Model Of States Research Articles

Inverse Electron Demand Diels-Alder Reaction of Pyrazines with 2,5-Norbornadiene as Acetylene Precursor

Herein, we report the reaction of pyrazines with 2,5-norbornadiene, an acetylene precursor, as a cascade of Diels-Alder reactions yielding substituted pyridines. In contrast to the known examples of intermolecular Diels-Alder type reaction of pyrazines with acetylene, which usually leads to a mixture of products, in our case the formation of a single isomer was observed. The proposed approach allows the conversion of pyrazines with different types of substitution, except those containing methyl and amino donor groups, to pyridines. In addition, highly functionalized aminonicotic esters were obtained, which can be used for the synthesis of bioactive compounds. An unusual course of the Diels-Alder reaction was found in the case of tetrasubstituted pyrazines, which cleave organic nitriles instead of HCN. Quantum-chemical modeling of possible transition states showed that this Diels-Alder reaction proceeds via cycloaddition of 2,5-norbornadiene to pyrazine with sequential elimination of HCN and cyclopentadene; the reaction pathway including initial formation of acetylene from norbornadiene and subsequent Diels-Alder reaction with the acetylene was rejected as kinetically unfavorable.

AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins are defined by the structural topology of the fold and favor conserved conformational motions driven by soft modes. Our findings provide evidence that AlphaFold2 combined with randomized alanine sequence masking can yield accurate and consistent results in predicting moderate conformational adjustments between apo and holo states, especially for proteins with localized changes upon ligand binding. For large hinge-like domain movements, the proposed approach can predict functional conformations characteristic of both apo and ligand-bound holo ensembles in the absence of ligand information. These results are relevant for using this AlphaFold adaptation for probing conformational selection mechanisms according to which proteins can adopt multiple conformations, including those that are competent for ligand binding. The results of this study indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and the detection of high-energy conformations. By incorporating a wider variety of protein structures in training datasets, including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

Valuation survey for SF-6Dv2 in Japan based on the international protocol.

The SF-6D Classification System was recently updated (SF-6Dv2). We performed a valuation survey to construct a value set for the SF-6Dv2 in Japan. An online discrete choice experiment (DCE) with duration was used to estimate a value set for the SF-6Dv2 for Japan based on public preferences. The target sample number was 3800. Respondents were asked to complete 15 choice tasks. A conditional logit model that estimates interactions between time and each dimension was used to develop the value set. The collected sample included 3933 respondents for the DCE tasks. The results of all the unconstrained models showed some inconsistencies. In particular, inconsistencies in the two most severe levels of the role limitation (RL) and vitality (VT) dimensions were observed in all models. The number of inconsistencies was smallest in a core model (n = 3) and in a model for core and common health states (n = 2). The physical functioning (PF) and pain (PA) dimensions had the greatest influence on utility at the overall level across all models. RL, VT, and social functioning (SF) had smaller overall impacts on utility. The PF weights for the two most severe levels are much lower than those in the UK and Australia. The Japanese scores tended to be lower compared with the UK SF-6Dv2 scores. We obtained a value set for Japan (model 5). With the development of this value set, it is now possible to calculate quality-adjusted life years for economic evaluation in Japan when the SF-6Dv2 has been used.

System Dynamics Modeling of Caries Severity States in Long-Term Care.

Dental caries among long-term care (LTC) residents is a persistent and complex problem driven by social and structural factors. Systems thinking may be useful in considering novel approaches to reducing disease. This study aimed to develop a system dynamics model to simulate the progression of dentate older adults in LTC through caries severity states and estimate the effects of 3 intervention scenarios on the progression of caries: preventive topical fluoride (TF), arrest of caries with silver diamine fluoride (SDF), and a combination of TF and SDF. Dentate older adults in LTC were categorized into 4 caries severity states by their number of untreated carious lesions. The model assumed that changes in severity states were consistent with incidence rates reported in the literature and available billing data for dental care and that individuals move in and out of the system by entering and exiting the facility or experiencing edentulism. For all scenarios, the proportion of dentate older adults in LTC with 1 or more untreated lesions stays stable, the distribution of disease shifts from a high severity state, and the system approaches equilibrium after 4 y. The TF intervention predicts minimal impacts on decreasing the proportion of dentate older adults with 1 or more untreated lesions (2.5% decrease), while the SDF intervention and the combination interventions were most disruptive. There was a 29.6% and 33.6% decrease, respectively. Given the specific population dynamics in LTC, these findings suggest that long-term (greater than 4 y) interventions should be designed to address both the management of existing lesions and their incidence. This system dynamics model allows researchers to render institution-specific data points from LTCs to estimate the effects of proposed interventions at the respective site.

Hydrogen Bonding and Noncovalent Electric Field Effects in the Photoconversion of a Phytochrome.

A profound understanding of protein structure and mechanism requires dedicated experimental and theoretical tools to elucidate electrostatic and hydrogen bonding interactions in proteins. In this work, we employed an approach to disentangle noncovalent and hydrogen-bonding electric field changes during the reaction cascade of a multidomain protein, i.e., the phytochrome Agp2. The approach exploits the spectroscopic properties of nitrile probes commonly used as reporter groups of the vibrational Stark effect. These probes were introduced into the protein through site-specific incorporation of noncanonical amino acids resulting in four variants with different positions and orientations of the nitrile groups. All substitutions left structures and the reaction mechanism unchanged. Structural models of the dark states (Pfr) were used to evaluate the total electric field at the nitrile label and its transition dipole moment. These quantities served as an internal standard to calculate the respective properties of the photoinduced products (Lumi-F, Meta-F, and Pr) based on the relative intensities of the nitrile stretching bands. In most cases, the spectral analysis revealed two substates with a nitrile in a hydrogen-bonded or hydrophobic environment. Using frequencies and intensities, we managed to extract the noncovalent contribution of the electric field from the individual substates. This analysis resulted in profiles of the noncovalent and hydrogen-bond-related electric fields during the photoinduced reaction cascade of Agp2. These profiles, which vary significantly among the four variants due to the different positions and orientations of the nitrile probes, were discussed in the context of the molecular events along the Pfr → Pr reaction cascade.

Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre-heart failure, has not been well established. This study evaluated the performance of machine learning prediction models for predicting elevated N terminal pro brain natriuretic peptide (NT-proBNP) levels in the US general population. Individuals aged 20-79years without cardiovascular disease from the nationally representative National Health and Nutrition Examination Survey 1999-2004 were included. Six prediction models (two conventional regression models and four machine learning models) were trained with the 1999-2002 cohort to predict elevated NT-proBNP levels (>125pg/mL) using demographic, lifestyle, and commonly measured biochemical data. The model performance was tested using the 2003-2004 cohort. Of the 10237 individuals, 1510 (14.8%) had NT-proBNP levels >125pg/mL. The highest area under the receiver operating characteristic curve (AUC) was observed in SuperLearner (AUC [95% CI]=0.862 [0.847-0.878], P<0.001 compared with the logistic regression model). The logistic regression model with splines showed a comparable performance (AUC [95% CI]=0.857 [0.841-0.874], P=0.08). Age, albumin level, haemoglobin level, sex, estimated glomerular filtration rate, and systolic blood pressure were the most important predictors. We found a similar prediction performance even after excluding socio-economic information (marital status, family income, and education status) from the prediction models. When we used different thresholds for elevated NT-proBNP, the AUC (95% CI) in the SuperLearner models 0.846 (0.830-0.861) for NT-proBNP>100pg/mL and 0.866 (0.849-0.884) for NT-proBNP>150pg/mL. Using nationally representative data from the United States, both logistic regression and machine learning models well predicted elevated NT-proBNP. The predictive performance remained consistent even when the models incorporated only commonly available variables in daily clinical practice. Prediction models using regularly measured information would serve as a potentially useful tools for clinicians to effectively identify targets of natriuretic-peptide screening.

What Explains Differences in Minimum Wage Growth Between EU Member States?

Abstract There are considerable differences in minimum wage growth between EU member states with national minimum wages. Potential sources for these differences are discrepancies in economic fundamentals and institutional differences in how minimum wages are adjusted. Using a novel dataset based on macroeconomic data, institutional information on minimum wage setting and data on economic policy orientation and elections, the article tests whether growth differences in the minimum wage of 21 EU member states during the time period 2000 to 2020 can be explained by a catch-up dynamic in new EU member states, by different growth models of EU member states or by differences in the actors that are responsible for the adjustment of minimum wages. The results show that across the entire sample and irrespective of actors, minimum wage growth follows consumer price inflation and wage growth most closely. Higher than average minimum wage growth rates in EU member states stem from overshooting inflation during the period of EU accession, reducing wage inequality and increasing the Kaitz index. Actors also mattered for minimum wage growth. Adjustments by social partner consensus led to higher minimum wage growth than the benchmark of indexed minimum wages, introducing a distributive element to minimum wage adjustments.

Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations.

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AlphaFold2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and detection of high energy conformations. By incorporating a wider variety of protein structures in training datasets including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

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 &gt;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.