Search Results (438)

This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Short recordings of repeated vowels /a/, /i/, and /o/ were acquired shortly after admission and again before discharge following treatment and clinical stabilization. The pipeline included active-RMS normalization, automatic segmentation of repeated vowels, optional edge trimming, alternative pitch-estimation variants, and extraction of three feature families: phonatory and temporal measures, spectral-shape descriptors, and MFCC-based cepstral features. Within-patient admission-to-discharge differences were evaluated using two-sided Wilcoxon signed-rank tests, with nominal p-values interpreted as exploratory. Phonatory and temporal measures produced the most consistent exploratory findings. The pause-duration trend for /a/ decreased between admission and discharge and was the most configuration-stable individual candidate. CPP maximum and CPP range for /o/ increased consistently across the evaluated phonatory configurations, indicating systematic changes in cepstral prominence. Shimmer-related measures provided additional exploratory findings. MFCC measures showed complementary changes, particularly in MFCC11 variability for /o/, whereas spectral-shape effects were generally weaker and less consistent. Because the study involved a small, single-center cohort without a control group or external validation, the findings should be regarded as hypothesis-generating candidate acoustic measures rather than clinically validated biomarkers. Full article

Product concept images combine symmetry, closure, balance, and repeated components. Existing shape analysis and computational aesthetic methods can quantify these properties; however, when evidence is reduced to global descriptors or aggregate scores, image-layer provenance and sensitivity to rasterization or heuristic settings may be obscured. This paper presents a traceable image-processing pipeline based on scenario framing, alternative specification, geometry-informed computation, evidence synthesis, and design embodiment (SAGE-D), evaluated on four controlled smart-speaker alternatives using separate body, light-band, and aperture masks. Seven dimensionless descriptors measure silhouette reflection, centroid balance, light-band closure, aperture regularity and gradient, component-scale retention, and contour compactness. Resolution resampling, one-pixel morphology, parameter perturbation, and synthetic controls assess sensitivity. At $512\times 512$ pixels, A, B, and R showed exact bilateral silhouette consistency; B and R showed complete light-band occupancy; and C and R showed strong downward aperture-radius gradients. Conventional same-mask measures gave concordant geometric readings, while leave-one-gate-out analysis showed that screening depended mainly on predefined closed-ring and linear-gradient requirements. Only R passed all six case gates. SAGE-D is used here as an auditable organization of layer-specific measurements and bounded screening rules, not as a superior descriptor set. The conclusions are limited to the supplied two-dimensional (2D) representations and do not establish population-level generalizability, preference, or engineering performance. Full article

Machine learning is increasingly used to relate molecular descriptors, formulation variables, and biophysical measurements to aggregation, viscosity, solubility, and shelf-life outcomes. The evidence is promising but uneven. Most published datasets contain tens to a few hundred antibodies, use different assays and endpoint definitions, and rely mainly on internal validation. Direct evidence for bispecific antibodies, antibody–drug conjugates, mRNA–lipid nanoparticles, and viral vectors remains limited. This structured critical review evaluates what current models can support, how data and validation choices shape reported performance, and where claims exceed the available evidence. We searched PubMed through 30 June 2026 using predefined queries for machine learning, biopharmaceutical formulation, colloidal stability, advanced modalities, and shelf-life modelling. Studies were assessed by molecular diversity, formulation coverage, endpoint quality, split strategy, external validation, and decision relevance. The strongest current use cases are early antibody developability screening, high-concentration viscosity classification, formulation ranking within a defined experimental domain, and image-based particle classification. Long-term shelf-life prediction may benefit from hybrid kinetic and machine learning models, but real-time confirmation remains necessary. Progress will depend less on larger algorithms than on better labels, molecule-level validation, shared reference datasets, and clear uncertainty reporting. Full article

Whereas existing approaches to financial regime detection calibrate thresholds to historical price-and-return statistics, we propose a framework built on immunisation: a particle filter whose observation model is calibrated to the distributional shape of market stress rather than specific historical episodes. The particle filter—with theoretical foundations in probabilistic robotics and autonomous driving, and adapted to financial markets—forms the inferential backbone. Its observation model stacks distributional shape descriptors—skewness, tail asymmetry, kurtosis, and the share of leading sector-eigenvalue energy in cross-asset return covariance—computed across a hierarchy of temporal windows and injected as structured distributional archetypes, replacing the random initialisation of Thrun and Burgard, and of Reisinger. Applied to S&P 500 across four distinct crises (Dotcom 2002, Lehman 2009, COVID-19 2020, and the 2022 inflation-driven bear market), the descriptors show pre-crisis discrimination, with effect sizes (Cohen’s d) of 2.9 or more for realised volatility, Bowley downside skewness, and tail-quantile features, and 1.3 for sector concentration (${\lambda }_1$ ratio). Out of sample (2015–2026), the pipeline confirms endogenous regime transitions with lead-time before the market trough. With the flexibility of particle filters, the richness of the observation model is the primary enabler of real-time regime detection. We frame the system as a distributional-shape monitor that detects regime transitions in real time, not a pre-peak forecaster: highly sensitive, it registers deformation as stress becomes measurable, with the attendant sensitivity–specificity trade-off. On the abrupt COVID-19 shock, it confirms the transition sixteen trading days before the trough, without claiming pre-peak detection; confirmation timing scales with each crisis’s own duration, from roughly two to three weeks for the fastest episodes to several months for the slowest. Full article

Evaluating whether an intellectual property (IP) prototype faithfully preserves the visual identity and semantic intent of its original concept design is an important yet challenging task in product design and creative prototyping. Existing evaluation practices mainly rely on manual inspection or global image-level similarity comparison, which are subjective, difficult to reproduce, and insufficient for localizing identity-critical deviations. To address this problem, this paper proposes PartSense-IP, a part-aware vision–language sensor fusion framework for visual–semantic consistency evaluation of IP prototypes. The proposed framework takes a 2D concept image, an optional textual design description, and multi-view RGB-D sensor observations of a prototype as inputs. It first constructs a multi-view prototype representation and decomposes both the concept and prototype observations into design-relevant parts. Dense visual features, color and shape descriptors, and vision–language semantic embeddings are then extracted to evaluate part-level consistency. A Part-Aware Visual–Semantic Consistency Fusion (PVCF) algorithm is further developed to integrate shape, color, local visual similarity, semantic alignment, and cross-view stability into a unified IP consistency score. In addition to scalar scoring, PartSense-IP generates localized difference maps, 3D inconsistency visualization, and interpretable design feedback for prototype refinement. Experiments on the proposed IP-ProtoSense evaluation protocol demonstrate that PartSense-IP outperforms representative vision–language, dense-visual, segmentation-based, and 3D multimodal baselines in consistency scoring, inconsistency detection, localization, ablation, and robustness evaluation. Full article

Long-chain n-alkane hydroisomerization is a key catalytic route for upgrading wax-rich, bio-derived, and synthetic hydrocarbon feedstocks into diesel fuels, sustainable aviation fuels, and lubricant base oils with improved low-temperature properties. However, selective hydroisomerization remains challenging because mismatches in the spatial proximity and relative strength of metal and acid sites can prolong the residence time of olefin/carbenium-ion intermediates, thereby promoting over-isomerization to multibranched species, deep cracking, and coke formation. This review summarizes recent advances in zeolite-based bifunctional catalysts for long-chain n-alkane hydroisomerization. The catalytic mechanisms are first discussed, including metal-catalyzed dehydrogenation/hydrogenation, acid-catalyzed skeletal rearrangement, and shape-selective pathways governed by pore-mouth and key-lock effects. Catalyst construction strategies are then outlined, with emphasis on the preparation of zeolite supports and the introduction and localization of metal sites. Subsequently, structure–performance relationships are reviewed from the perspectives of support properties, metal site characteristics, and promoter effects, followed by a concise assessment of catalyst performance with real feedstocks under industrially relevant conditions. Finally, this review provides guidance for the precise design of metal–acid bifunctional hydroisomerization catalysts by highlighting descriptor-guided optimization, spatially regulated metal–acid–pore architectures, multiscale characterization and modeling, and scalable catalyst construction under practical reaction conditions. Full article

Background/Objectives: Synthetic cathinones are a rapidly evolving class of new psychoactive substances whose structural diversity complicates toxicological risk assessment. Methylenedioxy cathinones occupy a pharmacological space between MDMA-like entactogens and more dopaminergic stimulant cathinones, but their direct cardiac liabilities remain poorly characterized. This study aimed to compare the cardiotoxic and neurobehavioral profiles of methylone, butylone, pentylone and their N,N-dimethyl analogues, and to determine how alkyl-chain extension and terminal amine substitution shape functional toxicity. Methods: Wild-type short-fin zebrafish (Danio rerio) embryos were used as a multiparametric New Approach Methodology. Cardiac rhythmicity was assessed in 3 days post-fertilization embryos after acute exposure to methylone, butylone, pentylone, dimethylone, dibutylone, dipentylone, dihexylone and diheptylone by high-speed video microscopy and dynamic pixel-based analysis, focusing on atrial chronotropy and atrioventricular conduction. Basal locomotor activity was evaluated in 5 days post-fertilization eleutheroembryos over 120 min using automated video tracking. Results: Negative chronotropy increased with alkyl-chain extension, with the monoalkyl subset following the rank order methylone < butylone < pentylone. Among dialkyl analogues, dihexylone and, especially, diheptylone produced the strongest atrial-rate inhibition. AV conduction impairment was more heterogeneous but became prominent among higher-liability analogues, with diheptylone showing the lowest AV-block midpoint descriptor and complete lethality at 1000 µM. Locomotor profiling revealed predominantly hypoactive phenotypes, with sustained late-phase inhibition especially for dipentylone, dihexylone and diheptylone. Conclusions: Alkyl-chain extension and terminal amine substitution shaped cardiac and neurobehavioral toxicity in a structure-dependent manner. The zebrafish workflow provides a structure-oriented framework for prioritizing emerging methylenedioxy cathinones with comparatively higher functional cardiac liability. Full article

Robotic teleoperation is a core enabling technology spanning remote surgery, industrial inspection, and virtual-reality applications. Despite growing deployment, operator experience assessment still relies almost exclusively on post hoc subjective questionnaires, which preclude real-time monitoring and adaptive intervention. Here, the wearable IMU chain is considered not only as a command interface but also as an implicit sensing channel for operator state. We test whether end-effector kinematics generated by the IMU-to-robot mapping contain information about ten post-task workload and user-experience dimensions, comprising NASA-TLX-inspired workload scales together with usability, responsiveness, realism, intuitiveness, and perceived performance. A secondary analysis of a publicly available dataset (16 participants, 144 motion recordings, simulated UR10e arm) is conducted through a three-stage pipeline: bivariate correlation analysis (Pearson and Spearman), multivariate regression (10 model families, 16 feature-set combinations, Leave-One-Subject-Out validation), and binary classification (median-split). Statistical validity is assessed via 1000-permutation nested testing. Target-specific regression models reach $R^2\ge 0.50$ on seven out of 10 subjective dimensions, with a peak of $R^2=0.787$ for usability; permutation testing confirms significance for eight out of 10 targets. Binary classification achieves AUC $\ge 0.75$ on nine out of 10 targets, with three dimensions reaching perfect AUC. SHAP analysis identifies temporal irregularity and distributional shape descriptors as the dominant kinematic explanatory families. These results support the feasibility of kinematics-based inference of operator experience and provide an offline proof of concept toward future real-time adaptive teleoperation systems. Full article

Reference labels for crop-type mapping are frequently coarse, and administrative land-use registries such as Japan’s eMAF (electronic Map of Agriculture and Forestry) database routinely group agronomically distinct crops under broad, ambiguous categories. This study addresses that problem for the Nakasatsunai region of Hokkaido, Japan, by combining Sentinel-1 synthetic aperture radar (SAR) time series with field-level optical vegetation-index analysis in a modular processing pipeline. The principal novelty of the work is not the pipeline architecture alone but a three-step, Normalized Difference Vegetation Index (NDVI)-driven label-refinement procedure—automatic removal of non-growing or low-amplitude field samples, Euclidean k-means subclass discovery within each coarse label, and trajectory-based label correction—that converts noisy nine-class eMAF labels into a more reliable training set prior to classifier training. The feature set combines the Radar Vegetation Index (RVI), VV and VH backscatter, the γVH/γVV polarization ratio, and NDVI, together with temporal-shape descriptors (phenological timing, peak magnitude, amplitude, maximum slope, and area under the curve) derived from monthly growth trajectories over the 2018 growing season. A Random Forest classifier, together with a gradient-boosting comparator, is evaluated before and after preprocessing under stratified k-fold cross-validation. Across n = 1208 field samples spanning the nine eMAF classes, classification accuracy improved from an overall accuracy of 71.8% on the raw labels to 82.6% after the three-step refinement; Cohen’s kappa increased from 0.63 to 0.77. Correlation analysis indicates that γVH/γVV tracks field-level NDVI more consistently (mean Pearson r = 0.68) than RVI does (mean Pearson r = 0.43) across the eight classes with sufficient samples, motivating its use as a SAR-only phenological proxy; this comparison is extended to the polarimetric PRVI, DPSVI, and DpRVI indices in the discussion. The underlying 80–90% label-accuracy estimate is derived from NDVI trajectory inspection rather than independent, field-surveyed ground truth, and a factorial ablation is used to characterize, to the extent the cross-validated evidence allows, how much of the reported accuracy gain is attributable to label-error correction as opposed to NDVI–SAR feature fusion; both this attribution and the label-accuracy estimate itself are identified as priorities for field validation in future work. The proposed framework is intended to convert coarse, noisy crop labels into a structured and reliable dataset while producing interpretable, field-level phenological insight for agricultural monitoring. Full article

Reliable traits are needed for identification of tea (Camellia sinensis) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD–RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions. Full article

With the implementation of the Western Development Strategy and the Belt and Road Initiative, engineering activities on the Loess Plateau have expanded substantially in both scale and depth. Consequently, the disintegration of Lishi loess, which has received relatively limited attention, has become an increasingly important concern in relation to geological hazards and engineering stability. This study investigated the disintegration behavior of remolded Lishi loess specimens with different initial water contents under controlled dry-density conditions and developed a mathematical model to characterize the disintegration process. In addition, three-dimensional particle flow code (PFC3D) simulations were performed to provide a particle-scale mechanical interpretation of the observed behavior. The experimental results showed that the disintegration curves of the specimens exhibited a typical asymmetric S shape at all tested initial water contents. The Gompertz model provided a compact empirical description of these curves, with coefficients of determination ranging from 0.993 to 0.999. In the model, α denotes the upper asymptote of the disintegration curve, λ characterizes the growth-rate behavior, and β represents the characteristic time associated with the inflection point. These parameters should be interpreted as empirical descriptors of the disintegration process rather than direct measures of the microscopic properties of loess. Although the model closely reproduced the experimental curves, further validation using independent datasets is required before it can be applied predictively to other specimens or test conditions. The PFC3D simulations provided an equivalent mesoscopic representation of contact-network weakening, bond breakage, and progressive particle detachment at different initial water contents. The simulated evolution qualitatively reproduced the progression from boundary-particle detachment to the gradual loss of specimen integrity and final particle accumulation. These findings characterize the water-content-dependent disintegration behavior of remolded Lishi loess and provide a preliminary basis for understanding the water sensitivity of disturbed or reworked Lishi loess in engineering applications. Full article

The mechanical behavior of calcareous sand differs significantly from that of conventional quartz sands, leading to challenges in offshore geotechnical engineering applications. This distinctive response is closely associated with the complex three-dimensional morphology of calcareous sand particles. However, existing characterization methods are often limited to specific morphological scales and cannot fully describe the multiscale complexity of particle shape. To address this issue, this study performs a comparative morphological analysis of calcareous sand (CS) and Fujian quartz sand (FS) across three particle-size ranges by integrating X-ray micro-computed tomography with spherical harmonic (SH) analysis. Individual particles are reconstructed using SH representation, and a multiscale morphology characterization framework is developed by decomposing particle morphology into three distinct scale levels: large-scale form represented by sphericity, medium-scale angular features represented by roundness, and small-scale surface texture represented by roughness. The results demonstrate that CS and FS exhibit distinct morphological characteristics across different scales, while particle-size effects remain less pronounced within the investigated range. Furthermore, the SH amplitude spectra reveal statistically self-similar characteristics of particle surfaces, allowing the fractal dimension to be correlated with multiscale morphological descriptors. The proposed framework provides a quantitative description of complex particle morphology across multiple scales and may facilitate further investigations of particle-scale mechanical behavior in granular materials. Full article

The spatial pattern of building damage produced by an earthquake carries information that classical building-by-building vulnerability indices cannot capture. This study presents one of the first frameworks to use Topological Data Analysis (TDA), a set of methods that quantify the “shape” of data, to characterise the spatial topology of seismic damage across an urban building inventory. Using the geo-referenced centroids of buildings as a point cloud, a sequence of connectivity graphs (a Vietoris–Rips filtration) is built at increasing distance scales, and persistent homology is used to track which spatial features appear and disappear. From this we extract four interpretable descriptors: Betti numbers (the numbers of connected building clusters and of enclosed gaps), persistence entropy (a measure of how disordered the damage pattern is), total persistence (the combined lifespan of all topological features), and the Wasserstein-2 distance (how far the post-earthquake pattern has moved from the intact pre-earthquake pattern). These descriptors form a physics-informed feature vector that is used to predict the building-cluster damage state. The developed framework was trained, tested, and validated on 1490 buildings over seven post-earthquake scenarios. Lognormal fragility parameters were estimated with maximum likelihood estimation, and an Artificial Neural Network (ANN) and a Random Forest (RF) were retrained on the same 593-building training dataset for comparison. On the 847-building benchmark, the TDA framework reached 93.3% accuracy (95% CI: 91.4–94.9%), F1 = 0.921 (0.902–0.940), and AUC = 0.933, using a stratified 70/15/15 split (training = 593, validation = 127, test = 127). This is a 6.0-percentage-point gain over the retrained ANN and a 12.1-percentage-point gain over the HAZUS-MH index (McNemar p = 0.017). Damage was recorded on the six EMS-98 states DS0–DS5, with DS4 and DS5 merged into a single class to give a five-class taxonomy, and building-type-specific inter-storey drift ratio thresholds were validated against EN 1998-3 (Eurocode 8 Part 3). Exact Rips computation is practical only for clusters up to about 2000 buildings; for larger populations, a CGAL (Computational Geometry Algorithms Library)-based sparse approximation with O(N log N) cost is recommended. It seems that the topological descriptions of the damage field may provide predictive information above and beyond that given by density and ground motion intensity and offer a reproducible tool for post-earthquake screening. Full article

Site investigation is costly, slow, and locally destructive, with boreholes spaced by budget rather than by ground variability. Portable electrical resistivity offers a rapid, low-disturbance alternative, but correlations built on absolute resistivity transfer poorly between locations. This study asks whether the limitation lies in the measurement or in its representation. In four boreholes in tropical sandy soils of the Khorat Plateau, Thailand (601–1729 m apart), Wenner-array resistivity was measured at 0.5 m depth increments alongside standard penetration tests (SPT) and index testing, yielding 63 paired observations. Each smoothed log-resistivity profile was described by three within-borehole quantities: the relative electrical state (x), its squared deviation (x²), and the transition intensity (g). SPT-N was predicted by linear regression under leave-one-borehole-out cross-validation. Absolute resistivity predicted SPT-N poorly (R² = 0.133) and added nothing to depth alone (0.597 versus 0.617); the descriptors raised cross-borehole performance to R² = 0.733 (RMSE 13.5 versus 16.2 blows), kept an advantage without smoothing (0.677), and laboratory indices transferred worst (−0.262). With only four boreholes, the gain is not yet statistically definitive (bootstrap 95% CI −0.04 to +0.26), so the study is presented as a proof of concept: the transferable information in a resistivity profile appears to lie in its shape rather than its magnitude, supporting borehole targeting and more resource-efficient site investigation. Full article

Assessment of left atrial remodeling in atrial fibrillation (AF) has traditionally relied on anteroposterior diameter, left atrial volume (LAV), and indexed left atrial volume (LAVI). Although these measures remain clinically useful, they reduce a complex, asymmetric, and anatomically constrained chamber to scalar descriptors and therefore cannot fully capture the spatial substrate that underlies AF persistence, thromboembolic risk, or arrhythmia recurrence after catheter ablation. Three-dimensional left atrial reconstruction provides a more refined framework by preserving chamber shape, regional deformation, pulmonary vein (PV) orientation, left atrial appendage (LAA) geometry, posterior wall and roof configuration, left lateral ridge anatomy, wall-thickness heterogeneity, and computational surface features. In this review, we examine how three-dimensional left atrial geometry can extend conventional remodeling assessment from measurement of atrial size toward imaging-based substrate characterization. We discuss the relative strengths and limitations of computed tomography (CT), cardiovascular magnetic resonance (CMR), three-dimensional echocardiography, and electroanatomic mapping (EAM), and summarize key geometry-derived metrics, including LAV, LAVI, left atrial sphericity, asymmetry index, atrial eccentricity index, PV anatomy, LAA morphology, posterior wall geometry, wall thickness, radiomics, and artificial intelligence (AI)-derived shape descriptors. We further synthesize evidence linking geometric remodeling with atrial cardiomyopathy, mechanical dysfunction, fibrosis, low-voltage substrate, and catheter ablation outcomes. The clinical relevance of three-dimensional left atrial geometry may be further redefined by pulsed field ablation (PFA), whose non-thermal lesion biology and tissue selectivity may modify predictors of recurrence established in radiofrequency and cryoballoon cohorts. Finally, we outline the need for standardized segmentation, harmonized metric definitions, prospective multicenter validation, and integration with AI, digital twin modeling, biomarkers, EAM data, and wearable-derived AF burden. Three-dimensional left atrial geometry is not yet a standalone determinant of ablation strategy, but it may become a central component of individualized atrial phenotyping and rhythm-control decision-making. Full article