Search Results (708)

Semantic segmentation of 3D urban meshes is a fundamental task for intelligent urban scene understanding. However, conventional methods face severe computational efficiency bottlenecks in large-scale scenarios. Meanwhile, state space models (SSMs) with inherent linear time complexity offer an efficient solution, but existing SSM-based approaches fail to fully exploit the intrinsic textural properties of 3D meshes, resulting in degraded feature discriminability and inferior performance on fine-grained structures and small urban objects. To address these issues, we propose TOTMSeg, a texture-aware octree-based Transformer-Mamba framework for large-scale urban mesh semantic segmentation. It follows a coherent pipeline of texture feature extraction, octree-guided local feature aggregation, and global semantic feature modeling. Specifically, we design a Point-template-based Texture Sampling (PTS) strategy and integrate a lightweight Texture Triangular Convolution (TTC) module to extract fine-grained texture details, effectively alleviating the texture information deficiency in conventional point cloud-based methods. Extensive experiments on two large-scale public datasets demonstrate that TOTMSeg outperforms state-of-the-art (SOTA) baselines on main evaluation metrics, achieves notable performance gains on fine-grained structures and small-object categories, and preserves the high computational efficiency of SSM-based architectures. Full article

This study evaluated unconditional synthetic mammography generation with Deep Convolutional Generative Adversarial Networks (DCGANs). The dataset comprised 690 anonymized mammograms, equally distributed across BI-RADS 1–6 (115 images/category). All images were standardized to 512 × 512 pixels, reoriented to a common right-breast view, and prepared in two input domains: grayscale and color-mapped intensity encoding. Two models were compared: a standard DCGAN trained directly at 512 × 512, and a progressive DCGAN trained through discrete stages from 8 × 8 to 512 × 512. Training used PyTorch (Python 3.10), latent dimension = 100, Adam, learning rate = 2 × 10⁻⁴, β1 = 0.5, binary cross-entropy loss, and batch size = 1. Both models learned the low-frequency mammographic manifold, generating breast-like silhouettes and heterogeneous internal intensity distributions. However, the progressive DCGAN produced smoother contours, more coherent internal organization, and fewer grid/line artifacts than the standard model. Grayscale-only training showed weak learning, whereas the color-mapped domain improved structural recovery, although this advantage should be interpreted as computational rather than clinical. Late-epoch checkpoint analysis showed a structurally invariant generator with 43 tensors and 19,531,127 parameters; from epochs 89–100, relative checkpoint drift remained within 0.294–0.317%, with epochs 93–96 showing the most stable regime. Despite these advances, generated images still exhibited background speckle, coarse mottled texture, extra-anatomical bright structures, and limited diversity. Thus, the results support feasibility of synthetic mammography generation, but not yet clinically reliable synthetic data for direct AI training. The generated images should presently be regarded as exploratory complementary data pending expert, metric-based, and downstream validation. Full article

Industrial production and logistics dashboards increasingly aggregate real-time data but remain monitoring tools with limited support for action prioritization, explainable recommendations, adaptive response, and organizational learning. This study proposes the Intelligent Acuity-Oriented Operations Dashboard (IAOOD), a conceptual, design-oriented framework that repositions operational dashboards toward active decision support. An exploratory user-preference study (10 interviews followed by a questionnaire with 41 professionals) provides preliminary evidence of the perceived relevance of the advanced capabilities to be added to the framework. Another evidence of relevance is a feature-availability comparison against selected industrial and academic dashboard approaches. This comparison is intended to assess conceptual coverage of key dashboard capabilities, not to demonstrate real-world performance improvement or operational superiority. The leading scientific contribution is a coherent integration of several recent key capabilities into a single operational design logic. These capabilities are: (1) closed-loop prediction–response–learning, (2) adaptive interfaces, (3) unified hard and soft metrics, (4) explainable AI, and (5) acuity classification. The second contribution is the empirical assessment of dashboard user preferences and the acuity dashboard hierarchy and its four-level acuity classification (urgent, acute, warning, weak link). A classification that organizes events by severity, urgency, confidence, impact, and transparent justification, enabling prioritized and governance-aligned interventions. Methodologically, IAOOD is developed through a structured conceptual framework-development process and illustrated via dashboard mock-ups of the proposed hierarchical structure. The study contributes architectural elements, evaluation dimensions, and six testable propositions linking framework mechanisms to expected outcomes (response timeliness, decision trust, recurrence reduction, strategic alignment, cognitive effectiveness, situational awareness). IAOOD is positioned as an integrative benchmark whose scientific value depends on subsequent empirical testing through industrial pilots, controlled user studies, and longitudinal KPI analysis. Full article

Interferometric synthetic aperture radar (InSAR) maps ground motion in permafrost regions, but deformation does not uniquely identify the underlying process. This systematic review assessed how far the field has progressed from deformation detection toward validated process inference. A Web of Science Core Collection search on 16 July 2026 returned 409 records; 262 publications met the core scope, and 175 contributed 372 coherent scientific results and 727 companion metrics. Research expanded rapidly after 2020 and diversified from seasonal mapping toward active-layer thickness (ALT), ground ice, hydrology, infrastructure, and predictive modelling. Evidence maturity nevertheless declined along the inference chain. Descriptive deformation/quality and model/algorithm results comprised 58.3% of the evidence, whereas direct validation against field observations, global navigation satellite system (GNSS) measurements, or levelling comprised 4.6%. Only 12.4% of results reported a defensible analytic sample size and 33.9% provided usable uncertainty. ALT evaluations using probing, ground-penetrating radar (GPR), or model references estimated non-equivalent quantities and could not support one pooled accuracy measure. The radar line-of-sight (LOS) displacement is also a projected, non-unique response whose process interpretation depends on motion geometry and thermal, hydrological, and mechanical assumptions. No evidence family met the prespecified requirements for global meta-analysis. Progress toward cumulative inference requires explicit estimands, matched independent validation, uncertainty propagation, and transparent dataset dependence. Full article

Seismic sensors deployed in land acquisition record wavefields that are strongly distorted by near-surface heterogeneity, which introduces trace-specific, frequency-dependent phase perturbations that persist even after advanced time processing. These distortions are more pronounced for dense single sensor acquisition, where individual sensor coupling, local site conditions, and receiver-level near-surface variability drive phase behavior that differs from one sensor to the next. Conventional processing relies primarily on surface-consistent deconvolution, which targets long- to mid-wavelength phase variability under the highly simplified assumption of surface consistency, equalizing large-scale trends and taming variability through overdetermination across many sensors. However, this approximation is inherently unable to correct localized, non-surface-consistent phase distortions, and its effectiveness further degrades when such effects dominate, as is often the case for modern high-density single-sensor data. A separate and equally important limitation is that conventional workflows provide no direct, quantitative per sensor measure of phase reliability, that is, trace-to-trace phase coherence. Phase quality is therefore assessed only indirectly, typically through amplitude behavior or visual inspection, leaving residual phase disorder largely undiagnosed. We introduce phase variance as a seismic quality-control attribute for seismic sensor-recorded data, by treating seismic phases as circular random variables and analyzing local trace ensembles using circular statistics. This data-driven measure quantifies localized phase dispersion without phase unwrapping, enabling analysis of local phase trends and sensor-to-sensor fluctuations without global assumptions or wavelet models. Phase variance provides frequency-by-frequency classification of the data, ranging from coherent signal behavior to fully randomized, noise-dominated phase. Synthetic tests confirm that phase variance reliably captures imposed phase perturbations and their frequency dependence. Application of phase variance analysis to field prestack land data shows that conventional processing reduces phase variability primarily in the low-to-intermediate frequency range and struggles within the noise cone, while the highest and lowest frequencies often show little improvement in phase coherence. Phase variance operates automatically over the full prestack volume, from shallow to deep, and frequency by frequency, providing a consistent, human-independent metric for defining effective bandwidth based on phase coherence and supporting phase-sensitive workflows such as AVO, migration, and full-waveform inversion. Full article

Multimodal large language models (MLLMs) and vision–language models (VLMs) have rapidly entered medicine, demonstrating promising performance in clinical reasoning, radiology report generation, and visual question answering (VQA). However, many current multimodal architectures and pretrained visual backbones remain fundamentally rooted in two-dimensional (2D) image processing, even though major clinical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and echocardiography, are inherently volumetric or temporal. This narrative review examines the transition from 2D vision–language systems to volumetric multimodal AI, tracing the evolution from 2D and slice- or projection-based approaches through sequential and video-like methods to three-dimensional (3D) vision foundation models and native 3D VLMs/MLLMs. We examine their representational and computational trade-offs, evaluation gaps, and clinically grounded benchmarks. Approaches differ substantially in how they represent and preserve 3D information. Slice- and projection-based methods offer computational efficiency but may discard spatial context, whereas sequential and native volumetric approaches increasingly model relationships across the full imaging study. Recent 3D foundation models and multimodal systems demonstrate the feasibility of reusable volumetric representations and language-enabled 3D image interpretation, but face barriers in computational cost, training-data scale, evaluation methodology, and clinical reliability. Only 53% of Med-Gemini-3D reports were judged clinically acceptable, and natural language processing (NLP) metrics such as BLEU and ROUGE correlate poorly with diagnostic correctness. True 3D multimodal medical intelligence remains in its early stages. Future progress requires efficient volumetric representation strategies, clinically grounded evaluation frameworks, standardized benchmarks, and robust cross-institution validation. Full article

Background/Objectives: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF–target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)–target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. Methods: We performed two complementary analyses: first, testing whether curated TF–target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF–gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. Results: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF–target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision–recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. Conclusions: Single-cell expression associations can provide useful but limited statistical clues for TF–target regulation and should be interpreted as complementary rather than definitive regulatory evidence. Full article

Background/Objectives: Wide-field swept-source optical coherence tomography angiography (WF-SS-OCTA) enables non-invasive, layer-specific retinal microvascular assessment. We explored treatment-associated microvascular change in acute leukemia and its concurrent association with post-induction response. Methods: In this prospective single-center cohort (ChiCTR2300077558), 111 newly diagnosed adults (58 AML, 53 ALL) and 40 healthy controls underwent pre-induction 16 × 16 mm WF-SS-OCTA; 77 were re-imaged at first response assessment. Response (complete remission (CR)/CRi vs. partial/no response (PR/NR)) was adjudicated independently of OCTA. The focal exploratory outcome was deep capillary plexus vessel density change (ΔDCP-VD). This outcome was designated after completion of data collection, and all analyses are exploratory and hypothesis-generating. Results: DCP-VD showed an AML < ALL < control baseline gradient (42.49 ± 3.15%, 43.45 ± 2.89%, 44.27 ± 2.78%; ANOVA p = 0.014), and 10 of 20 baseline metrics survived false-discovery-rate correction. ΔDCP-VD was higher in CR/CRi than PR/NR (1.50 (1.00, 2.00) vs. 0.50 ± 0.83 percentage points; difference 0.97, 95% CI 0.57–1.37; p < 0.01) and correlated with hemoglobin change (r_s = +0.546, p < 0.01); in-sample discrimination gave an AUC of 0.799 (95% CI 0.674–0.907). Conclusions: ΔDCP-VD differed by concurrent response and tracked hemoglobin change; findings establish no causality, prediction, or clinical threshold and require independent prospective validation. Full article

Productivity evaluation in industrial software projects is still predominantly based on aggregate Function Point-to-effort ratios. Such an approach overlooks the internal structural and functional heterogeneity of software projects, potentially leading to misleading comparisons between systems characterized by similar functional sizes but markedly different structural complexities. This work investigates the hypothesis that projects with similar functional structures, within the same application domain, tend to exhibit comparable productivity behavior. For this purpose, an adaptive and structurally informed productivity assessment approach is proposed, based on a Structural Productivity Index that integrates domain-specific productivity information with project functional composition. The proposed model is validated through real-world and synthetic project datasets, combined with consistency indicators and clustering-based analyses to examine its behavior under different conditions and its capability to distinguish regular productivity patterns from structurally anomalous projects. Experimental results suggest that the proposed adaptive productivity modeling approach provides a more coherent interpretation of productivity estimates compared with traditional productivity metrics. The proposed structural representation provides an interpretable basis for future AI-based productivity analytics. Full article

The 2021 eruption of Cumbre Vieja on La Palma, Canary Islands, produced extensive lava flows and major landscape transformation, providing an opportunity to evaluate remote sensing approaches for mapping the extent of an emplaced lava flow. This study assessed direct spectral, classic Rao’s quadratic entropy (RaoQ), and multidimensional RaoQ approaches using satellite observations acquired before and after the eruption. Optical, radar, thermal infrared, and night-time radiance datasets were evaluated within a common change-detection framework implemented in Google Earth Engine. Difference maps were converted into binary change maps using a histogram-based thresholding procedure calibrated on the reference delineation and evaluated against the Copernicus Emergency Management Service (CEMS) lava-flow reference and no-change validation areas derived from ESA WorldCover using multiple accuracy metrics. Because the change reference is the final CEMS lava-flow delineation and the no-change samples lie outside a 100 m buffer around it, the accuracy figures reported here quantify the mapping of lava-flow extent and not of other eruption-related effects such as ash deposition or vegetation damage beyond the flow margins. Among the direct spectral approaches, the NHI_SWIR index achieved the highest overall classification performance. Among the individual Sentinel-2 bands, B12 achieved the highest overall accuracy, whereas B8A achieved the highest true skill statistic; both exceeded the multidimensional RaoQ configurations in mean prevalence-independent discrimination. Within the classic RaoQ approach, MIRBI produced the strongest single-variable heterogeneity-based results. The best multidimensional configurations combined Sentinel-2 B8A and B12 with Sentinel-1 VV, demonstrating that radar backscatter provided complementary information to optical observations. Although multidimensional RaoQ did not surpass the best direct spectral variables, it produced competitive and spatially coherent representations of lava-flow disturbance. The evaluated thermal infrared and night-time radiance products did not provide competitive discrimination under the selected spatial and temporal conditions for different reasons: a thresholding limitation in the case of the Landsat thermal product, and an unfavourable ratio of pixel size to flow width in the case of the night-time radiance products, while the MODIS product returned no valid validation points and could not be evaluated. These product-specific explanations rest on a small number of comparisons and are provisional. These results show that carefully selected Sentinel-2 SWIR variables remain the strongest benchmark for detailed mapping of fresh lava-flow disturbance, while multidimensional RaoQ provides a framework for optical–radar integration that requires no training data or prior classification. Because the evaluation covers a single eruption in a single landscape, transfer of the framework to other events and settings remains to be demonstrated. Full article

Quantum communication is emerging as a foundation for next-generation networks, offering unprecedented capabilities in security, entanglement-based connectivity, and distributed computation. However, the classical Open Systems Interconnection (OSI) model, designed for deterministic, error-tolerant systems, is incompatible with quantum phenomena such as decoherence, probabilistic entanglement, and the no-cloning theorem. This paper surveys and redefines the OSI model for quantum networking in the context of 7G systems. We propose a Quantum-Converged OSI stack by extending the classical seven-layer model with two additional layers: (i) Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and (ii) Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. The survey synthesizes over 150 research works published between 2018 and 2025, classifying them by OSI layer, enabling technologies (e.g., Quantum Key Distribution, Quantum Error Correction, and Post-Quantum Cryptography), and application domains such as satellite quantum links, quantum IoT, and federated edge systems. We further provide a taxonomy of cross-layer enablers and discuss simulation tools, including NetSquid, QuNetSim, and QuISP. Finally, an evaluation framework with quantum-native metrics, such as entropy throughput, coherence latency, and entanglement fidelity, is introduced, along with open challenges for programmable stacks, digital twins, and AI-defined quantum agents. The specific and novel contribution of this work is a Quantum-Converged OSI stack that extends the classical seven-layer model with two additional layers: Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. Unlike prior technology-centric surveys, the proposed framework classifies over 150 research works by OSI layer, maps enabling technologies (QKD, QEC, PQC) and application domains (satellite quantum links, quantum IoT, federated edge systems) to their functional layers, and introduces a quantum-native evaluation framework based on entropy throughput, coherence latency, and entanglement fidelity. This layer-resolved synthesis, together with the formal definition of cross-layer quantum-native metrics, constitutes the principal novelty distinguishing this survey from existing quantum-networking reviews. Full article

The exponential growth of scientific literature has amplified the need for ranking mechanisms that prioritize recent publications while ensuring the relevance and authenticity of cited information. The current recency-based metrics, like Price’s Index and the mean/median age of references, primarily characterize citation age but ignore textual or lexical alignment and self-citation bias. Conversely, methods prioritizing relevance highlight lexical alignment but overlook citation recency and self-citation bias. The objective of this study is to propose an interpretable multi-signal ranking framework that integrates the reference recency, lexical relevance, and self-citation behavior into a unified ranking score. Two benchmark datasets, the AMiner corpus (DBLPV13) and OpenAlex, are used for empirical evaluation of the proposed method. The proposed approach demonstrates the greater score differentiation compared with conventional recency metrics like Price’s Index and citation half-life (mean/median age). Additionally, the resulting publication rankings are further compared with the well-known methods such as AttRank, PageRank, RAM, and raw citation counts. Furthermore, the top-ranked publications are qualitatively evaluated using Computer Science Ontology (CSO) to assess topical alignment and topical coherence complemented by statistical analysis. The results show that combining temporal, lexical relevance, and self-citation signals produces publication rankings that consistently differ from traditional citation- and recency-based methods while providing interpretable approach for examining multiple aspects of scientific publications. Full article

Poorly governed geoscience databases introduce subtle but systematic biases that propagate through mineral resource estimation workflows, distorting grade continuity and resource classification. While the importance of data quality in mining is widely acknowledged, quantitative demonstrations of how specific data management decisions translate into downstream technical and economic impacts remain limited. This study addresses that gap through three case studies derived from real-world industry examples. The first case study quantifies operator-dependent data extraction bias by comparing two independently generated datasets sourced from the same database on the same day. Despite nominal equivalence, the datasets differed materially in record counts and grade distributions, producing only negligible global volumetric differences (~0.1%) but up to ~5% local geometric variability and a 19.5% difference in estimated copper grade, resulting in a ~36% divergence in projected revenue. The second case study evaluates analytical uncertainty near detection limits using duplicate assay pairs, demonstrating that relative error increases markedly at low concentrations and that this behavior reflects inherent analytical limitations rather than laboratory non-compliance. The third case study examines the common practice of assigning detection limit placeholder values to unassayed intervals, showing that such substitutions can artificially generate ore in barren domains, whereas retaining null values and applying assignments during post-processing yields geologically and statistically coherent results. Collectively, these case studies demonstrate that hidden data biases can exert a stronger influence on resource outcomes than estimation methodology alone. The results highlight the need for standardized extraction workflows, explicit treatment of low-grade uncertainty, and validation practices that extend beyond global reconciliation metrics. Robust data governance and transparent, reproducible workflows are essential to reducing compounding uncertainty and improving confidence in mineral resource models. All datasets have been anonymized, scaled, or modified to prevent identification of any specific project, company, or operation. No confidential or proprietary datasets are disclosed. Full article

Drought persistence and delayed recovery remain major challenges for drought monitoring in semi-arid environments, where precipitation variability interacts with slowly evolving land-surface conditions. Although precipitation-based indicators effectively characterize meteorological drought, they often provide limited information on the persistence and recovery of integrated dry land-surface states. This study investigates soil moisture persistence and coupled drought-state variability in a semi-arid mountainous region of the southern Peruvian Andes using long-term satellite and reanalysis datasets of precipitation (CHIRPS), root-zone soil moisture (ERA5-Land), vegetation conditions (NDVI), and land surface temperature (LST). Soil moisture persistence was characterized using percentile-based metrics, duration analysis, and temporal persistence diagnostics to evaluate the continuity of dry conditions beyond precipitation anomalies. To synthesize hydroclimatic forcing and land-surface variability within a common framework, an Integrated Drought State (IDS) was developed by combining standardized precipitation, soil moisture, vegetation, and thermal indicators into a simplified diagnostic representation of coupled drought-state variability. The results show measurable temporal persistence in soil moisture together with distinct temporal responses in vegetation and surface thermal conditions, indicating that drought-state continuity reflects coupled land-surface behavior beyond precipitation anomalies alone. Persistent thermal anomalies and reduced vegetation activity co-evolve with soil moisture depletion, indicating that land-surface responses adjust more slowly than atmospheric forcing. The IDS captures the coherent temporal evolution of precipitation, soil moisture, vegetation, and surface thermal conditions during prolonged dry periods, providing an integrated representation of drought-state variability across the study region. Overall, the findings emphasize the importance of incorporating slowly varying land-surface conditions, particularly soil moisture persistence, alongside hydroclimatic forcing to improve the interpretation of drought persistence and recovery in semi-arid mountainous environments. Full article

Generating a virtual anchor from speech has to satisfy three demands at once: the face must stay recognizable as the same person, the motion has to be temporally coherent, and the lips must follow the audio. Existing methods often fall short on the first two points because identity and expression share one entangled representation, and temporal dynamics are modeled only implicitly. We propose DynaID-VAE to address these problems. At its core is an identity–expression disentangled conditional VAE (DC-VAE) that splits the latent space into a time-varying expression subspace and a static identity subspace, held apart by mutual-information minimization and orthogonality regularization. A temporal memory module (TMM) then regularizes the expression trajectory: a GRU propagates sequential state, attention retrieves from a learnable key–value prototype memory, and residual fusion combines the two. Multiscale adversarial supervision and lip–audio synchronization losses complete the training objective. We evaluate on VirtualAnchor-100, a benchmark we recorded ourselves (100 h, 10 anchors), under two complementary protocols. Cross-identity driving is scored only with non-paired measures, namely lip synchronization, distributional video quality, and identity preservation; full-reference image metrics are confined to a self-reenactment protocol, where a genuine paired ground truth exists. DynaID-VAE outperforms the one-reference baselines Wav2Lip, PC-AVS, SadTalker, and DiffTalk under both protocols and on unseen VoxCeleb2 identities. The margins are stable across five identity-disjoint, nested cross-validation folds and are confirmed by an external SyncNet evaluator that never takes part in training, while the model runs at 41.2 FPS with 14.3 M parameters. Ablations separate the contribution of each regularizer and each TMM component. Linear and capacity-matched non-linear probes quantify the factorization as a large reduction of decodable reference identity; full independence is not claimed. A user study confirms the perceptual gains. Full article