Universal Model Research Articles

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.

УНІВЕРСАЛЬНА МАТЕМАТИЧНА МОДЕЛЬ АВТОНОМНОГО АСИНХРОННОГО ГЕНЕРАТОРА З КОНДЕНСАТОРНИМ САМОЗБУДЖЕННЯМ

A universal mathematical model of an autonomous asynchronous generator with capacitor self-excitation in retarded phase coordinates is proposed, taking into account the electromagnetic connections between the windings of the stator and rotor phases, saturation of the main magnetic circuit, and active power losses in the magnetic circuit elements. The model provides an opportunity to analyze stable periodic modes and transient electromagnetic and electromechanical processes in autonomous power supply systems with arbitrary switching schemes of its elements and asynchronous generators. The model provides the possibility of taking into account the residual magnetization of the magnetic circuit of an asynchronous generator and the change in rotor speed during the course of the self-excitation process of an asynchronous generator with a grounded neutral of the stator winding and an asymmetric load. References 10, figures 2.

Recent Developments in Degenerate Higher Order Scalar Tensor Theories

AbstractDegenerate Higher Order Scalar Tensor (DHOST) theories are the most general scalar‐tensor theories whose Lagrangian depends on the metric tensor and a single scalar field and its derivatives up to second order. They propagate only one scalar degree of freedom, without being plagued by Ostrogradsky instabilities. This is achieved through certain degeneracies of the functions forming their Lagrangian. They generalize the Horndeski and beyond‐Horndeski theories. Originally proposed to describe the late‐time acceleration of the expansion of the universe, generalizing the cosmological constant, they can also be used to build models of the early universe, to describe inflation or alternatives to standard inflation. In the late universe, they modify the standard Vainstein screening mechanism from Horndeski theories (which can have observable consequences) and are suited to build black hole models, featuring non‐stealth Kerr black hole solutions. In this work, their phenomenology is reviewed, looking at their basic properties, their parameterizations and classifications, focusing on solutions in the early and the late universe and at cosmological and astrophysical constraints.

Восприятие родительского отношения и общая академическая успеваемость старшеклассников: российско-кыргызское исследование

<p style="text-align: justify;"><strong>Objective. </strong>Cross-cultural analysis of the perception of parental attitudes and its influence on the general academic performance of high school students from Russia and Kyrgyzstan. <br><strong>Background. </strong>The characteristics of parental attitudes are closely related not only to the child’s educational success during life in the family, but also to educational achievements in the future. At the same time, the sociocultural specifics of parent-child relationships, through the perception of a growing child, can change the direction and magnitude of such associations. <br><strong>Study design. </strong>The characteristics of the relationship between mothers and fathers – emotional acceptance and overprotection – by high school students from two countries differing in socio-economic status are analyzed. A cross-cultural analysis of the structure of associations between the characteristics of the perception of parental attitudes and academic performance is carried out. <br><strong>Participants. </strong>The study analyzed data from 528 students in grades 10–11 from Russia and Kyrgyzstan. Russian sample: 230 people (<em>M</em> = 17,31; <em>SD</em> = 0,7), 58,1% girls. Kyrgyz sample: 298 people (<em>M</em> = 17,21; <em>SD</em> = 0,7), 67,6% girls. <br><strong>Measurements. </strong>Russian-language version of the questionnaire “Parental Bonding Instrument” (T.N. Tikhomirova, D.A. Gaysina, S.B. Malykh, 2021). <br><strong>Results. </strong>At high school age, the perception of parental attitude has a culture-specific conditioning, most clearly expressed in the perception of emotional acceptance by the mother and the overprotective attitude of the father. <br><strong>Conclusions. </strong>Given the existence of a universal model of the influence of parental attitudes on the general academic performance of high school students, cross-cultural specificity of functional associations within the model is observed, which is more characteristic of the perception of the attitudes of fathers rather than mothers.</p>

Cosmological implications on two fluids model universe with parametrization of Hubble parameter in Lyra manifold

In this research, we explore the cosmological implication on five-dimensional FRW cosmological model for k = 0 in presence of two perfect fluids: ordinary baryonic fluid and an interacting dark energy in the context of Lyra’s manifold. We obtain an exact solution of the field equations by assuming the parametrization of the Hubble parameter H(a) = τ(1 + a −ν ), where a is a scale factor and τ > 0, ν > 1 are the arbitrary constants. This results in a universe model with a transition from a previously decelerating universe to the current accelerating universe, characterized by a time-dependent deceleration parameter. Moreover, we have used 46 observational Hubble data sets to restrict the parameters of our suggested model. Some cosmological parameters have been examined about the nature of the proposed model universe. It is shown that our model can be considered a realistic one.

Simulating adiabatic quantum computing with parameterized quantum circuits

Abstract Adiabatic quantum computing is a universal model for quantum computing whose implementation using a gate-based quantum computer requires depths that are unreachable in the early fault-tolerant era. To mitigate the limitations of near-term devices, a number of hybrid approaches have been pursued in which a parameterized quantum circuit prepares and measures quantum states and a classical optimization algorithm minimizes an objective function that encompasses the solution to the problem of interest. In this work, we propose a different approach starting by analyzing how a small perturbation of a Hamiltonian affects the parameters that minimize the energy within a family of parameterized quantum states. We derive a set of equations that allow us to compute the new minimum by solving a constrained linear system of equations that is obtained from measuring a series of observables on the unperturbed system. We then propose a discrete version of adiabatic quantum computing that can be implemented in a near-term device while at the same time is insensitive to the initialization of the parameters and to other limitations hindered in the optimization part of variational quantum algorithms. We compare our proposed algorithm with the variational quantum eigensolver on two classical optimization problems, namely MaxCut and number partitioning, and on a quantum-spin configuration problem, the transverse-field ising chain model, and confirm that our approach demonstrates superior performance.

Current Status of the MB-pol Data-Driven Many-Body Potential for Predictive Simulations of Water Across Different Phases.

Developing a molecular-level understanding of the properties of water is central to numerous scientific and technological applications. However, accurately modeling water through computer simulations has been a significant challenge due to the complex nature of the hydrogen-bonding network that water molecules form under different thermodynamic conditions. This complexity has led to over five decades of research and many modeling attempts. The introduction of the MB-pol data-driven many-body potential energy function marked a significant advancement toward a universal molecular model capable of predicting the structural, thermodynamic, dynamical, and spectroscopic properties of water across all phases. By integrating physics-based and data-driven (i.e., machine-learned) components, which correctly capture the delicate balance among different many-body interactions, MB-pol achieves chemical and spectroscopic accuracy, enabling realistic molecular simulations of water, from gas-phase clusters to liquid water and ice. In this review, we present a comprehensive overview of the data-driven many-body formalism adopted by MB-pol, highlight the main results and predictions made from computer simulations with MB-pol to date, and discuss the prospects for future extensions to data-driven many-body potentials of generic and reactive molecular systems.

No universal mathematical model for thermal performance curves across traits and taxonomic groups

In ectotherms, the performance of physiological, ecological and life-history traits universally increases with temperature to a maximum before decreasing again. Identifying the most appropriate thermal performance model for a specific trait type has broad applications, from metabolic modelling at the cellular level to forecasting the effects of climate change on population, ecosystem and disease transmission dynamics. To date, numerous mathematical models have been designed, but a thorough comparison among them is lacking. In particular, we do not know if certain models consistently outperform others and how factors such as sampling resolution and trait or organismal identity influence model performance. To fill this knowledge gap, we compile 2,739 thermal performance datasets from diverse traits and taxa, to which we fit a comprehensive set of 83 existing mathematical models. We detect remarkable variation in model performance that is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results reveal a surprising lack of well-defined scenarios in which certain models are more appropriate than others. To aid researchers in selecting the appropriate set of models for any given dataset or research objective, we derive a classification of the 83 models based on the average similarity of their fits.

A simple model of magnetic universe without singularity associated with a quadratic equation of state

A model of magnetic universe based on nonlinear electrodynamics has been introduced by Kruglov (Phys Rev D 92:123523, 2015). This model describes an early inflation era followed by a radiation era. We show that this model is related to the model of universe based on a quadratic equation of state introduced in our previous paper (Chavanis in Universe 1:357, 2015). This correspondence may provide a more fundamental justification of our equation of state. It may arise from quantum corrections to linear electrodynamics when the electromagnetic field becomes very high. We discuss two quantitatively different models of early universe. In Model I, the primordial density of the universe is identified with the Planck density. At t=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t=0$$\\end{document}, the universe had the characteristics of a Planck black hole (“planckion” particle). During the inflation, which takes place on a Planck timescale, the size of the universe evolves from the Planck length to a size comparable to the Compton wavelength of the neutrino. If we interpret the radius of the universe at the end of the inflation (neutrino’s Compton wavelength) as a minimum length related to quantum gravity and use Zeldovich’s first formula of the vacuum energy, we obtain the correct value of the cosmological constant. In Model II, the primordial density of the universe is identified with the electron density as a consequence of nonlinear electrodynamics. At t=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t=0$$\\end{document}, the universe had the characteristics of an electron. This can be viewed as a refinement of the “primeval atom” of Lemaître. During the inflation, which takes place on a gravitoelectronic timescale, the size of the universe evolves from the electron’s classical radius to a size comparable to the size of a dark energy star of the stellar mass. If we interpret the radius of the universe at the beginning of the inflation (electron’s classical radius) as a minimum length related to quantum gravity and use Zeldovich’s second formula of the vacuum energy, we obtain the correct value of the cosmological constant. This provides an accurate form of Eddington’s relation between the cosmological constant and the mass of the electron. We use these arguments to show that the present universe contains about 1080\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{80}$$\\end{document} protons (Eddington’s number). We also introduce a nonlinear electromagnetic Lagrangian that describes simultaneously the early inflation, the radiation era, and the dark energy era. It may account for a form of “generalized radiation” that is present from the beginning to the end of the cosmic evolution. Baryonic and dark matter are treated as independent noninteracting species. The dark energy era is due to the electromagnetic vacuum energy (zero-point radiation). In this approach, both the early inflation and the late acceleration of the universe (dark energy) arise as a consequence of nonlinear electrodynamics. This leads to a simple model of magnetic universe without singularity (aioniotic universe).

Matching and driving mechanism analysis of the supply and demand relationships of soil conservation services in karst peak-cluster depression basin in Southwest Guangxi, China

The karst peak-cluster depression basin in southwestern Guangxi, characterized by a fragile ecological environment and intensive human activities, is characterized by rocky desertification and ecological function degradation. This degradation not only impairs the region’s ecological integrity but also limits its ecological and welfare benefits. Traditionally, the estimation of soil conservation service supply and demand in karst areas relies on internationally recognized models, which often exclude the critical factor of karst desertification. This omission introduces significant uncertainty in the assessment of soil supply and demand in karst terrains. To address this gap, our study employs an adapted revised universal soil loss equation model tailored for karst regions to conduct a quantitative analysis of the soil supply–demand relationship in southwestern Guangxi. We explore its spatiotemporal evolution and spatial matching characteristics utilizing geographic detectors to identify the driving factors influencing the ecological supply-to-demand ratio (ESDR). Our findings revealed the following: (1) From 2000 to 2020, the soil conservation supply–demand ratio in the study area showed a fluctuating upward trend, with an overall increase of approximately 63.29%. Specifically, there was a fluctuating increase from 2000 to 2008 and from 2013 to 2020, while a downward trend was observed from 2009 to 2012. (2) The predominant matching pattern of the soil conservation services in the research area was “low supply, low demand”, which was primarily located in the southeastern part of southwestern Guangxi. (3) The supply–demand relationship of soil conservation services is significantly influenced by factors such as vegetation coverage, slope, soil type, and lithology, with each factor exhibiting a marked interactive effect on the ESDR. This study evaluates and reveals the supply–demand relationship and driving mechanisms of soil conservation services in the karst region of southwestern Guangxi, which contributes to the precise formulation of strategies for rocky desertification prevention and ecological restoration.

UTDM: a universal transformer-based diffusion model for multi-weather-degraded images restoration

UTDM: a universal transformer-based diffusion model for multi-weather-degraded images restoration

Universal model of Floquet operator Krylov space

Universal model of Floquet operator Krylov space

A universal drag model for liquid-solid fluidization: Experiment, data-driven modeling, CFD modeling and simulation

Various industrial applications are performed in liquid-solid fluidized beds where drag force plays a significant role in affecting the expansion characteristics of granular-bed, and then determines the mass/heat transfer performance. This study employs dimensionless learning data-driven modeling method, which is derived from the principle of dimensional invariance, to automatically discover the relationship between the drag coefficient and hydraulic dimensionless numbers from the liquid-solid fluidization data. It is found that the Fr number (=ul2/gds) also plays important role in improving the prediction accuracy of drag model except for Re number (=dsulρl/μl). The proposed data-driven modeling method has desired robustness, and the yielded drag model can be applicable to other liquid-solid systems, such as water-polystyrene spheres and water-coal particles, although it is derived from the fluidization of spherical glass beads in rising tap-water. The proposed drag model can also provide good CFD simulation results that agree very well with the experiment data with the relative error less than 5 %.

The Magellanic Clouds Are Very Rare in the IllustrisTNG Simulations

The Large and Small Magellanic Clouds (LMC and SMC) form the closest interacting galactic system to the Milky Way, therewith providing a laboratory to test cosmological models in the local Universe. We quantify the likelihood for the Magellanic Clouds (MCs) to be observed within the ΛCDM model using hydrodynamical simulations of the IllustrisTNG project. The orbits of the MCs are constrained by proper motion measurements taken by the Hubble Space Telescope and Gaia. The MCs have a mutual separation of dMCs=24.5kpc and a relative velocity of vMCs=90.8kms−1, implying a specific phase-space density of fMCs,obs≡(dMCs·vMCs)−3=9.10×10−11km−3s3kpc−3. We select analogues to the MCs based on their stellar masses and distances in MW-like halos. None of the selected LMC analogues have a higher total mass and lower Galactocentric distance than the LMC, resulting in >3.75σ tension. We also find that the fMCs distribution in the highest resolution TNG50 simulation is in 3.95σ tension with observations. Thus, a hierarchical clustering of two massive satellites like the MCs in a narrow phase-space volume is unlikely in ΛCDM, presumably because of short merger timescales due to dynamical friction between the overlapping dark matter halos. We show that group infall led by an LMC analogue cannot populate the Galactic disc of satellites (DoS), implying that the DoS and the MCs formed in physically unrelated ways in ΛCDM. Since the 20∘ alignment of the LMC and DoS orbital poles has a likelihood of P=0.030 (2.17σ), adding this χ2 to that of fMCs gives a combined likelihood of P=3.90×10−5 (4.11σ).

Advanced unconditional signal processing model for cross-section contour reconstruction using multi-channel measurements

In mechanical engineering the standard method to assess the geometric tolerances of rotational parts is by analysing parts’ rotational motion in relation to the measuring system. Traditional tolerance compliance measurement models are conditional signal processing models that require pre-defined contour parameters of measured sections. However, due to part diversity and complexity, pre-determining and pre-setting the section parameters before each measurement process is very time-consuming and, in some cases, unachievable. To address this challenge, this paper proposes a universal unconditional signal processing model, which uses multi-channel measurements for cross-section contour reconstruction that operates without predefined section parameters. This model can handle multi-point measurement signals and accurately estimate the contour shape and engineering center coordinates of measured sections through circumferential Fourier expansion and an approximated signal-contour transform matrix. An efficient iterative algorithm then reconstructs the contour and precisely locates the engineering center. The computational accuracy and robustness of the proposed model have been confirmed through rigorous theoretical analysis and comprehensive experimental validation. Due to its inherent unconditional advantage, the proposed signal processing model can achieve intelligent monitoring of rotational parts throughout their entire life, which not only adapts and remains stable across a wide variety of cross-section types but also significantly improves measurement efficiency, ensuring precise and accurate results.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Informative Data Selection With Uncertainty for Multimodal Object Detection.

Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust generalization of the models. To implement a general vision model, we need to develop deep learning models that can adaptively select valid information from multimodal data. This is mainly based on two reasons. Multimodal learning can break through the inherent defects of single-modal data, and adaptive information selection can reduce chaos in multimodal data. To tackle this problem, we propose a universal uncertainty-aware multimodal fusion model. It adopts a multipipeline loosely coupled architecture to combine the features and results from point clouds and images. To quantify the correlation in multimodal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation. In this way, our model reduces the randomness in fusion and generates reliable output. Moreover, we conducted a completed investigation on the KITTI 2-D object detection dataset and its derived dirty data. Our fusion model is proven to resist severe noise interference like Gaussian, motion blur, and frost, with only slight degradation. The experiment results demonstrate the benefits of our adaptive fusion. Our analysis on the robustness of multimodal fusion will provide further insights for future research.