Universal Model Research Articles

A semi-analytic universal model on elasticity across wide temperatures and pressures.

A semi-analytic model is presented universally for the elastic constants and moduli of solid phases in a wide range of temperatures and pressures. We derive in detail the model as a function of temperature and pressure, where the characteristic temperature is clearly associated with the Debye temperature. The abundant experiments of thermal elasticity for Cr-Mn-Fe-Co-Ni high entropy alloys are used to estimate the validity of the characteristic temperature of elasticity. The linear process of the analytical part significantly reduces the high computational and experimental cost of elasticity across a wide range of temperatures and pressures. We take the elastic property of beryllium within the range of up to 6000K and 500GPa as a prototype to investigate the accuracy, efficiency and extrapolation of this model. The application to Mg3Al2Si3O12-pyrope and CaSiO3-perovskite in the Earth's mantle further suggests that our model excellently describes the elasticity of different materials across a wide range of temperatures and pressures.

Predicting spatial curvature ΩK in globally CPT -symmetric universes

Boyle and Turok’s CPT-symmetric universe model posits that the Universe was symmetric at the big bang, addressing numerous problems in both cosmology and the Standard Model of particle physics. We extend this model by incorporating the symmetric conditions at the end of the Universe, which impose constraints on the allowed perturbation modes. These constrained modes conflict with the integer wave vectors required by the global spatial geometry in a closed universe. To resolve this conflict, only specific values of curvature are permissible, and in particular the curvature density is constrained to be ΩK∈{−0.016,−0.011,−0.004,…}, consistent with observations. Published by the American Physical Society 2024

Features of the representation of architectural space in the painting works of Giorgio de Chirico

This article describes the features of the depiction of architectural space in the paintings of the surrealist artist Giorgio de Chirico. In general terms, surrealism is described as an artistic style of the early twentieth century, its emergence, distinctive features and semantic tasks; The questions in relation to Giorgio de Chirico are examined in more detail: from what architectural objects and constituent elements is the urban space of his paintings formed, what objects are the main semantic parts, the general universal architectural model of the formation of such a space in de Chirico’s compositions is described. The article describes the semiotic structure used by the master, reveals the concept and meaning of the term “metaphysical painting”, ways of reading a surreal work of art, and the playful nature of the interaction between the artist and the viewer. The article analyzes in detail the methods and features of constructing the internal space of the metaphysical works of this master, showing the constituent elements, methods, techniques, and techniques for organizing the composition of the painting. The significance of the image of architectural space in de Chirico’s works on modern man and the use of his artistic experience in our time is described.

Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO2 concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security.

An analytical approach to incomplete spherical conformal contact: application on self-lubricating spherical plain bearings

Purpose Contact pressure is a critical factor that significantly influences the wear of self-lubricating spherical plain bearings. The purpose of this paper is to address the issue of conformal contact in spherical plain bearings with a self-lubricating fabric liner, and then a universal theoretical analytical model for conformal contact between frictionless spheres is proposed. Design/methodology/approach This study establishes an analytical model to calculate the conformal contact in spherical plain bearings and verifies the new model by finite element analysis. Findings The new model proposed in this paper overcomes the limitations of elastic half-space and small-deformation assumptions. After conducting accuracy validation, it was observed that the computational error of the new model has significantly decreased in comparison to the Johnson model. For a conformal contact with a clearance of 0, the error is nearly 0. Practical implications The analytical model can calculate the contact pressure distribution of self-lubricating spherical plain bearings bonded with a self-lubricating layer and can be extended to the conformal contact problem of spherical contact surfaces in biomechanics and other fields. Originality/value The model presented here overcomes the limitations of the elastic half-space and small deformation assumptions. It accurately calculates the contact pressure distribution of self-lubricating spherical bearings. Moreover, the complex nonlinear relationship between variables such as normal force, clearance, maximum contact pressure and contact radius was investigated using this model. The model can also be extended and applied to the conformal contact problem of spherical contact surfaces in various fields, including biomechanics. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0296/

Toward a Universal Model of Hyporheic Exchange and Nutrient Cycling in Streams

AbstractIn this paper we demonstrate that several ubiquitous hyporheic exchange mechanisms can be represented simply as a one‐dimensional diffusion process, where the diffusivity decays exponentially with depth into the streambed. Based on a meta‐analysis of 106 previously published laboratory measurements of hyporheic exchange (capturing a range of bed morphologies, hydraulic conditions, streambed properties, and experimental approaches) we find that the reference diffusivity and mixing length‐scale are functions of the permeability Reynolds Number and Schmidt Number. These dimensionless numbers, in turn, can be estimated for a particular stream from the median grain size of the streambed and the stream's depth, slope, and temperature. Application of these results to a seminal study of nitrate removal in 72 headwater streams across the United States, reveals: (a) streams draining urban and agricultural landscapes have a diminished capacity for in‐stream and in‐bed mixing along with smaller subsurface storage zones compared to streams draining reference landscapes; (b) under steady‐state conditions nitrate uptake in the streambed is primarily biologically controlled; and (c) median reaction timescales for nitrate removal in the hyporheic zone are 0.5 and 20 hr for uptake by assimilation and denitrification, respectively. While further research is needed, the simplicity and extensibility of the framework described here should facilitate cross‐disciplinary discussions and inform reach‐scale studies of pollutant fate and transport and their scale‐up to watersheds and beyond.

Comparative gut microbiome research through the lens of ecology: theoretical considerations and best practices.

Comparative approaches in animal gut microbiome research have revealed patterns of phylosymbiosis, dietary and physiological convergences, and environment-host interactions. However, most large-scale comparative studies, especially those that are highly cited, have focused on mammals, and efforts to integrate comparative approaches with existing ecological frameworks are lacking. While mammals serve as useful model organisms, developing generalised principles of how animal gut microbiomes are shaped and how these microbiomes interact bidirectionally with host ecology and evolution requires a more complete sampling of the animal kingdom. Here, we provide an overview of what past comparative studies have taught us about the gut microbiome, and how community ecology theory may help resolve certain contradictions in comparative gut microbiome research. We explore whether certain hypotheses are supported across clades, and how the disproportionate focus on mammals has introduced potential bias into gut microbiome theory. We then introduce a methodological solution by which public gut microbiome data of understudied hosts can be compiled and analysed in a comparative context. Our aggregation and analysis of 179 studies shows that generating data sets with rich host diversity is possible with public data and that key gut microbes associated with mammals are widespread across the animal kingdom. We also show the effects that sample size and taxonomic rank have on comparative gut microbiome studies and that results of multivariate analyses can vary significantly with these two parameters. While challenges remain in developing a universal model of the animal gut microbiome, we show that existing ecological frameworks can help bring us one step closer to integrating the gut microbiome into animal ecology and evolution.

Unified linear response theory of quantum electronic circuits

Modeling the electrical response of multi-level quantum systems at finite frequency has been typically performed in the context of two incomplete paradigms: (i) input-output theory, which is valid at any frequency but neglects dynamic losses, and (ii) semiclassical theory, which captures dynamic dissipation effects well but is only accurate at low frequencies. Here, we develop a unifying theory, valid for arbitrary frequencies, that captures both the small-signal quantum behavior and the non-unitary effects introduced by relaxation and dephasing. The theory allows a multi-level system to be described by a universal small-signal equivalent-circuit model, a resonant RLC circuit, whose topology only depends on the number of energy levels. We apply our model to a double-quantum-dot charge qubit and a Majorana qubit, showing the capability to continuously describe the systems from adiabatic to resonant and from coherent to incoherent, suggesting new and realistic experiments for improved quantum state readout. Our model will facilitate the design of hybrid quantum–classical circuits and the simulation of qubit control and quantum state readout.

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent anovel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning. Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice. Tumor boundaries were auto-segmented on contrast-enhanced T1w sequences. Out of the three auto-contours predicted by SA, accuracy was evaluated for the contour with the highest calculated IoU (Intersection over Union, "oracle mask," simulating interactive model use with selection of the best tumor contour) and for the tumor contour with the highest model confidence ("suggested mask"). Mean best IoU (mbIoU) using the best predicted tumor contour (oracle mask) in full MRI slices was 0.762 (IQR 0.713-0.917). The best 2D mask was achieved after amean of 6.6 interactive point prompts (IQR 5-9). Segmentation accuracy was significantly better for high- compared to low-grade glioma cases (mbIoU 0.789 vs. 0.668). Accuracy was worse using the suggested mask (0.572). Stacking best tumor segmentations from transverse MRI slices, mean 3D Dice score for tumor auto-contouring was 0.872, which was improved to 0.919 by combining axial, sagittal, and coronal contours. The Segment Anything foundation segmentation model can achieve high accuracy for glioma brain tumor segmentation in MRI datasets. The results suggest that foundation segmentation models could facilitate RT treatment planning when properly integrated in aclinical application.

Adaptive Capacity, Governance and Small Island Developing States: A Case Study of Sargassum Management in the Eastern Caribbean

Governance generally, and appropriate operational institutions specifically, are said to be crucial to increasing human adaptive capacity amidst environmental change. But existing conceptualizations tend to assume a universal model of governance will work for states of all sizes. This article questions this orthodoxy which disregards the lack of clarity on size-relevant institutional design and functioning. We do so by focusing on the role of governance in the adaptive capacity of small island developing states (SIDS) facing a new marine social-ecological threat. We draw on a unique dataset of capacity self-assessments undertaken by thirty-eight key agencies involved in the management of sargassum seaweed influx events in the Eastern Caribbean. We found support for the findings of public administration scholars who show that country size is a contextual factor affecting adaptive capacity beyond the control of managers. The implication is that the ability of SIDS to adapt to new or emergent environmental change is crucially inhibited by size-related governance constraints while solutions that mitigate these effects – informal networks and regional organizations – are not well captured by existing metrics. To ensure assessments of adaptive capacity are contextually appropriate we need a more nuanced appreciation of the impacts of state size on governance outcomes.

Universal Remaining Useful Life Prediction for OECTs under Different Aging Conditions

Abstract It is significantly important to predict remaining useful life (RUL) of organic electrochemical transistors (OECTs) for next-generation offshore electronics with stable and reliable performance. Most existing RUL prediction models are not suitable for OECT RUL prediction tasks as they are based on the premise that components have the same aging conditions. In fact, aging conditions of different OECTs often exist discrepancy, leading to performance degradation of RUL prediction models. Although a few methods addressed this issue via transfer learning methods, they still suffer from the challenge in terms of an obvious discrepancy of aging data distribution caused by different aging conditions. To address this issue, we developed a novel universal RUL prediction model for OECTs, called adaptive transformer-based network (ATFN), to reduce the obvious discrepancy among different aging data. First, a transformer-based feature extractor is used to capture the temporal and spatial aging features from some aging precursors. Then, a multi-scale feature alignment metric is adopted to align the aging features of OECTs by reducing discrepancy at different feature scales. Last, an adversarial manner is developed to obtain aging-condition-invariant features for further feature alignment. Extensive experiments are conducted on a real-world OECTs cycling stability aging tests dataset. The average MSE of our method is reduced by two orders of magnitude compared to the one of baseline, which indicates that our method achieves great progress for universal RUL prediction of OECTs under different aging conditions.

Singlet, triplet, and mixed all-to-all pairing states emerging from incoherent fermions

The electron-electron and electron-phonon coupling in complex materials can be more complicated than simple density-density interactions, involving intertwined dynamics of spin, charge, and spatial symmetries. This motivates studying universal models with complex interactions and whether BCS-type singlet pairing is still the “natural” fate of the system. To this end, we construct a Yukawa-SYK model with nonlocal couplings in both spin and charge channels. Furthermore, we provide for time-reversal-symmetry breaking dynamics by averaging over the Gaussian unitary ensemble rather than the orthogonal ensemble. We find that the ground state of the system can be an orbitally nonlocal superconducting state arising from incoherent fermions with no BCS-like analog. The superconductivity has an equal tendency to triplet and singlet pairing states separated by a non-Fermi liquid phase. We further study the fate of the system within the superconducting phase and find that the expected ground state, away from the critical point, is a mixed singlet/triplet state. Finally, we find that, while at Tc the triplet and singlet transitions are dual to one another, below Tc the duality is broken, with the triplet state more susceptible to orbital fluctuations just by its symmetry. Our results indicate that such fluctuation-induced mixed states may be an inherent feature of strongly correlated materials. Published by the American Physical Society 2024

A Systems Biology Approach Towards a Comprehensive Understanding of Ferroptosis.

Ferroptosis is a regulated cell death process characterized by iron ion catalysis and reactive oxygen species, leading to lipid peroxidation. This mechanism plays a crucial role in age-related diseases, including cancer and cardiovascular and neurological disorders. To better mimic iron-induced cell death, predict the effects of various elements, and identify drugs capable of regulating ferroptosis, it is essential to develop precise models of this process. Such drugs can be tested on cellular models. Systems biology offers a powerful approach to studying biological processes through modeling, which involves accumulating and analyzing comprehensive research data. Once a model is created, it allows for examining the system's response to various stimuli. Our goal is to develop a modular framework for ferroptosis, enabling the prediction and screening of compounds with geroprotective and antiferroptotic effects. For modeling and analysis, we utilized BioUML (Biological Universal Modeling Language), which supports key standards in systems biology, modular and visual modeling, rapid simulation, parameter estimation, and a variety of numerical methods. This combination fulfills the requirements for modeling complex biological systems. The integrated modular model was validated on diverse datasets, including original experimental data. This framework encompasses essential molecular genetic processes such as the Fenton reaction, iron metabolism, lipid synthesis, and the antioxidant system. We identified structural relationships between molecular agents within each module and compared them to our proposed system for regulating the initiation and progression of ferroptosis. Our research highlights that no current models comprehensively cover all regulatory mechanisms of ferroptosis. By integrating data on ferroptosis modules into an integrated modular model, we can enhance our understanding of its mechanisms and assist in the discovery of new treatment targets for age-related diseases. A computational model of ferroptosis was developed based on a modular modeling approach and included 73 differential equations and 93 species.

Cosmic analysis through dark energy models in fractal universe with non-linear interaction term

Cosmic analysis through dark energy models in fractal universe with non-linear interaction term

Programable shape recovery properties and snap-through instabilities of viscoelastic and shape-memory NS metamaterials

The combination of smart material and mechanical metamaterial has the potential to bring novel properties and additional functionalities, which, however, has been ignored for a long time. This work numerically, experimentally, and analytically examines the negative stiffness (NS) metamaterials based on shape memory polymers (SMPs) with a focus on the tunable and temperature-dependent properties induced by the interaction of material and geometry nonlinearity. A universal discrete model of a series of shape-memory NS metamaterial is developed by separating the bending effect and stretching effect, where the viscoelastic material is described by the generalized Maxwell-Wiechert model, and an SMP constitutive model is proposed based on multiplicative decomposition of the deformation gradient. The mechanical and shape-memory properties influenced by temperature, geometry, material and loading rate are experimentally observed through relaxation tests and cyclic compression tests. Then the discrete model and finite element analysis (FEA) is adopted to examine the novel mechanical responses in a wide range of parameters. Results show that viscoelasticity and loading rate play a significant role in mechanical responses, and tunable load-capacity and stability can be achieved for shape-memory metamaterial. A phase diagram of different stabilities is constructed showing strictly monotonic, S-shaped, and snapping responses. Additionally, repeated compression and recovery performance proves the possibility of reusable applications. This work provides new opportunities for functional metamaterial to obtain programmable shape recovery and mechanical responses.

Progressive evolution of the viscous dissipation mechanism from the macroscale to the nanoscale

Recent studies of viscous dissipation mechanisms in impacting droplets have revealed distinct behaviours between the macroscale and nanoscale. However, the transition of these mechanisms from the macroscale to the nanoscale remains unexplored due to limited research at the microscale. This work addresses the gap using the many-body dissipative particle dynamics (MDPD) method. While the MDPD method omits specific atomic details, it retains crucial mesoscopic effects, making it suitable for investigating the impact dynamics at the microscale. Through the analysis of velocity contours within impacting droplets, the research identifies three primary contributors to viscous dissipation during spreading: boundary-layer viscous dissipation from shear flow; rim geometric head loss; and bulk viscous dissipation caused by droplet deformation. This prompts a re-evaluation of viscous dissipation mechanisms at both the macroscale and nanoscale. It reveals that the same three kinds of dissipation are present across all scales, differing only in their relative intensities at each scale. A model of the maximum spreading factor (βmax) incorporating all forms of viscous dissipation without adjustable parameters is developed to substantiate this insight. This model is validated against three distinct datasets representing the macroscale, microscale and nanoscale, encompassing a broad spectrum of Weber numbers, Ohnesorge numbers and contact angles. The satisfactory agreement between the model predictions and the data signifies a breakthrough in establishing a universal βmax model applicable across all scales. This model demonstrates the consistent nature of viscous dissipation mechanisms across different scales and underscores the importance of integrating microscale behaviours to understand macroscale and nanoscale phenomena.

Is blockchain cost-effective in construction project management? A systematic review from the perspective of transaction cost

PurposeBlockchain technology (BCT) is considered a promising tool to improve the productivity of construction project management. Existing research has studied its potential costs and benefits for the construction industry. However, the potential costs and benefits of BCT failed to be compared as actual costs and benefits of specific applications for stakeholders. To fill this gap, this study seeks to analyze the cost-effectiveness of BCT-based applications in construction project management.Design/methodology/approachThis study is conducted with a customized systematic literature review based on transaction cost theory to enable qualitative comparison. With a deliberately designed structure confining extraneous variables, the costs and benefits of BCT-based applications are identified and compared. The inherent dependent relations of processes and the evolution relations of functions are identified. The cost-effectiveness of blockchain adoption is then analyzed.FindingsSeven functions and six challenges are identified within five processes. The result suggests all identified functions are cost-effective except for manual instruction (coding smart contracts manually). The smart contracts require explicit definition and logic to be effective. However, the construction projects essentially require the institution to be flexible due to unpredictability. The adoption of smart contracts and corresponding additional requirements can increase the transaction cost of bounded rationality.Research limitations/implicationsAs manual instruction is fundamental to realize other functions, and its advanced substitute relies on its broad adoption, its cost-effectiveness must be improved for applications to be acceptable to stakeholders. The establishment of a universal smart contract model and a universal, legitimate and efficient database structure are recommended to minimize the cost and maximize the effect of applications.Originality/valueThis study contributes to the knowledge by providing a comprehensive analysis of BCT adoption’s cost-effectiveness in construction project management. The adopted review structure can be extended to analyze the qualitative benefits and challenges of management automation in the early stages.

The cost of not getting care: income disparities in the affordability of health services across high-income countries

Abstract This presentation will present findings from the Commonwealth Fund 2023 International Health Policy Survey of the General Population, focusing on financial barriers in accessing healthcare among adults across 10 countries. It highlights significant income-related disparities in healthcare affordability, particularly in the United States, where nearly a quarter of the population lacks affordable access to care. While disparities exist in other countries, Germany and the Netherlands demonstrate lower rates of affordability issues and income disparities, possibly due to their universal health coverage models and cost-sharing caps based on income levels. These countries also prioritize mental health and dental care within their public health plans. The analysis emphasizes the critical need for affordable and comprehensive healthcare coverage to ensure equitable health outcomes. It suggests that policies such as cost-sharing caps and income-based subsidies could mitigate financial barriers to care, drawing insights from successful models in Germany and the Netherlands. Ultimately, addressing affordability is vital for achieving health equity and improving overall population health.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar functional connectivity characteristics through clustering, thereby enhancing the classification process. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.&#xD.

The model of the local Universe in the framework of the second-order perturbation theory

Abstract Recently, we constructed the specific solution to the second-order cosmological perturbation theory, around any Friedmann–Lema\^itre–Robertson–Walker (FLRW) background filled with dust matter and a positive cosmological constant. In this paper, we use the {\it Cosmicflows-4} (CF4) sample of galaxies from the Extragalactic Distance Database to constrain this metric tensor. We obtain an approximation to the local matter distribution and geometry. We numerically solve for null geodesics for randomly distributed mock sources and compare this model with the Lema\^itre-Hubble constant inferred from the observations under the assumption of perfect isotropy and homogeneity. We conclude on effects of realistic inhomogeneities on the luminosity distance in the context of the Hubble tension and discuss limitations of our approach.