Model Of States Research Articles

Building 1D lattice models with $G$-graded fusion category

We construct a family of one-dimensional (1D) quantum lattice models based on GG-graded unitary fusion category \mathcal{C}_G𝒞G. This family realize an interpolation between the anyon-chain models and edge models of 2D symmetry-protected topological states, and can be thought of as edge models of 2D symmetry-enriched topological states. The models display a set of unconventional global symmetries that are characterized by the input category \mathcal{C}_G𝒞G. While spontaneous symmetry breaking is also possible, our numerical evidence shows that the category symmetry constrains the models to the extent that the low-energy physics has a large likelihood to be gapless.

Bridging Tridosha Siddhanta and Biorhythm Theory: Uniting Ancient Dosha Principles with Modern Cyclic Patterns

This article explores the conceptual intersections between Tridosha Siddhanta, the foundational theory of Ayurveda, and the Biorhythm Theory, a modern psychological model of cyclical biological and emotional states. Tridosha Siddhanta posits that human health and wellness are governed by the balance of three fundamental Doshas Vata, Pitta, and Kapha each representing a combination of the five elements and their dynamic influences on physiological and psychological functions. In contrast, Biorhythm Theory suggests that individuals experience predictable cycles of physical, emotional, and intellectual states influenced by inherent rhythms. By analyzing both systems, this article seeks to elucidate how the cyclical nature of biorhythms might correlate with the doshic imbalances and fluctuations described in Ayurveda. It aims to provide a comprehensive understanding of how integrating these two paradigms can enhance approaches to personalized health and wellness, offering a holistic perspective that bridges ancient wisdom with contemporary scientific insights.

Toward Modeling Continental‐Scale Inland Water Carbon Dioxide Emissions

AbstractInland waters emit significant amounts of carbon dioxide (CO2) to the atmosphere; however, the global magnitude and source distribution of inland water CO2 emissions remain uncertain. These fluxes have previously been “statistically upscaled” by independently estimating dissolved CO2 concentrations and gas exchange velocities to calculate fluxes. This scaling, while robust and defensible, has known limitations in representing carbon source limitations and spatial variability. Here, we develop and calibrate a CO2 transport model for the continental United States, simulating carbon transport and transformation in >22 million hydraulically connected rivers, lakes, and reservoirs. We estimate 25% lower CO2 fluxes compared to upscaling estimates forced by the same observational calibration data. While precise CO2 source distribution estimates are limited by the resolution of model parameterizations, our model suggests that stream corridor CO2 production dominates over groundwater inputs at the continental scale. Our results further suggest that the lack of observational networks for groundwater CO2 and scalable metabolic models of aquatic CO2 production remain the most salient barriers to further coupling of our model with other Earth system components.

3D model of a stable triangle LiF–NaBr–KBr four-component reciprocal system Li+, Na+, K+ || F-, Вr-

A 3D model of the phase equilibrium states of the quasi-three-component system LiF–NaBr–KBr, which is a stable triangle of the four-component reciprocal system Li+, Na+, K+ || F-, Br-, has been constructed. Based on the 3D-model, polythermal, isothermal sections and the polytherm of phase crystallization were constructed for the first time. Two polythermal sections contain wide areas of boundary solid solutions based on sodium and potassium bromide. In an isothermal section at 650 оC, the fields of the liquid phase and the coexisting two and three phases are delimited. The crystallization polytherm is represented by three fields. In the crystallization field of lithium fluoride, the area of separation of two liquids is limited. The direction of the ion exchange reaction 2LiBr + NaF + KF = 2LiF + NaBr + KBr was confirmed by thermodynamic calculations at temperatures of 400, 600, 800, 1000K. The exothermic nature of the exchange reaction is confirmed by taking a DTA heating curve for a mixture of powders from 50% LiBr + 25% NaF + 25% KF, and the phase composition of the reaction products LiF + NaBr(OTR) + KBr(OTR) is confirmed by X-ray phase analysis data, where OTR is limited solid solution.

Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states

With mounting evidence for the benefits of meditation, there has been a growing interest in measuring and quantifying meditative states. This study introduces the Inner Dereification Index (IDI), a class of personalized models designed to quantify the distance from non-meditative states such as mind wandering based on a single individual’s neural activity. In addition to demonstrating high classification accuracy (median AUC: 0.996) at distinguishing meditation from thinking states moment by moment, IDI can accurately stratify meditator cohorts by experience, and correctly identify the practices most effective at training the dereification aspect of meditation (decentering from immersion with thoughts and perceptions and recognizing them as mental constructs). These results suggest that IDI models may be a useful real-time proxy for dereification and meditation progress, requiring only 1 min of mind wandering data (and no meditation data) during model training. Thus, they show promise for applications such as real-time meditation feedback, progress tracking, personalization of practices, and potential therapeutic applications of neurofeedback-assisted generation of positive states of consciousness.

Chirality Control of Magnetic Vortices in Ferromagnetic Disk–Nanowire System

The results of experimental studies and micromagnetic modeling of magnetic states in a one-dimensional array are presented. The array has the form of a chain of ferromagnetic disks coupled with a ferromagnetic nanowire made of the same material. The disks are located on opposite sides of the nanowire, which makes it possible to obtain distributions when the chiralities of the magnetic vortex shells in neighboring disks alternate, which can find application in vortex spin nanooscillators. Using the application in situ of a magnetic field to an excited objective lens using Lorentz transmission electron microscopy, it is shown that in this system it is possible to control the chiralities of the shells of magnetic vortices, which is realized by magnetization in the sample plane along various azimuthal directions. When wire magnetized along a nanowire, vortex states with opposite chiralities are realized in disks located on opposite sides of it. An antivortex is formed in the nanowire itself at the boundary with the disk, since the local direction of magnetization in the wire and in the disk are anticollinear. When magnetized perpendicular to the nanowire, states with the same chirality are realized in all disks. In this case, two perpendicular domain walls are formed between the disks in the nanowire, and the vortex in the disk is shifted to one of the edges along the nanowire.

A computational account of self-control

Self-control is core to human well-being. However, the lack of a well-specified, computationally tractable framework related to self-control makes it difficult to clarify underlying mechanisms, interpret relevant empirical phenomena, or develop interventions helpful in promoting self-control. To help address this gap, we invite consideration of the Comparison with Goal States Model (CGSM) for self-control. The CGSM amplifies activations related to available options whose representations are similar to representations of relevant goals and diminishes activations related to available options whose representations are dissimilar to representations of relevant goals. For example, influenced by healthy eating goals, the CGSM would amplify activations related to an apple and diminish activations related to a cookie, leading to an eventual preference for the apple, even though the cookie might be initially preferred. The CGSM successfully explicates observations related to reaction time in food choice, dynamics reflected in mouse-tracking trajectories, and showcases a mechanism by which hyperbolic discount curves in temporal discounting contexts might emerge. We use the CGSM to propose theoretical constraints on the nature of self-control and describe how multiple strategies have the potential to promote self-control.

First-principles simulation of electronic properties of MoB/Si3N4 superlattices via machine learning

MBene materials and Si3N4 materials have excellent properties, but there are still many technical bottlenecks in the preparation of high-performance and flexible structure electronic device materials. In this paper, MBene is combined with Si3N4 materials to form a superlattice structure, which is composed of six superlattice heterostru ctures. Firstly, the first-principles method was used to study the electronic properties of MBene/Si3N4 superlattice materials. The results show that with the increase of stacking times, the material structure becomes more stable, and the interlayer electrons are transferred from the MoB layer to the Si3N4 layer. Second, in the machine learning part, the Extreme Gradient Boosting (XGBoost) algorithm and the Convolutional Neural Network (CNN) algorithm were used to predict the density of states of six superlattice heterojunctions through ensemble learning, and it was found that the best prediction results were achieved when the input data with a combination of global descriptors and local descriptors were used. The lowest MAE is only 0.03, which not only indicates that the predicted density of states model is excellent, but also proves that the more refined the characterization of the surrounding environment of the atom, the more accurate the prediction of the density of states of the material and the higher the generalization performance of the prediction model.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

Research on The local clothing thermal insulation prediction model with different dress state of indoor occupants

In a dynamic and non-uniform thermal environment, adjusting personal dress states is an effective method for achieving thermal comfort. During this process, clothing thermal insulation, as a key parameter in human thermoregulation models, is not uniformly distributed across the body. However, current thermal comfort standards do not provide a direct method for obtaining local clothing thermal insulation. Based on field surveys, this study summarized typical year-round clothing ensembles and common dress states, and tested the local thermal insulation of 40 clothing ensembles and 60 individual garments using a thermal manikin. Subsequently, a prediction model for the local thermal insulation of clothing ensembles was developed based on the overall thermal insulation of individual garments. Meanwhile, it was found that changes in dress state significantly alter the thermal insulation of body parts such as the arms and chest. Simulations conducted using the JOS-3 and UCB models revealed that the lower the ambient temperature, the greater the impact of changes in local clothing thermal insulation on human skin temperature and thermal sensation. Finally, a correction model for different dress states was developed, and comparison with existing models showed the lower root mean square error (RMSE) between the predicted and measured values. This study provides a more accurate and convenient method for determining local thermal insulation, offering a data foundation for improving indoor environmental control and enhancing energy efficiency.

The tsarist policy of russification of education and the “unfavorable realities” in Bessarabia (second half of the 19th century)

The article reveals the process of radical transformation of the education system in Bessarabia in the second half of the nineteenth century, as a result of the tightening of the national policy of the Russian Empire and the intensification of the national-cultural Russification of the non-Russian peoples, the attempts of the Czarist authorities to modernize and homogenize the multinational Russian Empire, according to the model of European states. The author highlights two stages of the policy of Russification of education in Bessarabia. The first stage – years 1866-1872 – is characterized by the undermining of Romanian and mixed parochial primary schools (Romanian-Russian), the prohibition of teaching Romanian language at the regional gymnasium, theological seminary, county schools, etc, by creating a Russian primary and secondary education system in Bessarabia. The second stage – years 1873-1900 – is characterized by the attempt to radically transform the linguistic and cultural realities of the Bessarabian villages, causing the exponential growth of the number of Russian schools in rural areas, mass learning of the Russian language by the Romanian peasants in Bessarabia, the imposition of the Russian language not only in state institutions, but also in the privacy of families. The discussions in this regard within the Committee of Ministers of the Russian Empire highlighted the following dilemma: either the application of force to boost the policy of Russification, or the continuation of the policy of gradual Russification.Understanding the impasse that the Russian Empire would have entered, the authorities finally decided to achieve the goals of the policy of russification of the Romanians in Bessarabia, not through repressive methods, but „gradually and without violence”.

Dielectric Relaxation, Electrical Conductivity, Dielectric Permittivity, and Correlated Barrier Hopping Conduction Mechanism Analysis in Ag Doped Bi2S3 at Low Temperatures

AbstractPure and 6% Ag doped bismuth sulfide (Bi2S3) were synthesized via solid‐state reaction method at 500 °C. The X‐ray diffraction method confirmed the orthorhombic phase with average crystallite size ∼25 nm and average lattice strain ∼0.5% and ∼0.6% for pure and 6% Ag doped Bi2S3. The scanning electron microscope (SEM) images confirmed nanobelt morphology. A direct bandgap of ∼3.3 and ∼3.4 eV for pure and doped Bi2S3 was estimated using UV–vis spectroscopy, respectively. AC measurements were performed from 200 Hz to 2 MHz at temperature 223–298 K. Investigation of Nyquist plots of 6% Ag doped Bi2S3 nanobelts suggested space‐charge‐dependent behavior and indicated an negative temperature coefficient of resistance (NTCR). AC conductivity adhered to Jonscher's power law in which the decreasing trend of s‐parameter with increasing temperature indicates correlated barrier hopping conduction mechanism. From this model, hopping distance (∼10−9–10−11 m) and density of states (∼1022–1023 eV−1 cm−3) were estimated. The dielectric permittivity exhibited dispersion at low frequencies due to the combined effect of electronic, ionic, and interfacial polarizations. Presence of two semicircular arcs in the complex modulus analysis suggested the presence of both grain and grain‐boundary effects.

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.

Implementation Of New Mexico's 'No Behavioral Health Cost Sharing' Law: A Qualitative Study.

Out-of-pocket spending is a long-standing challenge for privately insured people. New Mexico passed the first US law prohibiting private insurers from applying cost sharing to behavioral health treatment, effective January1, 2022. We examined the perceptions of key informants, including clinicians, insurers, and state officials, about implementing the No Behavioral Health Cost Sharing law to explore how it might affect downstream outcomes such as spending and access. The law was viewed favorably and implemented without much difficulty. Clinicians noted widespread positive impacts, particularly for those needing intensive treatment. However, they worried about workforce capacity and the exclusion of people covered under self-insured employer plans, which are exempt from state regulation under the Employee Retirement Income Security Act (ERISA) of 1974. Insurers found the law to be in alignment with their organizational goals, but they expressed concern about the administrative burden caused by increased reviews of claims, and some were monitoring for unintended consequences (for example, waste and fraud) that could lead to increased premiums. Engagement strategies were needed to inform eligible members and facilitate enrollment in eligible plans. The law provides a potential model for states to improve access to behavioral health care, but impacts may be limited by factors such as workforce, awareness, and federal ERISA constraints.

ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning

Abstract Aiming at the difficulty in ensuring accuracy and performance of existing Simultaneous Localization And Mapping (SLAM) technology based on multi-sensor fusion in complex dynamic environments, a multi-sensor adaptive fusion SLAM framework based on degradation detection and deep reinforcement learning (ASLAM-FD) is proposed. This framework can achieve adaptive collaborative precise adjustment of fusion weights (FWs) based on the real-time self-degradation states and relative degradation states quantified by each sensor, and can adapt to different tightly coupled SLAM algorithms based on FWs. In addition, within this framework, the continuous quantification models for the degradation states of internal/external sensors with certain versatility (we refer to them as EX-DM and IN-DM) is proposed. These quantitative models can achieve continuous quantification of degradation states of various mainstream internal/external sensors. Based on the above sensor degradation state quantification models, this paper further proposes a deep reinforcement learning (DRL) network suitable for adaptive collaborative adjustment of FWs. This network focuses more on the temporal nature of sensor observation data and degradation states, making it more suitable for modeling relationships between data with temporal features. In the experimental section, we adapted the proposed ASLAM-FD to different multi-sensor fusion SLAM algorithms on multiple datasets, and compared it with multiple advanced fusion SLAM algorithms. We found that adapting ASLAM-FD can effectively improve the accuracy and performance of fusion SLAM in complex dynamic environments.

Identifying predictors of multi-year cannabis vaping in U.S. Young adults using machine learning

IntroductionIncreasing number of current cannabis users report using a vaporized form of cannabis and young adults are most likely to vape cannabis. However, the number of studies on cannabis vaping is limited, and predictors of cannabis vaping among U.S. young adults remain unclear. Previous studies on cannabis vaping have known limitations, as they (1) relied heavily on regression-based approaches that often fail to examine complex and non-linear interactive effects, (2) focused on examining cannabis vaping initiation but not on its use over multiple years, and (3) failed to account for recreational cannabis legalization (RCL) status. MethodsThis study was a secondary analysis of the restricted use files of the Population Assessment of Tobacco and Health Study, Waves 4–6 (December 2016-November 2021). A two-stage machine learning approach, which included Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Tree (CART), was used to identify predictors of multi-year cannabis vaping while accounting for state-level RCL status among a representative sample of U.S. young adults. ResultsStratified CART created a five-terminal-node prediction model for states with RCL (split by cannabis use, cigarette use, bullying behavior, and ethnicity) and a different five-terminal-node prediction model for states without RCL (split by cannabis use, heroin use, nicotine vaping, and hookah use). ConclusionsCharacteristics predicting multi-year cannabis vaping appear to differ from those of cannabis vaping initiation. Results also highlight the importance of accounting for RCL status because predictors of cannabis vaping may differ for individuals living in states with and without RCL.

Speed of Sound Measurements of R-1130(E) and an Azeotropic Blend of R-1336mzz(Z)/1130(E)

Sound speed data measured using a dual-path pulse-echo instrument are reported for pure trans-1,2-dichloroethene (R-1130(E)) and an azeotropic blend of cis-1,1,1,4,4,4-hexafluorobutene (R-1336mzz(Z)) and R-1130(E) with a composition of 74.8 mass % R-1336mzz(Z) with the balance being R-1130(E). The azeotropic blend of R-1336mzz(Z)/1130(E) is classified as R-514A in ANSI/ASHRAE standard 34. Liquid phase speed of sound data are reported from just above the saturation pressure of pure R-1130(E) or the bubble point pressure of R-514A to a maximum pressure of 26.7 MPa. The relative combined expanded uncertainty in the speed of sound varies from 0.032 % to 0.148 % with the greatest deviations occurring at the lowest sound speeds. At present, no reference Helmholtz-energy-explicit equation of state (EOS) is available for R-1130(E). Therefore, the reported data for pure R-1130(E) are compared to an extended corresponding states (ECS) model. Deviations between the pure R-1130(E) sound speed data and the ECS model were found to be consistently negative ranging between − 4.1 % and − 3.5 %. The R-514A data are compared to a multifluid model inclusive of the established reference Helmholtz-energy-explicit EOS for R-1336mzz(Z) and ECS model for R-1130(E) with estimated binary interaction parameters. Deviations between the experimental speed of sound data and the multifluid model were also found to be consistently negative. However, deviations from the multifluid model were found to be as great as − 17.1 %. The large deviations from the ECS model and multifluid model underscore the need for a robust Helmholtz-energy-explicit EOS for R-1130(E).

Virtual reality facilitated travel inspiration: the role of pleasure and arousal

ABSTRACT The paper investigates how Virtual Reality (VR)-facilitated travel inspiration increases visit intention through pleasure and arousal. The research rationale is based on our conceptual framework suggesting that the transmission model of inspiration (from the state of inspiration-by to the state of inspiration-to) and the model of emotional states of pleasure and arousal go in tandem. Partial least squares structural equation modelling (PLS-SEM) was used to test the hypotheses with data from 290 participants recruited through Prolific Academic who had visited a destination via a 360° VR activity. Our findings demonstrate that VR inspired-by has significant relationships with pleasure and arousal. Pleasure is a complementary partial mediator of the relationship between inspired-by and inspired-to. Similarly, our findings demonstrate that there is a positive effect of pleasure on visiting intention through inspired-to. Arousal neither appears to mediate the relationship between inspired-by and inspired-to nor has any significant relationship with visiting intention.

Multidimensional Edge Perception Model for Rail Vehicle Operational States Based on Artificial Intelligence of Things

Multidimensional Edge Perception Model for Rail Vehicle Operational States Based on Artificial Intelligence of Things

Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 °C, 25 °C, and 35 °C) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell ‘a’ is validated with a different set of cell ‘b’ on identical test parameters, and the results are tabulated and compared. At 25 °C, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.