Space Model Research Articles

Beyond borders: developing the core aspects of physically active learning enactment (CAPAbLE) model in the third space

ABSTRACT In this article, we employed a third-space methodology to bring together two researchers and four teachers who have sustained physically active learning in their practice to explore the real-world applicability and processes of enacting physically active learning in teaching. We co-developed the Core Aspects of Physically Active Learning Enactment (CAPAbLE) model through six meetings. The model outlines 12 core aspects that can support pedagogical considerations about the purposes of enacting PAL through the iterations of planning, organising and evaluating. The CAPAbLE model may support the sustainability of PAL in schools and set the stage for future empirical investigations.

Effective integration and models of information: lessons from integrative structure modeling

Integrative structure modeling is a method for using information from multiple sources to compute structural models of biomolecular systems. It proceeds via four steps: (i) defining the model representation, which determines the variables whose values will be computed; (ii) constructing a function for scoring alternative models according to how well they accommodate input information; (iii) searching a space of candidate models for acceptable models; and (iv) analyzing acceptable models to evaluate their fit with input information. These steps are iterated until a model adequate for addressing biological questions is found. In this paper, we draw lessons from integrative modeling about effective integration and about modeling. We describe what it means to integrate information from multiple sources: Integration amounts to distributing information among the four steps of integrative modeling. Theory and data alike can be sources of information; this process thus generates models of information, rather than models of theory or models of data. We then propose heuristics for distributing information and designing multiple iterations of modeling. Effective iteration requires prioritizing the most reliable information and minimizing the time required to obtain an adequate model. Rather than being constructed from theory and assessed using data, models are constructed from any available information and assessed in a coherentist manner.

Estimation of true dates of various flowering stages at a centennial scale by applying a Bayesian statistical state space model.

Evaluation of long-term detailed cherry flowering phenology is required for a deep understanding of the sensitivity of spring phenology to climate change and its effect on cultural ecosystem services. Neodani Usuzumi-zakura (Cerasus itosakura) is a famous cherry tree in Gifu, Japan. On the basis of detailed decadal flowering phenology information published on the World Wide Web, we estimated the probability distributions of the year-to-year variability of the true dates of first flowering (FFL), first full bloom (FFB), last full bloom (LFB), and last flowering (LFL) from 1924 to 2024 by applying a Bayesian statistical state space model explained by air temperature data. We verified the estimated values against flowering phenology records of the tree from the literature and a private collection. The true dates of FFL and FFB could be explained by means of daily minimum air temperature from 1 December to 28/29 February and that of daily mean air temperature from 1 to 31 March, and those of LFB and LFL by means of daily mean air temperature from 1 to 10 April. Results were similar when we used air temperature data recorded at weather stations both 1 km and 29 km from the tree. These results indicated that our proposed Bayesian statistical state space model can estimate cherry flowering phenology that takes into account centennial-scale air temperature data recorded at a nearby weather station with a coarse temporal resolution.

A toolkit for quantifying individual response to herbal extracts in metabolic and inflammatory stress

This study developed a health assessment tool to analyze dynamic stress responses and resilience with the PhenFlex challenge. This study integrated a health space model and machine learning to quantify and visualize the impact of herbal extracts on inflammatory and metabolic health at the individual level. Two randomized, double-blind, placebo-controlled crossover trials were conducted involving participants with PhenFlex challenge after overnight fasting. Blood samples were collected, and a machine learning algorithm was used to predict health estimation scores based on metabolic and inflammatory responses. The resulting health space model visually represents individuals’ health status in a 2-D space. Intervention with herbal extracts (e.g., Angelica keiskei, AK, and Capsosiphon fulvescens, CF) resulted in higher health scores in the health space, indicating improved health. This research emphasizes the quantification of phenotypic changes for personalized nutrition and health optimization. Overall, this study provides a valuable toolkit for validating herbal extract efficacy and extends its application to personalized nutrition.

Uncertainty analysis method for diagnosing multi-point defects in urban drainage systems

Urban drainage system (UDS) plays a key role in city urbanization, where defective pipes can lead to seepage. Previous studies have identified the locations of defects in UDS using inverse optimization models. However, the unique optimal solution neglects uncertainty analysis, which may lead to misdiagnosis. In addition, the multi-point defect diagnosis has heavy computational burden due to high dimensional parameters space. To address these issues, this paper proposes a hybrid method that leverages the genetic algorithm (GA) to identify probable space, and then utilizes the adaptive Metropolis (AM) to provide an estimation of the posterior probability distribution (PPD). Firstly, a multi-population GA is employed for the maximum exploration within the model space. Then, AM algorithm is used to explore the final PPD of each pipe defect parameter. The metrics accuracy (ACC), Matthews correlation coefficient (MCC) and mean absolute error (MAE) are used to evaluate the diagnosis performance. A synthetic UDS case with randomized multi-point seepage scenarios is used to validate the method. Results indicate that the proposed hybrid method is effective in diagnosing multi-point defect, with 0.91, 0.78 of the hybrid method and 0.87, 0.69 of the DiffeRential Evolution Adaptive Metropolis method for the ACC and MCC, respectively. Meanwhile, the diagnosis speed has increased by 32%. The result PPD passes the 90% confidence interval validation. The proposed method can provide effective uncertainty analysis to reduce misdiagnosis of the traditional method.

GASSM: Global attention and state space model based end-to-end hyperspectral change detection

GASSM: Global attention and state space model based end-to-end hyperspectral change detection

Koopman-Constrained Hierarchical Deep State Space Model for Industrial Quality Prediction via Cloud-Edge Collaborative Framework

Koopman-Constrained Hierarchical Deep State Space Model for Industrial Quality Prediction via Cloud-Edge Collaborative Framework

Predicting hospital admissions due to COVID-19 in Denmark using wastewater-based surveillance.

Wastewater surveillance has become a fundamental tool to monitor the circulation of SARS-CoV-2 in order to prepare timely public health responses. In this study we integrate available clinical data on hospital admissions with wastewater surveillance data and investigate if predictions of the number of hospital admissions due to COVID-19 in Danish hospitals are improved by including wastewater concentrations of SARS-CoV-2. We implement state space models to describe the relationship between the number of hospital admissions due to COVID-19, available with a three-week classification delay, and more recent numbers of total hospital admissions with COVID-19. Including wastewater concentrations of SARS-CoV-2, we consider five-week predictions of the number of hospital admissions due to COVID-19. As a result of the three-week classification delay, the predictions translate into two hindcasts, one nowcast and two forecasts. The predicted values for all time frames follow the observed numbers well. We find that log likelihood values are higher when including wastewater concentrations (across all horizons) and that lagging the wastewater observations to investigate whether changes in wastewater concentrations occur before changes in hospital admissions does not result in further improvements. Our study shows that including wastewater concentrations improve estimates of the number of hospital admissions due to COVID-19, implying that wastewater concentrations add valuable information about the underlying transmission and that the imminent development of the near-future disease burden from COVID-19 is better informed when carefully including wastewater concentrations.

Szegő Limit Theorem for Truncated Toeplitz Operators

We discuss generalizations of the Szegő Limit Theorem to truncated Toeplitz operators. In particular, we consider compressions of Toeplitz operators to an increasing sequence of finite dimensional model spaces. We present two theorems. The first is a new variant of the Szegő Limit Theorem in this setting. The second relates to the variant given by Strouse–Timotin–Zarrabi (J Approx Theory 220:12–30, 2017), and characterizes the sequences for which that result holds.

Efficient Weather and Air Quality Forecasts Through AI

Accurate and timely weather forecasts and air quality predictions are essential for designing effective strategies to manage weather-related events and air pollution. These forecasts also play a key role in understanding aerosol-meteorology interactions within weather systems. However, traditional numerical methods, such as chemical transport models (CTMs), are computationally intensive, and their high resource demands limit their practical use in real-time air quality management and weather forecasting. In response to these challenges, we develop a novel approach called DeepCTM4D, which leverages deep learning to replicate CTM simulations, enhancing the computational efficiency of meteorology and air quality modeling in the four-dimensional chemistry space. The DeepCTM4D model is trained to accurately predict atmospheric chemical concentrations based on inputs such as precursor emissions, meteorological factors, and initial conditions. The key advantage of DeepCTM4D lies in its ability to efficiently identify the main drivers of pollution formation and assess how changes in emissions and meteorological conditions influence air quality. The relationships between emissions, meteorology, and concentration that DeepCTM4D captures align with established atmospheric chemistry mechanisms, further supporting the model’s scientific validity. Overall, DeepCTM4D offers a promising solution for simulating complex atmospheric processes, providing policymakers with critical information needed to design effective pollution control strategies and weather-caused events. This AI-driven model can also be integrated into global weather and air quality forecasting systems, serving as a powerful tool for more efficient, real-time predictions.

AI-assisted discovery of quantitative and formal models in social science

In social science, formal and quantitative models, ranging from ones that describe economic growth to collective action, are used to formulate mechanistic explanations of the observed phenomena, provide predictions, and uncover new research questions. Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture non-linear and dynamical relationships in social science datasets. By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. Augmenting existing workflows with symbolic regression can help uncover novel relationships and explore counterfactual models during the scientific process. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research by systematically exploring the space of non-linear models and enabling fine-grained control over expressivity and interpretability.

Revisiting Pascale Casanova’s world literary space

ABSTRACT The claim that Pascale Casanova focuses too much on case studies from the West when articulating her model of a world literary space is well known in studies of world literature. Yet there remains no concrete examination of how the paradigms of her world literary space fail to fully account for the processes of transnational literary reception for Chinese writers who engage with literary works that are asynchronous with their own. This article is the first to use a comparative approach to reappraise the foundations of Casanova’s world literary space – temporalities, translation, and structural inequalities – by reading them alongside the works of the twentieth-century Chinese writer Yu Dafu. The ways in which Yu engages with European and Asian literature in his writings suggest a complex literary engagement for early twentieth-century Chinese writers. There is a need to re-evaluate the mechanisms underlying Casanova’s influential model of a world literary space in order to apprehend and open new lines of inquiries into other cosmopolitan encounters for twentieth-century Chinese writers.

Enhanced spatial modeling on linear networks using Gaussian Whittle-Matérn fields

Abstract Spatial statistics is traditionally based on stationary models on $${\mathbb{R}}^{d}$$ like Matérn fields. The adaptation of traditional spatial statistical methods, originally designed for stationary models in Euclidean spaces, to effectively model phenomena on linear networks such as stream systems and urban road networks is challenging. The current study aims to analyze the incidence of traffic accidents on road networks using three different methodologies and compare the model performance for each methodology. Initially, we analyzed the application of spatial triangulation precisely on road networks instead of traditional continuous regions. However, this approach posed challenges in areas with complex boundaries, leading to the emergence of artificial spatial dependencies. To address this, we applied an alternative computational method to construct nonstationary barrier models. Finally, we explored a recently proposed class of Gaussian processes on compact metric graphs, the Whittle-Matérn fields, defined by a fractional SPDE on the metric graph. The latter fields are a natural extension of Gaussian fields with Matérn covariance functions on Euclidean domains to non-Euclidean metric graph settings. A ten-year period (2010–2019) of daily traffic-accident records from Barcelona, Spain have been used to evaluate the three models referred above. While comparing model performance we observed that the Whittle-Matérn fields defined directly on the network outperformed the network triangulation and barrier models. Due to their flexibility, the Whittle-Matérn fields can be applied to a wide range of environmental problems on linear networks and more general metric graphs such as modeling of water contamination in stream networks or modeling air quality or accidents on urban road networks.

Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model

Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. Specifically, we develop a local-enhanced state space module to better aggregate rich local and global information, both of which are complementary and beneficial for high-quality image reconstruction. Furthermore, we design a multi-router scanning strategy for spatially varying feature extraction, alleviating the issue of redundant information caused by repeated scanning directions in existing methods. Extensive experiments on multiple benchmarks show that the proposed Weamba performs favorably against state-of-the-art approaches.

HMCNet: A Hybrid Mamba–CNN UNet for Infrared Small Target Detection

Using infrared technology to accurately detect small weak targets is crucial in various fields, such as reconnaissance and security. However, the infrared detection of small weak targets is challenged by complex backgrounds, tiny target sizes, and low signal-to-noise ratios, which significantly increase the difficulty of detection. Early studies in this domain typically utilized manually designed feature-extraction methods that performed inadequately in the presence of complex backgrounds. While advancements in deep learning have spurred rapid progress in this field, with CNN models effectively enhancing the detection performance, the problem of small weak target features being lost persists. HMCNet, which employs a hybrid architecture combining a state space model and a CNN, is proposed in this paper; its hybrid architecture demonstrates the capacity to extract the local features and model the global context, facilitating superior suppression of complex backgrounds and detection of small weak targets. Our experimental results on the public IRSTD-1k dataset and our own MISTD dataset indicate that, compared to the current mainstream methods, the method proposed achieves better detection accuracy while maintaining high-speed inference capabilities, thus validating the rationality and effectiveness of this research.

Integration of hyperplastic models in strain space

This paper presents a simple and flexible integration scheme designed to capture the elastic-plastic behaviour of materials directly from a free energy function, a dissipation function, and kinematic constraints, typically formulated in strain space. As such, it eliminates the need to establish expressions for yield functions and/or plastic flow potentials. This is beneficial as analytical transformation of complex dissipation functions, particularly when combined with realistic kinematic constraints, often involves mathematical complexities. However, such a transformation is unnecessary as all essential information is already embedded within the various elements of the framework, i.e. the free energy potential, dissipation function and kinematic constraint expressions. Moreover, the scheme can be utilized to numerically visualise yield surfaces. The method is successfully applied to a family of sand and clay models featuring different dissipation functions, kinematic constraints (dilatancy rules) and friction mobilisation. These applications include showcasing models that lack analytical transformation, as well as models where analytical transformation to the yield and potential surfaces is possible, allowing for comparison. Through numerical simulation of several drained and undrained element tests, and the generation of yield surfaces, the efficacy of the proposed integration scheme is demonstrated. Furthermore, the proposed scheme is used to implement the Matsuoka-Nakai model in a finite element program to demonstrate its applicability to boundary value problems.

On the space of 2d integrable models

We study infinite dimensional Lie algebras, whose infinite dimensional mutually commuting subalgebras correspond with the symmetry algebra of 2d integrable models. These Lie algebras are defined by the set of infinitesimal, nonlinear, and higher derivative symmetry transformations present in theories with a left(right)-moving or (anti)-holomorphic current. We study a large class of such Lagrangian theories. We study the commuting subalgebras of the 2d free massless scalar, and find the symmetries of the known integrable models such as sine-Gordon, Liouville, Bullough-Dodd, and Korteweg-de Vries. Along the way, we find several new sequences of commuting charges, which we conjecture are charges of integrable models which are new deformations of a single scalar. After quantizing, the Lie algebra is deformed, and so are their commuting subalgebras.

The disparities and development trajectories of nations in achieving the sustainable development goals

The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an ‘SDG space of nations’. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators — areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs.

Augmented reality and digital placemaking: enhancing quality of life in urban public spaces

ABSTRACT Public places affect residents’ and tourists’ quality of life. However, stakeholders may struggle to understand these environments’ features. Digital technology has made interactive urban space models possible, enabling more collaborative and inclusive urban planning. Tehran’s Vanak Square neighborhood uses digital placemaking. Augmented reality with interactive markers lets residents influence planning. The integration of augmented reality with emerging technologies is transforming urban environments. The emergence of urban metaverse cyberspaces and the transformation of cities into smart cities through technologies like AR are revolutionizing how residents do business and live, including virtual offices, stores, and tourism. This integration democratizes services, resources, and skills globally without borders, significantly altering the fabric of urban life. Digital placemaking improves public space design and inclusivity, increasing city life. Moreover, integrating concepts like urban metaverse cyberspaces and smart city digital twins can further revolutionize urban living by democratizing services and resources through immersive technologies.

Using the state-space separable cohort model modification for assessment of Pacific cod Gadus macrocephalus (Gadidae) stocks in the Petropavlovsk-Komandorskaya subzone

Results of using a state-space cohort model with smoothing unscented Kalman filter for assessment of Pacific cod stock abundance and the other population parameters in the Petropavlovsk-Komandorskaya Subzone are presented. Series of numerical experiments were carried out with various variants of the recruitment model. The results of the work performed can be used in preparing forecasts of the TAC of marine commercial fish in terms that the observational data allow the use of age-structured models in the state space.