Input-output Relationship Research Articles

Optimizing Microservice Deployment in Edge Computing with Large Language Models: Integrating Retrieval Augmented Generation and Chain of Thought Techniques

Large Language Models (LLMs) have demonstrated impressive capabilities in autogenerating code based on natural language instructions provided by humans. We observed that in the microservice models of edge computing, the problem of deployment latency optimization can be transformed into an NP-hard mathematical optimization problem. However, in the real world, deployment strategies at the edge often require immediate updates, while human-engineered code tends to be lagging. To bridge this gap, we innovatively integrated LLMs into the decision-making process for microservice deployment. Initially, we constructed a private Retrieval Augmented Generation (RAG) database containing prior knowledge. Subsequently, we employed meticulously designed step-by-step inductive instructions and used the chain of thought (CoT) technique to enable the LLM to learn, reason, reflect, and regenerate. We decomposed the microservice deployment latency optimization problem into a collection of granular sub-problems (described in natural language), progressively providing instructions to the fine-tuned LLM to generate corresponding code blocks. The generated code blocks underwent integration and consistency assessment. Additionally, we prompted the LLM to generate code without the use of the RAG database for comparative analysis. We executed the aforementioned code and comparison algorithm under identical operational environments and simulation parameters, conducting rigorous result analysis. Our fine-tuned model significantly reduced latencies by 22.8% in handling surges in request flows, 37.8% in managing complex microservice types, and 39.5% in processing increased network nodes compared to traditional algorithms. Moreover, our approach demonstrated marked improvements in latency performance over LLMs not utilizing RAG technology and reinforcement learning algorithms reported in other literature. The use of LLMs also highlights the concept of symmetry, as the symmetrical structure of input-output relationships in microservice deployment models aligns with the LLM’s inherent ability to process and generate balanced and optimized code. Symmetry in this context allows for more efficient resource allocation and reduces redundant operations, further enhancing the model’s effectiveness. We believe that LLMs hold substantial potential in optimizing microservice deployment models.

A re-sampling statistics based imprecise moment independent global sensitivity analysis methodology with limited data of uncorrelated and correlated geotechnical properties

Traditional Global Sensitivity Analysis (GSA) is often performed using variance-based Sobol’s GSA, considering statistics of inputs estimated from limited data to be precise. This can lead to inaccuracies in the importance ranking of inputs due to negligence of – i) epistemic uncertainties in their statistics, and ii) role of other moments of outputs in Sensitivity Indices (SIs). This study overcomes these limitations by proposing an imprecise moment independent GSA methodology. The methodology couples re-sampling statistical techniques, i.e., Jackknifing and Bootstrapping with Monte-Carlo Simulations based Borgonovo’s moment independent GSA to estimate distributions of SIs. The re-sampling techniques estimate epistemic uncertainties in the statistics of inputs by generating re-constituted samples. These epistemic uncertainties are then propagated via Borgonovo’s GSA to estimate their imprecise moment independent SIs. The methodology is further coupled with Nataf’s transformation and Moving Least Square-Response Surface Methods to deal with problems of correlated inputs and lack of explicit input-output relationships. The generalized nature of methodology is demonstrated for three real case studies (one tunnel and two slopes), with different correlation structures between inputs, input-outputs relationships (explicit/implicit), and failure mechanisms. The methodology estimated the distributions of SIs of inputs instead of their point estimates, indicating the propagation of epistemic uncertainties truthfully. The Jackknife-GSA was significantly efficient (∼99 %) with negligible differences in statistics of SIs as compared to Bootstrap-GSA. The correlation between inputs significantly affects the statistics of SIs. Further, a reduction in the imprecision of SIs of inputs with their increasing sample sizes was observed, which is consistent with the general understanding.

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning.

Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) co-tuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

Robust approximation of the conditional mean for applications of Machine Learning

Machine Learning approaches are increasingly used in a range of applications. They are shown to produce low conventional errors but in many real applications fail to model the underlying input–output relationships. This is because the error measures used only predict the conditional mean under some restrictive assumptions, often not met by the data we extract from applications. However, new approaches to Machine Learning, for example using Evolutionary Computation, allow a range of alternative error measures to be used. This paper explores the use of the Fit to Median Error measure in machine learning regression automation, using evolutionary computation in order to improve the approximation of the ground truth. When used alongside conventional error measures it improves the robustness of the learnt input–output relationships to the conditional median. It is compared to traditional regularisers to illustrate that the use of the Fit to Median Error produces regression neural networks which model more consistent input–output relationships. The problem considered is ship power prediction using a fuel-saving air lubrication system, which is highly stochastic in nature. The networks optimised for their Fit to Median Error are shown to approximate the ground truth more consistently, without sacrificing conventional Minkowski-r error values.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B<sub>4</sub>C Composites: A Comparative Study with Nature-Inspired Algorithms

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B4C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.

Unifying Principles of Generalization: Past, Present, and Future.

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.

Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D Printed Polyacrylamide Hydrogels.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G') and loss (G") moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G' and G". We employed a multilayer perceptron (MLP) and successfully predicted G' and G" from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student's t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input-output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

The productivity spillover effect of foreign divestment: Evidence from Chinese industrial enterprises

Foreign divestment in China is garnering increasing social attention. This study investigates the productivity spillover effects of foreign divestment based on Chinese industrial enterprise data from 2000 to 2007. Our findings indicate that foreign divestment negatively impacts the total factor productivity (TFP) of incumbent enterprises, especially through backward linkages. We examine potential mechanisms based on the characteristics of divested and incumbent enterprises. The closure of foreign-invested enterprise disrupts industrial connections, whereas the sale of foreign equity to domestic shareholders strengthens local industrial linkages, thus presenting an opposite effect. Hong Kong, Macau, and Taiwan (HMT)-invested enterprises tend to local sourcing, leading to divestment effects through backward linkages. Conversely, non-HMT enterprises, with a higher capability to provide differentiated intermediate goods, primarily exhibit forward linkage effects. Further analysis reveals that incumbent enterprises with lower technological capabilities are more dependent on their foreign suppliers and customers. Domestic non-state-owned enterprises and those in regions with lower economic development or marketization levels also have less stable input-output relationships. As a result, these enterprises are more susceptible to the vertical spillover effects of foreign divestment.

Simulation Model of One-Parameter Selective Assembly of Two Elements With Multivariate Set-Making

Analytical and simulation methods are used to model assembly processes and technological systems in order to determine and optimize their performance and functioning parameters. The first one, describes an object or process using mathematical relationships indirectly. Simulation modelling is one of the modern methods of production system experimental research, allowing to take the presence of various components in the system, nonlinear characteristics, non-determinism and dynamism of ongoing processes into account. This work is devoted to the simulation model development of the technological process of multivariate one-parameter selective assembly of two elements. Multivariate set-making is defined by generalized rules with a set of weights which elements are set the distribution of elements between sets of different types. Selectively assembled elements have parameters determined by the manufacturing stage, which are independent random variables with known probabilistic characteristics. It is assumed that the input-output relationship is linear with given coefficients, the set-making equations linking the numbers of selective groups and assembly sets are known. A simulation model of the assembly process was built in the GPSS World, which, in contrast to the known one, operates with multivariate set-making rules for the case of assembly of two elements. The model allows to determine the quantitative indicators of the assembly process, in particular, the probabilities of assembly set formation, work-in-progress and preliminary rejects. As an example, the process of multivariate set-making of two parts forming a precision engineering product is considered. The data of parts distributions of both types by selective groups before assembly are given, the technological process indicators are calculated. Based on the modelling results the advantages and disadvantages of the multivariate set-making process are outlined.

Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features

Abstract. Groundwater level (GWL) forecasting with machine learning has been widely studied due to its generally accurate results and low input data requirements. Furthermore, machine learning models for this purpose can be set up and trained quickly compared to the effort required for process-based numerical models. Despite demonstrating high performance at specific locations, applying the same model architecture to multiple sites across a regional area can lead to varying accuracies. The reasons behind this discrepancy in model performance have been scarcely examined in previous studies. Here, we explore the relationship between model performance and the geospatial and time series features of the sites. Using precipitation (P) and temperature (T) as predictors, we model monthly groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, applying a 1-D convolutional neural network (CNN) with a fixed architecture and hyperparameters tuned for each time series individually. The GWL observations range from 21 to 71 years, resulting in variable test and training dataset time ranges. The performances are evaluated against selected geospatial characteristics (e.g. land cover, distance to waterworks, and leaf area index) and time series features (e.g. autocorrelation, flat spots, and number of peaks) using Pearson correlation coefficients. Results indicate that model performance is negatively influenced at sites near waterworks and densely vegetated areas. Longer subsequences of GWL measurements above or below the mean negatively impact the model accuracy. Besides, GWL time series containing more irregular patterns and with a higher number of peaks might lead to higher model performances, possibly due to a closer link with precipitation dynamics. As deep learning models are known to be black-box models missing the understanding of physical processes, our work provides new insights into how geospatial and time series features link to the input–output relationship of a GWL forecasting model.

Rotor dynamic response prediction using physics-informed multi-LSTM networks

The deep learning method provides an effective alternative to numerical simulations for establishing the nonlinear input-output relationship and calculating dynamic responses of rotor systems. To overcome the low generalization capability of pure data-driven long short-term memory (LSTM) networks when predicting dynamic responses to out-of-distribution inputs, a dynamic response prediction method using physics-informed multi-LSTM networks is proposed. This approach incorporates required physical constraints into the deep LSTM network, allowing the model training process to optimize the network parameters within the feasible solution space that adheres to physical laws. Consequently, this enhances the physical interpretability of the deep learning model. Specifically, two physics-informed multi-LSTM network architectures are introduced, and physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. The feasibility of the proposed method is verified by a Bouc-Wen hysteresis model and a simulated gas generator rotor. The response prediction performance of the two networks is validated on a constructed fault rotor dataset with significant sample differences, along with cross-speed and cross-node prediction validation for the rotor system. The results demonstrate that the trained networks exhibit strong robustness and generalization capabilities, making them suitable as surrogate models for rotor systems.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

T-matrix of piezoelectric shunt inclusions on a thin plate

Developing piezoelectric-based plate-like metamaterials necessitates an effective modeling method to elucidate the omnidirectional wave properties of piezoelectric coupled inclusions on a thin plate. The commonly used methods, such as the transfer-matrix method and finite element method, are inadequate for analyzing the transmission and reflection of omnidirectional waves in a two-dimensional elastic medium. Unlike the existing methods, the multiple scattering method employs Bessel functions as the displacement-basis methods which can accurately describe the propagation and scattering characteristics of omnidirectional waves. This paper develops a novel T-matrix formulation to support the multiple scattering method, representing the input-output relationship between incident and reflected waves from a piezoelectric shunt inclusion on a host thin plate. The piezoelectric shunt inclusion comprises a varying-thickness substrate bonded with piezoelectric shunting patches. The T-matrix of the piezoelectric shunt inclusion is formulated by integrating the wave-based method with Rayleigh-Ritz method. The derived T-matrix is then used to semi-analytically analyze the far-field scattering and reflection properties of a single inclusion. Numerical results capture the scattering properties resulting from trapped mode resonances of the piezoelectric shunt inclusion. Additionally, the capability of the piezoelectric shunt damping to design and tune multiple critical coupling conditions for axisymmetric modes of thin plates is parametrically investigated by varying the values of inductors and resistors.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits.

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates dis-continuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

Encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian mode modulation

Optical cryptosystems are crucial for ensuring the security of optical information transmission and storage. The indirect measurement mechanism of single-pixel imaging (SPI) offers a feasible implementation channel for optical cryptosystems. Illumination patterns are encryption keys projected onto the plaintext object, while the intensity collected by the single-pixel detector forms the ciphertext. However, the variations in the object's angular position during SPI measurement generally introduce certain inaccuracies in image reconstruction. And due to SPI's input-output linear mapping relationship, the plaintext is vulnerable to exposure. This proposes an encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian (LG) mode modulation. The inherent circular symmetry of LG mode makes the angular position of the object information that can be encrypted, while the intrinsic properties of the object can be represented by polarization. Our system characterizes various polarization parameters of samples serving as reliable plaintext with an error of less than 4.2%, including depolarization, diattenuation, and retardance. For encryption demonstration, LG modes are randomly divided into 5 groups, corresponding to an object at different rotational states. This, combined with 16 polarization modulations, constructs pattern-angle-polarization joint keys, enabling high-security encryption as well as high-fidelity decryption of the mask image, optical axis orientation, and retardance of the test sample. Experimental results demonstrate the effectiveness of our scheme in enhancing the security and information complexity of optical cryptography, offering valuable insights for optical communication and quantum information security.

Interpretable physics-aware alkali-silica reaction expansion prediction

The alkali-silica reaction (ASR) is a major contributor to the aging and degradation of infrastructure. Understanding ASR-induced expansion in concrete structures and accurately predicting its future progression are critical components of effective risk assessment frameworks. This paper presents a study focused on developing and interpreting an advanced machine learning model specifically designed to predict ASR expansion. The model strategically integrates two powerful algorithms to achieve this goal. A comprehensive database comprising 2000 samples of ASR expansion data with various attributes was used to train the model. The first algorithm, eXtreme Gradient Boosting (XGBoost), was employed to establish a predictive model for ASR expansion, achieving approximately 90% validation accuracy. The second algorithm, SHapley Additive exPlanations (SHAP), was applied to assess the relative importance of the factors influencing the XGBoost model’s predictions. This approach provided valuable physical and quantitative insights into the input–output relationships, which are often obscured in conventional machine learning methods. The study revealed that higher silica content, elevated alkali levels, and longer reaction times are strongly correlated with increased ASR expansion. In contrast, larger aggregate sizes and higher water-to-cement ratios were associated with reduced expansion. Additionally, the analysis identified alkali content as the most influential factor in determining the ultimate ASR expansion, while silica content emerged as the key parameter affecting the characteristic time of the ASR curve.

RSVP for VPSA : A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis.

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights.