equivalence of canonical and microcanonical ensembles, describing systems with soft and hard constraints respectively, is a central concept in statistical physics. Traditionally, the breakdown of ensemble equivalence (EE) has been associated with nonvanishing relative canonical fluctuations of the constraints in the thermodynamic limit. Recently, it has been reformulated in terms of a nonvanishing relative entropy density between microcanonical and canonical probabilities. The earliest observations of EE violation required phase transitions or long-range interactions. More recent research on binary networks found that an extensive number of local constraints can also break EE, even in absence of phase transitions. Here we study for the first time ensemble nonequivalence in weighted networks with local constraints. Unlike their binary counterparts, these networks can undergo a form of Bose–Einstein condensation (BEC) producing a core–periphery structure where a finite fraction of the link weights concentrates in the core. This phenomenon creates a unique setting where local constraints coexist with a phase transition. We find surviving relative fluctuations only in the condensed phase, as in more traditional BEC settings. However, we also find a non-vanishing relative entropy density for all temperatures, signalling a breakdown of EE due to the presence of an extensive number of constraints, irrespective of BEC. Therefore, in presence of extensively many local constraints, vanishing relative fluctuations no longer guarantee EE. • Ensemble nonequivalence is found in weighted networks. • The presence of local constraints always leads the ensemble nonequivalence. • Bose-Einstein condensation can break the ensemble equivalence in weighted networks. [ABSTRACT FROM AUTHOR]

Keywords for this news article include: Palma de Mallorca, Spain, Europe, CDC and FDA, CSIC-UIB. Keywords: Palma de Mallorca; Spain; Europe; CDC and FDA EN Palma de Mallorca Spain Europe CDC and FDA 952 952 1 05/29/23 20230602 NES 230602 2023 JUN 4 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Current study results on CDC and FDA have been published. [Extracted from the article]

Facilities for handling uncertainty have long been an integral part of knowledge-based systems. In the early days of rule-based programming, the predominant methods used variants on probability calculus to combine certainty factors associated with applicable rules. These methods were favored because the probabilistic techniques available at the time required either specifying an intractable number of parameters or assuming an unrealistic set of independence relationships. A Bayesian network is an annotated directed graph that encodes probabilistic relationship among distinctions of interest in an uncertain-reasoning problem. The representation rigorously describes these relationships, yet includes a human-oriented qualitative structure that facilitates communication between a user and a system incorporating the probabilistic model. With the advent of small, powerful computers and graphic user interfaces, modeling tools based on Bayesian networks are seeing frequent use in real-world applications, including diagonals, forecasting, automated vision, sensor fusion and manufacturing control.

Endless Forms: The Secret World of Wasps by Serian Sumner, HarperCollins, 2022; 400 pages; $28.99 Serian Sumner, professor of behavioral ecology at University College London, wants to set the record straight: she is on a "high-octane mission to defend wasps, challenge perceptions of wasps, and especially to balance the scientific scales for the wasp against the bounty of scientific belovedness bestowed upon the bee." Those merit treatment at the end of the book, but Sumner devotes several preliminary chapters to species that have as few as a dozen members, and that illustrate how social behavior evolved since the first solitary wasp took flight about 235 million years ago. Among the grimmest of these predators, the emerald jewel wasp (Ampulex compressa), which hunts cockroaches too big to carry, injects a venom that effectively zombifies the roach, inducing it to walk, "like a well-trained poodle", to the nursery feast. [Extracted from the article]

The title molecule is abbreviated as PMDT has been investigated using experimental and computational techniques." Keywords: Drugs and Therapies EN Drugs and Therapies 1471 1471 1 05/08/23 20230512 NES 230512 2023 MAY 12 (NewsRx) -- By a News Reporter-Staff News Editor at Drug Week -- Investigators publish new report on drugs and therapies. [Extracted from the article]

Human behavior, and in particular vaccine hesitancy, is a critical factor for the control of childhood infectious disease. Here we propose a spatio-temporal behavioral epidemiology model where the vaccine propensity depends on information that is non-local in space and in time. The properties of the proposed model are analyzed under different hypotheses on the spatio-temporal kernels tuning the vaccination response of individuals. As a main result, we could numerically show that vaccine hesitancy induces the onset of many dynamic patterns of relevance for epidemiology. In particular we observed: behavior-modulated patterns and spatio-temporal chaos. This is the first known example of human behavior-induced spatio-temporal chaos in statistical physics of vaccination. Patterns and spatio-temporal chaos are difficult to deal with, from the Public Health viewpoint, hence showing that vaccine hesitancy can cause them could be of interest. Additionally, we propose a new simple heuristic algorithm to estimate the Maximum Lyapunov Exponent. • Human behavior-based spatio-temporal SIR type epidemic model is considered. • The model includes vaccine hesitancy in relation to childhood immunization. • The behavioral response is assumed to be fully non-local over space and time. • Resulting patterns are triggered by the vaccination response and the spatio-temporal information kernels. • Spatio-temporal chaos is among the observed patterns. [ABSTRACT FROM AUTHOR]

Climate Change, Clinical Research, Deutsche Forschungsgemeinschaft, Ecology, Global Warming, Health and Medicine, Mathematics, Social Science This will be the focus of the work to be done by the Research Unit Using clonal oak phytometers to unravel acclimation and adaptation mechanisms of long-lived forest tree holobionts to ecological variations and climate change, which is jointly funded with the Swiss National Science Foundation (SNSF) under the D-A-CH cooperation. Keywords: Climate Change; Clinical Research; Deutsche Forschungsgemeinschaft; Ecology; Global Warming; Health and Medicine; Mathematics; Social Science EN Climate Change Clinical Research Deutsche Forschungsgemeinschaft Ecology Global Warming Health and Medicine Mathematics Social Science 1408 1408 1 04/17/23 20230421 NES 230421 2023 APR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) is establishing 13 new Research Units. [Extracted from the article]

Keywords for this news article include: Viral, COVID-19, Virology, SARS-CoV-2, RNA Viruses, Epidemiology, Travel Medicine, Risk and Prevention, Statistical Physics, Travel Restrictions, American Institute of Physics, Severe Acute Respiratory Syndrome Coronavirus 2. American Institute of Physics, COVID-19, Coronavirus, Epidemiology, Physics, RNA Viruses, Risk and Prevention, SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2, Statistical Physics, Travel Medicine, Travel Restrictions, Viral, Virology Keywords: American Institute of Physics; COVID-19; Coronavirus; Epidemiology; Physics; RNA Viruses; Risk and Prevention; SARS-CoV-2; Severe Acute Respiratory Syndrome Coronavirus 2; Statistical Physics; Travel Medicine; Travel Restrictions; Viral; Virology EN American Institute of Physics COVID-19 Coronavirus Epidemiology Physics RNA Viruses Risk and Prevention SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 Statistical Physics Travel Medicine Travel Restrictions Viral Virology 4 4 1 04/10/23 20230414 NES 230414 2023 APR 14 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx COVID-19 Weekly -- WASHINGTON, - Air traffic is a significant and complex problem. [Extracted from the article]

Keywords: Beijing; People's Republic of China; Asia; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Physics; Social Science; Statistical Physics EN Beijing People's Republic of China Asia Cyborgs Emerging Technologies Health and Medicine Machine Learning Physics Social Science Statistical Physics 5964 5964 1 03/23/23 20230310 NES 230310 2023 MAR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Researchers detail new data in Machine Learning. Beijing, People's Republic of China, Asia, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Physics, Social Science, Statistical Physics. [Extracted from the article]

The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn–Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. We present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. We reimplement the scattering matrix calculation for GPUs with a block matrix inversion algorithm that only uses accelerator memory. Using the Cray XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code. [ABSTRACT FROM AUTHOR]

Previous work on iterated learning, a standard language learning paradigm where a sequence of learners learns a language from a previous learner, has found that if learners use a form of Bayesian inference, then the distribution of languages in a population will come to reflect the prior distribution assumed by the learners (Griffiths and Kalish 2007). We expand these results to allow for more complex population structures, and demonstrate that for learners on undirected graphs the distribution of languages will also reflect the prior distribution. We then use techniques borrowed from statistical physics to obtain deeper insight into language evolution, finding that although population structure will not influence the probability that an individual speaks a given language, it will influence how likely neighbors are to speak the same language. These analyses lift a restrictive assumption of iterated learning, and suggest that experimental and mathematical findings using iterated learning may apply to a wider range of settings. [ABSTRACT FROM AUTHOR]