Minimal Model Research Articles

Asymptotic symmetries of gravity in the gauge PDE approach

We propose a framework to study local gauge theories on manifolds with boundaries and their asymptotic symmetries, which is based on representing them as so-called gauge PDEs. These objects extend the conventional BV-AKSZ sigma-models to the case of not necessarily topological and diffeomorphism invariant systems and are known to behave well when restricted to submanifolds and boundaries. We introduce the notion of gauge PDE with boundaries, which takes into account generic boundary conditions, and apply the framework to asymptotically flat gravity. In so doing, we start with a suitable representation of gravity as a gauge PDE with boundaries, which implements the Penrose description of asymptotically simple spacetimes. We then derive the minimal model of the gauge PDE induced on the boundary and observe that it provides the Cartan (frame-like) description of a (curved) conformal Carollian structure on the boundary. Furthermore, imposing a version of the familiar boundary conditions in the induced boundary gauge PDE, leads immediately to the conventional Bondi–Metzner–Sachs algebra of asymptotic symmetries. Finally, we briefly sketch the construction for asymptotically (A)dS gravity.

Leptogenesis consequences of trimaximal mixing and μ−τ reflection symmetry in the most minimal seesaw model

In this paper, we have studied the realizations of the popular first trimaximal neutrino mixing and neutrino μ−τ reflection symmetry (which are well motivated from the neutrino oscillation data and lead to interesting phenomenological consequences) in the most minimal seesaw model with a pseudo-Dirac pair of right-handed neutrinos and their consequences for leptogenesis. In order to realize the low-scale resonant leptogenesis scenario, we have considered two possible ways of generating the tiny mass splitting between the two right-handed neutrinos: one way is to modify their Majorana mass matrix to a form as shown in Eq. (25); the other way is to consider the renormalization-group corrections for their masses. For the μ−τ reflection symmetry, in order for leptogenesis to work, we have further considered the flavor-dependent conversion efficiencies from the lepton asymmetry to the baryon asymmetry during the sphaleron processes and its breaking via the renormalization-group corrections. Published by the American Physical Society 2024

The top quark rare decays with flavor violation

Abstract In the present study, we investigate the decays of the top quark $t\rightarrow c\gamma$, $t\rightarrow cg$, $t\rightarrow cZ$ and $t\rightarrow ch$. They are extremely rare processes in the Standard Model (SM). As the $U(1)$ extension of the minimal supersymmetric standard model (MSSM), the $U(1)_X$SSM features new superfields such as the right-handed neutrinos and three Higgs singlets. We analyze the effects of different sensitive parameters on the results and make reasonable theorecial predictions, which provides a useful reference for future experimental development. Considering the constraint of the updated experimental data, the numerical results show that the branching ratios of all four processes $t\rightarrow c\gamma,~cg,~cZ,~ch$ can reach the same order of magnitude as their experimental upper limits. Among them, $\tan\beta$ has the most obvious effect on each process and is the main parameter. $g_X$, $g_{YX}$, $\mu$, $M_2$, $\lambda_H$, $M_{U23}^2$ and $M_{Q23}^2$ are important parameters for the processes, and have effects on the numerical results. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Growth couples temporal and spatial fluctuations of tissue properties during morphogenesis

Living tissues display fluctuations-random spatial and temporal variations of tissue properties around their reference values-at multiple scales. It is believed that such fluctuations may enable tissues to sense their state or their size. Recent theoretical studies developed specific models of fluctuations in growing tissues and predicted that fluctuations of growth show long-range correlations. Here, we elaborated upon these predictions and we tested them using experimental data. We first introduced a minimal model for the fluctuations of any quantity that has some level of temporal persistence or memory, such as concentration of a molecule, local growth rate, or mechanical property. We found that long-range correlations are generic, applying to any such quantity, and that growth couples temporal and spatial fluctuations, through a mechanism that we call "fluctuation stretching"-growth enlarges the length scale of variation of this quantity. We then analyzed growth data from sepals of the model plant Arabidopsis and we quantified spatial and temporal fluctuations of cell growth using the previously developed cellular Fourier transform. Growth appears to have long-range correlations. We compared different genotypes and growth conditions: mutants with lower or higher response to mechanical stress have lower temporal correlations and longer-range spatial correlations than wild-type plants. Finally, we used theoretical predictions to merge experimental data from all conditions and developmental stages into a unifying curve, validating the notion that temporal and spatial fluctuations are coupled by growth. Altogether, our work reveals kinematic constraints on spatiotemporal fluctuations that have an impact on the robustness of morphogenesis.

Adaptive Particle Swarm Optimization based improved modeling of Solar Photovoltaic module for parameter determination

Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.

The class group of a minimal model of a quotient singularity

The class group of a minimal model of a quotient singularity

A multiscale sensorimotor model of experience-dependent behavior in a minimal organism

To survive in ever-changing environments, living organisms need to continuously combine the ongoing external inputs they receive, representing present conditions, with their dynamical internal state, which includes influences of past experiences. It is still unclear in general, however 1) how this happens at the molecular and cellular levels and 2) how the corresponding molecular and cellular processes are integrated with the behavioral responses of the organism. Here, we address these issues by modeling mathematically a particular behavioral paradigm in a minimal model organism, namely chemotaxis in the nematode C. elegans. Specifically, we use a long-standing collection of elegant experiments on salt chemotaxis in this animal, in which the migration direction varies depending on its previous experience. Our model integrates the molecular, cellular, and organismal levels to reproduce the experimentally observed experience-dependent behavior. The model proposes specific molecular mechanisms for the encoding of current conditions and past experiences in key neurons associated with this response, predicting the behavior of various mutants associated with those molecular circuits.

Functional regimes define the response of the soil microbiome to environmental change.

The metabolic activity of soil microbiomes plays a central role in carbon and nitrogen cycling. Given the changing climate, it is important to understand how the metabolism of natural communities responds to environmental change. However, the ecological, spatial, and chemical complexity of soils makes understanding the mechanisms governing the response of these communities to perturbations challenging. Here, we overcome this complexity by using dynamic measurements of metabolism in microcosms and modeling to reveal regimes where a few key mechanisms govern the response of soils to environmental change. We sample soils along a natural pH gradient, construct >1500 microcosms to perturb the pH, and quantify the dynamics of respiratory nitrate utilization, a key process in the nitrogen cycle. Despite the complexity of the soil microbiome, a minimal mathematical model with two variables, the quantity of active biomass in the community and the availability of a growth-limiting nutrient, quantifies observed nitrate utilization dynamics across soils and pH perturbations. Across environmental perturbations, changes in these two variables give rise to three functional regimes each with qualitatively distinct dynamics of nitrate utilization over time: a regime where acidic perturbations induce cell death that limits metabolic activity, a nutrient-limiting regime where nitrate uptake is performed by dominant taxa that utilize nutrients released from the soil matrix, and a resurgent growth regime in basic conditions, where excess nutrients enable growth of initially rare taxa. The underlying mechanism of each regime is predicted by our interpretable model and tested via amendment experiments, nutrient measurements, and sequencing. Further, our data suggest that the long-term history of environmental variation in the wild influences the transitions between functional regimes. Therefore, quantitative measurements and a mathematical model reveal the existence of qualitative regimes that capture the mechanisms and dynamics of a community responding to environmental change.

Surprise-minimization as a solution to the structural credit assignment problem.

The structural credit assignment problem arises when the causal structure between actions and subsequent outcomes is hidden from direct observation. To solve this problem and enable goal-directed behavior, an agent has to infer structure and form a representation thereof. In the scope of this study, we investigate a possible solution in the human brain. We recorded behavioral and electrophysiological data from human participants in a novel variant of the bandit task, where multiple actions lead to multiple outcomes. Crucially, the mapping between actions and outcomes was hidden and not instructed to the participants. Human choice behavior revealed clear hallmarks of credit assignment and learning. Moreover, a computational model which formalizes action selection as the competition between multiple representations of the hidden structure was fit to account for participants data. Starting in a state of uncertainty about the correct representation, the central mechanism of this model is the arbitration of action control towards the representation which minimizes surprise about outcomes. Crucially, single-trial latent-variable analysis reveals that the neural patterns clearly support central quantitative predictions of this surprise minimization model. The results suggest that neural activity is not only related to reinforcement learning under correct as well as incorrect task representations but also reflects central mechanisms of credit assignment and behavioral arbitration.

Organisational teleology 2.0: Grounding biological purposiveness in regulatory control

AbstractThis paper critically revises the organisational account of teleology, which argues that living systems are first and foremost oriented towards a goal: maintaining their own conditions of existence. It points out some limitations of this account, mainly in the capability to account for the richness and complexity of biological systems and their purposeful behaviours. It identifies the reason of these limitations in the theoretical grounding of this account, specifically in the too narrow notion of closure of constraints, focused on self‐production. It proposes to ground an organisational account of biological teleology in the capability of living system not just to produce and replace their parts, but to control their own internal dynamics and behaviours in such a way as to maintain themselves. This theoretical framework has two advantages. It better captures the distinctive features of biological organisations and consequently the richness and active nature of their purposeful behaviours. By doing so, it makes it possible to apply this framework beyond minimal theoretical models to real biological cases.

“Sparse + Low-Rank” tensor completion approach for recovering images and videos

Recovering color images and videos from highly undersampled data is a fundamental and challenging task in face recognition and computer vision. By the multi-dimensional nature of color images and videos, in this paper, we propose a novel tensor completion approach, which is able to efficiently explore the sparsity of tensor data under the discrete cosine transform (DCT). Specifically, we introduce two “sparse + low-rank” tensor completion models as well as two implementable algorithms for finding their solutions. The first one is a DCT-based sparse plus weighted nuclear norm induced low-rank minimization model. The second one is a DCT-based sparse plus p-shrinking mapping induced low-rank optimization model. Moreover, we accordingly propose two implementable augmented Lagrangian-based algorithms for solving the underlying optimization models. A series of numerical experiments including color image inpainting and video data recovery demonstrate that our proposed approach performs better than many existing state-of-the-art tensor completion methods, especially for the case when the ratio of missing data is high.

#3140 Investigating the impact of Anti-IL5 therapy in eosinophilic granulomatosis with polyangiitis (EGPA): a three year longitudinal perspective

Abstract Background and Aims In the randomized, placebo-controlled MIRRA trial [1] for relapsing and refractory eosinophilic granulomatosis with polyangiitis (EGPA), adjuvant therapy with 300 mg anti-IL5 mAB Mepolizumab [MEPO] for 12 months (M), accrued longer times in remission, reduced steroid exposure and reduced relapse rates [1]. The aim of this study is to analyze the outcome of 100 mg MEPO monthly s/c for a minimum of 36 months. Changes to adjuvant immunosuppression and indications for anti-IL5 class switch from MEPO 100 mg s/c to Benralizumab (BRZ) or Reslizumab (Res) were assessed. Method In this observational study, 20 EGPA patients received anti-IL5 therapy for a minimum of 36M (range 49-68M). All commenced on 100 mg s/c MEPO every four weeks. Anti-IL5 therapy switched to BRZ or Res due to partial response or intolerance. Assessment time points included MEPO commencement, 6, 12, 18, 24 and 36 months. Results Overall, there was a 50% reduction in steroid dose by 12 months. This continued to reduce to 24M, by which time 2 were off steroids and a further 10/20 (50%) on weaning dose ≤ prednisolone 5 mg/day. Mean steroid dose continued to decrease to 36 months. The number on adjuvant conventional immunosuppressants (ACIS), reduced over time from 10/20 (50%) at M0 to 4/20 (20%) by M24. Clinical benefits included ANCA serology normalized in all four positive patients by 12 months. Mean eosinophil count reduced from 0.42 mg ± 0.33 × 109/L at M0 to 0.04 ± 0.03 × 109/L at 12 and 24M. BVAS reduced from median 5 [3-7], to 0 [0-1] by 24M. The change in mean FEV1 over 12 months was from (M0) 2.11 ± 0.66 to (M12) 2.39 ± 0.62 and FVC (M0) 3.42 ± 0.87 to (M12) 3.67 ± 0.93/105.60 ± 20.47 respectively. All 20 EGPA patients receiving anti -IL5 therapy, ranging from 49-68M remain on therapy. At 36M, 9 have remained on 100 mg s/c MEPO. 10 (50%) have switched to an alternative anti-IL5 agent—10 switched to benralizumab, 1 initially on benralizumab to reslizumab. 9/10 had achieved partial response prior to switch (reduction in steroids / relapse rate), 1/10 had no response.. During the duration of the study, 3 patients had a break of therapy, but all resumed anti-IL5 treatment with good response. Hence, all 20 remain on anti-IL5 beyond 24M. After 36M, one patient required cyclophosphamide along with anti-IL5 therapy for myocarditis. A further patient had Rituximab for EGPA/ Rheumatoid arthritis overlap between anti-IL5 agents. Conclusion In this study, there was a 50% reduction in steroid dosage by 12 months and steroid requirements continue to decrease to 36M. By 24 months 2 are steroid free and a further 10 on weaning dose ≤5 mg. Furthermore, the number on adjuvant conventional immunosuppression reduced over the 24M (n = 4 at 24M). This study demonstrates that anti-IL5 therapy serves as a favorable model for steroid and conventional immunosuppressant minimization in EGPA. Clinical benefits of reduction in BVAS, improved pulmonary function tests and reduced serum eosinophilia were recorded. The relapsing nature of EGPA places a dependency of therapy on steroids and this study demonstrated sustained and ongoing improvement annually with continued anti-IL5 therapy. Some participants required a switch in anti-IL5 agent.

Active transport of a passive colloid in a bath of run-and-tumble particles

The dispersion of a passive colloid immersed in a bath of non-interacting and non-Brownian run-and-tumble microswimmers in two dimensions is analyzed using stochastic simulations and an asymptotic theory, both based on a minimal model of swimmer-colloid collisions characterized solely by frictionless steric interactions. We estimate the effective long-time diffusivity D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathscr {D}}$$\\end{document} of the suspended colloid resulting from its interaction with the active bath, and elucidate its dependence on the level of activity (persistence length of swimmer trajectories), the mobility ratio of the colloid to a swimmer, and the number density of swimmers in the bath. We also propose a semi-analytical model for the colloid diffusivity in terms of the variance and correlation time of the net fluctuating active force on the colloid resulting from swimmer collisions. Quantitative agreement is found between numerical simulations and analytical results in the experimentally-relevant regime of low swimmer density, low mobility ratio, and high activity.

Growth-induced collective bending and kinetic trapping of cytoskeletal filaments.

Growth and turnover of actin filaments play a crucial role in the construction and maintenance of actin networks within cells. Actin filament growth occurs within limited space and finite subunit resources in the actin cortex. To understand how filament growth shapes the emergent architecture of actin networks, we developed a minimal agent-based model coupling filament mechanics and growth in a limiting subunit pool. We find that rapid filament growth induces kinetic trapping of highly bent actin filaments. Such collective bending patterns are long-lived, organized around nematic defects, and arise from competition between filament polymerization and bending elasticity. The stability of nematic defects and the extent of kinetic trapping are amplified by an increase in the abundance of the actin pool and by crosslinking the network. These findings suggest that kinetic trapping is a robust consequence of growth in crowded environments, providing a route to program shape memory in actin networks.

Models for Test Cost Minimization in Database Migration

Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints by appropriately instantiating the parameters of the framework. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs.

Convergence of cscK metrics on smooth minimal models of general type

Convergence of cscK metrics on smooth minimal models of general type

Quantum Slush State in Rydberg Atom Arrays.

In this Letter, we propose an exotic quantum state that does not order at zero temperature in a Rydberg atom array with antiblockade mechanism. By performing an unbiased large-scale quantum MonteCarlo simulation, we investigate a minimal model with facilitated excitation in a disorder-free system. At zero temperature, this model exhibits a heterogeneous structure of liquid and glass mixture. This state, dubbed quantum slush state, features a quasi-long-range order with an algebraic decay for its correlation function, and is different from most well-established quantum phases of matter.

In Silico Study of the Early Stages of Aggregation of β-Sheet Forming Antimicrobial Peptide GL13K.

Antimicrobial peptides (AMPs) are of growing interest as potential candidates that may offer more resilience against antimicrobial resistance than traditional antibiotic agents. In this article, we perform the first in silico study of the synthetic ß sheet-forming AMP GL13K. Through atomistic simulations of single and multi-peptide systems under different conditions, we are able to shine a light on the short timescales of early aggregation. We find that isolated peptide conformations are primarily dictated by sequence rather than charge, whereas changing charge has a significant impact on the conformational free energy landscape of multi-peptide systems. We demonstrate that the loss of charge-charge repulsion is a sufficient minimal model for experimentally observed aggregation. Overall, our work explores the molecular biophysical underpinnings of the first stages of aggregation of a unique AMP, laying necessary groundwork for its further development as an antibiotic candidate.

Design principles as minimal models

In this essay I suggest that we view design principles in systems biology as minimal models, for a design principle usually exhibits universal behaviors that are common to a whole range of heterogeneous (living and nonliving) systems with different underlying mechanisms. A well-known design principle in systems biology, integral feedback control, is discussed, showing that it satisfies all the conditions for a model to be a minimal model. This approach has significant philosophical implications: it not only accounts for how design principles explain, but also helps clarify one dispute over design principles, e.g., whether design principles provide mechanistic explanations or a distinct kind of explanations called design explanations.