As microwave sensing for cardiac monitoring advances, characterizing the population-level distributions of microwave cardiogram (MCG) and achieving robust statistical consensus are increasingly important. This article proposes a distribution-encoding technique based on dimensionality reduction that enables heatmap visualization and provides intuitive insight into the distribution of abnormal cardiac patterns. Four typical classes of arrhythmias are presented, with a particular focus on two arrhythmias: atrial premature beat (APB) and atrial fibrillation (AF). To leverage prior knowledge in distribution coordinates while automatically learning discriminative features, a distribution encoding neural network has been proposed. Validation on data from 64 patients (6115 samples) collected at two hospitals shows that both 24GHz continuous-wave and 60GHz frequency-modulated continuous-wave radars can effectively discriminate arrhythmias with the proposed learning mechanism. In a three-class classification (AF, APB, normal), accuracy reaches 96.15% on a small dataset of 257 samples and 90.99% on a larger dataset of 2964 samples. For common three-, four-, and five-class classifications, overall accuracies are approximately 80%, 75%, and 70%, respectively. Ablation experiments are presented to analyze the effectiveness of the learning mechanism.

Machine learning-empowered smartphone platforms for sustainable point-of-care bacterial detection.

Integrated multi-omics analysis to elucidate the genetic basis of seed traits in cotton.

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation (EAH) to address environmental changes in DMO. In light of mutual dependency between decision variables in Pareto-optimal solutions, VARE builds an efficient VAR model, capturing such mutual relationship while handling dense model parameterisation with dimensionality reduction, to predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity, for scenarios where predictive approaches are unsuitable, by making hypermutation aware of the significance of environmental changes in both decision and objective spaces. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a variety of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive empirical studies. Specially, the proposed algorithm is computationally much faster than popular transfer learning based approaches while producing significantly better results.

Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.

We present an exact dimensional reduction for ensembles of N identical dynamical units governed by ordinary differential equations of order M with quasi-linear structure. In these systems, each unit follows a linear differential equation whose coefficients depend nonlinearly on the ensemble of variables, such as a mean field, giving rise to a large class of network dynamical systems. We derive M+1 closed-form macroscopic equations of order M with variables that exactly capture the full microscopic dynamics and that allow for the exact reconstruction of individual trajectories from the reduced system. This dimensional reduction facilitates a simplified analysis of collective behavior in a new class of coupled oscillator networks and provides computationally efficient exact representations of large-scale dynamics. We illustrate our approach on two examples, highlighting new families of solvable models relevant to physics, biology, and engineering that are now amenable to simplified analysis.

Dimensionality reduction as information fusion enabler in large-scale group decision-making: Classification, challenges, and future directions

Global nonlinear Dimensionality Reduction (DR) methods excel at capturing complex features of datasets while preserving their overall high-dimensional structure when projecting them into a lower-dimensional space. Force-Scheme (FS) is one such method, used in a variety of domains. However, its use is still hindered by distortions and high computational cost. In this paper, we introduce Enhanced Force-Scheme (EFS), a revisited approach to solve the optimization problem posed by FS. We build on the core ideas of the original FS algorithm and introduce a more advanced optimization framework grounded in gradient-based optimization, which yields higher-quality layouts. Additionally, we elaborate on multiple strategies to accelerate the computation of projections using EFS, thereby facilitating its use on large datasets. Finally, we compare it with FS and other popular DR techniques and show that, among the methods tested, EFS best captures global structure while still performing well on local metrics. • Presentation of a new model (Enhanced Force-Scheme, EFS) that corrects major artifacts in Force-Scheme (FS) layouts by rethinking the way in which points are moved during the optimization. • Introduction of gradient descent concepts to obtain more reliable convergence and more detail in the resulting layouts. • Introduction of multiple strategies for scaling EFS and enable the projection of large datasets.

Viral proteases are essential enzymes in many viral strains, playing a crucial role in the viral replication cycle. They are key targets for antiviral drug development and have significant implications for viral pathogenesis. To address the issue of Flavivirus protease substrate promiscuity, Yellow Fever virus protease (YFP), West Nile Virus Protease (WNP), Zika virus protease (ZVP), Usutu Virus Protease (UVP), and Rocio Virus Protease (RVP) were recombinantly expressed in E. coli BL21-(DE3) and purified. Mass spectrometric Proteomic Identification of protease Cleavage Sites (PICSs) was performed using peptide libraries derived from a murine cell line lysate. A surprisingly high promiscuity in protease substrate specificity was detected for all five viral proteases, with a recurrence of arginine in the P1 position. Using homology modeling, specific subsites could be identified. However, the promiscuity of peptide binding was difficult to elucidate using these models. For these reasons, the ProtTrans protein language model (pLM) was used and fine-tuned with the obtained peptide sequences. The ProtTrans T5-Encoder model, originally trained to predict same protein-chain amino acids using a huge size of protein sequence data, when fine-tuned with target peptides from the PICS experiments and decoy peptides, could classify each of these groups with up to 76% test-set accuracy. Dimensionality reduction indicated that the T5 embeddings could indeed contain similar information, which was useful for recognizing protein-peptide interactions. These results confirm the usefulness of pLMs for the prediction of protein-protein interactions and thus have important implications for antiviral drug design.

Machine learning-coupled multi-elemental fingerprinting for high-accuracy source identification of distillation residues: A novel strategy for industrial waste traceability.

Abstract Out‑of‑equilibrium quantum many‑body systems – such as multi-electron atoms and molecules driven by strong laser fields, quenched ultracold gases, and ultrafast-excited solids – exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave‑function methods to describe these effects scale exponentially with particle number; simpler mean‑field approaches neglect essential particle correlations. The time-dependent two-particle reduced density matrix (TD2RDM) formalism offers a middle ground by propagating the two‑particle density matrix and closing the Bogoliubov–Born–Green–Kirkwood–Yvon hierarchy with a reconstruction of the three‑particle cumulant, which carries information about three-particle correlations. But the validity and existence of time‑local reconstruction functionals ignoring memory effects remain unclear across different dynamical regimes. We show that a neural ordinary differential equation (ODE) model trained on exact two-particle reduced density matrix data (no dimensionality reduction) can reproduce its full dynamics without any explicit three‑particle information – but only in parameter regions where the Pearson correlation between the two‑ and three‑particle cumulants is large. In contrast, in the anti‑correlated or uncorrelated regime, the neural ODE fails, indicating that no simple time‑local functional of the instantaneous two‑particle cumulant can capture the evolution. The magnitude of the time‑averaged three‑particle‑correlation buildup appears to be the primary predictor of successful extrapolation: For a moderate correlation buildup, both neural ODE predictions and existing TD2RDM reconstructions are accurate, whereas stronger values lead to systematic breakdowns. These findings pinpoint the need for memory‑dependent kernels in the three‑particle cumulant reconstruction for the latter regime. Our results place the neural ODE as a model‑agnostic diagnostic tool that maps the regime of applicability of cumulant expansion methods and guides the development of non‑local closure schemes. More broadly, the ability to learn high‑dimensional reduced‑density‑matrix dynamics from limited data opens a pathway to fast, data‑driven simulation of correlated quantum matter, complementing traditional numerical and analytical techniques.

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination method, "F-informed MDS," which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community's response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Optimal scheduling of hydropower and pumped storage hydropower for high renewable energy share in Thailand: A novel hybrid optimization approach with dimensionality reduction

Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.

ABSTRACT Aim Unmitigated climate change will subject species to environments unlike any experienced for millions of years, creating new risks and opportunities for species, especially where niches are truncated and novel climates open previously unavailable portions of the fundamental niche. Here we present a conceptual framework and explore methods for identifying niche edges, potentially truncated edges, and the risks and opportunities associated with changing climate prevalence. Innovation We provide conceptual and methodological frameworks to identify and quantify potentially truncated edges and associated risks and opportunities which can be incorporated into studies regarding conservation biology and novel ecosystems. This method uses multidimensional kernel density analysis to identify portions of the niche that are particularly susceptible to climate changes, the potentially truncated niche edges. Climate‐prevalence analysis then identifies which portions of the niche, including edges, may become more (opportunity edge) or less (risk edge) common in the future. Using three North American tree species, we test the sensitivity of this method to different representations of climate (via dimension reduction) and species‐specific available climates (via buffer distance), discuss the geographic and environmental distributions of potentially truncated edges and risk/opportunity edges, and recommend future implementations of this method in research and conservation. Main Conclusions Niche‐edge and climate‐prevalence analyses together help identify populations sensitive to changes in climate and assess climate risk or opportunity. Estimates of edge frequency and climate‐prevalence risk/opportunity are sensitive to climate dimensionality, with 4‐dimensional representations recommended over 2‐dimensional representations, and to buffer distance, with buffers > 100 km showing the best performance. This new method complements traditional approaches for assessing species‐level effects of climate change, such as species distribution models, and can help conservation planning by identifying populations that are particularly vulnerable to disappearing climates versus populations poised to leverage the opportunities associated with novel climates, with perhaps surprising effects.

Parallel attribute reduction algorithm based on simplified neighborhood matrix with Apache Spark

Statistically-informed surrogate models combining linear dimension reduction and neural networks for blast wave propagation

The soluble solids content (SSC) and firmness are two key parameters that determine edible quality and maturity of peaches; therefore, they have garnered significant attention from researchers in the peach industry. To enable rapid and nondestructive detection of SSC and firmness in peaches, a portable device based on visible–near infrared (Vis/NIR) spectroscopy was developed during this study. The device consists of three main components: a spectral acquisition module; a control and display module; and a power management module. Additionally, a corresponding data processing program was designed for this device. Using the developed instrument, diffuse reflectance spectra of peaches were collected. After spectral preprocessing to eliminate interference, feature wavelength selection algorithm was used for dimensionality reduction, and partial least squares regression (PLSR) model was established for SSC and firmness prediction. For the prediction set, the coefficients of determination ( ) for SSC and firmness were 0.884 and 0.852, the root mean square errors of prediction (RMSEPs) were 0.466°Brix and 5.532 N, and the residual predictive deviations (RPDs) were 2.938 and 2.602, respectively. The optimal models were embedded into the device to evaluate its stability and accuracy using an independent validation set. The results indicated that the average coefficients of variation for SSC and firmness measurements are 3.808% and 4.415%. The independent validation set yielded of 0.854 and 0.817 and the RMSEPs of 0.512°Brix and 5.659 N, respectively. Overall, the portable device developed for nondestructive multi-quality detection of peaches demonstrated stability and accuracy that fully meet the requirements of on-site and real-time measurements.

Energy management strategy (EMS) has a significant impact on the lithium-ion battery/supercapacitor combined hybrid energy storage systems (HESS) of electric vehicles. However, existing strategies often suffer from inefficient energy allocation and poor dynamic response. To address these challenges, this paper proposes a fuzzy reinforcement learning (RL)-based EMS to optimize the efficiency of HESS. Firstly, key road features are extracted from vehicle operation data using a sliding window, and then principal component analysis and an optimized K-nearest neighbors’ algorithm are used for feature dimensionality reduction and clustering, which can enable more precise feature classification under various road conditions. Secondly, a dueling double deep fuzzy Q-network (D3FQN) framework is proposed for real-time power allocation optimization. By combining the uncertainty handling capability of the fuzzy inference system with the efficient learning ability of the dueling architecture, the real-time adaptively decision-making ability of HESS can be enhanced under complex road conditions. Additionally, an adaptive reward function is designed to dynamically adjust the allocation strategy according to real-time road conditions, power demand, and battery status. Finally, the physical and comparative experiments are conducted, and the results validated the strong dynamic adaptability and efficient energy allocation of the proposed strategy across various road conditions.

A Novel Personal Best- Control Binary Particle Swarm Optimization (NPbest-BPSO) based electromyography (EMG) signal feature selection and classification