User-defined Parameters Research Articles

Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings

The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Innovative hybrid energy storage systems with sustainable integration of green hydrogen and energy management solutions for standalone PV microgrids based on reduced fractional gradient descent algorithm

This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.

Quantitative Analysis of Protein-Protein Equilibrium Constants in Cellular Environments Using Single-Molecule Localization Microscopy.

Current methods for determining equilibrium constants often operate in three-dimensional environments, which may not accurately reflect interactions with membrane-bound proteins. With our technique, based on single-molecule localization microscopy (SMLM), we directly determine protein-protein association (Ka) and dissociation (Kd) constants in cellular environments by quantifying associated and isolated molecules and their interaction area. We introduce Kernel Surface Density (ks-density,) a novel method for determining the accessible area for interacting molecules, eliminating the need for user-defined parameters. Simulation studies validate our method's accuracy across various density and affinity conditions. Applying this technique to T cell signaling proteins, we determine the 2D association constant of T cell receptors (TCRs) in resting cells and the pseudo-3D dissociation constant of pZAP70 molecules from phosphorylated intracellular tyrosine-based activation motifs on the TCR-CD3 complex. We address challenges of multiple detection and molecular labeling efficiency. This method enhances our understanding of protein interactions in cellular environments, advancing our knowledge of complex biological processes.

Statistically principled feature selection for single cell transcriptomics.

The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes (feature selection) for downstream analysis (e.g., unsupervised cell clustering). The evaluation of different approaches to feature selection is hampered by the fact that, as we show here, the performance of feature selection methods varies greatly with the task being performed. For routine cell type identification, even randomly chosen features can perform well, but for cell type differences that are subtle, both number of features and selection strategy can matter strongly. Here we present a simple feature selection method grounded in an analytical model that, without resorting to arbitrary thresholds or user-defined parameters, allows for interpretable delineation of both how many and which features to choose, facilitating identification of biologically meaningful rare cell types. We compare this method to default methods in scanpy and Seurat, as well as SCTransform, showing how greater accuracy can often be achieved with surprisingly few, well-chosen features.

High on sparsity: Interbin compensation of cardiac motion for improved assessment of left-ventricular function using 5D whole-heart MRI.

Cardiac magnetic resonance is the gold standard for evaluating left-ventricular ejection fraction (LVEF). Standard protocols, however, can be inefficient, facing challenges due to significant operator and patient involvement. Although the free-running framework (FRF) addresses these challenges, the potential of the extensive data it collects remains underutilized. Therefore, we propose to leverage the large amount of data collected by incorporating interbin cardiac motion compensation into FRF (FRF-MC) to improve both image quality and LVEF measurement accuracy, while reducing the sensitivity to user-defined regularization parameters. FRF-MC consists of several steps: data acquisition, self-gating signal extraction, deformation field estimations, and motion-resolved reconstruction with interbin cardiac motion compensation. FRF-MC was compared with the original 5D-FRF method using LVEF and several image-quality metrics. The cardiac regularization weight ( ) was optimized for both methods by maximizing image quality without compromising LVEF measurement accuracy. Evaluations were performed in numerical simulations and in 9 healthy participants. In vivo images were assessed by blinded expert reviewers and compared with reference standard 2D-cine images. Both in silico and in vivo results revealed that FRF-MC outperformed FRF in terms of image quality and LVEF accuracy. FRF-MC reduced temporal blurring, preserving detailed anatomy even at higher cardiac regularization weights, and led to more accurate LVEF measurements. Optimized produced accurate LVEF for both methods compared with the 2D-cine reference (FRF-MC: 0.59% [-7.2%, 6.0%], p = 0.47; FRF: 0.86% [-8.5%, 6.7%], p = 0.36), but FRF-MC resulted in superior image quality (FRF-MC: 2.89 ± 0.58, FRF: 2.11 ± 0.47; p < 10-3). Incorporating interbin cardiac motion compensation significantly improved image quality, supported higher cardiac regularization weights without compromising LVEF measurement accuracy, and reduced sensitivity to user-defined regularization parameters.

Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device

Time Scale Analysis (TSA) is an investigative tool used in engineering design to identify locations in processes that should be a focus of Process Intensification (PI). Furthermore, TSA points to process variables and parameters that could be used to advance and measure PI improvement. However, TSA cannot suggest any specific design solution to intensify process performance. Instead, design engineers should use their fundamental knowledge and creative intelligence to specify detailed design transformations. TSA will then provide a specific quantitative measure of the improvement. TSA implementation improves an explicitly defined process performance, thus helping achieve process intensification goals. TSA is based on first principles, and it utilizes Characteristic Times (CT) such as diffusion, mean residence, and reaction times to improve an existing process. In this study, we specifically consider microfluidic biomedical devices. To illustrate the genesis of CT and TSA, we start by developing a mathematical model of an enzymatic degradation process in a biomedical device called iCore based on mass, momentum, and kinetic equations. After introducing user-defined scaling parameters, we extract CTs pertinent to the enzymatic degradation of uric acid in this microfluidic biomedical device. Diffusion coefficients, microchannel architectural characteristics, enzyme loading, hydrogel thickness, and characteristic parameters of enzyme kinetics are the parameters and process variables incorporated in this analysis. Finally, we compared the extracted CTs with a COMSOL Multiphysics parametric study to demonstrate how time scale analysis as a design tool and adjusting design parameters, such as diffusion coefficient, hydrogel layer thickness, substrate concentration, and enzyme concentration, can enhance the enzymatic reaction process without a need for complex computational modeling. It is crucial to recognize that pertinent CTs can be determined by understanding the type and nature of the observed process, previous experience, published data, and other foundational engineering design work. There is no need for mathematical modeling and numerical simulations to identify and acknowledge the CTs relevant and essential to the observed process; in this work, we only illustrate the principal origin of CTs via a detailed mathematical model of the process, as previously reported by Jovanovic et al. Therefore, in a routine application of TSA, it is important to remember that mathematical modeling and detailed numerical simulations are not necessary. This is a very comforting fact when TSA is deployed as a tool in higher-level process design functions. The investigations on how best to apply TSA in these higher level design functions such as Process Intensification, scale-up/numbering-up, change of device architecture, change of operating conditions, change of process feed characteristics, change of material physical and chemical properties, parametric optimization of the system for various objective functions, and techno-economic analysis, are yet to be studied and reported.

Towards multisensory control of physical modeling synthesis

Physical models of musical instruments offer an interesting tradeoff between computational efficiency and perceptual fidelity. Yet, they depend on a multidimensional space of user-defined parameters whose exploration by trial and error is impractical. Our article addresses this issue by combining two ideas: query by example and gestural control. Prior publications have presented these ideas separately but never in conjunction. On one hand, we train a deep neural network to identify the resonator parameters of a percussion synthesizer from a single audio example via an original method named perceptual-neural-physical sound matching (PNP). On the other hand, we map these parameters to knobs in a digital controller and configure a musical touchpad with MIDI polyphonic expression. Hence, we propose a multisensory interface between human and machine: it integrates haptic and sonic information and produces new sounds in real time as well as visual feedback on the percussive touchpad. We demonstrate the interest of this new kind of multisensory control via a musical game in which participants collaborate with the machine in order to imitate the sound of an unknown percussive instrument as quickly as possible. Our findings show the challenge and promise of future research in musical "Human-AI parternships".

Optimisation of open pit slope design considering groundwater effects using particle swarm optimisation and scaled boundary finite element method

Slopes are a crucial structures in open pit mines. Their design has implications on the economic, safety and environmental operation of the mining industry. Designing stable slopes can be challenging due to the complexities introduced by the stratigraphy and hydrology of the strata. With rising commodity costs and inflation rates, mining operating costs are increasing. Reducing operational costs is necessary for mining industries to remain competitive. While steepening the pit slope can decrease stripping materials and save money, it also increases the risk associated with slope surges. Therefore, optimising slopes is crucial for both financial and safety reasons. Numerical models such as the finite element method experience challenges in mesh generation of heterogeneous systems characterised by varying material properties and stratigraphies. Moreover, the need for repetitive geometry update necessitates recursive mesh regeneration that increases the computational burden. Moreover, previous slope optimisation studies focus solely on dry conditions. To consider the complex condition of hydrology along with heterogeneity in the soil stratigraphy, this study develops an optimisation procedure by combining the particle swarm optimisation algorithm and the scaled boundary finite element with an image-based meshing technique to optimise slopes with groundwater and achieve the desired factor of safety (FoS). The method changes the slope design parameters and the phreatic surface of groundwater simultaneously, considering user-defined parameters while iteratively re-meshing the optimisation processes. Several cases are presented, demonstrating the optimisation of bench width, bench angle, backfill parameters, and groundwater pumping levels.

Optical Investigation of Sparks to Improve Ignition Simulation Models in Spark-Ignition Engines

The use of renewable fuels in place of fossil fuels in internal combustion engines is regarded as a viable method for achieving zero-impact-emission powertrains. However, to achieve the best performance with these fuels, these engines require further optimization, which is achieved through new combustion strategies and the use of advanced ignition systems such as prechambers. Since simulations greatly accelerate this development, accurate simulation models are needed to accurately predict the combustion phenomenon, which requires a deep understanding of the ignition phenomenon as it significantly affects combustion. This work presents a comprehensive experimental methodology to study sparks under engine conditions, providing quantitative data to improve and validate ignition simulation models. The goal was to determine the volume generated by sparks under engine conditions that can initiate combustion and use this information to improve simulation results to match the experimental results. The visible sparks were observed with high-speed cameras to understand their time-resolved evolution and interaction with the flow. The heat transfer from the plasma was also visualized using a modified Background-Oriented Schlieren technique. The information gained from the experimental observations was used to improve an ignition simulation model. Since the velocity of the plasma was found to be slower than the surrounding flow, a user-defined parameter was included to calibrate the velocity of the simulated plasma particles. This parameter was calibrated to match the simulated spark length to the experimental spark length. In addition, since the previous simulation model did not take the heat transfer from the plasma into account, the simulated plasma particles were coupled to have heat transfer to the surroundings. Based on a comparison of the simulation results with the experimental results, the improved approach was found to provide a better physical representation of the spark ignition phenomenon.

Targeted mechanical and energy absorption properties of 3D printed aluminium metamaterials

The potential of 3D-printed AlSi10Mg auxetic structures for diverse mechanical and energy-absorbing needs remains untapped. This article reveals a multi-criteria framework for the laser powder bed fused (L-PBF) −υ architecture considering elastic modulus (E), yield strength (σy), specific energy absorption (SEA), peak crush force (PCF) and crush force efficiency (CFE). The framework seamlessly combines trial data, multi-criteria decision-making, and performance indicators. Five auxetic structures were 3D-printed, characterised for mechanical and energy absorption traits within a 0.17–0.26 relative density range. The outcomes revealed a range of values for various parameters, including the Poisson’s ratio (−0.03 to −0.22), porosity (80.87–87.60 %), CFE (33–83 %), elastic modulus (100–632 MPa), yield strength (1.8–10 MPa), and SEA (0.5–6.8 kJ/kg). The reliability of these structures was ensured through a meticulous selection process based on an extensive literature review and empirical validation. To address the limitations of theoretical models, our work goes beyond theoretical predictions by experimentally validating these properties and integrating advanced methodologies such as the ‘analytic hierarchy process’ (AHP) and the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS). This allows us to determine the best-performing auxetic architecture. The decision-making process was informed by five user-defined parameters prioritised in the order of CFE>−υ> E>σy> SEA based on their relative closeness identifying AUX5 as the best performing auxetic architecture. This study introduces an innovative method for crafting scenario-based auxetic architectures with varying performance levels based on their relative importance.

Techno-Economic Assessment of Coaxial HTS HVAC Transmission Cables with Critical Current Grading between Phases Using the OSCaR Tool

In recent years, the scientific and industrial interest regarding alternative technologies for transmission cables has increased. These conductors should efficiently transmit significant amounts of power between grid nodes, which are expected to be particularly congested due to the projected global increase in electricity production. Superconducting cables are considered a promising solution in this context, offering the potential to transmit large amounts of energy with minimal losses and compact dimensions, thereby potentially benefiting the environment. To evaluate the feasibility of integrating superconducting cables into existing grids, techno-economic approaches should be adopted. Such techniques enable the conceptual design of a specific cable structure, allowing users to explore a wide range of operating parameters to derive optimal designs. This paper reports a comprehensive techno-economic analysis of High Voltage Alternating Current (HVAC) cables realized with High-Temperature Superconducting (HTS) tapes, with the aim to transmit extremely high-power level. The optimal coaxial design is selected using Optimization Tool for Superconducting Cable Research (OSCaR) by implementing a graded approach to the critical current of the HTS tapes used for the different phases. This optimization aims to achieve the most effective balance between the cost of the coated conductors and their electrical properties. The whole set of model equations, the user-defined parameters, and the applied constraints are detailed. The OSCaR tool is then applied to assess the impact on the optimized design of the cable system and the corresponding cost indexes of several crucial parameters, such as the maximum transmitted power, the voltage level, and the line length.

Velocity model-based adapted meshes using optimal transport

In geophysical numerical models using the finite-element method or its variant, the spectral-element method, to solve seismic wave equations, a mesh is employed to discretize the domain. Generating or adapting a mesh to complex geological properties is a challenging task. To tackle this challenge, we develop an r-adaptivity method to generate or adapt a two-dimensional mesh to a seismic velocity field. Our scheme relies on the optimal transport theory to perform vertices relocation, which generates good-shaped meshes and prevents tangled elements. The mesh adaptation can delineate different regions of interest, like sharp interfaces, salt bodies, and discontinuities. The algorithm has a few user-defined parameters that control the mesh density. With typical seismic velocity examples (e.g., Camembert, SEAM Phase, Marmousi-2), mesh adaptation capability is illustrated within meshes with triangular and quadrilateral elements, commonly employed in seismic codes. Besides its potential use in mesh generation, the method developed can be embedded in seismic inversion workflows like multiscale full waveform inversion to adapt the mesh to the field being inverted without incurring the I/O cost of re-meshing and load rebalancing in parallel computations. The method can be extended to three-dimensional meshes.

An enhanced stochastic subspace identification incorporating time variables and its application to large-span sea-crossing suspension bridges

Structural health monitoring and assessment are essential to large-span sea-crossing bridges, and modal identification is the most effective method for achieving them, with the stochastic subspace identification (SSI) method dominating. However, when analyzed in open traffic scenarios with SSI for a large-span sea-crossing bridge, the contour of the vibration mode is distorted. Related affecting factors such as the measuring conditions, user-defined parameters, and traffic are explored, and traffic is verified as the essential component. To address this issue, an enhanced SSI algorithm incorporating time variables is created to automatically identify and rectify vibration mode shapes. Feature heterotopic signals are first matched with a common reference signal to perform modal identification. Advanced particle swarm optimization is applied to estimate critical parameters. The SSI algorithm with the density-based spatial clustering of applications with noise (DBSCAN) algorithm is then utilized to extract the modal characteristics. Both simulated misalignment and real records are applied to verify the suggested approach's performance. Comparative analysis with state-of-the-art techniques, such as the error norm, cross correlation, phase transform-β, mean phase deviation, and genetic algorithm, shows that the recommended approach is dependable and successful at identifying the large-span sea-crossing bridge's vibration mode shapes and computationally more accurate and efficient.

Parameter Optimization in Cluster Identification Algorithms for Characterizing Nanoclusters in Al-Mg-Si-Cu Alloys.

Optimization of user-defined parameters (Dmax, Nmin, order (K)) in the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, used to characterize nanoclusters in Al-0.9% Mg-1.0% Si-0.3% Cu (mass %), was conducted. Ten combinations of parameters with a given K were considered for samples naturally aged (NA) and preaged (PA) at 100°C. We confirmed four types of unphysical clusters, artificially formed, by analyzing composition with size, atomic density, and atomic arrangement inside clusters. The optimum combinations minimizing those unphysical clusters were obtained for both NA and PA samples. Meanwhile, to evaluate the reliability of the optimum combination, volume rendering and isosurfacing were performed. As a result, regions of high solute concentration were confirmed, and those regions are in good agreement with the position of the clusters obtained by applying the optimum combination in DBSCAN. Furthermore, by comparing the optimum combinations with the fixed parameters widely used until now, we showed that for each dataset, considering independent parameters obtained in the same method is desirable rather than using fixed parameters. Consequently, an idea of determining the algorithm parameters for characterizing the nanoclusters in Al-Mg-Si(-Cu) alloys was introduced.

Fatigue damage identification based on Kullback-Leibler relative entropy for raw acoustic emission waveform

Acoustic emission (AE) as a non-destructive testing technique is used to identify structural fatigue damage through the evolution of AE characteristic parameters, aiming to prevent significant economic losses and catastrophic consequences caused by fatigue failure. Traditional AE characteristic parameters (counts, hit rate, duration, rise time) are susceptible to the influence of threshold and user-defined acquisition parameters (PDT, HDT, HLT). Inaccurate parameter settings can miss the AE signals of the structural damage, resulting in inaccurate assessment. To reduce the dependency on threshold and other user-defined parameters, the Kullback-Leibler relative entropy (KLRE) is proposed as a feature parameter for identifying structural fatigue damage and performing fatigue life analysis. The effectiveness and feasibility of KLRE were verified through the fatigue tension–compression test on medium carbon steel (MCS) 1045 V-notch specimens. Based on different waveform recording methods, KLRE is divided into the hit KLRE (HKLRE) and waveform stream KLRE (SKLRE). Experimental results demonstrate that:(1) SKLRE outperforms the traditional AE time–frequency domain features in assessing fatigue damage, exhibiting an inflection point in its evolution trend before reaching fatigue critical failure. (2) Node-independent wavelet packet denoising method improves the initial instability of SKLRE caused by work hardening and crack face friction. (3) Amplitude attenuation does not affect the evolution trends of Shannon entropy (SE) and SKLRE. (4) The evolution trend of HKLRE is similar to traditional AE time–frequency domain features, necessitating the use of multiple features together to reduce uncertainty.

EDMD: An Entropy based Dissimilarity measure to cluster Mixed-categorical Data

The effectiveness of clustering techniques is significantly influenced by proximity measures irrespective of type of data and categorical data is no exception. Most of the existing proximity measures for categorical data assume that all attributes contribute equally to the distance measurement which is not true. Usually, frequency or probability-based approaches are better equipped in principle to counter this issue by appropriately weighting the attributes based on the intra-attribute statistical information. However, owing to the qualitative nature of categorical features, the intra-attribute disorder is not captured effectively by the popularly used continuum form of entropy known as Shannon or information entropy. If the categorical data contains ordinal features, then the problem multiplies because the existing measures treat all attributes as nominal. To address these issues, we propose a new Entropy-based Dissimilarity measure for Mixed categorical Data (EDMD) composed of both nominal and ordinal attributes. EDMD treats both nominal and ordinal attributes separately to capture the intrinsic information from the values of two different attribute types. We apply Boltzmann’s definition of entropy, which is based on the principle of counting microstates, to exploit the intra-attribute statistical information of nominal attributes while preserving the order relationships among ordinal values in distance formulation. Additionally, the statistical significance of different attributes of the data towards dissimilarity computation is taken care of through attribute weighting. The proposed measure is free from any user-defined or domain-specific parameters and there is no prior assumption about the distribution of the data sets. Experimental results demonstrate the efficacy of EDMD in terms of cluster quality, accuracy, cluster discrimination ability, and execution time to handle mixed categorical data sets of different characteristics.

GB-AFS: graph-based automatic feature selection for multi-class classification via Mean Simplified Silhouette

This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not require any user-defined parameters such as the number of features to select. The minimum number of features is selected using our newly developed Mean Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the clustering results for the feature selection task. To illustrate the effectiveness and generality of the method, we applied the GB-AFS method using various combinations of statistical measures and dimensionality reduction techniques. The experimental results demonstrate the superior performance of the proposed GB-AFS over other filter-based techniques and automatic feature selection approaches, and demonstrate that the GB-AFS method is independent of the statistical measure or the dimensionality reduction technique chosen by the user. Moreover, the proposed method maintained the accuracy achieved when utilizing all features while using only 7–30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\%$$\\end{document} of the original features. This resulted in an average time saving ranging from 15%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15\\%$$\\end{document} for the smallest dataset to 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} for the largest. Our code is available at https://github.com/davidlevinwork/gbfs/.

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.

At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

The gamma-variate in contrast-enhanced imaging: a unified view and method from computed to electrical impedance tomography

Objective. Modern medical imaging plays a vital role in clinical practice, enabling non-invasive visualization of anatomical structures. Dynamic contrast enhancement (DCE) imaging is a technique that uses contrast agents to visualize blood flow dynamics in a time-resolved manner. It can be applied to different modalities, such as computed tomography (CT) and electrical impedance tomography (EIT). This study aims to develop a common theoretical and practical hemodynamic extraction basis for DCE modelling across modalities, based on the gamma-variate function. Approach. The study introduces a framework to generate time-intensity curves for multiple DCE imaging modalities from user-defined hemodynamic parameters. Thus, extensive datasets were simulated for both DCE-CT and EIT, representing different hemodynamic scenarios. Additionally, gamma-variate extensions to account for several physiological effects were detailed in a modality-agnostic manner, and three corresponding fitting strategies, namely nonlinear, linear, and a novel hybrid approach, were implemented and compared on the basis of accuracy of parameter estimation, first pass reconstruction, speed of computation, and failure rate. Main results. As a result, we found the linear method to be the most modality-dependent, exhibiting the greatest bias, variance and failure rates, although remaining the fastest alternative. The hybrid method at least matches the state-of-the-art nonlinear method’s accuracy, while improving its robustness and speed by 10 times. Significance. Our research suggests that the hybrid method may bring noteworthy accuracy and efficiency improvements in handling the high-dimensionality of DCE imaging in general, being a step towards real-time processing. Moreover, our generative model presents a potential asset to produce benchmarking and data augmentation datasets across modalities.