State Model Research Articles

The effect of the non-uniform critical current density on the magnetoelastic behavior of bulk superconductors: Case of a long cylindrical superconductor

In this paper, the effect of non-uniform critical current density on bulk superconductors is studied, the case of a long cylindrical superconductor with transport current is chose to discussed. The critical current density is distributed non-uniformly along the radius of the cylinder. Based on the Bean critical state model, the distributions of trapped magnetic flux and shielding current in the cylinder are investigated. Combined with the plane strain approach, the analytical expressions of magnetic flux pinning force and stress are obtained. The magnetostriction of the cylinder is also discussed. Results show that the non-uniform critical current density changes the distribution law of the shielding current and trapped magnetic flux in the cylinder. The increase of non-uniform parameters n leads to an obvious increase in the flux pinning force. Thus, a larger extreme value of the pinning stress is obtained, a bigger structure deformation is produced inside the superconducting cylinder. All those conclusions will provide a helpful guide for engineering application.

Elucidating the conformational change of dengue envelope protein using the Markov state model

ABSTRACT Dengue virus, an arbovirus of genus flavivirus, is one of the most prevalent infectious disease causing organisms in the tropical environment leading to numerous deaths every year. The envelope protein plays a crucial role in the viral genome entry into the host cell. This is achieved through structure and oligomeric status change in the dengue envelope protein. The pre-fusion and post-fusion states’ structures of the dengue envelope protein are known although the pathway and intermediate structures involved in this process have remained in the dark till date. In this study, we have used targeted molecular dynamics (MD) simulation followed by conventional MD simulation and the Markov state model analysis to elucidate this pathway of change in conformation from the pre-fusion state to the post-fusion state. Intermediate conformations which may in the future serve as therapeutic targets have also been deduced from the Markov state model. This pathway determination may pave the way for the design of transition state analog inhibitors which prevent the dengue virus envelope protein from changing its conformation and thus establishing infection. The results obtained could lead to development of novel drugs to combat the viral infection.

CO2 solubility in aqueous solution of salts: Experimental study and thermodynamic modelling

AbstractThere are many economic obstacles and complex engineering problems associated with CO2 capture and storage in saline aquifers that need to be addressed. Overcoming such challenges requires precise knowledge on the fluid phase equilibria of CO2‐brine systems. Having accurate CO2 solubility data over a wide range of temperature and pressure can greatly assist in resolving these obstacles by improving the performance and accuracy of the thermodynamic modeling and subsequent CCS engineering success.CO2 solubility in pure water and NaCl solutions has been widely studied in the literature, however, there is a lack of data on CO2 solubility at lower temperatures (below 298 K). Furthermore, limited phase equilibria data are available for CO2 solubility in CaCl2, MgCl2, and KCl solutions at elevated temperatures (i.e., T > 323.15 K).In this work, the phase equilibria of CO2 and brine systems are investigated experimentally and theoretically. In this study, solubilities of CO2 in pure water and various concentrations of NaCl (10, 15, 20, and 22 wt%), KCl (10, 15, and 22 wt%), CaCl2 (7.5, 10, 15.7, and 23.4 wt%), and MgCl2 (6.7, 11, 18, and 29 wt%) aqueous solutions are reported. All CO2 solubilities were measures at 323.15, 373.15, and 423.15 K and over various pressure ranges, while solubilities in 10 and 20 wt% NaCl aqueous solutions were also measured over the temperature range of 263 to 298 K and pressures up to the hydrate dissociation pressure of each system. Equation of state modelling using the PC‐SAFT and the Cubic Plus Association equations of state, is performed in the theoretical part of the study to validate the measured solubility data. © 2024 The Author(s). Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

Metamagnetic anomalies in the kinetic Ashkin–Teller model

This study investigates the metamagnetic anomaly in the Ashkin–Teller spin-1/2 model in a ferromagnetic state, utilizing dynamic mean-field theory on a cubic lattice from a stochastic theory. We analyze instantaneous magnetizations over time, dynamic order parameters, and magnetic susceptibilities concerning the constant bias field, as well as examining dynamic phase diagrams. Our observations reveal that dynamic phase transitions occur at specific points of the constant bias field, where instantaneous magnetization transitions from oscillating between negative and positive states to a strictly positive state. We also observe a narrow region in the dynamic phase diagram between phase transitions of different order parameters of the model, for some conditions among the couplings of these order parameters. These results contribute to an understanding of the dynamic behavior of the spin-1/2 AT model under ferromagnetic conditions, providing interesting insights into phase transitions and the metamagnetic anomaly.

Impact assessment of multiple pressures on ecosystem services with a state and transition model: Application to Posidonia oceanica seagrass meadows

A significant challenge in the integration of ecosystem services into decision-making processes lies in effectively capturing the dynamics of marine socio-ecological systems, including their evolutionary pathways, equilibrium states, and tipping points. This paper explores the evolutionary trajectories of a vital marine ecosystem endemic to the Mediterranean Sea: the Posidonia oceanica seagrass meadows, in response to various drivers of change. A state-and-transition model is employed to assess the ecosystem services provided by P. oceanica across different states defined by selected transitions, such as overfishing, fragmentation, pollution, and invasion by non-native species. To apply this model, scientific expertise is combined with field data generated using the Ecosystem-Based Quality Index to evaluate the conservation status of P. oceanica. This integrated approach allows for the representation of the ecosystem services offered by the meadows across different states, leveraging ecological data. The findings highlight the disproportionate impact on provisioning services, particularly sea urchins and commercial fish production, which suffer the most under various stressors. Notably, when these services decline to critical levels, the meadows cease to provide significant benefits. Finally, a synthesized representation is presented, merging ecological insights with monitoring data, offering a framework that is more accessible to stakeholders and decision-makers.

Analysis on the Improvement of Energy Efficiency by using its Models in Wireless Sensor Networks

Aims and Background: A self-configured and infrastructure-less wireless network is named as a wireless sensor network (WSN), which has the role of monitoring physical or environmental conditions like sound, motion, temperature, vibration, and pollutants for passing their data throughout the network to a center of location where the data could be easily observed as well as analyzed. Methodology: In WSN, the small-sized sensor node is working with a very small battery with limited energy. Replacing the battery or recharging the battery is not feasible, and so, the energyefficient operation of WSN is the key factor. While designing routing protocols (RPs) for WSNs, one among the significant goals is energy conservation owing to this lower power. Totally, three models, namely, state, cluster, and content, were utilized for enhancing energy efficiency (EE). Each protocol has its own way of routing that varies from the other in terms of the parameters selected or the approach. Results: This paper explicates a survey on WSNs, upgrading EE in WSN based on the state model, EE improvement in WSN based on the cluster model, and EE enhancement in WSN using a contentbased model with its performance comparison. Conclusion: This paper evaluates the number of cluster heads (CHs) of CADS in different nodes with different schemes for WSNs and a comparison of the four schemes in WSNs.

Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

Abstract The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).

Insight into structural dynamics involved in activation mechanism of full length KRAS wild type and P-loop mutants

KRAS protein is known to be frequently mutated in various cancers. The most common mutations being at position 12, 13 and 61. The positions 12 and 13 form part of the phosphate binding region (P-loop) of KRAS. Owing to mutation, the protein remains in continuous active state and affects the normal cellular process. Understanding the structural changes owing to mutations in GDP-bound (inactive state) and GTP-bound (active state) may help in the design of better therapeutics. To understand the structural flexibility due to the mutations specifically located at P-loop regions (G12D, G12V and G13D), extensive molecular dynamics simulations (24 μs) have been carried for both inactive (GDP-bound) and active (GTP-bound) structures for the wild type and these mutants. The study revealed that the local structural changes at the site of mutations allosterically guide changes in distant regions of the protein through hydrogen bond and hydrophobic signalling network. The dynamic cross correlation analysis and the comparison of the correlated motions among different systems manifested that changes in SW-I, SW-II, α3 and the loop preceding α3 affects the interactions of GDP/GTP with different regions of the protein thereby affecting its hydrolysis. Further, the Markov state modelling analysis confirmed that the mutations, especially G13D imparts rigidity to structure compared to wild type and thus limiting its conformational state in either intermediate state or active state. The study suggests that along with SW-I and SW-II regions, the loop region preceding the α3 helix and α3 helix are also involved in affecting the hydrolysis of nucleotides and may be considered while designing therapeutics against KRAS.

STRAIN TRANSFER ANALYSIS OF A FOUR-LAYER EMBEDDED FIBER OPTIC SENSING MODEL IN AN ELASTIC STATE

Optical fiber grating strain sensors are currently utilized in a variety of structural health monitoring applications. The encapsulated fiber optic sensor is unable to completely detect the strain of the structure, so the strain transfer theory should be established to maximize the strain sensing of fiber. It is required to explore the embedded four-layer fiber optic sensing model to create a more plausible strain transfer error hypothesis. Based on the three-layer fiber optic sensing model, the Goodman assumption and Fourier series approach were presented to study the strain transfer efficiency of the four-layer model in the elastic state. First, the physical quantity to be analyzed is determined, and finally the radius, interlayer bonding coefficient and elastic modulus are selected as the parameters affecting the strain transfer efficiency. The length range of efficiency evaluation is 0–2.5[Formula: see text]m, and the transfer efficiency under different radii is above 0.90 when the length [Formula: see text][Formula: see text]m. The interlayer bonding coefficient [Formula: see text] between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 has little impact on the transfer efficiency, and the same is true for [Formula: see text], so it cannot be considered in practice. When [Formula: see text] is between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 and the length [Formula: see text][Formula: see text]m, the strain transfer coefficient reaches 95%. The influence of elastic modulus on the transfer efficiency is very significant when [Formula: see text][Formula: see text]m. The four-layer model performs similarly to the three-layer model within the paste length range of 1.0[Formula: see text]m, but has a superior strain transfer effect when the pasted length exceeds 1.0[Formula: see text]m. As the radius of the protective layer rises, the effect of strain transfer deteriorates.

Structural generation by inverse transformation using principal component analysis enhances conformational sampling of protein

Abstract Molecular dynamics (MD) simulations are frequently used to elucidate the molecular mechanisms underlying protein behavior. Based on a conformational search with MD simulations, protein structures rich in high-dimensional data can be quantitatively evaluated in free-energy landscapes (FELs). Generally, FELs are defined in low-dimensional subspaces spanned by reaction coordinates to characterize biological functions. When calculating FELs of proteins, principal component analysis (PCA) is particularly useful for capturing large-amplitude motions via dimensionality reduction into low-dimensional subspaces. In this study, to efficiently calculate FELs, a simple and convenient method is proposed by accelerating conformational search in a PCA subspace, which is achieved by quick generation of protein configurations. Specifically, inverse transformation driven by PCA facilitates the quick generation of diverse protein configurations from arbitrary grids in a defined PCA subspace. In our conformational search, a set of newly generated configurations serves as initial structures for multiple MD simulations, enabling one to calculate FELs of proteins by building Markov state models from their multiple trajectories. In conclusion, the conformational search from protein configurations broadly distributed in a PCA subspace accelerates FEL calculations, which supports a comprehensive approach to understanding collective protein dynamics.

The Entropy and Energy for Non-Mechanical Work at the Bose-Einstein Transition of a Harmonically Trapped Gas Using an Empirical Global-Variable Method.

Quantum thermal engines have received much attention in recent years due to their potential applications. For a candidate group, harmonically trapped gases under Bose-Einstein condensation (BEC), we see little investigation on the energy transference around that transition. Therefore, we present an empirical study with rubidium-87 gas samples in a magnetic harmonic trap. We developed an empirical equation of state model to fit to our experimental dataset, expressing the pressure parameter in terms of temperature, and six technical coefficients, functions of the volume parameter and the number of atoms. By using standard thermodynamic relations, we determine the system's entropy, shown to be constant at the BEC transition, as expected. Being isentropic makes the BEC transition an energy source for non-mechanical work. Hence, we observed that the enthalpy at the BEC transition, at fixed values of the volume parameter, grows fairly linearly with the number of atoms. We fitted a linear function to that data, finding the specific enthalpy of the BEC transformation and the intrinsic enthalpic loss for BEC. We deem this study to be a step closer to practical quantum-based engines.

A dynamic object removing 3D reconstruction system based on multi-sensor fusion

Abstract Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.

Interactions between Inhibitors and 5-Lipoxygenase: Insights from Gaussian Accelerated Molecular Dynamics and Markov State Models.

Inflammation is a protective stress response triggered by external stimuli, with 5-lipoxygenase (5LOX) playing a pivotal role as a potent mediator of the leukotriene (Lts) inflammatory pathway. Nordihydroguaiaretic acid (NDGA) functions as a natural orthosteric inhibitor of 5LOX, while 3-acetyl-11-keto-β-boswellic acid (AKBA) acts as a natural allosteric inhibitor targeting 5LOX. However, the precise mechanisms of inhibition have remained unclear. In this study, Gaussian accelerated molecular dynamics (GaMD) simulation was employed to elucidate the inhibitory mechanisms of NDGA and AKBA on 5LOX. It was found that the orthosteric inhibitor NDGA was tightly bound in the protein's active pocket, occupying the active site and inhibiting the catalytic activity of the 5LOX enzyme through competitive inhibition. The binding of the allosteric inhibitor AKBA induced significant changes at the distal active site, leading to a conformational shift of residues 168-173 from a loop to an α-helix and significant negative correlated motions between residues 285-290 and 375-400, reducing the distance between these segments. In the simulation, the volume of the active cavity in the stable conformation of the protein was reduced, hindering the substrate's entry into the active cavity and, thereby, inhibiting protein activity through allosteric effects. Ultimately, Markov state models (MSM) were used to identify and classify the metastable states of proteins, revealing the transition times between different conformational states. In summary, this study provides theoretical insights into the inhibition mechanisms of 5LOX by AKBA and NDGA, offering new perspectives for the development of novel inhibitors specifically targeting 5LOX, with potential implications for anti-inflammatory drug development.

Collaborative Decision Making with Responsible AI: Establishing Trust and Load Models for Probabilistic Transparency

In responsible AI development, the construction of AI systems with well-designed transparency and the capability to achieve transparency-adaptive adjustments necessitates a clear and quantified understanding of user states during the interaction process. Among these, trust and load are two important states of the user’s internal psychology, albeit often challenging to directly ascertain. Thus, this study employs transparency experiments involving multiple probabilistic indicators to capture users’ compliance and reaction times during the interactive collaboration process of receiving real-time feedback. Subsequently, estimations of trust and load states are established, leading to the further development of a state transition matrix. Through the establishment of a trust–workload model, probabilistic estimations of user states under varying levels of transparency are obtained, quantitatively delineating the evolution of states and transparency within interaction sequences. This research lays the groundwork for subsequent endeavors in optimal strategy formulation and the development of transparency dynamically adaptive adjustment strategies based on the trust–workload state model constraints.

Cognitive Analytic Therapy (CAT) in borderline personality disorder: a case report on the efficacy of longer treatment duration

ABSTRACT This article presents a case report on the use of Cognitive Analytic Therapy (CAT) in treating a patient with features of Borderline Personality Disorder (BPD) over an extended treatment duration. The case report highlights the effectiveness of CAT in addressing complex and entrenched issues and emphasises the use of the Sequential Diagrammatic Reformulation (SDR) and Multiple Self State model as key therapeutic tools. The findings demonstrate the benefits of longer treatment duration in CAT for individuals with features of BPD and underscore the importance of tailoring treatment to individual needs.

Acute aerobic exercise and creative thinking: comparison of transient hypofrontality state and strength model of self-controls’ theoretical predictions

Creativity is a high-level cognitive activity that can" be affected by acute aerobic exercise. This paper aims to determine how acute aerobic exercise intensities may influence aspects of divergent and convergent creative thinking. In this regard, to give an insight into the results, the predictions of the transient hypofrontality thesis and the strength model of self-control have been taken into consideration. Ninety-three healthy male college students (age = 21.18 ±1.73 years) were randomly assigned to anaerobic threshold intensity (%85 HRmax, n = 31), low intensity (%60 HRmax, n = 31), and control (n = 31) groups Participants performed divergent (Alternate Uses Task) and convergent (Raven's Advanced Progressive Matrices Test) creative thinking tasks before, during, and after cessation of two anaerobic thresholds and low intensities and control conditions. For the divergent creative thinking, flexibility and fluency performances during and after cessation of exercise in anaerobic threshold and low-intensity groups were improved in comparison to the control group.These results were consistent with the transient hypofrontality thesis and inconsistent with the strength model of self-control pre-dictions. In contrast, the performance of convergent creative thinking was relatively stable during and after cessation of exercise in anaerobic threshold and low-intensity groups which was inconsistent with both the transient hypofrontality thesis and the strength model of self-control predictions. The findings suggest that theoretical predictions of transient hypofrontality thesis are more adaptable with literature than the strength model of self-control to consider the exercise-creative processes relationship and further study of the issue would be of interest. Key words: Cognition, Convergent, Creativity, Divergent, Self-depletion, Physical Activity

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments often leads to varied outcomes. Understanding how individuals interpret the causes of unexpected feedback is crucial for adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility is about how quickly conditions change, impacting results. Stochasticity, on the other hand, refers to outcomes affected by random chance or "luck". Understanding these factors enables individuals to have more effective environmental analysis and strategy implementation (explore or exploit) for future decisions. This study investigates how anxiety and apathy, two prevalent affective states, influence the perceptions of uncertainty and exploratory behavior. Participants (N = 1001) completed a restless three-armed bandit task that was analyzed using latent state models. Anxious individuals perceived uncertainty as more volatile, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

Capturing the Effects of Single Atom Substitutions on the Inhibition Efficiency of Glycogen Synthase Kinase-3β Inhibitors via Markov State Modeling and Experiments.

Small modifications in the chemical structure of ligands are known to dramatically change their ability to inhibit the activity of a protein. Unraveling the mechanisms that govern these dramatic changes requires scrutinizing the dynamics of protein-ligand binding and unbinding at the atomic level. As an exemplary case, we have studied Glycogen Synthase Kinase-3β (GSK-3β), a multifunctional kinase that has been implicated in a host of pathological processes. As such, there is a keen interest in identifying ligands that inhibit GSK-3β activity. One family of compounds that are highly selective and potent inhibitors of GSK-3β is exemplified by a molecule termed COB-187. COB-187 consists of a five-member heterocyclic ring with a thione at C2, a pyridine substituted methyl at N3, and a hydroxyl and phenyl at C4. We have studied the inhibition of GSK-3β by COB-187-related ligands that differ in a single heavy atom from each other (either in the location of nitrogen in their pyridine ring, or with the pyridine ring replaced by a phenyl ring), or in the length of the alkyl group joining the pyridine and the N3. The inhibition experiments show a large range of half-maximal inhibitory concentration (IC50) values from 10 nM to 10 μM, implying that these ligands exhibit vastly different propensities to inhibit GSK-3β. To explain these differences, we perform Markov State Modeling (MSM) using fully atomistic simulations. Our MSM results are in excellent agreement with the experiments in that they accurately capture differences in the binding propensities of the ligands. The simulations show that the binding propensities are related to the ligands' ability to attain a compact conformation where their two aromatic rings are spatially close. We rationalize this result by sampling numerous binding and unbinding events via funnel metadynamics simulations, which show that indeed while approaching the bound state, the ligands prefer to be in their compact conformation. We find that the presence of nitrogen in the aromatic ring increases the probability of attaining the compact conformation. Protein-ligand binding is understood to be dictated by the energetics of interactions and entropic factors, like the release of bound water from the binding pockets. This work shows that changes in the conformational distribution of ligands due to atom-level modifications in the structure play an important role in protein-ligand binding.

UAV air combat autonomous trajectory planning method based on robust adversarial reinforcement learning

The poor robustness of the air combat autonomous trajectory planning strategy (ATP) trained through vanilla reinforcement learning (RL) methods is attributed to its dependence on the source environment. When this ATP is transformed from the source to the target environment, it may fail to lead to an effective dogfight victory due to disturbances, which can potentially threaten the flight safety of the unmanned combat aerial vehicle (UCAV). In order to enhance the robustness of ATP, we propose a robust RL-based method for generating an ATP strategy. This method can effectively consider uncertainties in the state and potential model misspecifications. First, a jointly trained adversary is designed to apply disturbances in the environment, forming a two-player zero-sum game with the ATP agent. To solve this game problem and learn a robust ATP, the robust adversarial RL method (RARL) is applied. Additionally, in order to prevent non-convergence of strategies resulting from the introduction of the RARL algorithm, a curriculum learning method is proposed that emulates the way human pilots learn, gradually progressing from simpler to more challenging courses. Finally, a quantitative method for evaluating the robustness of the ATP is proposed, taking into consideration the differences in ATP performance between the source and target combat environments. The results of robustness estimations demonstrate that the RARL algorithm, which is proposed herein, effectively enhances the ATP's robustness.

2 - Thandika Mkandawire’s Model for an African Developmental State, and the Ethiopian Experiment (2001–2018)

2 - Thandika Mkandawire’s Model for an African Developmental State, and the Ethiopian Experiment (2001–2018)