Search Results (143)

Expansive clays exhibit shrink–swell behavior driven by microscale physicochemical interactions that are not fully captured by conventional macroscopic descriptors. This study presents a quantitative framework for evaluating microstructural evolution in expansive clays using Environmental Scanning Electron Microscopy (ESEM) combined with fractal dimension and lacunarity analysis under controlled suction paths. ESEM micrographs were collected along primary drying and secondary wetting paths across multiple magnification scales. Fractal dimension quantifies surface complexity, while lacunarity characterizes pore distribution and clustering. Fractal dimension increases with magnification and suction, reflecting greater exposure of particle surfaces as pore water is removed. Lacunarity decreases with magnification and shows soil-dependent trends with suction, indicating changes in pore heterogeneity. Hysteresis in both metrics reveals irreversible microstructural rearrangement associated with particle aggregation and fluid redistribution. These results demonstrate that fractal dimension and lacunarity provide complementary descriptors of soil fabric and establish a quantitative link between microstructure and suction-driven behavior in expansive clays. Full article

Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) provides an endpoint for treatment evaluation in breast cancer. Multi-sequence breast MRI can support pCR prediction, but routine examinations may lack usable T1-weighted or T2-weighted sequences. Many models merge radiomic and deep features by concatenation, leaving the interaction between handcrafted descriptors and learned representations weakly specified. We developed a radiomics-guided framework for pCR prediction from multi-sequence breast MRI. The model uses a multi-branch 2.5D encoder for sequence-specific features, radiomics-guided channel recalibration, and masked token fusion to aggregate available sequence tokens. We evaluated the framework on 157 patients from the I-SPY1 Trial cohort with patient-level five-fold cross-validation, fixed sequence-combination analysis, and slice-window sensitivity analysis. The full model achieved 78.4% accuracy and 0.809 AUC, compared with 75.8% accuracy and 0.788 AUC for the strongest channel-concatenation baseline. In this cohort, radiomics-guided multi-sequence learning was feasible, with external validation required before clinical interpretation. Full article

Whole-slide imaging has transformed histopathology into a data-rich domain; however, many computational pathology models encode tissue morphology within latent representations, limiting interpretability, reproducibility, and generalization. This review positions histomics as an intermediate phenotype representation layer linking histological images with downstream clinical inference through structured descriptors of tissue morphology, spatial organization, and tissue architecture. Unlike prior reviews focused primarily on feature extraction or predictive performance, the study adopts a representation-centric perspective of histomics. A taxonomy of histomic features across biological scales is presented, and artificial intelligence frameworks, including machine learning, deep learning, weakly supervised learning, and multimodal approaches, are systematically examined. Key challenges, including segmentation dependence, feature instability, aggregation variability, and domain shift, are critically analyzed alongside emerging developments in foundation models, representation learning, and multimodal pathology. The review provides a unified framework for understanding histomic representations and identifies future directions for developing robust, interpretable, and generalizable computational pathology systems. Full article

LiDAR-based place recognition is critical for long-term autonomous navigation in Global Navigation Satellite System (GNSS)-denied environments, yet existing methods struggle to balance accuracy and efficiency under substantial yaw rotations. This paper proposes a robust framework based on a multi-channel polar bird’s-eye-view (BEV) representation. Under yaw-dominated revisits, the polar BEV image transforms yaw rotation into cyclic column shifts, providing a useful structural prior for rotation-equivariant feature extraction. Raw point clouds are projected onto polar BEV grids encoding density, height, and intensity. A rotation-equivariant feature extractor comprising a Radial Compression Module and a rotation-equivariant Transformer module captures long-range azimuthal dependencies via Conditional Positional Encoding and Circular Relative-Position Bias. The equivariant features are aggregated by NetVLAD into a compact global descriptor, trained end-to-end with a hard-example mining triplet loss. Extensive experiments on the public KITTI and NCLT datasets, as well as our self-constructed LiDAR Place Recognition Revisit (LPRR) dataset, demonstrate competitive performance on KITTI and superior performance on NCLT and LPRR among the compared methods. The proposed framework achieves a favorable trade-off between performance and computational cost, and shows promising cross-dataset generalization on the evaluated NCLT and LPRR datasets without fine-tuning. Full article

Enterprise infrastructure operators face a critical challenge in prioritizing post-quantum migration, as quantum-related risk is not uniformly distributed across data in transit, in use, and at rest. Existing assessments rely on system-level evaluations or protocol-specific analyses, which do not capture the heterogeneity of exposure across infrastructure layers. This paper extends the Quantum-Adjusted Risk Scoring (QARS) model introduced in into an evidence-based, layer-specific framework that evaluates in-transit, in-use, and at-rest data separately. QARS applies a unified five-factor scoring framework separately to each data state and introduces a quantum-vulnerability attenuation mechanism grounded in Grover-bounded residual security that prevents overstating urgency for non-Shor-vulnerable symmetric protection. Observable host-level evidence determines the binary and ratio descriptors used by the model, while the fixed affine mapping coefficients are treated as transparent semi-quantitative calibration parameters. These coefficients are documented separately and subjected to coefficient-level sensitivity analysis to evaluate whether the reported layer ordering depends on their nominal values. The model is demonstrated through an illustrative controlled experiment using real infrastructure observations. Strengthening storage protection reduces the aggregate system risk from 0.707 (high) to 0.414 (moderate), a 41.5% reduction. However, the maximum-layer score remains high (0.657), indicating that the transport layer continues to dominate migration urgency. Sensitivity analysis confirms that the dominance of the transport layer is stable under wide perturbations of the calibration parameters. These findings demonstrate that risk reduction in one layer does not eliminate overall exposure but shifts the dominant vulnerability. By distinguishing between overall system posture and the most critical remediation priority, QARS supports infrastructure operators in identifying high-risk components and planning structured, evidence-based post-quantum migration. Full article

Sodium caseinate (SC)-stabilized emulsions are highly susceptible to flocculation and phase separation near the protein isoelectric point (pI), limiting their application in acidified food systems. In this study, high acyl gellan gum (HA) was introduced to construct pH-responsive protein–polysaccharide complexes to modulate the interfacial assembly and stability of SC emulsions. Results demonstrated that HA interacts with SC primarily through electrostatic attraction and multi-site hydrogen bonding. This interaction induces protein conformational rearrangement and, as evidenced by combined structural and computational analyses, facilitates the assembly of a denser, interconnected composite network. The formation of HA–SC complexes significantly enhanced interfacial adsorption, reduced oil–water interfacial tension. Rheological and microrheological analyses revealed the composite system formed an elasticity-dominated weak gel network, restricting droplet mobility and suppressing aggregation. Consequently, HA–SC emulsions exhibited markedly improved pH tolerance and physical stability compared to SC-only emulsions, particularly near the pI, evidenced by reduced droplet size, lower Turbiscan stability indices, and more homogeneous microstructures. Crucially, utilizing a well-defined mechanistic model of fixed HA and SC concentrations, this study quantitatively links molecular interactions, interfacial network reconstruction, and macroscopic emulsion stability across a broad pH continuum. Rank-correlation analysis of pH-resolved descriptors shows the molecular charge state co-varies monotonically with the interfacial network and macroscopic stability, and is inversely coupled to droplet mobility. These findings provide new insights into protein–polysaccharide interfacial engineering, establishing the essential physical-stability foundation for the future rational design of acid-tolerant food emulsions and functional delivery systems. Full article

We develop a finite-resolution empirical framework for applying nonnegative Mages–Anastasiadi–Rohner partial information decomposition (MAR-PID) to continuous and non-binary discrete variables. The variables are represented by recursive quantile binarization. This provides a balanced binary-tree representation at each finite depth. MAR-PID is then applied to binary target components, and the resulting atoms are aggregated back to the original target and source variables. The construction gives nonnegative target-relative information summaries for observed variables up to the chosen empirical resolution. The pipeline consists of conditional channel estimation, bit-level MAR-PID computation, projection of source atoms to the original variables, and aggregation of the resulting information contributions. The obtained quantities are empirical estimates of finite-resolution population quantities. XOR and mixed redundancy–synergy examples show how the representation separates informational mechanisms, which signed interaction-information summaries can conflate. We focus here on the finite-resolution MAR-PID construction and its information-level quantities. Downstream summaries, such as resolution-normalized PID-dimension descriptors and thresholded support-based degrees of freedom, are indicated but left for separate work. Full article

Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an ontology-aware sparse Perceiver framework for interpretable, multi-horizon retrospective forecasting of DBM abundance from historical pest records and rich meteorological descriptors. Each feature-lag value is encoded as a token carrying feature identity, ecological group, descriptor type, lag position, and seasonal information; in the rich setting, 138 descriptors across 12 months yield 1656 tokens per sample. Sparse cross-attention compresses these tokens into a compact latent representation, while horizon-specific queries produce one- to four-month-ahead forecasts. Attention tensors and a common-plus-residual branch are aggregated into feature-, group-, descriptor-, lag-, horizon-, and residual-level explanations. Using DBM records from Huiyang and Shantou, Guangdong, MeteoSCOPE achieved the strongest overall retrospective performance, with robust gains at Shantou and metric-dependent gains at Huiyang. The explanations identified pest history as the leading attended group at both sites and surfaced site-specific secondary attributions for soil moisture, weather state, wind, soil temperature, and humidity, treated as model evidence rather than causal ecological effects and corroborated by independent occlusion and KernelSHAP analyses. Strict zero-shot cross-site transfer degrades substantially, so prospective field validation and broader multi-site testing remain required before operational deployment. MeteoSCOPE thus provides a transferable methodological framework (not a deployable forecaster) for interpretable analysis of high-dimensional agricultural time series. Full article

Early stopping is a standard form of implicit regularization in neural sequence models, but criteria based solely on validation loss can become unstable or weakly informative in noisy, non-stationary, or weakly separated regimes. We propose Symbolic Early Stopping (SES), a representation-aware hybrid stopping criterion that monitors the evolution of validation hidden-state organization during training. At each epoch, SES constructs a Mapper-based symbolic abstraction of hidden representations extracted from a fixed monitored layer, transforms latent trajectories into symbol sequences, and summarizes them through a compact set of symbolic–dynamic descriptors capturing sequential complexity, transition uncertainty, and geometric dispersion. These descriptors are aggregated into a single symbolic stability score, which is combined with validation-loss monitoring to detect convergence of the learned representation. We evaluate SES on recurrent, bidirectional recurrent, and encoder-only Transformer architectures across multiple time-series regimes with different levels of structural regularity and noise. The results indicate that SES frequently terminates training substantially earlier than conservative loss-based baselines while preserving a competitive quality–efficiency trade-off relative to oracle validation-based stopping. Robustness experiments under additive input noise show that the symbolic monitoring signal remains informative under moderate perturbations, although its advantage is not uniform across all datasets and model classes. A layer-wise analysis further suggests that useful stopping signals may emerge before the final validation curve fully stabilizes, reflecting earlier organization of latent representations. Overall, SES provides an interpretable and computationally tractable framework for representation-level early stopping in neural sequence modeling. Full article

Accurate anatomical landmark detection is important for orthodontic analysis, surgical planning, and morphometric measurement, but fully supervised methods usually require large expert-annotated datasets. This work studies a one-shot setting, where only a single annotated template image is used for training. We propose a foundation-model-based landmark detection framework using a frozen DINO Vision Transformer (ViT) backbone. The proposed framework integrates three complementary components: a Multi-Layer Multi-Facet (MLMF) module that adaptively fuses key and value features from multiple ViT layers through global source-wise reweighting; a Mamba-Based Long-Range Context Aggregation (MLCA) module that injects global anatomical context into fused patch descriptors with linear complexity; and a Topology-Constrained Graph Refinement (TCGR) module that refines the predicted landmark configuration using anatomical graph constraints. Experiments on the Cephalometric dataset and the Hand X-ray dataset demonstrate that the proposed method achieves strong performance. Overall, the results show that jointly exploiting multi-source foundation-model representations, efficient long-range context aggregation, and topology-aware refinement improves annotation-efficient anatomical landmark detection. Full article

This research presents a robust multivariate statistical framework for the non-destructive prediction of geomechanical state parameters in quartz-rich coastal sands through acoustic emission (AE) monitoring. Granular media under symmetrical compressive stress function as complex natural systems, where microscopic energy dissipation—arising from particle rearrangement and grain microcracking—radiates as transient elastic waves. To decode these stochastic processes, 24 confined uniaxial compression tests were conducted across diverse soil typologies and moisture contents (0–12%). A high-dimensional data matrix was constructed, integrating 13 geotechnical variables with 48 acoustic descriptors formulated through three distinct temporal aggregations: stage-specific, history average and weighted history average. The statistical results identify the logarithmic effective vertical stress (${\log }_{10}\left({{\mathsf{\sigma }}^{\prime }}_{\mathrm{v}}\right)$) and the cumulative axial strain ($\mathsf{\epsilon }$) as the most significant geomechanical drivers, exhibiting Pearson correlation coefficients $\left|p\right|$ ≥ 0.85 with acoustic activity. In the acoustic domain, the analysis reveals that Signal Strength (${\mathrm{S}}_{\mathrm{s}}$) and cumulative energy ($\mathrm{E}$) flux are the most reliable predictors for volumetric deformation, while the amplitude ($\mathrm{A}$), b-value ($\mathrm{b}$), and average frequency ($\mathrm{F}$) emerge as critical indicators for identifying the transition between spatial rearrangement and the onset of grain fragmentation. Furthermore, the inclusion of dimensionless parameters, particularly earliness ($\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{l}$), enhances model stability by standardising waveform symmetry across varying stress regimes. High-order polynomial regression models (up to the third degree) were derived, demonstrating that the statistical complexity of acoustic signatures allows for the high-fidelity inference of the soil matrix’s initial and state parameters. This methodology establishes a unified mathematical architecture for the in situ characterisation of granular skeletons, balancing computational efficiency with predictive power in intricate geological domains. Full article

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems. Full article

Surface urban heat island intensity is shaped not only by land-cover composition but also by the spatial heterogeneity of urban surfaces. This study evaluates whether Shannon entropy derived from Dynamic World class probabilities can serve as a robust indicator of pointwise SUHI intensity across seven major Romanian cities. Summer daytime Landsat 8/9 observations for 2021–2025 were harmonized into multi-year median land surface temperature composites, while Dynamic World probabilities were used to compute normalized Shannon entropy at 90, 150, 300, and 600 m aggregation windows. SUHI was defined relative to a rural reference whose delineation was examined through a multi-parameter sensitivity analysis, after which entropy–SUHI relationships were modeled using generalized additive models with and without an additional spatial smooth. Across all seven cities, the entropy–SUHI relationship was consistently negative, with higher entropy values tending to be associated with lower local thermal excess. The best-supported models were usually obtained at 150 m and more broadly within the 150–300 m range, while very coarse aggregation weakened performance. Spatially adjusted models explained 57.2–82.4% of SUHI deviance, showing that entropy is consistently associated with a stable but partial component of intra-urban thermal variability. Alternative tied-best rural delineations mainly shifted the SUHI baseline and left the fitted entropy response essentially unchanged. Our findings support probability-based entropy as a reliable, scale-sensitive descriptor of urban surface mixture relevant to intra-urban thermal patterning across diverse geographical and climatic settings. Full article

This paper investigates solar energy production forecasting at a monthly temporal resolution using a pooled neural network framework applied to the Chikalov photovoltaic systems in southwestern Bulgaria. The study considers several related PV installations with unequal time-series lengths and formulates the forecasting task as one-step-ahead prediction of the next monthly total energy yield, measured in kWh, in a global pooled setting. Two complementary neural architectures are compared: a multilayer perceptron (MLP), which serves as a nonlinear feed-forward benchmark based on lagged observations and seasonal descriptors, and a gated recurrent unit (GRU), which explicitly models sequential temporal dependence. In both cases, seasonality is represented through cyclical calendar encodings, while model selection is performed by chronological hyperparameter search using a separate validation block. Forecast accuracy is assessed by RMSE, MAE, coefficient of determination (R²), MAPE, and sMAPE, and uncertainty is quantified through validation residual prediction intervals. The results show that the MLP achieves stronger validation performance, whereas the GRU provides better final out-of-sample generalization after refitting on the combined training and validation data. For both architectures, the best configurations are obtained with a 12-month input horizon, indicating that one full annual cycle contains the most informative memory for forecasting monthly aggregated photovoltaic energy yield in the considered dataset. After refitting on the combined training and validation data, the GRU achieved the best final out-of-sample performance, with RMSE = 296.38 kWh, MAE = 213.16 kWh, R² = 0.9231, MAPE = 7.52%, and sMAPE = 7.49%. Overall, the findings demonstrate that pooled neural modeling is an effective framework for monthly PV production forecasting and can provide practically useful support for sustainable energy planning, monitoring, and optimization. Full article

Amyloid-β (Aβ) aggregation is a central mechanistic feature of Alzheimer’s disease, involving heterogeneous conformational ensembles that evolve through monomeric, oligomeric, and fibrillar states. Understanding how small molecules modulate these state-dependent processes remains a major challenge in medicinal chemistry. This review examines the molecular mechanisms by which (-)-epigallocatechin-3-gallate (EGCG) perturbs Aβ aggregation, with a focus on insights derived from molecular dynamics (MD) simulations integrated with experimental data. MD studies employing structural, dynamical, and interaction-based descriptors (e.g., β-sheet content, contact maps, and salt bridge persistence) reveal that EGCG acts as a state-dependent modulator: it redistributes monomeric ensembles by masking aggregation-prone regions, induces topology switching in oligomers that suppresses seeding competence, and destabilizes protofibrillar β-sheet networks through interfacial and node-targeting interactions. Methodological analysis highlights the importance of force field selection, sampling depth, and aggregate model dependence, leading to a hierarchy of mechanistic confidence that distinguishes well-supported trends from model-specific observations. From a medicinal chemistry perspective, EGCG is best interpreted as a mechanistic probe rather than as a lead compound, informing the design of biostable modulators through principles such as bioisosteric replacement, topology control, and interfacial targeting. Collectively, this work provides a framework for translating the state-dependent aggregation mechanisms into rational therapeutic strategies. Full article