State Model Research Articles

On-the-fly spectral unmixing based on Kalman filtering

This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at https://github.com/HKouakou/KF-OSU.

Investigation on dynamic rubbing characteristics of a bladed rotor system with multi-mode rubbing fault

Dynamic behavior analysis of blades under high cycle fatigue with rubbing fault is vital for estimating the reliability of rotating machinery. This paper focuses on a novel rubbing model and the flexible dynamic characteristics of a shaft-blade-disk system (SBDS) model with considerations of various forms of rub-impacts. Firstly, the deformation relations and the motion equations of the SBDS model in steady state are derived using Lagrange's principle. Secondly, a new quasi-static blade rubbing model involving shaft deformation and blade coupling effect is established, and the influence of rub-impact correlation parameters on rubbing force is further explored. Then, both the effect of shaft-disk coupling on the blades at various states and the dynamic characteristics of numerous rubbing modes are also investigated. The results revealed that the asymmetric form of rub-impact will induce more complex vibrational behavior (such as frequency components and trajectory differences), while the symmetrical rubbing modes can keep the system relatively stable.

Transformation of the functions of the institution of the welfare State: the evolution of views and modern challenges

The relevance of the chosen issues is determined by the subject of research - the institution of the welfare state, which plays a crucial role in ensuring the normative order in society and the balance of interests between different social groups. It also guarantees the realization of citizens' political and social rights.The study focuses on the transformation of the welfare state's functions and priorities in social policy, influenced by a rapidly changing internal and external environment. The main methodological foundation of the study is theoretical scientific methods and a systematic approach. Due to the interdisciplinary nature of the research, the authors have drawn on both the results of sociological studies and a significant amount of data from international and domestic socio-economic statistics to address the identified issues. The authors have conducted a comprehensive analysis of the factors that contribute to socio-economic change and how these changes directly affect the functioning of the welfare state. They conclude that, despite differences in each country's stable preferences, external and internal socio-economic conditions influence the social functions of states, institutions, and the mechanisms for implementing these functions. Despite the fact that the principles and functions of the welfare state are implemented through specific measures that take into account the economic capabilities of the state, the peculiarities of state-building, national mentality, and the system of basic values, the idea of state paternalism remains at the heart of each model of the welfare state. This idea reflects the concept of state guardianship over the social sphere. In addition, there is a trend towards convergence among models of the welfare state in the modern world, which manifests itself in a steady trend towards unification of mechanisms for implementing social functions of the state and convergence in the scale of redistribution processes.

Speed of sound for understanding metals in extreme environments

Knowing material behavior is crucial for successful design, especially given the growing number of next-generation energy, defense, and manufacturing systems operating in extreme environments. Specific applications for materials in extreme environments include fusion energy, semiconductor manufacturing, metal additive manufacturing, and aerospace. With increased applications, awareness of foundational science for materials in extreme environments is imperative. The speed of sound provides insights into phase boundaries, like shock-induced melting. Thermodynamic integration of the speed of sound enables the deduction of other desirable properties that are difficult to measure accurately, like density, heat capacity, and expansivity. Metrology advancements enable the speed of sound to be measured at extreme conditions up to 15 000 K and 600 GPa. This comprehensive review presents state-of-the-art sound speed metrology while contextualizing it through a historical lens. Detailed discussions on new standards and metrology best practices, including uncertainty reporting, are included. Data availability for condensed matter speed of sound is presented, highlighting significant gaps in the literature. A theoretical section covers empirically based theoretical models like equations of state and CALPHAD models, the growing practice of using molecular dynamics and density functional theory simulations to fill gaps in measured data, and the use of artificial intelligence and machine learning prediction tools. Concluding, we review how a lack of measurement methods leads to gaps in data availability, which leads to data-driven theoretical models having higher uncertainty, thus limiting confidence in optimizing designs via numerical simulation for critical emerging technologies in extreme environments.

Earth shadow time/accelerometer/GNSS-based geosynchronous transfer orbit determination method for electric propulsion satellite

Autonomous orbital maneuvering of an electric propulsion satellite in geosynchronous transfer orbit (GTO) requires more accurate autonomous orbit determination (OD). Implementing the traditional autonomous OD method for the electric propulsion satellites may result in the accumulation of measurement errors and dynamic errors from orbit extrapolation, which significantly restrict the OD accuracy. Fortunately, an electric propulsion satellite in GTO can sense the transit time in or out of the Earth shadow according to the output power change of the solar array. Therefore, an Earth shadow time/accelerometer/GNSS-based geosynchronous transfer OD method for electric propulsion satellites is proposed in this paper. First, a system state model with dynamics is established for the entire orbit transfer process. Second, measurement models that use the measurement information from the accelerometer, GNSS, and the transit time into or out of the Earth shadow are established. Third, a sequential filtering estimation approach is designed to solve the problem of asynchronous data fusion for autonomous OD. Comparison simulations demonstrate the effectiveness and superiority of the proposed method.

Automatic modeling of the current state components of ancient buildings based on 3D deformation algorithm

AbstractAncient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model.

Design of a tubular segmented-in-series solid oxide fuel cell (SOFC): One-dimensional steady state modeling

In this study, a one-dimensional steady-state model has been established for rapidly predicting the output performance of tubular segmented-in-series solid oxide fuel cells (SOFCs), which will be used for the structural and material design of T-SIS-SOFC to improve output performance. This model simplifies ohmic polarization, activation polarization, and concentration polarization into equivalent resistances, while also considering the impact of contact resistance, interconnects, supports, and current collectors on the output performance. After validation, the model has proven reliable in predicting the output performance of cells with different structures. For a single cell, ohmic polarization is the primary influencing factor, and increasing conductivity through methods such as raising temperature, modifying materials, or reducing thickness can significantly reduce ohmic polarization. For the entire tubular SOFC system, when the total length is fixed, there exists an optimal number of cells and an optimal length for each cell. Based on the material and structure of the single cell unit employed in this paper, the maximum output power, approximately 48 W, is achieved when the number of cell units is 30.

Automatization of theoretical kinetic data generation for tabulated TS models building - Part 1: Application to 1,3-H-shift reactions

Estimation of kinetic parameters is a key aspect of chemical combustion modeling and several approaches were developed to approximate unknown data. In this work, a code in Python was developed to build tables of transition state (TS) models automatically for intramolecular H-shift reactions in alkyl radicals. The code generates the kinetic rules for all the possible combinations of methyl-substituted reactions based on the structures of the minimal, non-substituted, reactant, TS, and product. The code is able to create and differentiate multiple transition state configurations, considering the axial and equatorial positions for the cyclic substituents and including all the possible pathways for the reactions, which is shown to be an important feature in performing accurate automatic kinetic calculations. Each structure is automatically submitted to geometry optimization and electronic energy calculations, as well as the relaxed scans of the torsional modes identified by the code. From the results of electronic calculations, the rate constants for each pathway are obtained automatically by the application of the transition state theory with tunneling corrections, in a defined temperature range. The kinetic coefficients, as well as the modified Arrhenius parameters, are then assembled and organized to create a final table that connects the kinetic data with TS structure characteristics. These tables can be directly applied as a kinetic data source for reaction mechanism development. The ability of the code to generate reliable rate constants was tested for 1,3-H-shift reactions and the results were compared with theoretical data manually produced, and showed a good agreement. In particular, the code was able to create all the transition state configurations, with an exhaustive description of all possible reaction pathways, using a rigorous and systematic counting based on symmetry, stereocenters, and diastereomers. The proposed method leads to more accurate results on these aspects, compared to repetitive hand calculations of dozens of rate constants.

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Building Resilience, Agility, and Trust: The Neo-Weberian State as a Referential Model for Brazilian Public Administration

The paper explores the challenges and potential solutions for enhancing Brazilian public administration through the lens of the Neo-Weberian State (NWS) model. It discusses the historical context of Brazil’s public administration, highlighting the coexistence of different administrative mechanisms and the need for a stable, resilient, and agile bureaucracy to address contemporary societal challenges effectively. The article advocates for a paradigmatic shift toward a more coordinated, inclusive, and socially driven public sector, emphasizing the importance of hierarchical mechanisms and public service ethos in safeguarding democracy and resisting illiberal political agendas. By analyzing the trajectory of public administration reforms in Brazil and comparing them to global challenges, the essay underscores the NWS’s potential to bring legitimacy and effectiveness to state actions, ultimately aiming to restore trust in public administration and strengthen democratic governance in Brazil.

Green Facades

Abstract This article examines the development of ‘Forest City’ in the Iskandar Malaysia development Corridor within the broader context of China’s international expansion, focusing on Forest City portrayal as a model of sustainability. Despite being promoted as ‘eco-cities’ and ‘green cities’, these mega-projects often fall short of these ideals, revealing a significant gap between their marketed image and their actual environmental practices (Ouis 2011). Through the lens of Practice Theory enriched by the developmental state model, this study analyses the alignment between the promotional rhetoric of Chinese-backed green city projects and their real-world execution. Practice Theory offers a valuable tool for identifying inconsistencies between stated commitments to sustainability and the tangible outcomes observed in Chinese-finance development projects in Southeast Asia. The developmental state model helps to understand the practices that are translated, transplanted, and embedded by Chinese developers across borders. This analytical framework is critical as it sheds light on the underlying dynamics of ‘global China’ initiatives, which have gained increased global attention. Such scrutiny is particularly pertinent in highlighting the contrast between the marketed sustainability of these cities and the principles of genuine sustainable development. This critique provides a deeper understanding of the environmental implications of China’s overseas investments. Based on this analysis, the overarching research question is: why do Chinese-backed green city projects, such as Forest City in Iskandar Malaysia, frequently exhibit discrepancies between their promoted environmental sustainability and the actual environmental outcomes?

1H-MRS reveals abnormal energy metabolism and excitatory-inhibitory imbalance in a chronic migraine-like state induced by nitroglycerin in mice

BackgroundChronic migraine is closely related to the dysregulation of neurochemical substances in the brain, with metabolic imbalance being one of the proposed causes of chronic migraine. This study aims to evaluate the metabolic changes between energy metabolism and excitatory and inhibitory neurotransmitters in key brain regions of mice with chronic migraine-like state and to uncover the dysfunctional pathways of migraine.MethodsA chronic migraine-like state mouse model was established by repeated administration of nitroglycerin (NTG). We used von Frey filaments to assess the mechanical thresholds of the hind paw and periorbital in wild-type and familial hemiplegic migraine type 2 mice. After the experiments, tissue was collected from five brain regions: the somatosensory cortex (SSP), hippocampus, thalamus (TH), hypothalamus, and the spinal trigeminal nucleus caudalis (TNC). Proton magnetic resonance spectroscopy (1H-MRS) was employed to study the changes in brain metabolites associated with migraine, aiming to explore the mechanisms underlying metabolic imbalance in chronic migraine-like state.ResultsIn NTG-induced chronic migraine-like state model, we observed a significant reduction in energy metabolism during central sensitization, an increase in excitatory neurotransmitters such as glutamate, and a tendency for inhibitory neurotransmitters like GABA to decrease. The TNC and thalamus were the most affected regions. Furthermore, the consistency of N-acetylaspartate levels highlighted the importance of the TNC-TH-SSP pathway in the ascending nociceptive transmission of migraine.ConclusionAbnormal energy metabolism and neurotransmitter imbalance in the brain region of NTG-induced chronic migraine-like state model are crucial mechanisms contributing to the chronicity of migraine.

Monitoring the thermal state of an ingot with beveled and rounded corners in a slab crystallizer of a CCM

The organization of ingot cooling in the continuous caster mold affects the reliability, productivity and quality of the resulting ingots. Insufficient heat exchange between the solidifying metal and the wall of the mold leads to a decrease in the thickness of the solid shell of the ingot, which causes the formation of surface defects, and when the ingot is pulled out of the mold, emergency breakthroughs occur. Excessive heat transfer leads to an increase in the thickness of the solid shell, which entails excessive shrinkage of the ingot surface with the formation of a gap between the solid shell of the ingot and the walls of the mold. The appearance of an air gap leads to increased wear of the copper walls of the mold. There is a design of copper walls that makes it possible to reduce the normal component of forces that occurs during wear, reduce the temperature gradient and stress concentration in the corner of the ingot, and also bring the thickness of the hard crust stocking in the corner closer to the perimeter of the mold. However, the thermal state of the formed ingot with such a crystallizer design remains poorly understood. The goal of the work is to create a virtual tool for monitoring the operation of the mold of a slab continuous caster based on the development of a mathematical and computer model of the thermal state of an ingot with beveled and rounded corners. The temperature distribution in the ingot was calculated using the quasi-equilibrium theory of solidification. The numerical implementation of the mathematical model and obtaining an approximate solution using a computer were carried out using the finite dif-ference method and an implicit difference scheme. A computer program was developed in the Matlab development environment. Using computer modeling, the temperature distribution over the volume of the slab at the outlet of the mold with beveled and rounded corners was obtained. The program for the selected mold design allows you to analyze the solidification of the ingot skin and monitor this process for the selected steel grade and specified casting technological parameters

Two state model for the ML-BOP potential

The coarse-grained machine-learning derived ML-BOP model [Chan et al., Nature Commun. 10, 379 (2019)] provides a monoatomic representation of the water-water interaction potential in which orientational interactions are included as three-body contributions. Despite its simplicity, the model reproduces the phase diagram of water and its anomalies. Here, we show that a two-state Gibbs free energy expression – fitted simultaneously on the temperature and pressure dependence of the density and internal energy – predicts the existence of a liquid–liquid critical point, with critical parameters consistent with previous estimates. We also show that in this model: (i) while the low density liquid is pre-empted by crystal nucleation, the high-density liquid and its spinodal are accessible in numerical studies down to 100 K; (ii) crystallisation requires the presence of a local low density region. Thus, for densities larger than the critical density, spinodal decomposition (or nucleation of the low-density liquid) is a pre-requisite for ice nucleation.

Signatures of low-mass black hole–neutron star mergers

ABSTRACT The recent observation of the GW230529 event indicates that black hole–neutron star binaries can contain low-mass black holes. Since lower mass systems are more favourable for tidal disruption, such events are promising candidates for multimessenger observations. In this study, we employ five finite-temperature, composition-dependent matter equations of state and present results from ten 3D general relativistic hydrodynamic simulations for the mass ratios $q = 2.6$ and 5. Two of these simulations target the chirp mass and effective spin parameter of the GW230529 event, while the remaining eight contain slightly higher mass black holes, including both spinning ($a_{\mathrm{ BH}} = 0.7$) and non-spinning ($a_{\mathrm{ BH}} = 0$) models. We discuss the impact of the equation of state, spin, and mass ratio on black hole–neutron star mergers by examining both gravitational-wave and ejected matter properties. For the low-mass ratio model we do not see fast-moving ejecta for the softest equation of state model, but the stiffer model produces on the order of $10^{-6}\,\mathrm{ M}_\odot$ of fast-moving ejecta, expected to contribute to an electromagnetic counterpart. Notably, the high-mass ratio model produces nearly the same amount of total dynamical ejecta, but yields 52 times more fast-moving ejecta than the low-mass ratio system. In addition, we observe that the black-hole spin tends to decrease the amount of fast-moving ejecta while increasing significantly the total ejected mass. Finally, we note that the disc mass tends to increase as the neutron star compactness decreases.

Development of Model and Graphical User Interface for Leak Localization in Crude Oil Pipeline

Pipelines provide the most efficient and cost-effective means for fluid transport but the challenge posed by leaks has substantially increased the risks and hazards in pipeline fluid transportation. The development of efficient leak detection system pays off in quicker leak detection and localization, leading to quicker responses and remediation works by the pipeline emergency response team. This would ultimately bring about less severity of the pipeline leak in terms of financial losses, human and environmental consequences. Steady state modeling of crude oil pipeline flow with attempt to determine and localize leak has been achieved in this study. Mathematical models have been developed to detect and localize leaks in crude oil pipeline during leak occurrence. Leak detection was modeled using the conservation of mass equation while leak localization was modeled by modifying the Darcy-Weisbach pipeline liquid flow equations and utilizing the Swamee-Jain friction factor correlation. Equations for flowrate and pressure drop along the pipeline were developed for two cases: a case where there is no leak in the pipeline and a case where there is leak in the pipeline. Leak localization equation was determined by equating the fluid flow equation when there is no leak and when there is leak. The model was structured and simulated in Matlab software with the model tested for four field cases. The results from the simulation revealed swift leak detection, accurate localization of the leak, and determination of the pressure at the point of leak. Experimentally determined results from actual field measurements of leak incidences were used to validate the results. The result determined from the model developed proved accurate with only an average error of 0.216 miles.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits- shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

The prediction of bolted joint loosening state under transverse load based on a data-driven model

The impact of transverse load results in the loosening of bolted joints. The loosening condition is determined based on the transverse load and relevant parameters of the bolted joint. To efficiently forecast the loosening state of bolts under various transverse loads and relevant parameters, this study proposes a predictive model for the loosening state of bolted joints. Initially, the relationship between the loosening criteria and relevant parameters is established according to the bolt’s force conditions. Through elementary effect analysis, the high-dimensional representation model is improved, breaking down the loosening criteria function with high-dimensional parameter inputs into a series of low-dimensional functions. Subsequently, the prediction of the loosening state is achieved by training artificial neural networks to approximate multiple low-dimensional functions. The improved predictive model is utilized to determine the critical load for bolted joint loosening. Validation indicates the accuracy of the proposed model. Notably, the influence of the bearing friction coefficient and preload on the critical transverse load is more pronounced compared to the thread friction coefficient.

Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a significant challenge. Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitations. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are identified using Koopman-reweighted time-lagged independent component analysis (KTICA), which are subsequently leveraged by on-the-fly probability enhanced sampling (OPES) to efficiently explore the FES. The effectiveness of our pipeline is demonstrated and further compared with the common Markov State Model (MSM) approach on two model systems with increasing complexity: alanine dipeptide (Ala2) and deca-alanine (Ala10), underscoring its applicability across diverse biomolecular simulations.

Ward Development Committees as 4th Level Governance of Primary Health Care in Nigeria: The Rivers State Model

Ward Development Committees as 4th Level Governance of Primary Health Care in Nigeria: The Rivers State Model