Representation In Models Research Articles

Early Detection of Water Stress in Kauri Seedlings Using Multitemporal Hyperspectral Indices and Inverted Plant Traits

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (gs), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (gs and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm2), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale.

The Role of Leaf Area Changes Within Plant CO2 Physiological Impacts on the Global Hydrological Cycle

AbstractRising atmospheric CO2 concentrations enhance greenhouse warming and drive changes to plant physiology, leading to innumerable climate impacts. This study explores the impacts of plant responses on hydrological cycling at 2x preindustrial CO2 concentrations by analyzing simulations that isolate plant physiological effects using the Community Earth System Model versions 1 and 2. We find that leaf area growth increases canopy evaporation, which offsets transpiration declines, and dampens changes in global mean evapotranspiration, precipitation, and runoff in a CESM2 experiment with dynamic leaf area. These leaf area impacts are also evident in the differences between CESM1 and CESM2, with CESM2 better capturing observed leaf area magnitudes but potentially overestimating leaf area‐CO2 sensitivity, highlighting the importance of plant CO2 physiology on hydrological cycle changes and the need to improve its representation in climate models.

Continuous goal representations: Distance in representational space affects goal switching.

Theorists across all fields of psychology consider goals crucial for human action control. Still, the question of how precisely goals are represented in the cognitive system is rarely addressed. Here, we explore the idea that goals are represented as distributed patterns of activation that coexist within continuous mental spaces. In doing so, we discuss and extend popular models of cognitive control and goal-directed behavior, which implicitly convey an image of goals as discrete representational units. To differentiate empirically between discrete and continuous formats of goal representation, we employed a set-shifting paradigm in which participants switched between color goals that varied systematically in their distance in representational space. Across three experiments, we found that previous goals biased behavior during goal switches and that the extent of this bias decreased gradually with the previous goal's distance in color space from color information in the current trial. These graded effects of goal distance on performance are difficult to reconcile with the assumption that goals are discrete representational entities. Instead, they suggest that goals are represented as distributed, partly overlapping patterns of activation within continuous mental spaces. Moreover, the monotonous effects of distance in representational space on performance observed across all conditions in all experiments imply that the spreading of goal activation in representational space follows a monotonous (e.g., bell-shaped) distribution and not a nonmonotonous (e.g., Mexican-hat shaped) one. Our findings ask for a stronger consideration of the continuity of goal representations in models and investigations of goal-directed behavior.

Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework

PurposeEffective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems.Design/methodology/approachThis study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs.FindingsThe final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally.Practical implicationsThe proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research.Originality/valueAcademic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking.

Speech Emotion Recognition Model Based on Joint Modeling of Discrete and Dimensional Emotion Representation

This paper introduces a novel joint model architecture for Speech Emotion Recognition (SER) that integrates both discrete and dimensional emotional representations, allowing for the simultaneous training of classification and regression tasks to improve the comprehensiveness and interpretability of emotion recognition. By employing a joint loss function that combines categorical and regression losses, the model ensures balanced optimization across tasks, with experiments exploring various weighting schemes using a tunable parameter to adjust task importance. Two adaptive weight balancing schemes, Dynamic Weighting and Joint Weighting, further enhance performance by dynamically adjusting task weights based on optimization progress and ensuring balanced emotion representation during backpropagation. The architecture employs parallel feature extraction through independent encoders, designed to capture unique features from multiple modalities, including Mel-frequency Cepstral Coefficients (MFCC), Short-term Features (STF), Mel-spectrograms, and raw audio signals. Additionally, pre-trained models such as Wav2Vec 2.0 and HuBERT are integrated to leverage their robust latent features. The inclusion of self-attention and co-attention mechanisms allows the model to capture relationships between input modalities and interdependencies among features, further improving its interpretability and integration capabilities. Experiments conducted on the IEMOCAP dataset using a leave-one-subject-out approach demonstrate the model’s effectiveness, with results showing a 1–2% accuracy improvement over classification-only models. The optimal configuration, incorporating the joint architecture, dynamic weighting, and parallel processing of multimodal features, achieves a weighted accuracy of 72.66%, an unweighted accuracy of 73.22%, and a mean Concordance Correlation Coefficient (CCC) of 0.3717. These results validate the effectiveness of the proposed joint model architecture and adaptive balancing weight schemes in improving SER performance.

A multiscale model for multivariate time series forecasting

Transformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain. However, most of the existing methods, mainly represent the time-series from a single scale, making it challenging to capture various time granularities or ignore inter-series correlations between the series which might lead to inaccurate forecasts. In this paper, we address the above mentioned shortcomings and propose a Transformer based model which integrates multi-scale patch-wise temporal modeling and channel-wise representation. In the multi-scale temporal part, the input time-series is divided into patches of different resolutions to capture temporal correlations associated with various scales. The channel-wise encoder which comes after the temporal encoder, models the relations among the input series to capture the intricate interactions between them. In our framework, we further design a multi-step linear decoder to generate the final predictions for the purpose of reducing over-fitting and noise effects. Extensive experiments on seven real world datasets indicate that our model (MultiPatchFormer) achieves state-of-the-art results by surpassing other current baseline models in terms of error metrics and shows stronger generalizability.

Features of GEC representation in operational ionospheric models of Klobuchar, BDGIM and NeQuickG

Features of GEC representation in operational ionospheric models of Klobuchar, BDGIM and NeQuickG

Satellite-Driven Hydrological Modelling in Ungauged Moroccan bassins: Case study Ouzoud river

Predicting streamflow is vital for flash-flood early warning systems and managing water resources under climate change. However, limited streamflow observations restrict advanced prediction techniques to gauged basins, leaving most of the world's ungauged basins at a disadvantage. Developing reliable prediction methods for ungauged basins (PUB) is therefore essential. Over the past two decades, satellite-driven products, such as ERA5, have become crucial for enhancing precipitation and meteorological measurements, especially in complex terrains and changing climatic conditions. This study focuses on Morocco's arid and semi-arid regions, where water resource management is critical for agriculture, urbanization, and economic stability. Using the ERA5 dataset, which provides high-resolution atmospheric information, the study evaluates satellite-derived precipitation against ground measurements from the Sgatt station at Bernat River on daily timescales. Various statistical metrics assess ERA5's performance in representing daily precipitation and its integration into the GR4J-CemaNeige model for flow simulation. Results highlight the potential of ERA5 in improving rainfall representation and hydrological modelling in ungauged basins, verifying its effectiveness in simulating rainfall events compared to ground-based observations. This work underscores the importance of satellite-driven products for enhancing hydrological predictions and supporting water management in data-scarce regions.

Mathematical representation and nonlinear modelling of the Wheatley mitral valve

Mathematical representation and nonlinear modelling of the Wheatley mitral valve

On the Understandability of Design-Level Security Practices in Infrastructure-as-Code Scripts and Deployment Architectures

Infrastructure as Code (IaC) automates IT infrastructure deployment, which is particularly beneficial for continuous releases, for instance, in the context of microservices and cloud systems. Despite its flexibility in application architecture, neglecting security can lead to vulnerabilities. The lack of comprehensive architectural security guidelines for IaC poses challenges in adhering to best practices. We studied how developers interpret IaC scripts (source code) in two IaC technologies, Ansible and Terraform, compared to semi-formal IaC deployment architecture models and metrics regarding design-level security understanding. In a controlled experiment involving ninety-four participants, we assessed the understandability of IaC-based deployment architectures through source code inspection compared to semi-formal representations in models and metrics. We hypothesized that providing semi-formal IaC deployment architecture models and metrics as supplementary material would significantly improve the comprehension of IaC security-related practices, as measured by task correctness . Our findings suggest that semi-formal IaC deployment architecture models and metrics as supplementary material enhance the understandability of IaC security-related practices without significantly increasing duration . We also observed a significant correlation between task correctness and duration when models and metrics were provided.

A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model

Abstract Bayesian network are crucial components for uncertainty reasoning and knowledge representation in probabilistic graphical models. As model complexity increases, traditional digital methods face challenges in efficiency and resource utilization. In this work, a novel Bayesian network optimization method based on one-transistor one-memristor (1T1M) units for storing and accessing probability values was proposed. This method leverages the sigmoid-like characteristics of 1T1M for computational optimization, integrates probability storage into sigmoid belief network (SBN), and extends to the variational autoencoder (VAE) framework. By storing and manipulating probabilities at hardware level, this approach enhances storage efficiency and access speed while enhancing computational precision and model expressiveness through the sigmoid-like space of 1T1M. Validation experiments using the MNIST digit generation task demonstrate that this method exhibits unique advantages in SBN structures, particularly in handling complex conditional probabilities and inference tasks. Compared to traditional digital implementations, the proposed method exhibits potential advantages in inference speed and hardware utilization.

Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models

In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R2: 0.76) and Morgan fingerprints (MAPE: 24%, R2: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science.

Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming

AbstractUnderstanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO2 effluxes during the initial years of soil warming. The bias against observed CO2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models.

Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing

In the era of Industry 4.0, Zero Defect Manufacturing (ZDM) has emerged as a prominent strategy for quality improvement, emphasizing data-driven approaches for defect prediction, prevention, and mitigation. The success of ZDM heavily depends on the availability and quality of data typically collected from diverse and heterogeneous sources during production and quality control, presenting challenges in data interoperability. Addressing this, we introduce a novel approach leveraging Asset Administration Shell (AAS) and Large Language Models (LLMs) for creating interoperable information models that incorporate semantic contextual information to enhance the interoperability of data integration in the quality control process. AAS, initiated by German industry stakeholders, shows a significant advancement in information modeling, blending ontology and digital twin concepts for the virtual representation of assets. In this work, we develop a systematic, use-case-driven methodology for AAS-based information modeling. This methodology guides the design and implementation of AAS models, ensuring model properties are presented in a unified structure and reference external standardized vocabularies to maintain consistency across different systems. To automate this referencing process, we propose a novel LLM-based algorithm to semantically search model properties within a standardized vocabulary repository. This algorithm significantly reduces manual intervention in model development. A case study in the injection molding domain demonstrates the practical application of our approach, showcasing the integration and linking of product quality and machine process data with the help of the developed AAS models. Statistical evaluation of our LLM-based semantic search algorithm confirms its efficacy in enhancing data interoperability. This methodology offers a scalable and adaptable solution for various industrial use cases, promoting widespread data interoperability in the context of Industry 4.0.

Improving Urban Climate Adaptation Modeling in the Community Earth System Model (CESM) Through Transient Urban Surface Albedo Representation

AbstractIncreasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity.

The need for carbon-emissions-driven climate projections in CMIP7

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.

1.Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook. However, it is still an open question how we can best combine gene structure representation modelling and interaction learning into an end-to-end model for gene interaction searching. Here, in the current study, we developed a neural genetic block interaction searching model that can effectively process large SNP chip inputs and output the potential genetic block interaction heatmap. Our model augments a previously published hierarchical transformer architecture (Liu and Lapata, 2019) with the ability to model genetic blocks. The cross-block relationship mapping was achieved via a hierarchical attention mechanism which allows the sharing of information regarding specific phenotypes, as opposed to simple unsupervised dimensionality reduction methods e.g. PCA. Results on both simulation and UK Biobank studies show our model brings substantial improvements compared to traditional exhaustive searching and neural network methods.

Contrasting Response of Mesoscale Convective Systems Occurrence Over Tropical Land and Ocean to Increased Sea Surface Temperature

AbstractMesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm‐resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land‐ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation.

PerCNet: Periodic complete representation for crystal graphs

Crystal molecules are considered as graph structures in different representation methods. A reasonable crystal representation method should capture the local and global information. However, existing methods only consider the local information of crystal molecules by modeling the bond distance and bond angle of first-order neighbors of atoms, which leads to the issue that different crystals will have the same representation. To solve this many-to-one issue, we consider the global information by further considering dihedral angles. We propose a periodic complete representation of graph modeling and a calculation algorithm for infinite extended crystal materials. A theoretical proof for the representation that satisfies the periodic completeness is provided. Based on the proposed representation, we then propose a network for predicting crystal material properties, PerCNet, with a specially designed message-passing mechanism. To our best known, we are the first work that ensures the representation corresponds one-to-one with the crystal material based on graph modeling. Extensive experiments are conducted on two large-scale real-world material benchmark datasets. The PerCNet achieves the best performance among baseline methods in terms of MAE. Our code is available at https://github.com/JiaoHuang111/PerCNet.

The LTAR Grazing Land Common Experiment at Walnut Gulch Experimental Watershed.

The Walnut Gulch Experimental Watershed (WGEW) Long-Term Agroecosystem Research (LTAR) network common experiment addresses the aspirational practice of brush management (BM) to reverse the prevailing condition of woody plant encroachment (WPE) and increase perennial native grass production. Across the western United States, the decision to implement BM includes consideration of management objectives, cost, and the expected impact on a diverse suite of ecosystem services. Maintaining or restoring grass cover will help meet the LTAR sustainable production, economic, and social goals, and averting degradation will meet environmental goals. This common experiment, focused on hydrologic and erosion impacts of BM, aims to inform land management decisions on three major plant communities in the Southwestern United States: creosote bush (Larrea tridentata), mesquite (Prosopis velutina), and pinyon-juniper (PJ, Pinus and Juniperus spp.). On the WGEW, applying tebuthiuron pellets to creosote bush increased grass cover and reduced runoff and erosion. The 2016BM experiment on the Santa Rita Experimental Range applied a commonly used liquid herbicide cocktail but achieved only 7% mortality on mesquite, probably because of the timing of the aerial application. Experiments manipulating rainfall amount and intensity on plots receiving fire, chemical, or mechanical BM treatments on PJ communities aim to improve process representation in simulation models. The deliverables of these BM experiments will be to (i) improve the performance of runoff and erosion models, (ii) enhance our ability to identify areas most at risk from reduced hydrologic function and soil erosion after shrub proliferation, and (iii) better predict how landscapes will respond to BM interventions. Ranchers, land management agencies, and watershed conservation organizations will benefit from training and availability of improved tools to focus treatments on areas where greatest net benefits might be realized.