True State Research Articles

A cycle or a tunnel? A study on unemployment and low-pay dynamics in Italy

We study the extent of persistence in unemployment and low-pay employment in Italy in the period 2014-2017, using the Italian component of the EU-SILC survey merged with administrative data. We model persistence in unemployment and low-pay employment using different dynamic random-effects models accounting for observed and latent individual heterogeneity as well as endogeneity of the initial conditions. We find evidence of true state dependence in place for both unemployment and low-pay employment. Moreover, past unemployment spells increase the probability of being low-paid, conditional on being employed, while the opposite effect is limited. For both processes the degree of reliance on the previous state is considerably greater than the magnitude of cross-effects. Thus, evidence is presented that these processes shape almost independent no-pay/low-pay tunnels leading individuals into two different traps, rather than a cycle between the two states.

Current Status of Haploidization in Cool-Season Grain Legume Crop Species

Doubled haploid technology is, so far, the fastest route to induce a true homozygous state in plants. True homozygous plants are particularly important for breeders, as they can facilitate hybrid breeding and are useful in fixing traits in a breeding line. Fabaceae species are of great importance in food and feed production; however, they are far behind other families with respect to the development of effective haploidization protocols. Here, we present the most recent status of research on haploidization protocols in cool-season grain legume crops, including dry peas, chickpeas, faba beans, lentils, lupines, and grass peas. The first four species are primarily for human consumption; the latter are utilized as forage. All the mentioned species have been subject to haploidization trials; however, repeatable protocols, including the regeneration of confirmed haploid or doubled haploid plants, have not been elaborated. Research in field pea, chickpea, grass pea, and lupine is promising, with the reported regeneration of microspore-derived embryos in all four species. Repeatable plant regeneration has been reported only in field peas and chickpeas. The most recent achievements on haploidization through male and female gametophytes in faba bean are also presented. The key factors for the effective stimulation of haploid cell development in cool-season legumes are reviewed, providing a useful basis for future efforts toward haploidization in this group.

COMMUNIST PARTY’S AND POLITICS’ ELITES IN THE TRANSFORMATION OF MONGOLIAN SOCIETY IN THE 1920s TO 30s

This study shows the multifactorial process of formation of new communist and political elite in Mongolia in the 1920s and 30s during the hardest period of the struggle for sovereignty and the formation of true Mongolian state. The author focuses on the analysis of domestic political and foreign policy factors influencing the development of the basic principles and mechanisms of the formation of the Mongolian Communist Party’s and politics’ elite and its participation in the process of transformation of traditional Mongolian society based on the socialist modernisation model. The characteristics of the process of formation of the Mongolian communist and political elite from the very first revolutionary circles of Mongolia People’s Party to the fundamental measures of the country’s socialist reorganisation are given. Particular attention is paid to the formation of a “tactical alliance” at the initial stage of the revolutionary process, the internal political struggle in the ranks of the Mongolian communist and political elite in carrying out fundamental social changes and the transformation of Mongolian society in the period of the 1920s-40s. On the basis of archival material and taking into account the achievements of modern Russian and foreign historiography, problematic and key issues of Russian historiography of the process of formation of the political elite and the transformation of the socialist model of the socialist model in Mongolia in the 1920s-30s are considered. The author concluded that the period of the 1920s and 30s, the struggle between old and new social forces, the formation of a new communist and political elite unfolded against a very unique background characterised, on the one hand, by the desire for national unity, the revival of Mongolian statehood, and on the other hand, by the choice of the optimal model of social development in those historical conditions.

Nanostructures in Organic Light‐Emitting Diodes: Principles and Recent Advances in the Light Extraction Strategy

AbstractOrganic light‐emitting diodes (OLEDs) in recent years have emerged as a leading display technology and the popularity of OLEDs is attributed to their numerous advantages, including the ability to produce natural color, achieve a true black state, consume low consumption, exhibit fast response, and be compatible with flexible devices. However, limitations in the performance persist, e.g., the out‐coupling efficiency, which currently stands at ≈20% due to issues such as trapped modes and plasmon loss. Many researchers, therefore, have actively proposed the integration of various nanostructures to address the challenges and enhance OLED performance. The structures play a crucial role in facilitating strong optical interaction with surface plasmon and waveguide modes, thereby improving the extraction of trapped modes. To mitigate the confinement, layers to modulate the refractive index are introduced to extract the confined light and redirect it into the out‐coupled mode. In this review, a comprehensive overview of the principle and effectiveness of these nanostructures in enhancing OLED performance is provided. Various applications of OLEDs are explored based on nanostructures such as nanoparticles, nanomeshes, metasurface, bioinspired structures, and scattering layers. By implementing and refining these nanostructures, significant advancements in OLED performance are anticipated.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Frequentist belief update under ambiguous evidence in social networks

In this paper, we study a frequentist approach to belief updating in the framework of Dempster-Shafer Theory (DST). We propose several mechanisms that allow the gathering of possibly ambiguous pieces of evidence over time to obtain a belief mass assignment. We then use our approach to study the impact of ambiguous evidence on the belief distribution of agents in social networks. We illustrate our approach by taking three representative situations. In the first one, we suppose that there is an unknown state of nature, and agents form belief in the set of possible states. Nature constantly sends a signal which reflects the true state with some probability but which can also be ambiguous. In the second situation, there is no ground truth, and agents are against or in favor of some ethical or societal issues. In the third situation, there is no ground state either, but agents have opinions on left, center, and right political parties. We show that our approach can model various phenomena often observed in social networks, such as polarization or bounded confidence effects.

Validation of a Hybrid Domain Overlapping Coupling Between SAM and CFD Against the TALL-3D Transients

The system thermal-hydraulic (STH) code SAM has been coupled to the computational fluid dynamics (CFD) code Simcenter STARCCM+ utilizing a hybrid domain overlapping coupling method in space and an explicit coupling in time. The coupling aims to extend the STH code’s applicability to scenarios where local momentum and energy transfers are important yet difficult for STH standalone models to capture, such as three-dimensional (3D) mixing and thermal stratification. The coupling method’s numerical stability was previously verified against multiple test cases including two closed-loop configurations, and it was validated against a double T-junction experiment with 3D scalar mixing. In the present work, the method is validated against the TALL-3D STH/CFD coupling benchmark facility. TALL-3D is a liquid-metal facility with a large, pool-type enclosure (test section) that exhibits 3D flow effects to be modeled by a CFD code. The rest of the system exhibits approximately one-dimensional behavior that is well predicted by a STH code. First, a STAR-CCM+ CFD model of the 3D test section is validated against experimental temperature data. Then a SAM-STARCCM+ coupled model is validated against six different TALL-3D steady states and includes SAM standalone results for comparison. Last, the coupled model is validated against two TALL-3D transients: one exhibiting flow reversal in the test section and one exhibiting nonlinear, limit cycle oscillations (LCOs). For the first transient, the SAM-STARCCM+ coupled model properly predicts an increase in the test section’s inlet temperature during flow reversal, and this increase is not predicted by the SAM standalone model. The coupled model also better predicts initial flow recovery and subsequent oscillations as the system approaches a final natural circulation state. For the second transient, no true final steady state is observed due to LCOs. Neither the SAM-STARCCM+ coupled model nor the SAM standalone model can perfectly capture the experiment’s changing oscillation frequency during the transient. Nevertheless, the coupled model does reproduce the general oscillatory feedback observed. This is a significant achievement because the present coupling only uses an explicit coupling in time, as opposed to a tighter, semi-implicit coupling. In comparison, STH/CFD coupling efforts previously performed by other authors required a semi-implicit coupling to obtain similar results to this work. The strengths of the novel coupling scheme validated in the present paper lie in the superior convergence characteristics and the much simpler, nonintrusive implementation.

A physics-informed Bayesian data assimilation approach for real-time drilling tool lateral motion prediction

In this study, a Bayesian data assimilation method that fuses physics with motion sensor data is demonstrated to infer the dynamic states at points of interest on the bottomhole assembly (BHA) with proper uncertainty quantification. A 4.75 inch-LWD (Logging-while-drilling) tool has been used as a use case, where the dynamic states at the formation evaluation sensor can be predicted in real time with the measurements at the motion sensor as the required inputs. This was achieved with a developed transfer function that utilizes unscented Kalman filtering technique. The robustness of the transfer function was evaluated with synthetic data obtained from finite element analysis (FEA) simulations for various BHA configurations and drilling conditions. It was found that the prediction by the transfer function agrees favorably well with the true states of motion at the formation evaluation sensor. Specifically, using the developed transfer function can help reduce the relative errors for the motion trajectories at the formation evaluation sensor by a factor of 3, and can significantly enhance measurement quality risk classification. The developed transfer function method was further assessed with experimental roll test data, which is considered as close to drilling conditions. The prediction by the transfer function was found consistently close to the ground truth in the presence of backward whirl. The developed modeling method can potentially have broader impacts by enabling fit-for-basin virtual V&V (Verification and Validation) to accelerate LWD tool development, or enabling future drilling optimization.

Optimal estimation of pure states with displaced-null measurements

We revisit the problem of estimating an unknown parameter of a pure quantum state, and investigate ‘null-measurement’ strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state. Such strategies are known to approach the quantum Fisher information for models where the quantum Cramér-Rao bound (QCRB) is achievable but a detailed adaptive strategy for achieving the bound in the multi-copy setting has been lacking. We first show that the following naive null-measurement implementation fails to attain even the standard estimation scaling: estimate the parameter on a small sub-sample, and apply the null-measurement corresponding to the estimated value on the rest of the systems. This is due to non-identifiability issues specific to null-measurements, which arise when the true and reference parameters are close to each other. To avoid this, we propose the alternative displaced-null measurement strategy in which the reference parameter is altered by a small amount which is sufficient to ensure parameter identifiability. We use this strategy to devise asymptotically optimal measurements for models where the QCRB is achievable. More generally, we extend the method to arbitrary multi-parameter models and prove the asymptotic achievability of the the Holevo bound. An important tool in our analysis is the theory of quantum local asymptotic normality which provides a clear intuition about the design of the proposed estimators, and shows that they have asymptotically normal distributions.

Biological subgroups in melanoma: A correlation of tumor genomic and transcriptomic analysis with spatial immunophenotyping.

e14667 Background: A prominent challenge advancing immunotherapy is the heterogeneity of resistance mechanisms leading to drug development failure despite efficacy in subgroup of patients. To address these shortcomings, precision medicine platforms aimed at prospectively stratifying patients into treatment groups based on tumor mutational burden (TMB) and tumor inflammation scores by gene expression profiling (GEP) are now being explored. Whether these gene-based variables reflect the true immunologic state of the tumor remains an open question. Here, we sought to evaluate TMB and GEP as markers of intratumoral inflammatory state by single-cell spatial analysis. Methods: Under an IRB approved protocol through the Huntsman Cancer Institute, 25 melanoma tumors with available clinical, WES, and RNAseq data were stained with a multiplex immunofluorescence-based protocol using Opal dye and Leica Bond Rx (CD8, CD68, PD1, PD-L1, Ki67, SOX10, DAPI) and scanned with Akoya Polaris whole slide scanner. Images were analyzed using HALO. Results: Melanoma tumors were stratified into 4 subgroups based on TMB (<10 vs ≥10 m/Mb) by WES, and inflammatory (Inflm) gene signature expression (GSEA >0 vs ≤0) by RNAseq, denoted as S1 (TMBhi/Inflmhi; n=4), S2 (TMBhi/Inflmlo; n=4), S3 (TMBlo/Inflmhi; n=8), and S4 (TMBlo/Inflmlo; n=9). The rate of disease progression was 0% (S1), 25% (S2), 25% (S3), and 56% (S4). Infiltration analysis of CD8 T cells showed elevated intratumoral CD8 density in S1 (249/mm2) and S3 (157/mm2) vs S2 (65/mm2) and S4 (65/mm2; p=0.0002), consistent with GEP findings. Spatial analysis showed a similar pattern of CD8 distribution across subgroups with peak CD8 density in the tumor periphery. Proximity analyses showed average distance of CD8 T cell to the nearest tumor cell was not different across subgroups. Furthermore, we found Inflmhi tumors had a higher frequency of PD1+ CD8 T cells relative to Inflmlo tumors (12.9 vs 2.8 cells/HPF; p=0.0002), suggesting GEP also reflects CD8 activation and functional status. Increased frequency of PD1+ CD8 T cells was correlated with a much improved overall survival (HR=3.3; p=0.017). A similar analysis performed on CD68+ macrophages showed significantly increased peri-tumoral macrophage recruitment in S4 (445/mm2) vs S1 (209/mm2; p<0.0001), S2 (91/mm2; p<0.0001), and S3 (267/mm2; p=0.0003) subgroups. Interestingly, Inflmhi tumors were associated with an increased proportion of PDL1+ macrophages regardless of TMB status (43% vs 3%; p<0.0001), suggestive of an upregulated adaptive immune response in tumors with active inflammation. Conclusions: Our findings illustrate that biological groupings based on TMB and GEP biomarkers correlate with CD8 spatial location and activation state. Each subgroup has unique tumor immune features that can be related to immune resistance and warrant further investigation.

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

Abstract Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model,” of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction. Significance Statement The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes.

Effects of local time on the variations of the total electron contents at an American and Asian longitudes and their comparison with IRI-2016, IRI-Plas2017 and NeQuick-2 models during solar cycle 24

Ionospheric modelling is one of the major tools to study the behavior of the ionosphere. Ionospheric models have been useful in predicting the true state of the ionosphere particularly in regions where Global Positioning System (GPS) are not readily available. This research paper aims to study the longitudinal variations and the effects of local time on the total electron content (TEC) recorded in two different sectors (Asia and America) during the ascending, maximum and descending phases of solar cycle 24 (2011–2017) and also to compare its values to IRI-2016, IRI-Plas2017 and NeQuick-2 models in order to evaluate their performances. An hourly interval profile computed on seasonal basis were used to study the behaviors of TEC diurnally and seasonally. A monthly interval error profile plotted on annual basis was also used to investigate the deviations of the models from the GPS values. Our results showed that the peak values of TEC in the Asian and American sectors were recorded around the dawn,06:00UT (13:00LT) and dusk, 18:00UT (15:00LT) respectively. We also affirmed from our results that seasonal/winter anomalies were recorded in all the phases of the solar cycle in both sectors. Equinoctial Asymmetry was also observed to be predominant during different phases of the solar cycle in both sectors except during ascending and descending phases in the Asian and American sectors respectively. Out of the 168 months of data collated for this study, only 162 months of data were available. The IRI-2016, IRI-Plas2017 and NeQuick-2 models have 11.7%, 23.5% and 64.8% better performance in all the months under consideration. Therefore, the NeQuick-2 model had the best performance in both the Asian and American sectors. Finally, from the results of our statistical analysis, Mean Absolute Error (MAE) has ∼3 TECU lower than the Root Mean Square Error (RMSE) values in both sectors and in all the solar cycle phase. Hence, MAE can evaluate the performance of ionospheric models better than RMSE.

Online estimation of vertical velocity for deep-sea carriers based on depth data

Seabed residential stations are essential for the future exploration of the deep-sea exploration. Deep-sea carriers accompany transport equipment for underwater resident stations. To accommodate the low-energy consumption and low-cost nature of deep-sea carriers, an online vertical velocity estimation method based on depth data is proposed. This method serves as a cost-effective alternative to the expensive acoustic and inertial velocity measurement equipment conventionally used in underwater navigation systems. To meet the demand for low-cost online velocity measurements, a dynamic model was created for buoyancy-driven vehicles such as deep-sea carriers during unpowered ascent. Two distinct methods were used to process depth sensor data: one involves central differencing followed by low-pass filtering, and the other entails the differentiation of the data after fitting it to dynamic equations. This approach yields two different types of estimated velocities. A sliding window was defined and used the variance of the velocity and fitting coefficient within the window as indicators to assess the uncertainty of the velocity data. Based on this evaluation, an adaptive weight allocation rule for the fusion process was formulated. This approach allows us to derive the weighted estimated velocity. Through lake trials, the weighted estimated velocity was validated to closely match the ideal velocity data and showed a substantial reduction in velocity variance compared to the differenced estimated velocity that was not fused. This indicates that the fluctuations in the velocity data were significantly reduced. In addition, the integrated displacement showed an average absolute error of 0.1769 m compared with the measured displacement. This indicates that the weighted estimated velocity effectively reflects the true state of motion of the deep-sea carrier. Furthermore, when unexpected motion occurs in deep-sea carriers, the weighted estimated velocity can prevent falling into the "smoothness trap.” This demonstrated the robustness of the proposed algorithm.

An Examination of Property Crime in Light of Left Realism's Theoretical Capacity

This article reviews property crime through the concept of left realism by focusing on the basic principles of left realists. This paper explores the influence of socio-economic and demographic factors on property crime within the framework of left realist theory, highlighting the complex dynamics between these elements and crime rates in England and Wales. It critiques the reliability of crime statistics, suggesting that they might exaggerate or fail to capture the true state of property crime and its relationship with unemployment. The analysis also considers unemployment as a source of relative deprivation, potentially exacerbating property crime and victimization in the absence of adequate opportunities and support systems. However, the paper acknowledges that other factors, such as effective policing, criminal justice measures, and social provisions, may also play significant roles. It further examines how demographic factors, particularly in socially marginalized and economically deprived areas, can contribute to higher rates of property crime and increase the number of victims. Left realism provides valuable insights into the underlying causes of property crime and its victimization, advocating for a comprehensive understanding that goes beyond conventional analyses to address the root conditions of environmental deprivation and their impact on vulnerable communities even though left realism has weaknesses. Keywords: Left realism, property crime, unemployment

Simulation testing performance of ensemble models when catch data are underreported

Abstract Ensemble model use in stock assessment is increasing, yet guidance on construction and an evaluation of performance relative to single models is lacking. Ensemble models can characterize structural uncertainty and avoid the conundrum of selecting a “best” assessment model when alternative models explain observed data equally well. Through simulation, we explore the importance of identifying candidate models for both assessment and short-term forecasts and the consequences of different ensemble weighting methods on estimated quantities. Ensemble performance exceeded a single best model only when the set of candidate models spanned the true model configuration. Accuracy and precision depended on the model weighting scheme, and varied between two case studies investigating the impact of catch accuracy. Information theoretic weighting methods performed well in the case study with accurate catch, while equal weighting performed best when catch was underreported. In both cases, equal weighting produced multimodality. Ensuring that an ensemble spans the true state of nature will be challenging, but we observed that a change in sign of Mohn’s rho across candidate models coincided with the true OM being bounded. Further development of protocols to select an objective and balanced set of candidate models, and diagnostics to assess adequacy of candidates are recommended.

Sketching phase diagrams using low-depth variational quantum algorithms

Mapping out phase diagrams of quantum systems using classical simulations can be challenging or intractable due to the computational resources required to simulate even small quantum systems far away from the thermodynamic limit. We investigate using quantum computers and the variational quantum eigensolver (VQE) for this task. In contrast to the task of preparing the exact ground state using VQE, sketching phase diagrams might require less quantum resources and accuracy, because low fidelity approximations to the ground state may be enough to correctly identify different phases. We used classical numerical simulations of low-depth VQE circuits to compute order parameters for four well-studied spin and fermion models which represent a mix of 1D and 2D, and exactly-solvable and classically hard systems. We find that it is possible to predict the location of phase transitions up to reasonable accuracy using states produced by VQE even when their overlap with the true ground state is small. Further, we introduce a model-agnostic predictor of phase transitions based on the speed with which the VQE energy improves with respect to the circuit depth, and find that in some cases this is also able to predict phase transitions.

Tailoring data assimilation to discontinuous Galerkin models

AbstractIn recent years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is approximated as a linear combination of basis functions. Ensemble data assimilation (DA) aims to approximate the true state by combining model outputs with observations using error statistics estimated from an ensemble of model runs. Ensemble data assimilation in geophysical models faces several well‐documented issues. In this work we exploit the expansion of the solution in DG basis functions to address some of these issues. Specifically, it is investigated whether a DA–DG combination (a) mitigates the need for observation thinning, (b) reduces errors in the field's gradients, and (c) can be used to set up scale‐dependent localisation. Numerical experiments are carried out using stochastically generated ensembles of model states, with different noise properties, and with Legendre polynomials as basis functions. It is found that strong reduction in the analysis error is achieved by using DA–DG and that the benefit increases with increasing DG order. This is especially the case when small scales dominate the background error. The DA improvement in the first derivative is, on the other hand, marginal. We think this to be a counter‐effect of the power of DG to fit the observations closely, which can deteriorate the estimates of the derivatives. Applying optimal localisation to the different polynomial orders, thus exploiting their different spatial length, is beneficial: it results in a covariance matrix closer to the true covariance than the matrix obtained using traditional optimal localisation in state space.

Compact objects in and beyond the standard model from nonperturbative vacuum scalarization

We consider a theory in which a real scalar field is Yukawa coupled to a fermion and has a potential with two nondegenerate vacua. If the coupling is sufficiently strong, a collection of N fermions deforms the true vacuum state, creating energetically favored false-vacuum pockets in which fermions are trapped. We embed this model within general relativity and prove that it admits self-gravitating compact objects where the scalar field acquires a nontrivial profile due to nonperturbative effects. We discuss some applications of this general mechanism: (i) “neutron soliton stars” in low-energy effective QCD, which naturally happen to have masses around 2M⊙ and radii around 10 km even without neutron interactions; (ii) “Higgs false-vacuum pockets” in and beyond the standard model; (iii) “dark soliton stars” in models with a dark sector. In the latter two examples, we find compelling solutions naturally describing centimeter-size compact objects with masses around 10−6M⊙, intriguingly in a range compatible with the Optical Gravitational Lensing Experiment (OGLE)+Hyper Suprime-Cam (HSC) microlensing anomaly. In addition to these interesting examples, the mechanism of nonperturbative vacuum scalarization may play a role in various contexts in and beyond the standard model, providing a support mechanism for new compact objects that can form in the early Universe, can collapse into primordial black holes through accretion past their maximum mass, and serve as dark matter candidates. Published by the American Physical Society 2024

Studying Lyrics as the True State of Subconscious Devotement to the God with a Special Reference to Rabindranath Tagore’s Gitanjali, Song 1, 2 and 3

Rabindranath Tagore produced his literary manifestation through art, and vice versa. His artistic creation and literature are known in every corner of the literary world for their sublimity, mirth, vigour, rejuvenation, mourning, despair, death, connection with mankind, God, and nature, as an ensemble ecstasy of experiencing shades of life. Those ecstatic qualities of ultimate demonstration published Gitanjali in life in the year 1910 with 156 songs in togetherness, which brought Tagore the honour in 1913 of becoming the first Nobel laureate as an Asian in literature. Tagore has shown the world his musical devotion towards nature, love, humanity, and God. In every aspect of life, it has become relevant to its core and hidden subconsciousness. The true inspiration of Bhakti Andolon (Bhakti Movement), which made its resonance in the 16th century, especially in Bengal, was inculcated in the verses of Gitanjali, which murmur the sole devotee of Tagore at the feet of Lord Krishna, the incarnation of Lord Vishnu, the one who preserves the creation. The hearty samarpan (surrender) of a poetic persona like Rabindranath Tagore thoroughly influences readers, scholars, and researchers for the study of literary and psychological assessment simultaneously over the years. His choice of rendition through the words and gradual escalation towards the almighty signify the subconscious state of his core devotion as worship. It clearly seems Tagore’s conscious and deliberate movement from his own body to the cosmic devotion of Lord Krishna. This article aims to assess a brief study of the subconscious state of Tagore’s spiritual mind through his lyrics in Gitanjali, specifically in songs 1, 2, and 3. The descriptive research method is taken as the methodological approach for the study. For the expected substantiation of the stated facts, this article has taken a few references from anonymous sources as a secondary study of research.

Bayesian inference of vorticity in unbounded flow from limited pressure measurements

We study the instantaneous inference of an unbounded planar flow from sparse noisy pressure measurements. The true flow field comprises one or more regularized point vortices of various strength and size. We interpret the true flow's measurements with a vortex estimator, also consisting of regularized vortices, and attempt to infer the positions and strengths of this estimator assuming little prior knowledge. The problem often has several possible solutions, many due to a variety of symmetries. To deal with this ill posedness and to quantify the uncertainty, we develop the vortex estimator in a Bayesian setting. We use Markov-chain Monte Carlo and a Gaussian mixture model to sample and categorize the probable vortex states in the posterior distribution, tailoring the prior to avoid spurious solutions. Through experiments with one or more true vortices, we reveal many aspects of the vortex inference problem. With fewer sensors than states, the estimator infers a manifold of equally possible states. Using one more sensor than states ensures that no cases of rank deficiency arise. Uncertainty grows rapidly with distance when a vortex lies outside of the vicinity of the sensors. Vortex size cannot be reliably inferred, but the position and strength of a larger vortex can be estimated with a much smaller one. In estimates of multiple vortices their individual signs are discernible because of the nonlinear coupling in the pressure. When the true vortex state is inferred from an estimator of fewer vortices, the estimate approximately aggregates the true vortices where possible.