Universal Ontogenetic Growth without Fitting Parameters: Implications for Life History Invariants & Population Growth

Since the work of Von Bertalanffy (1957), several models have been proposed that relate the ontogenetic scaling of energy assimilation and metabolism to growth, being able to describe ontogenetic growth trajectories for living organisms and collapse them onto a single universal curve (West et al. 2001; Barnavar et al. 2002). Nevertheless, all these ontogenetic growth models critically depends on fitting parameters and on the allometric scaling of the metabolic rate. Using a new metabolic rate relation (Escala 2019) applied to a Bertalanffy-type ontogenetic growth equation, we find that ontogenetic growth can also be described by an universal growth curve for all studied species, but without the aid of any fitting parameters. We find that the inverse of the heart frequency fH, rescaled by the ratio of the specific energies for biomass creation and metabolism, defines the characteristic timescale for ontogenetic growth. Moreover, our model also predicts a generation time and lifespan that explains the origin of several 'Life History Invariants' (Charnov 1993) and predicts that the Mathusian Parameter should be inversely proportional to both the generation time and lifespan, in agreement with the data in the literature (Duncan et al. 1997; Dillingham et. al 2016; Hatton et al 2019). In our formalism, several critical timescales and rates (lifespan, generation time & intrinsic population growth rate) are all proportional to the heart frequency fH, thus their allometric scaling relations comes directly from the allometry of the heart frequency, which is typically fH ∝ M-0.25 under basal conditions.

Nano-Particles Carried by Multiple Dynein Motors Self-Regulate Their Number of Actively Participating Motors

Intra-cellular active transport by native cargos is ubiquitous. We investigate the motion of spherical nano-particles (NPs) grafted with flexible polymers that end with a nuclear localization signal peptide. This peptide allows the recruitment of several mammalian dynein motors from cytoplasmic extracts. To determine how motor–motor interactions influenced motility on the single microtubule level, we conducted bead-motility assays incorporating surface adsorbed microtubules and combined them with model simulations that were based on the properties of a single dynein. The experimental and simulation results revealed long time trajectories: when the number of NP-ligated motors Nm increased, run-times and run-lengths were enhanced and mean velocities were somewhat decreased. Moreover, the dependence of the velocity on run-time followed a universal curve, regardless of the system composition. Model simulations also demonstrated left- and right-handed helical motion and revealed self-regulation of the number of microtubule-bound, actively transporting dynein motors. This number was stochastic along trajectories and was distributed mainly between one, two, and three motors, regardless of Nm. We propose that this self-regulation allows our synthetic NPs to achieve persistent motion that is associated with major helicity. Such a helical motion might affect obstacle bypassing, which can influence active transport efficiency when facing the crowded environment of the cell.

Variable-Order Fractional Models for Wall-Bounded Turbulent Flows

Modeling of wall-bounded turbulent flows is still an open problem in classical physics, with relatively slow progress in the last few decades beyond the log law, which only describes the intermediate region in wall-bounded turbulence, i.e., 30–50 y+ to 0.1–0.2 R+ in a pipe of radius R. Here, we propose a fundamentally new approach based on fractional calculus to model the entire mean velocity profile from the wall to the centerline of the pipe. Specifically, we represent the Reynolds stresses with a non-local fractional derivative of variable-order that decays with the distance from the wall. Surprisingly, we find that this variable fractional order has a universal form for all Reynolds numbers and for three different flow types, i.e., channel flow, Couette flow, and pipe flow. We first use existing databases from direct numerical simulations (DNSs) to lean the variable-order function and subsequently we test it against other DNS data and experimental measurements, including the Princeton superpipe experiments. Taken together, our findings reveal the continuous change in rate of turbulent diffusion from the wall as well as the strong nonlocality of turbulent interactions that intensify away from the wall. Moreover, we propose alternative formulations, including a divergence variable fractional (two-sided) model for turbulent flows. The total shear stress is represented by a two-sided symmetric variable fractional derivative. The numerical results show that this formulation can lead to smooth fractional-order profiles in the whole domain. This new model improves the one-sided model, which is considered in the half domain (wall to centerline) only. We use a finite difference method for solving the inverse problem, but we also introduce the fractional physics-informed neural network (fPINN) for solving the inverse and forward problems much more efficiently. In addition to the aforementioned fully-developed flows, we model turbulent boundary layers and discuss how the streamwise variation affects the universal curve.

Evidence of Odderon-exchange from scaling properties of elastic scattering at TeV energies

We study the scaling properties of the differential cross section of elastic proton–proton (pp) and proton–antiproton ($$p\bar{p}$$ p p ¯ ) collisions at high energies. We introduce a new scaling function, that scales – within the experimental errors – all the ISR data on elastic pp scattering from $$\sqrt{s} = 23.5$$ s = 23.5 –62.5 GeV to the same universal curve. We explore the scaling properties of the differential cross-sections of the elastic pp and $$p\bar{p}$$ p p ¯ collisions in a limited TeV energy range. Rescaling the TOTEM pp data from $$\sqrt{s} = 7$$ s = 7 TeV to 2.76 and 1.96 TeV, and comparing it to D0 $$p\bar{p}$$ p p ¯ data at 1.96 TeV, our results provide an evidence for a t-channel Odderon exchange at TeV energies, with a significance of at least 6.26$$\sigma $$ σ . We complete this work with a model-dependent evaluation of the domain of validity of the new scaling and its violations. We find that the H(x) scaling is valid, model dependently, within $$200~\hbox {GeV}\le \sqrt{s} \le 8$$ 200 GeV ≤ s ≤ 8 TeV, with a $$-t$$ - t range gradually narrowing with decreasing colliding energies.

Dynamics of cascades on burstiness-controlled temporal networks

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

A Theory of Synaptic Transmission

Rapid and precise neuronal communication is enabled through a highly synchronous release of signaling molecules neurotransmitters into the synapse within just milliseconds of the action potential. We present an analytic theory that captures general principles of synaptic transmission while generating concrete predictions for particular synapses. A universal scaling is established, and demonstrated through a collapse of experimental data from different synapses onto a universal curve. The theory shows how key characteristics of synaptic function -- plasticity, fidelity, and efficacy -- emerge from molecular mechanisms of neurotransmitter release machinery.

Macroscopic and microscopic perspectives for adoption of technologies in the USA

Adoption of a new technology depends on many factors. Marketing, advertising, social interactions, and personal convictions are relevant features when deciding to adopt, or not, a new technology. Thus, it is very important to determine the relative weight of these factors when introducing a new technology. Here we discuss an agent based model to investigate the behavior of agents exposed to advertising and social contacts. Agents may follow the social pressure, or maybe contrarians, acting against the majority, to decide if they adopt or not a new technology. First, we solve analytically the model that relies on the above quoted factors. Then, we compare the theoretical results with empirical data concerning the adoption of innovations by American households during the 20th century. The analysis of the diffusion dynamics process is done either for the whole period, or by periods based on the so-called technical-economic paradigms, according to Freeman and Perez. Three different periods are considered: before 1920, from 1920 to 1970, and after 1970. We study the evolution of the model parameters for each technical-economic period. Finally, by adjusting the key parameters we are able to collapse all the data into a universal curve that describes all the adoption processes.

A Study of the Radiation Tolerance of CVD Diamond to 70 MeV Protons, Fast Neutrons and 200 MeV Pions

We measured the radiation tolerance of commercially available diamonds grown by the Chemical Vapor Deposition process by measuring the charge created by a 120 GeV hadron beam in a 50 μm pitch strip detector fabricated on each diamond sample before and after irradiation. We irradiated one group of samples with 70 MeV protons, a second group of samples with fast reactor neutrons (defined as energy greater than 0.1 MeV), and a third group of samples with 200 MeV pions, in steps, to (8.8±0.9) × 1015 protons/cm2, (1.43±0.14) × 1016 neutrons/cm2, and (6.5±1.4) × 1014 pions/cm2, respectively. By observing the charge induced due to the separation of electron–hole pairs created by the passage of the hadron beam through each sample, on an event-by-event basis, as a function of irradiation fluence, we conclude all datasets can be described by a first-order damage equation and independently calculate the damage constant for 70 MeV protons, fast reactor neutrons, and 200 MeV pions. We find the damage constant for diamond irradiated with 70 MeV protons to be 1.62±0.07(stat)±0.16(syst)× 10−18 cm2/(p μm), the damage constant for diamond irradiated with fast reactor neutrons to be 2.65±0.13(stat)±0.18(syst)× 10−18 cm2/(n μm), and the damage constant for diamond irradiated with 200 MeV pions to be 2.0±0.2(stat)±0.5(syst)× 10−18 cm2/(π μm). The damage constants from this measurement were analyzed together with our previously published 24 GeV proton irradiation and 800 MeV proton irradiation damage constant data to derive the first comprehensive set of relative damage constants for Chemical Vapor Deposition diamond. We find 70 MeV protons are 2.60 ± 0.29 times more damaging than 24 GeV protons, fast reactor neutrons are 4.3 ± 0.4 times more damaging than 24 GeV protons, and 200 MeV pions are 3.2 ± 0.8 more damaging than 24 GeV protons. We also observe the measured data can be described by a universal damage curve for all proton, neutron, and pion irradiations we performed of Chemical Vapor Deposition diamond. Finally, we confirm the spatial uniformity of the collected charge increases with fluence for polycrystalline Chemical Vapor Deposition diamond, and this effect can also be described by a universal curve.

Universal epidemic curve for COVID-19 and its usage for forecasting

We construct a universal epidemic curve for COVID-19 using the epidemic curves of eight nations that have reached saturation for the first phase, and then fit an eight-degree polynomial that passes through the universal curve. We take India’s epidemic curve up to September 22, 2020 and overlap it with the universal curve by minimizing square-root error. The constructed curve is used to forecast epidemic evolution up to January 1, 2021. The predictions of our model and those of supermodel for India are reasonably close to each other considering the uncertainties in data fitting.

Electronic quality factor for thermoelectrics

Development of thermoelectrics usually involves trial-and-error investigations, including time-consuming synthesis and measurements. Here, we identify the electronic quality factor BE for determining the maximum thermoelectric power factor, which can be conveniently estimated by a single measurement of Seebeck coefficient and electrical conductivity of only one sample, not necessarily optimized, at an arbitrary temperature. We demonstrate that thousands of experimental measurements in dozens of materials can all be described by a universal curve and a single material parameter BE for each class of materials. Furthermore, any deviation in BE with temperature or doping indicated new effects such as band convergence or additional scattering. This makes BE a powerful tool for evaluating and guiding the development of thermoelectrics. We demonstrate the power of BE to show both p-type GeTe alloys and n-type Mg3SbBi alloys as highly competitive materials, at near room temperature, to state-of-the-art Bi2Te3 alloys used in nearly all commercial applications.