Space Model Research Articles

Designing for health: A study of architecturally oriented models of public space

AbstractIn the post-pandemic period, cities are committed to creating healthier and more healing urban environments. In high-density urban environments, urban complexes are increasingly assuming the function of enriching the external space and activating the vitality of cities, and their architecturally oriented public spaces have great potential for the promotion of residents' health. However, current research on public space and health has focused on green spaces and neighborhoods, and has not yet systematically sorted out the pathways that influence how building-oriented public space can contribute to health. In order to fill the gap, this study attempt to establish a model of health-promoting architecturally oriented public space, which can provide a reference for the similar projects.

Shell model study of 92Mo and systematic features of the N = 50 isotones

The level structures of 92Mo have been investigated using the shell model code nushellx@msu within two large model spaces containing neutron core excitations across the N = 50 closed shell. The calculated results are compared with the available experimental data, and reasonable agreements are obtained. The level structures of 92Mo, especially the levels come from neutron core excitations, are interpreted by the present calculations. The systematic features of neutron core excitations in the even–even N = 50 isotones have also been investigated.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

Effectiveness of nonpharmaceutical policy interventions in reducing population mobility during the COVID-19 pandemic

Abstract Nonpharmaceutical policy interventions (NPIs) are intended to reduce population mobility in mitigating the spread of COVID-19. This paper evaluates their effect on population mobility during the COVID-19 pandemic. State space models are applied to estimate the time-varying effects of NPI stringency on weekly pedestrian counts from location-based sensors installed before the pandemic. Different models are developed that evaluate compliance with NPIs over time, identify the most effective NPI, and identify regional differences. An efficient parsimonious alternative is proposed for the multivariate Seemingly Unrelated Time Series Equation model if full covariance matrices are of full rank. Kalman filter estimates of the regression coefficients show that NPI stringency initially had a negative effect on population mobility. The effect weakened during the pandemic, suggesting a reduced willingness to comply with regulations. Four of nine NPIs were identified as the most effective. The multivariate model confirmed the findings across federal states. This paper highlights how combining new data sources, routinely collected administrative data, and sound methodology fosters modern policy evaluation.

Truncated Hankel operators on model spaces

The paper by Sarason in 2007 initiated and inspired the study of truncated Toeplitz operators which are the compression of Toeplitz operators to coinvariant subspace of H2. We study the truncated Hankel operators which are the compression of Hankel operators to coinvariant subspace of H2. Several parallel characterizations and properties of such operators are obtained. In addition, boundedness, finite rank and compactness of truncated Hankel operators were characterized in terms of their symbols.

Integration of internet of things (IoT) technology in the design model of sustainable green building spaces

Integration of internet of things (IoT) technology in the design model of sustainable green building spaces

Humpback whales in the Southwest Atlantic Ocean: investigating their breeding movements by satellite tracking

AbstractUnderstanding animals' movements is essential to assess habitat use, life‐history strategies, and population dynamics. Here, we investigate the movement and behavior patterns of 153 humpback whales in the Southwest Atlantic Ocean (SWA) using data obtained by satellite telemetry between 2003 and 2019 during the species' breeding season (August–December) off the Brazilian coast. Switching state space models were applied to estimate behavior states (bmode) classified as Area Restricted Search (ARS), Transiting (TRANS), or uncertain. Whales were distributed from 4°S to 24°S, and five clusters of ARS behavior were identified along the Brazilian coast. Generalized linear mixed modeling revealed three main results: (1) a transition towards more sinuous behavioral states with increasing latitude; (2) more sinuous movement behavior around new moons; (3) movement behavior was temporally dynamic throughout the breeding season over the years, particularly in 2019. The results then revealed important regions where humpback whales cluster to engage in mating and nursing behaviors, highlighting the influence of spatial location and environmental cycles on their behavior. Estimated movement behavior presented here improves the knowledge about the habitat use and movement patterns of SWA humpback whales in their breeding ground and can be used to mitigate potential human‐related impacts.

First report of betalain production from endolichenic Bacillus sp. LDAB-1 from Dirinaria aegilita: Insights from novel quantification methodology of image processing.

Pigments are widely used in food supplements envisaging attractive colors along with health benefits. The desired advancements in the nutraceutical and antioxidant properties of pigments utilized in food products necessitate the search for novel additives. The present study is the first in the field to report the pigment-producing endolichenic bacteria, Bacillus sp. LDAB-1 from Dirinaria aegilita. Morphological, biochemical, and molecular characterization of the bacterium emphasizes that ideal pigment production occurs when utilizing sucrose and sodium nitrate. The pigment was salted out and dialyzed for further qualitative characterization using ultraviolet-visible, fluorescence, and Fourier transforminfraredspectra and the results corroborated the presence of betalains. The antioxidant activity of betalain is closer to the efficiency of α-tocopherol, which confers the pigment properties for antioxidant and nutraceutical significance. An optimal methodology for pigment affirmation is an issue when using an alternative methodology. Hence, the present assessment employs a comparative analysis of findings from both a spectrophotometric method and image processing technology encompassing RGB, CMYK, YCbCr, and L*a*b*color space models. Amongst these, the L*a*b* model potentially provides an effective modality for determining the pigment concentration. Bland-Altman plot analysis indicates similar consistency levels in betalain quantification by both methods at 95% confidence intervals, affirming the integrity and consistency of color image processing technology. Consequently, the present study represents novelty and innovativeness in reporting endolichenic Bacillus sp. LDAB-1 from D. aegilita and a rational image optimization protocol for pigment elucidation characteristics.

Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergence

We revisit the Bayesian Context Trees (BCT) modelling framework for discrete time series, which was recently found to be very effective in numerous tasks including model selection, estimation and prediction. A novel representation of the induced posterior distribution on model space is derived in terms of a simple branching process, and several consequences of this are explored in theory and in practice. First, it is shown that the branching process representation leads to a simple variable-dimensional Monte Carlo sampler for the joint posterior distribution on models and parameters, which can efficiently produce independent samples. This sampler is found to be more efficient than earlier MCMC samplers for the same tasks. Then, the branching process representation is used to establish the asymptotic consistency of the BCT posterior, including the derivation of an almost-sure convergence rate. Finally, an extensive study is carried out on the performance of the induced Bayesian entropy estimator. Its utility is illustrated through both simulation experiments and real-world applications, where it is found to outperform several state-of-the-art methods.

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models.

To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction. We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of "contrast-weighted" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion. We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction. Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.

Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship

Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship

Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.

SD2S: Multiscale Granger causality analysis based on serial decomposition state space models

SD2S: Multiscale Granger causality analysis based on serial decomposition state space models

Spatiotemporal evolution and driving mechanisms of desertification on the Mongolian Plateau

Desertification poses a severe ecological and environmental challenge in the Mongolian Plateau (MP). It is difficult to quantify desertification distribution using unified indicators in the entire MP, because of its complex physical geographic conditions and various climatic zones covered. To accurately address this challenge, the spatial distribution of desertification at a 30-m resolution from 1990 to 2020 were mapped in this study. The desertification potential occurrence zone was identified by using a moisture index on the MP firstly. The feature space model and five machine learning models were constructed to make the map based on Google Earth Engine and Landsat data. The spatiotemporal distribution of desertification were further analyzed, and the dominant drivers of desertification distribution and evolution were identified using Geodetector model. The results indicate that the potential occurrence area of desertification accounted for 83.88 % of the total land area. The gradient boosted tree model for desertification assessment has the best performance with the highest overall accuracy of 88.18 %. The year 2010 marked a pivotal transition from land degradation to land restoration in the MP. Between 2010 and 2020, desertified land continued to deteriorate extensively in the southern Mongolia, while Inner Mongolia, China, essentially entered a full recovery phase. Precipitation and land use emerged as the primary drivers of the spatial distribution of desertification on the Mongolian Plateau and Mongolia, with potential evapotranspiration and precipitation influencing the distribution of desertification in Inner Mongolia, China. Land use change was the primary driver of desertification evolution on the MP and Mongolia. This study constructs an indicator system and methodology suitable for desertification monitoring on the MP, addresses the lack of refined desertification data over a long time series, and provides scientific reference for decision-making support in combating desertification in this region, and other large arid and semi-arid areas in the world.

Multigranulation variable-scale fuzzy neighborhood measures and corresponding Choquet-like integrals for feature selection

Choquet-like integrals, a nonlinear fuzzy aggregation function, are diffusely applied in several real problems. However, the corresponding fuzzy measures in them are provided by human intervention and not by data-driven methods. As an effective knowledge representation tool, rough set models in different approximation spaces are established for data processing. Particularly, the multigranulation fuzzy β-covering approximation space has been concerned with only a single scale β∈(0,1]. However, different granulation structures should have different scales. Therefore, establishing a multigranulation approximation space with a variable scale by inducing fuzzy measures through data-driven methods and using corresponding Choquet-like integrals in practical problems is a significant challenge. To address this challenge, the notion of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented here, as well as multigranulation variable-scale fuzzy neighborhood measures in it. Furthermore, Choquet-like integrals with the presented measures are constructed to solve the issue of feature selection (i.e., attribute reduction) under the new approximation spaces. Firstly, the concept of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented, where different fuzzy β-coverings have different scales β∈Θ. Moreover, multigranulation variable-scale fuzzy neighborhood measures in it, as fuzzy measures, are presented. Subsequently, Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures are constructed. A novel attribute reduction method under Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures is proposed for any decision information table. Finally, the presented method is selected for solving problems of classification and diagnosis of rolling bearing faults. Several public data sets are used to demonstrate the effectiveness and feasibility of the presented methods mentioned above.

ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing Images with State Space Model and Spatial Context

Super-resolution (SR) for satellite remote sensing images has been recognized as crucial and has found widespread applications across various scenarios. Previous SR methods were usually built upon Convolutional Neural Networks and Transformers, which suffer from either limited receptive fields or a lack of prior assumptions. To address these issues, we propose ESatSR, a novel SR method based on state space models. We utilize the 2D Selective Scan to obtain an enhanced capability in modeling long-range dependencies, which contributes to a wide receptive field. A Spatial Context Interaction Module (SCIM) and an Enhanced Image Reconstruction Module (EIRM) are introduced to combine image-related prior knowledge into our model, therefore guiding the process of feature extraction and reconstruction. Tailored for remote sensing images, the interaction of multi-scale spatial context and image features is leveraged to enhance the network’s capability in capturing features of small targets. Comprehensive experiments show that ESatSR demonstrates state-of-the-art performance on both OLI2MSI and RSSCN7 datasets, with the highest PSNRs of 42.11 dB and 31.42 dB, respectively. Extensive ablation studies illustrate the effectiveness of our module design.

Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting

Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods.

Prediction of neural activity in connectome-constrained recurrent networks.

We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

ДИСИПАЦІЯ ЕНЕРГІЇ НИЗЬКОМОЛЕКУЛЯРНИМИ РЕЧОВИНАМИ ПІД ЧАС ЗОНДУВАННЯ ЕЛЕКТРИЧНИМ ПОЛЕМ БАГАТОЖИЛЬНИХ КАБЕЛІВ АТОМНИХ ЕЛЕКТРИЧНИХ СТАНЦІЙ

A model of the piece-homogeneous interphase space of stranded cables is proposed, taking into account the influence of cracks oriented along the lines of force of the electric field. The cracks are filled with low molecular weight substances. The case of a probing electric field with a significant tangential component is considered.. The equivalent values of the tangent of the dielectric loss angle at various parameters of low molecular weight substances in the cracks of the polyethylene insulation of the conductor with a cross section of 2.5 mm2 were determined. The equivalent tangent of the angle of dielectric loss increases by (3-20) times when the dissipation of electrical energy by low molecular weight substances changes from 10% to 100% in a crack, which is 1% of the thickness of an intact section of polyethylene insulation. The distribution of the electric field for the general case of accumulation of low-molecular products in capillaries formed by slit-like gaps between isolated veins is obtained. A strong electric field in the smaller part of the gap between the insulated cores, which occurs at a core potential of 1 kV, contributes to the capillary absorption of decomposition products of both solid polyethylene insulation and water vapor from the atmosphere in the case of unshielded structures of multi-core cables. The asymmetric configuration of the probing electric field with a significant tangential component makes it possible to detect low-molecular decomposition products of solid polymer insulation, which are a sign of its aging. Differences experimentally observed in the value of tgδ with different schemes of examination of multi-core cables are related, to a greater extent, to the uneven distribution of substances dissipating electrical energy across the cross-section of the cable, and not to a change in the structure of the probing electric field. References 11, figure 5.